# Patent

## Title
Sharding of in-memory objects across NUMA nodes

## Abstract

Techniques are provided for sharding objects across different compute nodes. In one embodiment, a database server instance generates, for an object, a plurality of in-memory chunks including a first in-memory chunk and a second in-memory chunk, where each in-memory chunk includes a different portion of the object. The database server instance assigns each in-memory chunk to one of a plurality of computer nodes including the first in-memory chunk to a first compute node and a second in-memory chunk to a second local memory of a second compute node. The database server instance stores an in-memory map that indicates a memory location for each in-memory chunk. The in-memory map indicates that the first in-memory chunk is located in the first local memory of the first compute node and that the second in-memory chunk is located in the second local memory of the second compute node.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
Benefit Claim
This application claims priority to and is a Continuation-in-part of U.S. patent application Ser. No. 14/337,164, filed Jul. 21, 2014, entitled “FRAMEWORK FOR NUMA AFFINITIZED PARALLEL QUERY ON IN-MEMORY OBJECTS WITHIN THE RDBMS”, which claims priority to U.S. Provisional Patent Application No. 61/880,852 filed Sep. 21, 2013, the contents for each of which are hereby incorporated by reference in their entirety herein.


FIELD OF THE DISCLOSURE
The present disclosure relates to database systems and, more specifically, to managing database objects that reside in memory in one format and on disk in another format.
BACKGROUND
Given that main memory is becoming cheaper and larger, new data formats are needed to speed query processing when data is stored in memory. Existing formats are designed for disk and, when stored in memory (e.g. in the buffer cache), the formats are not optimal for queries. For example, it is common for database systems to store data persistently in “disk blocks”. Typically, within each disk block, data is arranged in row-major format. That is, the values of all columns of one row are followed by the values of all columns for the next row.
To speed up performance, some of the disk blocks may be cached in a “buffer cache” within volatile memory. Accessing the data from volatile memory is significantly faster than accessing the data from disk. However, even within the volatile memory, the data is still in the format of row-major disk blocks, which is not optimal for certain types of database operations.
In contrast to row-major disk blocks, columnar formats have many attractive advantages for query processing in memory, such as cache locality and compression. Consequently, some database servers now employ new table types for persistently storing data in column-major formats. In column-major format, the data may be read into volatile memory where it can be used to process certain queries more efficiently than would be possible if the data were stored in row-major disk blocks.
Unfortunately, the task of migrating existing databases that persistently store data in row-major disk blocks to use of the new column-major table types is not trivial. Further, after performing such a migration, query processing will be less efficient for the class of queries that can be performed more efficiently on data that is stored in row-major disk blocks.
As an alternative, some database systems keep the data in row-major disk blocks, but employ column store indexes. Column store indexes do not replace existing tables, and therefore do not require the entire database to be migrated to new table structures. Rather, column store indexes act more as a traditional secondary index. For example, such column store indexes are still persisted to disk. Unfortunately, a significant amount of overhead may be required to maintain such indexes as updates are performed on the data indexed thereby.
As yet another alternative, one may replicate a database, where a first replica of the database stores the data in conventional row-major disk blocks, while a second replica stores the data in a column-major format. When a database is replicated in this manner, queries that are most efficiently processed using row-major data may be routed to the first replica, and queries that are most efficiently processed using column-major data may be routed to the second replica.
Unfortunately, this technique does not work well due to the lag that occurs between replicated systems. Specifically, at any given point in time, some changes made at one of the replicas will not yet have been applied to the other replica. Consequently, the lag inherent in the replication mechanism may result in unpredictable artifacts and, possibly, incorrect results.
Further, each transaction generally needs to see its own changes, even before those changes have been committed. However, database changes are not typically replicated until the changes have been committed. Thus, a transaction may be limited to using the replica at which the transaction's uncommitted changes were made, even though the format of the data at the other replica may be more efficient for some operations.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.



BRIEF DESCRIPTION OF THE DRAWINGS
In the drawings:
FIG. 1 is a block diagram of a database system that concurrently maintains mirror format data in volatile memory and persistent format data on persistent storage, according to an embodiment;
FIG. 2a is a block diagram of a table used for examples;
FIG. 2b is a block diagram of how data items for a table may be concurrently maintained in two formats, one of which is an in-memory format, according to an embodiment;
FIG. 3 is a block diagram that illustrates journals stored in volatile memory in conjunction with mirror format data, according to an embodiment;
FIG. 4 is a block diagram illustrating how the data from a single table may be divided between IMCUs based on row ranges, according to an embodiment;
FIG. 5a is a block diagram illustrating how different database server instances may be assigned to manage different sets of MF data, where the sets are based on row ranges;
FIG. 5b is a block diagram illustrating how different database server instances may be assigned to manage different sets of MF data, where the sets are based on columns;
FIG. 6 is a block diagram illustrating an SMU that stores a changed-row bitmap and a record of bit changes, according to an embodiment;
FIG. 7 is a flowchart illustrating steps for performing a scan operation, according to an embodiment;
FIG. 8 is a block diagram illustrating a structure for implementing a changed-row bitmap, according to an embodiment; and
FIG. 9 is a block diagram illustrating a computer system that may be used to implement the techniques described herein;
FIG. 10 is a block diagram illustrating a system for managing space and objects within an in-memory area, according to an embodiment;
FIG. 11 is a block diagram illustrating an example layout of stripes within an in-memory area, according to an embodiment;
FIG. 12 is a block diagram illustrating an example in-memory segment layout, according to an embodiment;
FIG. 13 is a block diagram illustrating a multiprocessor system that allows NUMA affinitized parallel queries on in-memory objects, according to an embodiment;
FIG. 14 is a block diagram of a set of NUMA nodes that maintain different in-memory chunks for an in-memory object, according to an embodiment;
FIG. 15 is a table illustrating an auxiliary map that includes NUMA affinity information, according to an embodiment.



DETAILED DESCRIPTION
In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention.
NUMA Overview
Non-uniform memory access (NUMA) refers to an architecture in which:

    
    
        multiple central processing units (CPUs) have access to a pool of volatile memory, and
        each of the CPUs can access a respective portion of that volatile memory faster than the CPU can access the rest of the volatile memory.
    
    


In a NUMA system, the portion of the volatile memory that a given CPU can access faster than the rest of the volatile memory is referred to at that CPU's “local memory”, though the entire pool of volatile memory is “local” in the sense that it is directly accessible by the CPU. Within a NUMA system, a set of CPUs that have the same local memory are referred to as a “cluster” or “compute node”. Thus, each cluster or compute node, includes one or more CPUs and is assigned or otherwise associated with one or more local memory areas. A compute node and its local memory are collectively referred to as a “NUMA node”. A process executing on a particular compute node may have direct access to all memory areas of main memory within the SMP machine. However, the process generally may access a local memory area at much lower latencies than a memory area assigned to another NUMA compute node.
Within multiprocessor machines, a NUMA architecture may significantly reduce memory bottleneck caused when multiple CPUs try and access the same memory area at the same time. Processes executing on different NUMA nodes may access their corresponding local memory areas without conflicts. Memory access times may, therefore, be optimized by having processes operate on data within the local area and minimizing accesses to data stored in memory areas assigned to other NUMA nodes.
Sharding Data Objects in NUMA Systems
Database server instances frequently operate on large data objects. As an example, a database server instance may execute one or more database commands by processing hundreds of thousands or even millions of rows in a table. The database server instance may parallelize execution of the command on computing devices with more than one processor to reduce execution times by having each processor operate on a subset of the data object. In a NUMA multiprocessor system, the database server instance distributes the workload across different NUMA nodes.
The database server instance may optimize parallel execution of database commands and increase manageability of storage space by sharding data objects across different NUMA nodes. In the context of a NUMA system, sharding a data object refers to storing different chunks of the data object into different areas within the volatile memory pool. When loaded into an area within the volatile memory pool, such chunks are referred to as “in-memory” chunks.
As part of the sharding process, a database server instance assigns each chunk of the data object to one or more NUMA nodes, and causes the chunk assigned to each NUMA node to be loaded into the local volatile memory of that NUMA node. As an example, a first in-memory chunk may include a first set of rows or some other portion of a table, and a second in-memory chunk may include a second set of rows or a different portion of the same table. The first in-memory chunk may be assigned and stored in the local memory area of a first compute node, and the second in-memory chunk may be assigned and stored in the local memory area of a second compute node.
When an object is to be stored in memory, the database server determines how to shard the object into in-memory chunks and allocates space for each in-memory chunk from at least one NUMA node. If the object has a corresponding on-disk counterpart (e.g. if the object is an on-disk table), the database server instance may map on-disk page ranges of the object to NUMA nodes, and for each on-disk page range, cause the data stored on disk in that page range to be loaded into the NUMA node to which that page range is mapped. As a result, when the in-memory chunk is refreshed or otherwise reloaded, memory for the in-memory chunk is allocated from the same NUMA node on which the in-memory chunk was previously loaded. If the object is purely in-memory with no corresponding on-disk counterpart, then the database server instance may allocate space in a round-robin fashion such that the data of the object is uniformly spread across different NUMA nodes.
In order to track the location of different in-memory chunks, the database server instance maintains a map that associates each in-memory chunk with a corresponding NUMA node. When the database server instance receives a database query that requires access to the data object, the database server instance may use the in-memory map to determine which memory area in volatile memory is storing each in-memory chunk that contains data from the data object. The database server instance parallelizes execution of the database query by assigning processes executing on different NUMA nodes to operate on the data chunk that resides in the memory area(s) that is/are local to that NUMA node.
Mirror Format Overview
Different data formats have different benefits. Therefore, techniques are described herein for maintaining data persistently in one format, but making that data available to a database server in more than one format. In one embodiment, one of the formats in which the data is made available for query processing is based on the on-disk format, while another of the formats in which the data is made available for query processing is independent of the on-disk format.
The format that corresponds to the on-disk format is referred to herein as the “persistent format” or “PF”. Data that is in the persistent format is referred to herein as PF data. An in-memory format that is independent of the on-disk format is referred to as a “mirror format” or “MF”. Data that is in the mirror format is referred to herein as MF data. For example, in one embodiment, the persistent format is row-major disk blocks, and the mirror format is a column-major format.
According to one embodiment, the mirror format is completely independent of the persistent format. However, the MF data is initially constructed in memory based on the persistently stored PF data, not based on any persistent MF structures. Since persistent MF structures are not required, users of existing databases need not migrate the data or structures in their existing databases to another format. Thus, a conventional database system that uses row-major disk blocks may continue to use those disk blocks to persistently store its data without performing any data migration, while still obtaining the performance benefit that results from having a column-major representation of the data available in volatile memory.
In-memory MF data is maintained transactionally consistent with the PF data. The MF data is transactionally consistent in that any data items provided to a transaction from the MF data will be the same version that would have been provided if the data items were provided from the PF data. Further, that version reflects all changes that were committed before the snapshot time of the transaction, and no changes that were committed after the snapshot time of the transaction. Thus, when a transaction, that made a change to a data item that is mirrored in the MF data, is committed, the change is made visible relative to both the PF data and the MF data. On the other hand, if a transaction that made a change is aborted or rolled back, then the change is rolled back relative to both the PF data and the MF data.
In one embodiment, the same transaction manager that ensures consistency among the reads and writes of the PF data is also used to ensure consistency among the reads and writes of the MF data. Because the MF data is kept current in a transactionally consistent manner, if the in-memory MF data includes the data required by a database operation, then the database operation may be satisfied either from the in-memory MF data, or from the PF data.
The MF data mirrors data that already exists in the PF data. However, while all items in the MF data are mirror versions of corresponding items in the PF data (albeit organized in a different format), not all items in the PF data need be mirrored in the MF data. Thus, the MF data may be a subset of the PF data.
Because not all of the PF data is necessarily mirrored in the MF data, in some situations queries may require data that can only be satisfied by the PF data. For example, if a table has columns A, B and C, and only column A is mirrored in the MF data, then a query that requires values from column B must obtain those values from the PF data.
However, even in those circumstances, the MF data may still be used to (a) satisfy a portion of the query, and/or (b) speed up the retrieval of required data from the PF data. For example, the MF data may be used to identify the specific rows that must be retrieved from the PF data.
According to one embodiment, to reduce overhead, no on-disk copy of the MF data is maintained. In an alternative embodiment, a copy of the MF may be stored, but no attempt is made to keep the on-disk copy of the MF data in sync with updates that are being performed on the PF data. Consequently, after a failure, the in-memory MF data must be reconstructed based on the persistent copy of the PF data.
In some embodiments, the MF data is compressed. The compression can be performed at various compression levels, either specified by the user or based on access patterns.
While examples shall be given hereafter in which the mirror format is columnar, the mirror format may be any format, different from the persistent format, that is useful for running in-memory queries. For example, in an alternative embodiment, the PF format is column-major, while the MF format is row-major. Regardless of the particular mirror format used, the mirror format data is created in memory based on existing PF structures (e.g. tables and indexes) without causing a change to the format of those structures.
General Architecture
FIG. 1 is a block diagram of a database system according to one embodiment. Referring to FIG. 1, database system 100 includes volatile memory 102 and persistent storage 110. Volatile memory 102 generally represents the random access memory used by the database system, and may be implemented by any number of memory devices. Typically, data stored volatile memory 102 is lost when a failure occurs.
Persistent storage 110 generally represents any number of persistent storage devices, such as magnetic disks, FLASH memory, and/or solid state drives. Unlike volatile memory 102, data stored on persistent storage 110 is not lost when a failure occurs. Consequently, after a failure, the data on persistent storage 110 may be used to rebuild the data that was lost in volatile memory 102.
Within volatile memory 102, a database server 120 is executing database commands that are submitted to the database server by one or more database applications (not shown). The data used by those applications is illustrated as PF data 112. PF data 112 resides on persistent storage device 110 in PF data structures 108. The PF structures 108 may be, for example, row-major disk blocks. While row-major disk blocks are used for the purposes of illustration, the PF structures may take any form, such as column-major disk block, hybrid compression units where some data is arranged in column-major format and other data is arranged in row-major format, etc.
The volatile memory 102 further includes a cache 106 of PF data. Within cache 106, the data is stored in a format that is based on the format in which the data resides within the PF data structures 108. For example, if the persistent format is row-major disk blocks, then cache 106 may contain cached copies of row-major disk blocks.
On the other hand, MF data 104 is in a format that is unrelated to the persistent format. For example, in the case where the persistent format is row-major disk blocks, the mirror format may be column-major compression units. Because the mirror format differs from the persistent format, the MF data 104 is produced by performing transformations on the PF data. These transformations occur both when volatile memory 102 is initially populated with MF data 104 (whether at start-up or on-demand), and when volatile memory 102 is re-populated with MF data 104 after a failure.
Significantly, the existence of MF data 104 may be transparent to the database applications that submit database commands to the database server that makes use of the MF data 104. For example, those same applications, designed to interact with database systems that operate exclusively on PF data 112, may interact without modification with a database server that maintains MF data 104 in addition to the PF data 112. Further, transparent to those applications, that database server may use the MF data 104 to more efficiently process some or all of those database commands.
The Mirror Format Data
MF data 104 may mirror all of the PF data 112, or a subset thereof. In one embodiment, a user may specify what portion of the PF data 112 is “in-memory enabled”. The specification may be made at any level of granularity. For example, the specification of what is in-memory enabled may be made at least at the following levels of granularity:
the entire database
specified tables
specified columns
specified row ranges
specified partitions
specified segments
specified extents
As shall be described hereafter, in-memory enabled data is converted to the mirror format and stored as MF data 104 in volatile memory. Thus, when in-memory enabled data is required by a query, the database server has the option of providing the data from either the PF data 112 or the MF data 104. The conversion and loading may occur at the time the database is started, or in a lazy or on-demand fashion. Data that is not in-memory enabled is not mirrored in the MF data 104. Consequently, when such data is required by a query, the database server does not have the option of obtaining the data from the MF data 104.
For the purpose of explanation, it shall be assumed that PF data structures 108 include the table 200 illustrated in FIG. 2A. Table 200 includes three columns c1-c3, and six rows r1-r6. While the illustration of table 200 in FIG. 2A portrays how the data is logically organized on persistent storage 110, the actual format in which the data is physically stored may be quite different.
Specifically, referring to FIG. 2B, it illustrates how the data that resides in table 200 may be physically organized on persistent storage 110. In the present example, the data for table 200 is stored in three row-major disk blocks 202, 204 and 206. Block 202 stores the values for all columns of row r1, followed by the values for all columns of row r2. Block 204 stores the values for all columns of row r3, followed by the values of all columns of row r4. Finally, block 206 stores the values of all columns of row r5, followed by the values of all columns of row r6.
Copies of some of those disk blocks may be temporarily stored in cache 106. In the example illustrated in FIG. 2B, a cached copy 212 of block 204 resides in cache 106. Cache 106 may be managed using any one of a variety of cache management techniques, and the embodiments described herein are not limited to any particular cache management technique. In general, such techniques attempt to retain in volatile memory 102 copies of the disk blocks that are most likely to be requested in the near future. Consequently, when cache 106 runs out of space, cached copies of disk blocks that are less likely to be requested are replaced by copies of blocks that are more likely to be requested.
In contrast to the data in cache 106, the mirror format data 104 is not formatted in a manner that is based on the persistent format. In the illustrated example, mirror format data 104 includes two column vectors 220 and 222. Each column vector stores a contiguous series of values from a single column of table 200. In the present example, column vector 220 stores values from column 1 of table 200, and column vector 222 stores values from column 3 of table 300. In this example, the MF data 104 mirrors a subset of the PF data because MF data 104 does not include column vectors for column 2 of table 200.
Organization of the MF Data
According to one embodiment, even though the MF data uses a different format than the PF data, the MF data is organized in a manner that corresponds to the organization of the PF data. For example, on persistent storage 110, the PF data may be stored in blocks that reside in extents which, in turn, are organized into segments. Under these circumstances, within volatile memory 102, the MF data 104 may be organized based on the extents and/or segments to which the data belongs. Thus, column vector 220 may be divided into vector portions, each of which corresponds to a particular range of extents and/or segments.
Within the extents, data is typically ordered by rowid. Similarly, in one embodiment, the MF data 104 is ordered based on rowid. For example, the values in column vector 220 are ordered based on the same rowids that are used to order the PF data in blocks 202, 204 and 206. Specifically, rowid r1 immediately precedes rowid r2, so r1c1 immediately precedes r2c1 in column vector 220, and r1c1 to r1c3 immediately precede r2c1 to r2c3 in block 202.
In alternative embodiments, some or all of the data items in the MF data 104 are not ordered, within the MF data 104, by rowid. Storing the data items in a different order may be useful, for example, if the different ordering produced significantly better compression. As another example, the column vectors may initially be ordered by rowid. However, when new updates are “merged into” the column vectors (as shall be discussed in greater detail hereafter), the updated values may appended to the end of the existing column vectors to avoid having to decompress and recompress the existing column vectors.
When the data items within the column vectors are not in rowid order, an in-memory index may be built on rowid to quickly locate within the MF data 104 the data items associated with any given rowid.
Whether or not the data items within the column row vectors are ordered based on rowid, a rowid-to-item mapping may be established by maintaining a vector of rowids in conjunction with the column vectors. For example, FIG. 3 illustrates a rowid vector 330 that is maintained in addition to the column vectors 220 and 222. The first value (R1) in the vector of rowids is the rowid of the first data item in each of the column vectors. Similarly, the second value in the vector of rowids (R2) is the rowid of the second data item in each of the column vectors.
In embodiments where the organization of the MF data corresponds to the organization of the PF data, it is easier for the database server to split database operations between the MF data and the PF data. For example, the database server may determine that the MF data is to be used to satisfy a query relative to one range of extents (e.g. extent 1 to extent 10), while PF data is to be used to satisfy the query relative to another range of extents (e.g. extent 11 to extent 20).
Using the MF Data to Satisfy Queries
A conventional database system may operate normally by responding to every query by first searching for the requested data in cache 106. If the data is in cache 106, the data is accessed from cache 106. Otherwise, the needed data is loaded from PF data structures 108 into cache 106, and then accessed from cache 106. However, because the data in both cache 106 and PF data structures 108 is in the persistent format, performing operations based exclusively on the PF data does not always provide the best performance.
Therefore, according to one embodiment, the database server uses the MF data 104 to supply data items required by at least some requested database operations. For example, if a database query requests the values from column 1 of all rows, the database server may obtain those values from the column vector 220 without accessing persistent storage 110. In the absence of MF data 104, the database would only be able to obtain R3C1 and R4C1 without accessing persistent storage 110 (because currently only block 204 is in cache 106). To obtain R1C1 and R2C1, block 202 must be loaded into cache 106, and to obtain R5C1 and R6C1, block 206 must be loaded into cache 106. The time it would take to load blocks 202 and 206 into cache would be significantly more than the time required to obtain the values directly from column vector 220.
Using the MF Data to Evaluate Predicates
Even in situations where the data required by a database operation is not included in the mirror format data 104, the mirror format data 104 may be used to evaluate predicates, and thereby speed up the database operations in the same manner as conventional indexes. For example, assume that table 200 has thousands of rows, and in only three of those rows does column c1 have the value “joe”. Under these circumstances, a database server may receive a database command that requests the values, from column c2, of all rows where c1=“joe”.
In this example, the data that needs to be returned by the database command is from column c2, which is not in the MF data 104. However, the column vector 220 for column 1 may be used to quickly identify the three rows where c1=“joe”. This operation can be performed efficiently because the data items required to evaluate the predicate (values from c1) are stored contiguously in volatile memory. Once those rows have been identified using column vector 220, the database server may retrieve from disk only those blocks needed to obtain the data from those three rows.
Without using the MF data, a conventional index built on column c1 may be used to evaluate the predicate “where c1=joe”. However, some disk I/O may be necessary to use of the conventional index to perform this evaluation, whereas no disk I/O is needed to evaluate the predicate using column vector 220. Further, maintaining such an index can incur significant overhead.
Without using mirror format data 104 or a conventional index, the database server would have to load from persistent storage 110 every disk block that (a) is not already in cache 106, and (b) stores data for table 200. These blocks would have to be loaded merely to compare the values of column c1 against “joe” to identify the three rows for which c2 is required by the database command.
Because MF data 104 may be used for the same function as conventional indexes (i.e. to efficiently identify which rows satisfy criteria specified in a database command), a database system that uses MF data 104 need not have as many conventional indexes as would otherwise be necessary for efficient predicate evaluation. For example, if the MF data 104 includes a column vector for c1 and a column vector for c3, then the database server need not maintain conventional indexes for columns c1 or c3. By reducing the number of conventional indexes that need to be maintained by a database server, the overhead associated with making updates may be significantly reduced.
In-Memory Indexes
As explained above, when a predicate references a column, the column vector for that column may be used to evaluate the predicate. In this way, column vectors may be used instead of conventional indexes. To provide even faster predicate evaluation, in-memory indexes may be used. An in-memory index is an index stored entirely within volatile memory. The nature of the in-memory index may vary based on the characteristics of the data being indexed. For example, if low-cardinality keys are being indexed, the in-memory index may be a binary index. If high-cardinality keys are being indexed, then the in-memory index may be a B-tree. Regardless of the nature of the in-memory index, the entries in the index point to in-memory location of the data items in question, rather than on-disk locations.
Compression
As mentioned above, the MF data may be compressed. However, according to one embodiment, not all MF data need be compressed in the same way, or to the same degree. For example, if it is determined that the data from column c1 of table 200 is used frequently, and the data from column c3 is used infrequently, then the data in column vector 220 may be lightly compressed, or uncompressed, whereas the data in column vector 222 is highly compressed.
The compression algorithm, and the level of compression used by the algorithm, that is used to compress each portion of the MF data may be specified by a user, or may be determined automatically by a database server based on various factors. Possible compression algorithms include, but are not limited to, dictionary-based compression, run-length encoding (RLE), Ozip compression, etc.
The factors used by the database server to determine how each portion of MF data is compressed may include, for example, the frequency with which each portion is accessed, and how much data is in the portion, and how much volatile memory is available. In general, the more frequently a portion of the MF data is accessed, the less compressed the data. As another general rule, the less volatile memory that is available to store the MF data and/or the larger the size of the portion of the MF data, the higher the compression.
Even though data items may be compressed within the MF data, it may not be necessary to decompress the MF data to use it. For example, vector processing operations may be performed directly on compressed values, as described in U.S. patent application Ser. No. 13/708,054, filed Dec. 7, 2012, the entire contents of which are incorporated herein by reference. As also described in that application, it is also possible for the decompression to be performed on-chip after the compressed column vector values have been transferred to the CPU.
In-Memory Compression Units (IMCUs)
In an embodiment in which the MF data is compressed, the MF data may be organized, within volatile memory 102, into “in-memory compression units” (IMCUs). Each IMCU stores a different set of MF data. For example, as illustrated in FIG. 4, IMCU 402 stores half of column vectors 220 and 222, and IMCU 404 stores the other half of column vectors 220 and 222. Specifically, IMCU 402 includes a vector portion 420 that stores half the values from column c1, and a vector portion 422 that stores half the values from column c3. Similarly, IMCU 404 includes a vector portion 424 that stores the other half of the values from column c1, and a vector portion 426 that stores the other half the values from column c3.
In this example, the IMCUs divide the MF data based on the rows to which the data belongs, where IMCU 402 corresponds to rows r1 to r3 of table 200, and IMCU 404 corresponds to rows r4-r6 of table 200. However, this is only one of many different ways that the MF data may be spread among IMCUs. For example, different IMCUs may store MF data for different tables, different partitions of a table, different columns of a table, different segments, different extents, etc.
Metadata for the MF Data
To determine whether the MF data has the data required to process a query, and if so, to find the MF data required to process the query, the database server needs to know which PF data is mirrored in the MF data, and specifically which specific PF data is mirrored by each IMCU. Therefore, according to one embodiment, metadata 430 for the MF data is maintained in volatile memory 102, as illustrated in FIG. 4.
In one embodiment, metadata 430 includes a data-to-IMCU mapping. The data-to-IMCU mapping indicates which data is contained in each IMCU. This indication may be made in a variety of ways, including storing data that indicates, for each IMCU, one or more of the following:

    
    
        the table(s) whose data is stored in the IMCU
        the column(s) whose data is stored in the IMCU
        the range of rows stored in the IMCU
        the range of the disk blocks whose data is stored in the IMCU
        the segments whose data is stored in the IMCU
        the table partitions whose data is stored in the IMCU
        the extents whose data is stored in the IMCU
        the manner in which the data, within the IMCU, has been compressed
        the dictionary for decompressing the data stored in the IMCU (when a dictionary-type encoding has been used to compress the PF data)
    
    


In the situation illustrated in FIG. 4, the data-to-IMCU mapping may indicate, for example, that rows r1-r3 of columns c1 and c3 of table 200 are stored in IMCU 402, and that rows r4-r6 of columns c1 and c3 of table 200 are stored in IMCU 404.
Multi-Instance Environments
In some environments, the same PF data is accessed by multiple database server instances. Such environments are referred to herein as multi-instance environments. In a multi-instance environment, each database server instance may have access to volatile memory that the other database server instances cannot access directly. In such situations, one may populate the volatile memory of each of the database server instances with the same MF data, or may cause different portions of the MF data to be stored in the volatile memories of different database server instances. In the case where different portions of the MF data is stored in the volatile memories of different database server instances, metadata 430 may also include an IMCU-to-instance mapping.
For example, referring to FIG. 5a, it illustrates an embodiment in which IMCU 402 is stored in the volatile memory 502 of one database server instance (instance 1), and IMCU 404 is stored in the volatile memory 504 of another database server instance (instance 2). For the database servers to know where specific portions of the MF data reside, each maintains metadata (530 and 532) to indicate both (a) where IMCUs 402 and 404 reside, and (b) what data they contain.
In FIG. 5a, MF data from the same two columns (c1 and c3) is distributed between two database instances. However, it is also possible to distribute the MF data among database servers on other basis. For example, different instances may have the MF data for different tables, different columns, different partitions, different segments, different extents, etc.
FIG. 5b is a block diagram of a scenario in which the MF data is distributed among the database instances based on column. Specifically, in FIG. 5b, the IMCU 402 stored in the volatile memory 502 of instance 1 includes the entire column vector 220 for column c1, while the IMCU 404 stored in the volatile memory 504 of instance 2 includes the entire column vector 222 for column c3.
Because it is more efficient to access local data than to obtain data from a remote instance, the location of the MF data may be a factor in determining whether to obtain a particular data item from the MF data or the PF data. For example, in the scenario illustrated in FIG. 5b, if a query that is being executed by the database server of instance 1 requires data from column c1, the database server may decide to obtain the data from column vector 220, rather than from the PF data. On the other hand, if the same query being executed by the same database server requires data from column c3, then the database server may decide to obtain the data from the PF data.
When a database server determines that it is more efficient to perform an operation using MF data that resides in a remote instance that to use the PF data, the database server requests the remote instance to perform the operation. For example, in the scenario illustrated in FIG. 5b, if the database server of instance 1 is executing a query with the predicate “where c3=X”, the database server of instance 1 would request the database server of instance 2 to evaluate “where c3=X” using column vector 222. In response to evaluating the predicate, the database server of instance 2 would return to the database server of instance 1 data that indicates which rows satisfy the predicate.
Keeping the Mirror Format Data in Sync
The MF data 104 is only useful if the MF data 104 is kept up to date with all changes being made to the PF data. For example, if a query calls for the current values from column c1, then column vector 220 can only be used if its values are current. Similarly, if a query calls for current values of c2 from rows where c1=“joe”, then column vector 220 can only be used to identify the rows where c1=“joe” if the values in the column vector 220 are current.
Consequently, a mechanism is provided for keeping the mirror format data 104 in sync with the PF data as updates, inserts and deletes are performed on the PF data. Specifically, in one embodiment, the transaction manager of a relational database server, which is conventionally designed to transactionally update the PF data, is modified to concurrently transactionally update the MF data. For example, when the transaction manager updates a particular item in the PF data as part of a transaction, the transaction manager also updates the particular item in the MF data (if the particular item is in the MF data) as part of the same transaction.
By maintaining MF data 104 and the PF data transactionally synchronized, the result set of a query will be the same regardless of whether the query was processed using data items obtained exclusively from the MF data 104, or data items obtained exclusively from the PF data.
The result set will also be the same if the query is processed using some data items from MF data 104, and other data items from the PF data.
In-Place Updates to MF Data
For the MF data to remain transactionally consistent with the PF data, changes are made permanent to the MF data at the same time the changes are made permanent to the PF data. For example, when a transaction that changed r1c1 from X to Y commits, r1c1 must be changed from X to Y in both the PF data and the MF data.
In some situations, it is possible to directly update the MF data to reflect a change made by a transaction when the transaction commits. For example, if column vector 220 is either uncompressed, or compressed in a manner that produces fixed-width values, it is possible to directly change the value of r1c1 from X to Y in the column vector 220 when the transaction commits, without otherwise affecting the column vector 220 or incurrent significant overhead.
However, in other situations, it may be necessary to update the MF data implicitly. When updated implicitly, the MF data itself does not necessarily change, but metadata is stored to indicate that the values contained therein have been updated. As shall be described in greater detail hereafter, the metadata used to record implicit updates to the MF data may include journals and changed-row bitmaps.
Journals
In some embodiments, keeping the MF data in sync with updates to the PF data is complicated by the fact that the MF data may be in a compressed format. For example, if column vector 220 is compressed, then directly updating a value within the column vector 220 may require the entire column vector to be decompressed, the update performed, and then the entire column vector to be compressed again. It would not be efficient to perform such operations in response to every update performed on the PF data.
To reduce the amount of decompression and decompression operations required to keep the MF data in sync, one embodiment makes use of journals to make implicit updates to the MF data. In general, journals store information about updates (a) made to the PF data, and (b) not yet made directly to the MF data.
Referring to FIG. 3, it illustrates an embodiment in which journals 304 are maintained in conjunction with column vectors 220 and 222. In the embodiment illustrated in FIG. 3, column vectors 220 and 222 store compressed MF data 302. Because the data within the column vectors 220 and 222 is compressed, a significant amount of overhead would be required to directly update the data within the column vectors 220 and 222.
While journals 304 are also in volatile memory 102, journals 304 generally contain uncompressed data 302 that indicates changes made to the PF data that is not yet reflected in column vectors 220 and 222. For example, if the value of R3C1 of table 200 is updated from X to Y, rather than change the value of R3C1 in column vector 220, an entry is stored in one of journals 304 to indicate that R3C1 has been changed, and to record the new value for R3C1.
Journals 310 include a global journal 310 and numerous private journals. In general, global journal 310 records only those changes that have been made by committed transactions. Before transactions commit, the changes made by the transactions are stored in the private journals, as explained in greater detail hereafter.
Journals 310 may include entries for rows that do not exist in the MF data. For example, assume that the MF data for table 200 is created at time T1, and at time T2 a new row is inserted into table 200. Under these circumstances, an entry for the new row will initially be added to private journal of the transaction that inserted the row, and the entry for the new row will be moved to the global journal for table 200 when that transaction commits.
According to one embodiment, all journals support full transactional semantics (e.g. queries, DMLs, rollback to savepoint, rollback/abort, parallel queries/DMLs, and distributed transactions). In addition, journals can interoperate with the on-disk database system. For example, when the data is purged from an in-memory journal, required changes can be obtained from the PF data on disk, if a query needs them.
Private Journals
As mentioned above, journals 304 are used to store data that indicates (a) changes made to the PF data that (b) are not yet reflected in the MF data stored in IMCUs. Such changes are typically made by database servers as part of transactions. According to one embodiment, in addition to having a single “global” journal, such as journal 310, for all such changes, a separate “private” journal is maintained for each transaction.
For example, FIG. 3 illustrates the situation in which three transactions TX1, TX2 and TX3 are making changes to PF data that is mirrored in the compressed MF data 302. In addition to making the changes to the PF data, the transactions make the same changes to the MF data by storing, in their respective private journals, data that indicates what the changes are.
Similar to the changes made to the PF data, those changes reflected in the private journal of a transaction are not considered permanent until the transaction commits. Consequently, the changes reflected in the private journal of any given transaction will not be visible to other transactions until the given transaction commits. In the example shown in FIG. 3, the contents of journal 312 will be ignored by transactions TX2 and TX3. The contents of journal 314 will be ignored by transactions TX1 and TX3. The contents of journal 316 will be ignored by transactions TX1 and TX2.
Moving Journal Entries Upon Commit
The global journal is visible system-wide, since all changes reflected therein have been committed. Thus, in response to transaction TX1 committing, the changes reflected in the private journal 312 of TX1 are moved to the global journal 130. Similarly, in response to transaction TX2 committing, the changes reflected in the private journal 314 of TX2 are moved to the global journal 130. Likewise, in response to transaction TX3 committing, the changes reflected in the private journal 316 of TX6 are moved to the global journal 130.
As mentioned above, when a transaction commits, the contents of that transaction's private journal are moved to the appropriate global journals. In embodiments where the global journals are maintained on a per-IMCU basis and the private journals are maintained on a per-transaction basis, moving the private journal entries of a committed transaction may involve moving some of the entries to the global journal of one IMCU, and some entries to the global journal of another IMCU.
For example, assume that a transaction modifies a first set of data that maps to a first IMCU, and modifies a second set of data that maps to a second IMCU. Prior to commit, entries for both sets of modifications are stored in the private journal of the transaction. However, when the transaction commits, the entries for modifications to the first set of data are moved to the global journal for the first IMCU, and entries for modifications to the second set of data are moved to the global journal for the second IMCU.
After a transaction's changes are persistently committed to the PF data, the transaction is assigned a commit time. In response to being assigned a commit time, the journal entries of the transaction are updated to reflect the commit time. Once a transaction's journal entries are moved to the appropriate global journals and updated with the commit time of the transaction, the changes reflected in those entries become visible to other transactions.
As mentioned above, data within an IMCU need not be arranged in rowid order. When not in rowid order, the column vector of rowids (e.g. vector 330) may be used to locate data within an IMCU based on rowid. Specifically, the position of a rowid within vector 330 is the position of the values for the corresponding row within the other vectors 220 and 222. According to one embodiment, even when the data within an IMCU is not arranged in rowid order, the entries in the corresponding private and global journals are organized based on rowid. Thus, when data in an IMCU is invalidated due to an update made to the corresponding PF data, the rowid of the invalidated data is recorded, rather than the position of that data within the IMCU.
Journal Entry Contents
In general, each journal entry contains all information required to determine (a) what data items are in the entry, and (b) what version of those data items does the entry reflect. In one embodiment, each journal entry includes:
the rowid of the row associated with the entry
a timestamp that indicates when the data contained in the row was “current”
values for one or more columns of the corresponding row
With respect to the column values, in one embodiment, each journal entry includes full row images that result from all Data Manipulation Language (DML) operations. In such an embodiment, the journals are initially row-major data stores. However, under certain circumstances (such as when the journals grow too large), the contents of the journal may be converted to a column-major row store. The column-major information in the journals would only need to include values for those columns that are mirrored in the MF data.
In on embodiment, a threshold is established for how many rows a journal may have in row-major format. Once that threshold is exceeded, a conversion operation is triggered for converting some or all of the journal's row-major data to a column-major format. The threshold may be, for example, that a journal may have no more than 1000 rows of row-major data.
Journal Indexes
According to one embodiment, an index, maintained in volatile memory 102, is built on the rowid column of each private journal. In addition to the rowid column, indexes may be built on any other column of the private journals that will improve overall query processing efficiency. These journal indexes may be used, for example, during query processing to perform look-ups, or range-based scans of the journals.
Journal Structure
According to an embodiment, journals are organized, within volatile memory 102, as a series of temporally-ordered extents. For example, assume that the version time for MF data 104 is T1, and that the current system time is time T10. Under these circumstances, journal 310 may be organized into three extents, the first of which includes journal entries for changes made between time T1 and time T3, the second of which includes journal entries for changes made between time T3 and time T6, and the third of which includes journal entries for changes made between time T6 and the current system time.
When structured in this manner, extent pruning may be used to reduce the number of extents that are processed during a table scan. For example, for a table scan performed for a transaction with a snapshot time of T2, only the first extent of journal 310 would need to be scanned. The other journals contain only changes that the transaction is not allowed to see.
On the other hand, for a table scan performed for a transaction with a snapshot time of T7, all three extents of journal 310 would have to be scanned, because all three could contain journal entries for changes that must be seen by the transaction.
Merging Global Journals into the MF Data
As mentioned above, journals are used because it is inefficient to update the MF data directly every time a database operation makes a change to the corresponding PF data. This is particularly true when the MF data is compressed. However, it is also inefficient to allow the journals to grow indefinitely, both because eventually the journals will require too much volatile memory, and because the larger the journals grow, the less efficient it becomes to use the MF data to satisfy queries.
Consequently, according to one embodiment, the content of the global journals is periodically merged into the MF data. When the MF data is compressed, this merger operation typically involves decompressing the MF data, updating the MF data to reflect the most current committed versions of the items contained therein, and then compressing the MF data.
After data has been merged into the MF data contained in a particular IMCU, the metadata associated with the IMCU is updated to indicate a new version timestamp for the IMCU. For example, if the MF data in an IMCU reflected all changes made as of time T1, then prior to the merger the version timestamp for the IMCU would be T1. If the update involves merging into the MF data of the IMCU all changes that were made up to time T3, then after the merger the version timestamp for the IMCU would be updated to T3.
Post-Merger Retention of Global Journal Entries
As shall be described in greater detail hereafter, in some embodiments, changed-row bitmaps may be used to indicate which data items in the MF data have become stale. A data item in the MF data becomes stale when a change (not reflected in the MF data) is committed to the data item. Once the contents of a global journal have been merged into the corresponding MF data, the old entries in the journals may be purged and the changed-row bitmap is updated to reset all bits (thereby indicating that no data items in the newly-merged MF data are stale). However, in some embodiments, rather than purge all old journal entries in response to merging changes into the MF data, some of the old data may be retained in order to continue to use the MF data for transactions whose snapshot-times are before the merger time.
For example, if the post-merger version timestamp for the IMCU is T3, then a transaction with a snapshot time of T2 cannot use the MF data in the IMCU, because that data contains changes that the transaction is not allowed to see. However, if all the journal entries as of time T1 have been retained, it is possible to use those journal entries, in conjunction with the IMCU, to obtain some data items as of time T2. Specifically, for a data item whose journal entries have been retained, a transaction with a snapshot time of T2 would use the version of the data item from the most recent journal entry that precedes T2, the snapshot time of the transaction.
For example, assume that the journal only has a single entry, and the entry indicates that r5c1 was changed at time T3 from X to Y. Consequently, the post-merger IMCU will have value Y for r5c1. However, to provide the correct value to the transaction, the database server inspects the journal to see that the row of r5c1 was changed between the snapshot time T2 and the version time T3 of the IMCU. Based on this information, the database server knows that the value Y for r5c1 is too recent for the transaction to see, and that the transaction must instead see value X for r5c1. Consequently, in the data obtained for the transaction, the database server changes the value of r5c1 Y to X.
Unfortunately, it is not feasible to retain old journal entries indefinitely. Therefore, according to one embodiment, a configuration parameter is provided for specifying the retention policies associated with IMCUs or the database objects to which they correspond. For example, a retention policy may be that, for table 200, journal entries are retained for at least one hour. Thus, for IMCUs that contain data for table 200, when purging journal entries after a merger, only those journal entries that are associated with snapshot times that are less than an hour old are retained. Retaining already-merged journal entries in this manner ensures that transactions that have snapshot times less than an hour old will always be able to obtain the correct version of data items from the MF data.
According to one embodiment, old journal entries are retained until the database server determines that no currently executing queries will need the old journal entries. For example, if changes are merged into an IMCU at time T10, then journal entries, in the global journal of that IMCU, that are associated with changes made before time T10 may automatically be purged by the database server when there are no more currently-running transactions that have snapshot times before T10.
In some embodiments, the journal entries may store only an indication of which row was changed, and when, without storing the actual values involved. In such an embodiment, the pre-merger journal entries are still useful for indicating which values from the post-merger IMCU cannot be used by a transaction. In the example given above, the version of r5c1 that is in the post-merger IMCU cannot be used for a transaction with a snapshot time of T2, because the journal would indicate that r5c1 was changed between the snapshot time T2 and the version time T3 of the post-merger IMCU. Under these circumstances, if the journal does not have the actual pre-update value of r5c1 (i.e. X), the database server may obtain that value from the PF data, and the rest of the values that it needs from the MF data.
Global Journals and Memory Constraints
As explained above, both global and private journals are maintained in volatile memory. Private journals are used to record changes made by transactions that have not yet committed. Global journals, on the other hand, generally record changes made by transactions that have committed.
The more entries that a global journal has, the more volatile memory is consumed. Under some circumstances, there may simply not be enough volatile memory to store excessively large global journals. One way of handling these situations is to purge older extents of the journals.
For example, assume that the global journal of an IMCU has three extents E1, E2 and E3. Assume further that E1 contains entries for transactions that committed between time T1 and time T5, E2 contains entries for transactions that committed between time T5 and time T9, and E3 has journal entries for transactions that committed between time T9 and the current system time.
Assume further that the version time of the IMCU is T5. Under these circumstances, the entries in E1 may be used to “roll back” the values in the IMCU for transactions that have snapshot times between T1 and T5. On the other hand, the entries in E2 and E3 may be used to “roll forward” the values in the IMCU for transactions that have snapshot times after T5.
When faced with memory constraints, the database server may purge extents E1 only, E1 and E3, or E1, E2 and E3, depending on how much memory is needed. Purging an extent has an effect on performance of certain transactions. For example, assume that E1 is purged. After E1 is purged, a transaction with a snapshot time of T3 may require data items that maps to the IMCU. The transaction may obtain data items that did not change between T3 and T5 from the IMCU. Data items that did change between T3 and T5 are obtained from the PF data, because those items were recorded in E1 which has been purged.
Even after the purging of its journals, an IMCU may be used to supply data that did not change between (a) the version time of the IMCU and (b) the snapshot time of the transaction requesting the data. For example, if the IMCU version time is T1, a transaction with a snapshot time of T5 may obtain data items from the IMCU that were not changed between T1 and T5. As shall be described in greater detail hereafter, those changed data items may be identified using a delete vector generated for the transaction.
Snapshot Metadata Units
As mentioned above, metadata is maintained for each IMCU. In one embodiment, a Snapshot Metadata Unit (SMU) is responsible for maintaining at least some of that metadata. Referring to FIG. 6, an IMCU 600 is illustrated with its corresponding SMU 604. In the illustrated embodiment, SMU 604 stores the IMCU version time and a changed-row bitmap 606. The IMCU version time is the time at which the values in the IMCU 600 were current. Changed-row bitmaps shall be described in greater detail hereafter.
Among other things, the SMU for an IMCU captures all updates that affect the MF data contained in the IMCU. Consequently, the SMU for an IMCU may indicate, for example, whether the corresponding IMCU has valid values for a given rowid/snapshot-time combination. As another example, the SMU may generate a list of rowids of all rows for which the corresponding IMCU has invalid values, relative to a given snapshot-time. This list may then be used in conjunction with the rowid column vector to identify the rows for which values must be obtained from other sources (e.g. from journals or from the PF data).
Changed-Row Bitmaps
In one embodiment, the updates captured by an SMU are indicated by a “changed-row bitmap” maintained within the SMU. Referring again to FIG. 6, the changed row bitmap 606 for IMCU 600 is maintained in SMU 604. A changed-row bitmap is a bitmap that indicates the rows (a) for which the corresponding IMCU has values, and (b) that have been changed by transactions that committed since the version timestamp of the IMCU.
For example, when a transaction performs an update to rows r1, r3 and r5 of table 200, the SMU 604 for IMCU 600 updates the changed-row bitmap of IMCU 600 by setting the bits that correspond to rows r1, r3, and r5 because those are the updated rows that fall within the MF data of IMCU 600.
According to one embodiment, when a change is made to data that is mirrored in IMCU 600, the SMU 604 stores a record of which bits of the changed-row bitmap 606 were set, and when. These records are collectively represented in FIG. 6 as record of bit changes 608. For example, if an update made at time T1 modifies row r1, then the bit for row r1 would be set, and a record is stored to indicate that the bit for r1 was set at time T1.
According to one embodiment, the changed-row bitmap is created on an as-needed basis. For example, if the changed-row bitmap is to reflect whether a change has occurred to a million rows, a one million bit data structure is not pro-actively initialized. Instead, data is only stored for row ranges that have at least one bit set. For any range for which no data is stored, all bits are deemed to be “0”.
Referring to FIG. 8, it illustrates a hierarchical structure 800 for representing a changed-row bitmap, according to one embodiment. In the illustrated embodiment, the hierarchical structure 800 has levels that correspond to extents, blocks, and rows. The extent-level information 802 includes a record for each extent in which there is any set bit. Extent-level records link to other extent-level records (not shown), thereby forming a linked list of records for the extents that have one or more set bits.
In addition, the extent records include a pointer to a linked list of block level information 804 for the blocks that reside in the extent. In the illustrated example, the record for extent E1 points to the record for blocks B1, B2, B3 and B4. The block-level record can be the first record in a linked list of block-level records for the blocks that belong to extent E1.
The block-level records, in turn, point to row-level information 806 stored in the form of bitmap pieces. Specifically, in the illustrated embodiment, the record for block B1 points to bitmap piece 850.
Each position in the bitmap piece 850 corresponds to a row whose data items are stored in block B1. In the illustrated embodiment, bitmap piece 850 has six bit positions, which correspond to six rows are stored in B1. For each bit position, bitmap piece 850 includes two bits, one of which is a row-changed bit 820 and the other of which is an in journal bit 830. For any given row, the row-changed bit indicates that the row changed since data items for the row were stored in the IMCU. The in journal bit for a row indicates whether the updated values for the row are stored in the IMCU's journal.
Based on the information in data structure 800, the database server may determine whether the current version of a data item resides in the IMCU, in the journals of the IMCU, or in neither. Specifically, if the structure 800 has no information for a given row, then the IMCU has the current version of the data items from the row. The IMCU also has the current version of the data items from the row if the structure 800 has information for the row, and the row-changed bit for the row is “0”. If structure 800 has information for the row, the row-changed bit is set and the in journal bit is set, then the IMCU does not have the current version of the item, but the journal for the IMCU does have the current version of the item. Finally, if structure 800 has information for the row, the row-changed bit is set, and the in journal bit is not set, then neither the IMCU nor the journals have the current version of the data item, and the current version must be retrieved from the PF data.
The records of structure 800 are created on an as-needed basis. Thus, if the IMCU is current for all data items in a particular extent, then structure 800 may not have any records for the extent. Similarly, if the IMCU is current for all data items in a particular block, then structure 800 may not have any block level information 804 for that block. By only storing changed-row information for extents/blocks that have been changed or added since the version time of the IMCU, structure 800 may be significantly small than it would otherwise be if bits were pre-allocated for every row.
Using the Record of Bit Changes
For a transaction that requires the most recent version of data items, a set bit in the changed-row bitmap 606 indicates that the MF data has stale data for that row, and therefore the IMCU 600 cannot be used to supply data from that row. However, not all transactions require the most recent version of data items.
For example, in many database systems, transactions are assigned a snapshot time, and return data that reflects the state of the database as of that snapshot time. Specifically, if a transaction is assigned a snapshot time of T3, then the transaction must be provided versions of data items that include all changes that were committed before T3, and no changes that were not committed as of T3 (except for changes that the transaction makes itself). For such transactions, a set bit in the changed-row bitmap 606 does not necessarily indicate that the IMCU 600 cannot be used to be the source for items for the corresponding row. Specifically, such transactions may still use the IMCU 600 to obtain data for a particular row, even though the bit for that row is set in changed-row bitmap 606, if the bit was first set after the snapshot time of the transaction.
For example, assume that the column vectors 220 and 222 contain data as it existed at time T1, as indicated by the IMCU version time stored in SMU 604. At a later time T5, an update operation changes row r1. Specifically, the update changes the value of r1c1 for X to Y. In response to this update, the changed-row bitmap 606 of IMCU 600 would change from 000000 to 100000, setting the bit that corresponds to row r1 to “1”. In addition, a record is stored within SMU 604 indicating the bit for r1 was changed at T5.
At yet a later time T9, another update operation changes row r3. Specifically, the second update changes the value of r2c3 from A to B. In response to this update, the changed-row bitmap 606 of IMCU 600 would change from 100000 to 101000, setting the bit that corresponds to row r3 to “1”. In addition, a record is stored within SMU 604 indicating that the bit for row r3 was set at time T9.
After these updates have occurred, the database server may execute a transaction that reads the values of columns c1 and c3. If the snapshot time of the transaction is earlier than T5, then the transaction may read all the values from column vector 220 and 222. The database may determine this by comparing the snapshot time of the transaction to the times indicated in the record of bit changes 608. If the snapshot time of the transaction is after the IMCU version time, but before any times in the record of bit changes 608, than all values in the IMCU 600 are valid relative to that transaction.
If the snapshot time of the transaction is after T5 but before T9, then the transaction may read all values from column vectors 220 and 222 except the values from row r1, which must be obtained elsewhere (e.g. from a journal or from the PF data). If the snapshot time of the transaction is after T9, then the transaction may read all values from column vectors 220 and 222 except the values from rows r1 and r3, which must be obtained elsewhere.
Delete Vectors
In one embodiment, to account for the snapshot time of transactions that read values that are mirrored in IMCU 600, the changed-row bitmap 606 is used in conjunction of the record of bit changes 608 to create a delete vector for each transaction that seeks to read data from IMCU 600. A delete vector is snapshot-time specific, because bits in the delete vector are only set for rows that were updated before the snapshot-time associated with the transaction for which the delete vector is constructed. Stated another way, each delete vector reflects the version of the changed-row bitmap that was current as of the snapshot-time. Thus, the older the snapshot-time associated with a delete vector, the older the version of the changed-row bitmap the delete vector reflects, and thus fewer the number of bits that will be set in the delete vector.
For a transaction with a snapshot time after the version time of the IMCU, a delete vector is made for the transaction by “rolling back” changes that occurred to the changed-row bitmap 606 after the snapshot-time of the transaction. For example, if a transaction has a snapshot time of T5, the database server searches the record of bit changes 608 to identify changes that occurred after time T5. A copy of the changed-row bitmap 606 is made, and within that copy the bits that correspond to the changes that occurred after time T5 are reset to “0”. For transactions with snapshot times before the version time of the IMCU, the delete vector may be generated by making a copy of the changed-row bitmap 606, and within that copy setting to “1” the bits of rows that were changed between the snapshot time of the query and the version time of the IMCU.
Because delete vectors are transaction-specific, at any given time, five distinct transactions may be performing scans of the rows that map to a particular IMCU. Each of the five transactions may have been assigned a different snapshot-time. Consequently, each of the five transactions will have a different delete vector, though all five delete vectors are generated based on the same changed-row bitmap of the SMU that corresponds to the IMCU.
Post-Merge Retention of Pre-Merge Changed-Row Bitmaps
As mentioned above, when changes are merged into an IMCU, all values in the changed-row bitmap of the IMCU are reset to “0” to indicate that no rows have been changed since the new version time of the IMCU (which will be the time at which the IMCU is refreshed/merged). However, rather than simply discard or overwrite the existing changed-row bitmap, a copy of pre-merge changed-row bitmap may be saved. A saved copy of a pre-merge changed-row bitmap is referred to herein as a “retained bitmap”. As shall be described in greater detail hereafter, such retained bitmaps allow a post-merge IMCU to be used to provide data items to transactions that have snapshot times before the merge.
For example, assume that an IMCU is constructed at time T1. From time T1 to time T10, the changes made to the data items in the IMCU are recorded in its global journal, rather than being made directly to the data items themselves within the IMCU. While those changes are being recorded within the journal, the changes are also causing corresponding bits to be set in the changed-row bitmap of the IMCU. At time T10, the changes are merged into the IMCU, causing the version time of the IMCU to change from T1 to T10.
Under these circumstances, the state of the changed-row bitmap immediately before the merger reflects which rows, within the IMCU had changed between time T1 and time T10. By indicating which rows had changed between time T1 and time T10, the changed-row bitmap likewise indicates which rows had not changed between time T1 and time T10. Within the post-merger IMCU, those rows that had not changed between time T1 and time T10 can be provided to transactions that have snapshot times between T1 and T10.
Specifically, a copy of the pre-merge version of the changed-row bitmap is retained after the merger. Along with the retained bitmap, the version timestamp of the pre-merge IMCU is also stored. In the example given above, the retained bitmap would be associated with the version timestamp of T1.
When a transaction (a) requires data items that map to an IMCU, and (b) has a snapshot time that falls between the retrained bitmap time and the current IMCU time, the retained bitmap is used to identify the rows that were not changed between the retained bitmap time and the current IMCU time. Values for the identified rows may be provided to the transaction from the current IMCU. Values for the remaining rows are obtained elsewhere. Specifically, values for the remaining rows may be obtained from the global journal of the IMCU if the relevant journal entries have not yet been purged, or from the PF data.
IMCU Refresh Undo
Rather than store a single retained bitmap in response to the most recent merge, a separate retained bitmap may be stored in response to each merge. The retained bitmaps for a given IMCU may be linked in chronological order. The linked set of retained bitmaps for an IMCU constitutes an “IMCU refresh undo” for the IMCU.
For example, assume that an IMCU was created at time T1, and then refreshed/merged at times T10, T15 and T30. Under these circumstances, the IMCU refresh undo for the IMCU would contain three retained bitmaps RB1, RB2 and RB3. These three retrained bitmaps would be associated with times T1, T10 and T15, respectively.
In the present example, the “0” bits of RB1 indicate the rows that were not changed between times T1 and T10. The “0” bits of RB2 indicate the rows that were not changed between the times T10 and T15. The “0” bits of RB3 indicate the rows that were not changed between the times T15 and T30.
Given any snapshot time, the IMCU refresh undo may be used to identify which rows, within the current IMCU can be provided to a transaction with that snapshot time. For example, for a transaction with the snapshot time T18, the “0” bits in RB3 would indicate which rows can be provided to the transaction from the current IMCU. As another example, for a transaction with the snapshot time of T12, RB2 and RB3 can be combined using a logical OR operation to produce a bitmap that indicates which rows can be provided to the transaction from the current IMCU. As yet another example, for a transaction with the snapshot time of T5, RB1, RB2 and RB3 can be combined using a logical OR operation to produce a bitmap that indicates which rows can be provided to the transaction from the current IMCU.
Thus, given a transaction with the snapshot time of TX, the retained bitmap with the highest timestamp that is below TX is combined, using a logical OR operation, with all more recent retained bitmaps of the same IMCU. The logical “OR” operation produces a bitmap where the “0”s correspond to rows that have not changed since TX and the version time of the current IMCU. Consequently, data items for those rows may be provided by the IMCU.
Transaction Downgrade Based on Memory Constraints
As mentioned above, changes made to items in an IMCU are recorded in journals rather than made directly to the items in the IMCU. The journals are maintained in volatile memory. Unfortunately, long-running transactions that make changes to large numbers of items may cause so many journal entries to be generated that there is insufficient room to store the entries in volatile memory.
Under these circumstances, the journal entries may be flushed to persistent storage to free up space in volatile memory. However, flushing the journal entries to persistent storage, and reading the entries from persistent storage thereafter, incurs a significant performance penalty. Therefore, according to one embodiment, transactions that are generating journal entries in sufficient quantity to cause memory issues are “downgraded”.
According to one embodiment, such transactions are downgraded by pushing their existing private journal entries to the global journal of the IMCU, and ceasing to generate further private journal entries. Although in the global journal of the IMCU, such journal entries are not visible to other transactions because the journal entries are for an uncommitted transaction, and therefore initially associated with an “indeterminate” timestamp. When the downgraded transaction commits, the timestamps of the transaction's entries in the global journal are changed from indeterminate to the commit time of the transaction.
Rather than cease the generation of journal entries when in downgraded mode, transactions may continue to generate journal entries until the size of their private journal once again reaches the specified threshold. At that point, the private journal entries may once again be moved to the global journal, where the entries will not be visible to other transaction due to their indeterminate timestamp. This process of filling the private journal to a threshold, and then moving the entries to the global journal, may be repeated any number of times until the transaction either commits or is rolled back.
Regardless of whether a transaction that is operating in the downgraded mode continues to generate further private journal entries to record its changes, the changes are still recorded in the record of bit changes associated with the IMCU. Once the transaction commits, those bit changes are made to the changed-row bitmap.
By using the changed-row bitmap to record the fact that a change occurred, future transactions will avoid reading stale data items from the IMCU. When the changed-row bitmap indicates that data items associated with a particular row are invalid, transactions that require data items from that row must obtain the data items for a source other than the IMCU. In the case that the changes were made by a downgraded transaction that ceased generating journal entries, the changes will not appear in the global journal, so the data items are retrieved from the PF data.
In one embodiment, not all transactions that are using an IMCU are downgraded at once. Rather, the downgrades are performed on a per-transaction basis, where transactions are only downgraded if they satisfy certain criteria. The criteria may be, for example, that the amount of journal entries that they have generated exceeds a particular threshold.
In general, transactions must see the uncommitted changes that they have made themselves. Consequently, a downgraded transaction that has ceased generating journal entries may have to obtain the values of some data items that the transaction previously changed from the PF data, since no journal entry exists for those changes.
Maintaining Sync without Journals
In the sections above, it is explained that the MF data may be kept in sync with the PF data by recording changes in journals, while leaving the compressed MF data intact until the journals are merged into the compressed MF data. However, in an alternative embodiment, for one or more of the IMCUs, the MF data may be maintained in sync merely by invalidating the data in response to changes made to the corresponding PF data without using journals to record the changes.
In such an embodiment, delete vectors may be generated for a transaction, as described above. For those bits that are not set, the data may be obtained from the appropriate IMCU. For those bits that are set, the data must be retrieved from the PF data, since obtaining data from in-memory journals is not an option when no such journals are maintained.
The benefit of invalidating the MF data without recording the changes in journals is that the processing overhead and memory consumption of maintaining the journals is avoided. However, when data items in the IMCU are too stale to be used to process a transaction, accessing the appropriate version of the data items from the PF data will generally incur more overhead than would be required to obtain the data items from journals. In addition, refreshing the IMCUs in the absence of in-memory journals will also typically incur more overhead, because the changes that need to be merged into the IMCUs must be obtained from the PF data rather than from in-memory journals.
In some embodiments, journals may be maintained for some IMCUs, but not others. In addition, it is possible for the journal of an IMCU to be dropped, and yet continue to use the IMCU for data that has not been invalidated due to changes between the IMCU version time and the snapshot time of the transactions that require the data.
Determining from where to Obtain Data
Because MF data 104 is merely a mirror of some of the PF data (albeit in a different format), all data items contained in MF data 104 are also in the PF data. Therefore, for any query that requires access to data items that are mirrored in the MF data, the database server has the choice of obtaining that data from MF data 104, from the PF data, or partially from the MF data 104 and partially from the PF data.
In general, when the requested data is a row of a table, the location from which to most efficiently retrieve the data is cache 106 (assuming that the persistent format is row-major). If the requested row does not currently reside in cache 106, but the MF data 104 has all columns of the row, then MF data 104 is the location from which to most efficiently retrieve the row. Assuming that MF data 104 is column-major, MF data 104 is less efficient than cache 106 for retrieving a row because, in column-major format, the values for the row must be pieced together from various places within the MF data 104.
If not all of the data for the requested row is in the MF data 104, then at least some of the row must be retrieved from persistent storage 110. Typically, persistent storage 110 is the least efficient location from which to retrieve data, because disk accesses are significantly slower than operations on data stored in volatile memory.
According to one embodiment, the decision of where to the data may be made at any of numerous levels of granularity. For example, the decision of from where to obtain the data may be made on a per-table basis, a per-column basis, a per extent basis, a per segment basis, a per-table-partition basis, etc. Thus, even though all data from column c1 is in column vector 220, the database server may decide to execute a scan by obtaining some of the values of column c1 from column vector 220, and by obtaining the rest of the values of column c1 from the PF data on persistent storage 110.
According to one embodiment, database objects, such as tables, can be “in-memory enabled”. A table that has been in-memory enabled has at least a portion of its data mirrored in the MF data. For example, table 200 is in-memory enabled because data from two of its columns (c1 and c3) are mirrored in mirror format data 104. Specifically, data from column c1 of table 200 is mirrored in column vector 220, and data from column c3 of table 200 is mirrored in column vector 222.
When a table is not mirror-enabled, a scan of the table is performed by reading PF data from cache 106 and/or from persistent storage 110. On the other hand, when a table is mirror-enabled, it may be also possible to get some or all of the table's data from MF data 104. More specifically, it may be possible to obtain data of a mirror-enabled table from any of the following locations:

    
    
        the persistently-stored PF data
        the locally cached PF data
        the PF data in the cache of another instance
        the locally stored MF data
        the MF data stored in the volatile memory of another instance
        the locally stored MF data updated with information from journals
        entirely from the journals
        MF data stored in the volatile memory of another instance updated with information from journals
        any combination of the above.
    
    


Further, the data can be obtained without the use of any indexes, with the use of conventional indexes on the PF data, and/or with the use of in-memory indexes. Further, indexes need not be used in conjunction only with the format based upon which the indexes were built. Thus, conventional indexes built on the PF data may be used to identify rows that must be retrieved, and then data for those rows may be retrieved from the MF data. Similarly, an in-memory index may be used to identify rows that must be retrieved, and some or all of those rows may be retrieved from the PF data.
According to one embodiment, a cost-based optimizer is used to determine, for any given database operation, which of the sources (or which combination of these sources) will be used to supply the data needed by the database operation. Additional factors used by the cost-based optimizer include whether conventional and/or in-memory indexes exist for quickly locating the desired data.
Scanning Operations
According to one embodiment, when it is determined that a table scan operation is to obtain at least some of the requested data from MF data 104, a determination is made as to whether the timestamp associated with the MF data 104 is earlier than the snapshot timestamp being used by the scan. In embodiments where the MF data 104 is contained in an IMCU, the determination is made by comparing the IMCU version time, stored in the SMU of the IMCU, to the snapshot time of the transaction associated with the table scan.
If the MF data timestamp is earlier than the snapshot timestamp being used by the scan, then is possible that some of the data in the IMCU is stale relative to that snapshot time. Under these circumstances, it is possible that the required versions of data items that are stale in the IMCU reside in the global journal of the IMCU or the private journal of the transaction. In this case, the journals associated with the IMCU may also be scanned to obtain the correct version of the data that is stale in the IMCU.
Referring to FIG. 6, assume that column vector 220 has the current version of all values from column c1 of table 200 as of time T1. However, at time T3, R3C1 was changed from X to Y. For R3C1, column vector 220 has the old value X, while journal 602 has the new value Y. Thus, when a table scan with a snapshot time of T5 uses IMCU 600 as a source for any of its data, both the compressed MF data in IMCU 600 and the global journal 602 of IMCU 600 are scanned.
In addition to scanning the global journal 602, the private journal of the transaction that is performing the scan is also scanned. For example, if the transaction performing the scan is TX1, then private journal 662 is also scanned.
Thus, any given table scan may involve scanning the compressed MF data in IMCU 600, scanning global and private journals (e.g. journals 602 and 662), and scanning the PF data (some of which may be in cache 106). Each of these scans can be performed independently and in parallel. Thus, in response to a query that requests values from columns c1 and c2 of table 200, the database server may, in parallel, (a) scan column vector 220 for values from c1, (b) scan journal 602 for updated values from c1, (c) scan journal 662 for updated values of c1, and (d) scan the PF data structures 108 to obtain the values for c2 of table 200.
Scanning Operation Example
Referring to FIG. 7, it is a block diagram of the steps performed by a database server in response to a request to scan a table. The table that is being scanned is split into segments, where each segment includes a set of extents, and each extent includes a set of blocks. In this context, database server determines which blocks contain data that needs to be scanned, and whether to scan the blocks from the PF data, or to obtain the data from the MF data.
Specifically, at step 700, the database server determines where the scan operation is “memory-enabled”. An operation is “memory-enabled” if the operation is permitted to obtain some or all of the data it requires from the MF data. The scan operation may automatically be treated as memory-enabled, for example, if the table that is being scanned (the “target table”) is designated as memory-enabled. A table is “memory-enabled” if data from the table is to be mirrored in the MF data. As described elsewhere, data items from a memory-enabled table maybe proactively loaded into IMCUs, or may be loaded into IMCUs on an on-demand basis. Even if the target table is designated as memory-enabled, a switch may be provided to designate the scan operation as either memory-enabled or not-memory-enabled. A scan operation may be designated as not-memory-enabled to force the scan to be executed against only the PF data.
According to one embodiment, the memory-enabled designation may be made at any of a number of levels of granularity. For example, the designation may be made on a per-table basis, a per-partition basis, a per-segment basis, or a per-extent basis. For the purpose of illustration, it shall be assumed that the memory-enabled designation is made at the per-extent basis.
Referring again to FIG. 7, if the scan is not memory-enabled, then control passes to step 780 and the scan is performed only against the PF data. After the PF data has been used to perform the scan, the scan operation is done (step 782).
On the other hand, if the scan operation is memory-enabled, then control proceeds to step 702. At step 702, the database server determines the range of blocks that contain data required by the scan. Once the range has been determined, control passes to step 704. For the purpose of illustration, it shall be assumed that blocks B1 to B500 contain the data required by the scan operation.
Step 704 is the beginning of a loop that iterates through each block in the range identified in step 704. If at step 704 it is determined that there are no more blocks to scan, then control passes to step 782 and the scan operation is done. If some blocks have not yet been scanned, than control passes from step 704 to step 706.
At step 706, the database server determines the next block, from the range identified in step 702, to scan. At step 708, it is determined whether the address of the block selected in step 706 maps to an IMCU. If the address maps to an IMCU, then the IMCU stores MF versions of at least some data items from the segment. If the IMCU stores MF versions of data items from the segment, then control passes to step 710. Otherwise, control passes to step 712, where the segment that includes the block is obtained from the PF data.
In one embodiment, upon obtaining the PF version of a segment that is mapped to an IMCU, the database server converts the segment into the in-memory format, and stores the MF data thus produced in the IMCU. Such an embodiment employs on-demand loading, which is described in greater detail hereafter. The conversion and loading of the data into the IMCU may take some time. Therefore, at step 714, the database server determines whether to wait for the data from the segment to be converted and loaded. If the database determines to wait, then the database server waits, and control passes to step 708 when the data from the segment has been converted and loaded into the IMCU. If the database server determines not to wait, the data items are obtained from the PF data (step 720), and control returns to step 704.
As mentioned above, control passes to step 710 when it is determined that the address of the block maps to an IMCU. When the address of the block maps to an IMCU, the IMCU contains an MF version of at least some of the data items in the block. However, the versions of the data items that are contained in the IMCU are not necessarily valid relative to the snapshot time of the scan. Therefore, at step 710, it is determined whether the version of those data items in the IMCU is valid for the transaction that is executing the scan. In one embodiment, determining whether the data in the IMCU is valid involves generating a delete vector for the scan operation based on the snapshot time associated with the scan operation, the changed-row bitmap of the IMCU, and the record of bit changes for the IMCU. As described above, the delete vector is a snapshot-specific bitmap where each set bit indicates that the row corresponding to the bit is invalid relative to the snapshot time.
If, at step 710, it is determined that no data items for the current block are valid in the IMCU, control passes to step 716 where data items are obtained from the PF data until the end of the current extent. Then control passes back to step 704.
If the IMCU has valid versions for at least some of the items, then control passes to step 722. At step 722, the data items for which the IMCU has valid versions are fetched from the IMCU. The data items for which the IMCU does not have valid versions are fetched either from entries in the global journal of the IMCU, or from the PF data. As explained elsewhere, various factors may affect the selection of the source from which to obtain data items. Such factors may include, for example, whether the PF disk block that stores the correct version of the data items currently resides in cache. It is possible that only a subset of the data in a segment is mapped to an IMCU. For example, it may be that only a subset of a table's columns are mapped to an IMCU. Under these circumstances, any data items in the segment that are required by the scan but not mapped to the IMCU must be obtained from the PF data.
If the private journal of the transaction performing the scan has updated versions of any of the data obtained from the IMCU or the global journal, those updated versions are provided in place of any version otherwise obtained. This ensures that the scanning transaction sees its own changes, even though those changes have not been committed.
Even when the delete vector indicates that the IMCU has valid data for all rows, the global journal is checked to identify rows that were inserted after the IMCU was created. If the journal does not contain the actual data items for those rows, then the rows are retrieved from the PF data. Similarly, the private journal of the transaction is checked for rows newly inserted by the transaction, and for data items that have been changed by the transaction.
After fetching all necessary data items, control passes from step 722 back to step 704. At step 704, the loop is repeated until data items required by the scan have been obtained, either from an IMCU, from journal entries, or from the PF data.
When to Create the MF Data
Before MF data may be used to satisfy a query, or to improve performance of a query whose results are ultimately obtained from the PF data, the MF data must be present in volatile memory. Unlike cache 106, mirror format data is not simply a copy of the data that is stored on persistent storage 110. Rather, because the mirror format is not based on the persistent format, volatile memory 102 is initially populated by (a) reading the PF data from persistent storage 110 and (b) converting the PF data thus obtained to the MF format.
The amount of overhead that is required to perform the PF-to-MF conversion will vary from situation to situation, based on how different the mirror format is from the persistent format. For example, if the persistent format is row-major disk blocks that have been compressed one way, and the mirror format is column vectors that are compressed another way, the amount of overhead required to perform the conversion may be extensive.
The decision about when to create the MF data may be based on a variety of factors. For example, if sufficient time is available at system start-up, all of the PF data that has been selected for mirroring may be pre-loaded into volatile memory 102 on start up. As mentioned above, loading the MF data involves reading the corresponding PF data from persistent storage 110 and then converting that PF data into the mirror format.
Pre-Loading the MF Data
In one embodiment, the MF data is pre-loaded into volatile memory at database system start up. The pre-loading may be performed, for example, by background processes before any database operation is executed against the memory-enabled data structures that contain the data items that will be mirrored by the MF data.
The MF data may be created one-IMCU at a time. In multi-instance environment, durably stored metadata may be used to determine which MF data is pre-loaded into which database instance. Such metadata may include, for example, a MF-data-to-IMCU mapping and an IMCU-to-instance mapping.
In a simple example, the MF-data-to-IMCU mapping may indicate that IMCU 402 is to store the column vector 220 for c1, and that IMCU 404 is to store the column vector 222 of column c3. The IMCU-to-instance mapping may indicate that IMCU 402 is to be loaded into the volatile memory 502 of instance 1, while IMCU 404 is to be loaded into the volatile memory 504 of instance 2. Based on these mappings, the MF data would be pre-loaded into volatile memory in the manner illustrated in FIG. 5b. 
On-Demand Loading of MF Data
Rather than simply pre-load the MF data, some or all of the MF data may be generated at the time that the corresponding PF data is accessed by a database operation. For example, assume that database instance 1 is assigned to host the column vectors for columns c1 and c3 of table 200. Rather than construct and load those column vectors on start-up, database instance 1 may initially generate no MF data. Instead, the database instance 1 may wait until a database command requires a scan of table 200. Because no MF data has been created yet, the scan is performed based entirely on the PF data. During that scan, the values needed to construct the column vectors for c1 and c2 will be accessed. Therefore, the column vectors for c1 and c2 may be built at that time without incurring any additional disk accesses.
On-demand loading of MF data may be used in conjunction with pre-loading. For example, some of the MF data that is to be hosted on instance 1 may be created at the time instance 1 is started. Other portions of the MF data may be constructed at the time the data is accessed by queries.
In one embodiment, users may set configuration options to indicate which MF data to pre-load, and which MF data to load on-demand. In an alternative embodiment, the database server automatically determines which portions of the MF data are pre-loaded and which are loaded on-demand. In general, the more frequently a data item is used, the more likely the database server will automatically pre-load the data item into MF data so that even the first database operation that requires the data item has the option of obtaining the data from the MF data.
Persistent Storage of IMCU Images
As mentioned above, the MF data may be created on start-up, on-demand, or any combination thereof. In one embodiment, images of IMCUs may be periodically stored to disk. Such persistently-stored images may be used to re-populate volatile memory 102 with MF data after a crash. The image of any given IMCU will be current as of a “checkpoint time”, which may be when the IMCU image was persistently stored. However, that checkpoint time may be before the time that the crash occurred. Consequently, between the checkpoint time of an IMCU image and the time of the crash, additional changes may have been made to the IMCU. Since those changes are not reflected in the stored image, the IMCU image may be stale.
To use an otherwise stale IMCU image, the IMCU image may first be loaded into volatile memory. The IMCU data thus loaded may be usable, in conjunction with durably stored undo information, for database commands that have snapshot times before the checkpoint time associated with the IMCU image. To be usable with database commands that have snapshot times after the checkpoint time, redo information that was durably stored for the associated PF data prior to the crash may be used to populate the stale journals of the IMCU image with journal entries for the changes that occurred after the checkpoint time of the IMCU.
Depending on how many changes were made after the checkpoint time and before the crash, reconstructing an IMCU using a stale persistently stored image of the IMCU may consume significantly less overhead than completely re-generating the IMCU data from the PF data.
Selecting which PF Data to Mirror
The decision of which PF data to mirror, and when to load it, may be based on a variety of factors. For example, if a system has an enormous about of volatile memory 102, and a relatively small database, it may be desirable to mirror the entire database. Thus, all PF data would also be mirrored in the MF data. On the other hand, if there is a relatively small amount of volatile memory 102 relative to the size of the database, then it may be optimal to only mirror a very small fraction of the database.
Typically, when not all of the database is to be mirrored, the portion that is selected to be mirrored is based on which portion will most improve overall performance of the system. Typically, mirroring data that is used frequently will provide more benefit than mirroring data that is used less frequently. Thus, if one table, one column of a table, or one partition of a table is access more frequently than other data in the database, that table, column or partition may be selected to be mirrored in volatile memory 102. The selection of which portions of a database to mirror may be made at any level of granularity. For example, the selection may be made on a per-table basis, a per-column basis, a per extent basis, a per segment basis, a per-table-partition basis, etc.
Self-Verification
In systems that maintain MF data in addition to the PF data, multiple sources of the same data are available to process some queries. In the foregoing sections, it has been explained that when multiple sources of the same data are available, a database server may select from among the possible sources based on which source will result in the most efficient processing of the requested database operation.
However, rather than select one of the possible sources, a database server may alternatively execute the database operation, in parallel, against each of the two or more sources. For example, a query that selects data from column c1 of table 200 may be answered with MF data from column vector 220, or with PF data from PF data structures 108. Rather than select one or the other, the database server can execute the operation, separately and independently, against both sources. Once finished, the results produced by the various sources may be compared against each other. If the result sets do not match, then an error occurred during the processing of at least one of the operations.
The database server may take any number of possible actions when such an error is detected. For example, in one embodiment, an alert is generated to indicate the occurrence of the error. The alert may indicate what the discrepancy is between the two results sets. Instead of or in addition to generating an alert, the database server may perform additional debugging operations, including but not limited to re-executing the operation turning off or on different database features to determine the feature whose use produces the error.
When the results sets match, the user may have a greater degree of confidence that the results of the operation are accurate. Thus, parallel execution, by the same database instance, of the same operation against multiple sources of the same data (the MF data and the PF data) provides an on-the-fly “double check” to verify the result set of the operation.
Typically, the execution of the database operations against the two sources may be done in parallel, so that performing self-verification has little performance impact on the operation relative to performing the operation only on the PF data. According to one embodiment, self-verification may be enabled at a highly granular level. For example, self-verification may be enabled on a per-session basis. Thus, the additional overhead incurred by self-verification may be incurred in only those sessions a user wishes to “test” for accuracy.
Self-verification operations may also be initiated by the system itself. For example, rather than receive a request from an application to execute a database command, the database system may be configured to identify and select “critical” database commands from those that have already been executed by the database system. During periods of low use, the database server may execute one or more of those selected database commands in the background. The selected database commands are executed in self-verification mode to concurrently produce multiple copies of the result set, one based on the MF data and one based on the PF data. The result sets are compared to ensure that the result sets are identical. If not identical, an error message may be sent to a user and/or recorded in a log. If identical, data may be stored to indicate that the selected database command passed a self-verification test. After passing a threshold number of tests (where the threshold may be 1), the database server may be configured to cease to select the database command for automated background self-verification.
In one embodiment, rather than simply generate an alert when a self-verification test fails, the database command is repeatedly retested under different conditions. To ensure that the repeats of the operation are as similar as possible to the original operation that produced the self-verification error, the same database operation may be executed with the same snapshot time as was used during the session that encountered the error.
In many database systems, numerous advanced query processing features may have virtual “on-off” switches, where the default state is “on”. During the repeats of a previously-failed self-verification test, those features may be selectively turned on and off. If the self-verification passes when a particular feature is turned off, and fails when the same particular is turned on, then there is a likelihood that the error is related to that feature.
Having determined that use of a particular feature causes a self-verification problem with a particular database operation, a quarantine may be enforced. The scope of the quarantine may vary. For example, the database server may automatically turn off the particular feature for all future database commands, for all future database commands that target the same data as the database operation that encountered the error, or for only future executions of the specific database command that encountered the error.
Hardware Overview
According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. The special-purpose computing devices may be desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.
For example, FIG. 9 is a block diagram that illustrates a computer system 900 upon which an embodiment of the invention may be implemented. Computer system 900 includes a bus 902 or other communication mechanism for communicating information, and a hardware processor 904 coupled with bus 902 for processing information. Hardware processor 904 may be, for example, a general purpose microprocessor.
Computer system 900 also includes a main memory 906, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 902 for storing information and instructions to be executed by processor 904. Main memory 906 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 904. Such instructions, when stored in non-transitory storage media accessible to processor 904, render computer system 900 into a special-purpose machine that is customized to perform the operations specified in the instructions.
Computer system 900 further includes a read only memory (ROM) 908 or other static storage device coupled to bus 902 for storing static information and instructions for processor 904. A storage device 910, such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to bus 902 for storing information and instructions.
Computer system 900 may be coupled via bus 902 to a display 912, such as a cathode ray tube (CRT), for displaying information to a computer user. An input device 914, including alphanumeric and other keys, is coupled to bus 902 for communicating information and command selections to processor 904. Another type of user input device is cursor control 916, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 904 and for controlling cursor movement on display 912. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.
Computer system 900 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 900 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 900 in response to processor 904 executing one or more sequences of one or more instructions contained in main memory 906. Such instructions may be read into main memory 906 from another storage medium, such as storage device 910. Execution of the sequences of instructions contained in main memory 906 causes processor 904 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, or solid-state drives, such as storage device 910. Volatile media includes dynamic memory, such as main memory 906. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.
Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 902. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.
Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 904 for execution. For example, the instructions may initially be carried on a magnetic disk or solid-state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 900 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 902. Bus 902 carries the data to main memory 906, from which processor 904 retrieves and executes the instructions. The instructions received by main memory 906 may optionally be stored on storage device 910 either before or after execution by processor 904.
Computer system 900 also includes a communication interface 918 coupled to bus 902. Communication interface 918 provides a two-way data communication coupling to a network link 920 that is connected to a local network 922. For example, communication interface 918 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 918 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 918 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
Network link 920 typically provides data communication through one or more networks to other data devices. For example, network link 920 may provide a connection through local network 922 to a host computer 924 or to data equipment operated by an Internet Service Provider (ISP) 926. ISP 926 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 928. Local network 922 and Internet 928 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 920 and through communication interface 918, which carry the digital data to and from computer system 900, are example forms of transmission media.
Computer system 900 can send messages and receive data, including program code, through the network(s), network link 920 and communication interface 918. In the Internet example, a server 930 might transmit a requested code for an application program through Internet 928, ISP 926, local network 922 and communication interface 918.
The received code may be executed by processor 904 as it is received, and/or stored in storage device 910, or other non-volatile storage for later execution.
In the foregoing specification, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction.
In-Memory Space and Object Management Overview
In a conventional relational database management system (RDMS), a space management layer is primarily responsible for defining and maintaining persistent database object containers such as tablespaces and segments. According to techniques described herein, a space management layer of an RDBMS, in addition to or as an alternative to defining and managing persistent database object containers, is used to define and maintain in-memory object containers. The space management layer described herein may support a variety of tasks for defining and maintain in-memory objects including:

    
    
        creating, extending, truncating, and dropping in-memory object containers or areas;
        de-allocating unused space within in-memory object containers;
        creating in-memory segments corresponding to persistent database segments (“on-disk segments”) for tables, table partitions, and table subpartitions;
        loading data from on-disk segments to in-memory segment counterparts;
        supporting serial/parallel query full table scans, ad-hoc queries, and single row lookups of in-memory objects;
        allowing different processes to concurrently perform operations on several in-memory objects; and/or
        tracking and monitoring free space using in-memory space metadata structures.
    
    


An “in-memory object container” as used herein refers to a logical or physical unit of space allocation within memory that may be used to store data for an in-memory object. The example in-memory object containers described herein may be used to form highly-scalable data structures, allowing for concurrent operations on several in-memory objects. The in-memory object containers may further be used to support Structured Query Language (SQL) queries in both online transaction processing (OLTP) and data warehouse (DW) environments. Example in-memory object containers may include:

    
    
        in-memory segments;
        in-memory extents;
        stripes; and/or
        stripe lists.
    
    


An “in-memory object” as used herein refers to a data object that resides in memory. For example, the in-memory object may correspond to a table, cluster, index, or some other database object. In order to store data, the in-memory object comprises a collection of one or more in-memory segments. The in-memory objects may transparently inherit features that are applicable to persistent database objects such as SQL queriability, parallel query, DMLS, and DDLs. By inheriting such features, existing database applications that are designed to interact with a traditional RDBMS may access the in-memory objects with little to no re-architecture.
An “in-memory segment” as used herein refers to a collection of in-memory extents used to store data for a particular in-memory object. In one embodiment, an in-memory segment corresponds to and stores data for a non-partitioned table, a table partition, or a table subpartition. An in-memory segment can coexist with and reference an existing or new on-disk segment or can be purely in-memory with no corresponding on-disk segment. When referenced with a corresponding on-disk segment, the in-memory segment stores a group of pages from the corresponding on-disk segment, albeit, the in-memory segment stores data for the database object in a mirror format whereas the on-disk segment stores data for the database object in a persistent format.
An “in-memory extent” as used herein refers to a contiguous chunk of memory and corresponds to a specific number of bytes of physical storage space within memory. In-memory extents may come from different memory pools that handle allocations in units of different sizes as described in further detail herein. Space within an in-memory area is allocated to in-memory objects in units of in-memory extents.
A “stripe” as used herein refers to a continuous chunk of in-memory extents. Depending on available memory, the size of a stripe may vary from a single in-memory extent to a system-set upper bound. In-memory extents in a stripe are managed using a bitmap, where each bit represents the state of a respective in-memory extent in the stripe and indicates whether the in-memory extent is allocated for a specific segment or is free (unallocated). An individual in-memory segment includes in-memory extents from one or more stripes.
A “stripe list” as used herein refers to a collection of stripes. Depending on available memory, a stripe list may vary from a single stripe to a system-set upper bound. According to one embodiment, the stripe list is the unit of affinity during space search operations. Processes searching for in-memory extents are affined to individual stripe-lists to identify free space within an in-memory area.
General Space and Object Management Architecture
FIG. 10 is a block diagram illustrating a system for managing space and objects within an in-memory area, according to an embodiment. System 1000 generally comprises space management clients 1010a to 1010n, space management layer 1020, and in-memory area 1030.
Space management clients 1010a to 1010n represent one or more system components that interact with space management layer 1020. For example, space management clients 1010a to 1010n may include a set of server processes that are responsible for creating and managing IMCUs such as described above. When a space management client would like to store an IMCU within in-memory area 1030, the space management client submits a space request to space management layer 1020 that identifies the amount of space needed to store the IMCU. Space management layer 1020 responds by allocating space for the IMCU as described in further detail below.
Space management layer 1020 includes a set of one or more background processes that provide space management clients 1010a to 1010n with services for managing space and objects within in-memory area 1030. Space management layer 1020 generally includes in-memory area space management services 1022 and in-memory segment space management services 1024. The services provided by space management layer 1020 may be exposed to space management clients 1010a to 1010n as an application programming interface (API) or through another provided interface that is accessible to other layers within system 1000.
In-memory area space management services 1022 provide a variety of services for managing an in-memory area. These services are herein referred to as “in-memory area operations” or “area operations”. Example area operations include:
creating an in-memory area;
extending an in-memory area;
searching for space within an in-memory area;
freeing space from the in-memory area; and/or
dropping an in-memory area.
In-memory segment space management services 1024 provide a variety of services for managing in-memory segments within an in-memory area. These services are herein referred to as “in-memory segment operations” or “segment operations”. Example segment operations include:
creating an in-memory segment;
allocating in-memory extents for an in-memory segment;
de-allocating in-memory extents for an in-memory segment;
dropping/truncating an in-memory segment;
loading an in-memory segment;
scanning an in-memory segment; and/or
refreshing an in-memory segment.
In-memory area 1030 is a section of volatile memory 102 that is accessible to one or more processes that belong to a database server 120. In one embodiment, in-memory area 1030 is managed as a set of stripe lists and stripes represented through stripe control blocks 1032. Space management layer 1020 uses stripe control blocks 1032 to search the set of stripes for free in-memory extents and allocates the in-memory extents for in-memory segments 1034 in response to space requests received from space management clients 1010a to 1010n. In-memory segments 1034 store data in an in-memory format, such as MF data 104, for one or more in-memory objects.
In-Memory Stripe Layout
As noted above, in-memory area 1030 comprises a set of stripe lists, where each individual stripe list is a collection of stripes and each stripe is a contiguous set of in-memory extents. FIG. 11 depicts an example layout of stripes within an in-memory area, according to an embodiment. In-memory area layout 1100 generally includes one or more stripe lists represented by stripe list control blocks 1102a to 1102i and one or more stripes represented by stripe control blocks 1104a to 1104j, 1106a to 1106k, and 1108a to 1108b. 
Each respective stripe list control block includes pointers that map to a set of one or more stripe control blocks. A stripe “belongs” to a particular stripe list (the “parent”) when the stripe list control block representing the particular stripe list includes a pointer that maps to the stripe control block representing the stripe. Thus, stripes represented by stripe control blocks 1104a to 1104j belong to the stripe list represented by stripe list control block 1102a, and stripes represented by stripe control blocks 1106a to 1106k and 1108a, 1108b, etc. belong to the stripe list represented by stripe list control block 1102i. 
A stripe list control block may point to a stripe control block either directly or indirectly. For example, stripe list control block 1104j directly points to stripe control blocks 1106a to 1106k and 1108a, but does not directly point to stripe control block 1108b and subsequent control blocks. Rather, stripe control block 1108a is the first block in a linked list of stripe control blocks, with each stripe control block in the linked list including a pointer to the next and previous stripe control block in the linked list. Thus, stripe list control block 1104j includes a direct pointer to the first stripe control block in the linked list (stripe control block 1108a) and indirectly points to other stripe control blocks in the linked list (stripe control block 1108b, etc.).
Each respective stripe list control block may include one or more fields that are used to manage and define characteristics of the stripe list represented by the respective stripe list control block. For example, the stripe list control block may include one or more of the following header control fields:

    
    
        an index of the stripe list control block within an array of stripe list control blocks for the in-memory area;
        the number of stripe control blocks directly mapped by the stripe list control block;
        the total space being mapped by the stripe list control block; and/or
        a data block address for a corresponding on-disk image.
    
    


A stripe list control block further includes fields that define characteristics of the stripes that belong to the stripe list. For example, the stripe list control block may include fields that indicate the current status of a stripe, the amount of free space within the stripe, and where the stripe resides in memory. According to an embodiment, to define the characteristics of the corresponding stripes, the stripe list control block includes an array of stripe control blocks that includes one or more of the following fields for each stripe control block in the array:

    
    
        a flag variable indicating a status of the stripe represented by the stripe control block (e.g., the flag may indicate whether the stripe control block has been formatted for use with the stripe list control block, whether the stripe control block is being freed or has been freed back to memory for other uses, whether the stripe is allocated or unallocated, etc.);
        a memory address of the stripe control block;
        the amount of free space in the stripe control block; and/or
        a latch and latch recovery area to serialize allocation and de-allocation of in-memory extents from the stripe control block (the latch may be used to prevent multiple processes from allocating or de-allocating in-memory extents from the same stripe at the same time).
    
    


Each individual stripe control block from stripe control blocks 1104a to 1104j, 1106a to 1106k, and 1108a, 1108b, etc. maps a number of contiguous in-memory extents allocated when the in-memory area is created or extended. The number of extents mapped by an individual stripe control block may vary between stripe control blocks or may be fixed, depending on the particular implementation. In one embodiment, each stripe control block maps to the maximum possible contiguous physical memory guaranteed by an operating system that manages volatile memory. For example, if the maximum possible contiguous physical memory that the operating system may guarantee for a stripe is 100 megabytes, and in-memory extents are allocated in one megabyte chunks, then the stripe is mapped to 100 contiguous in-memory extents.
Each stripe control block includes a plurality of fields for defining and managing the respective stripe. For example, the stripe control block may include a set of header control fields. The header control field may include a set of parameter values such as:

    
    
        an index of the stripe within an array of stripe control blocks within a parent stripe list control block;
        a back pointer to the parent stripe list block;
        a starting memory address of the first extent in the stripe
        the length of the stripe;
        the number of extents in the stripe;
        the first free bit within the stripe from which space for an in-memory segment may be allocated;
        the number of free bits in the stripe; and/or
        a link to the next and/or previous stripe control block in a linked list of stripe control blocks.
    
    


Each stripe control block further includes a bitmap for identifying free in-memory extents within the stripe. Each bit in the bit-vector maps to a different in-memory extent within the stripe. A first bit value is used to indicate that the in-memory extent is currently allocated to a segment, and the second bit value is used to indicate that the in-memory extent is unallocated and free for allocation. For example, for a stripe with five in-memory extents, the stripe control block may store the bit-vector “11010” to indicate that the first, second, and fourth in-memory extents within the stripe are allocated to in-memory segments while the third and fifth in-memory extents are free for reuse.
When a process is accessing an individual stripe, the process may acquire a latch from the stripe control block to prevent other processes from concurrently accessing the corresponding stripe. Once the process has finished accessing the stripe, the process releases the latch to allow other processes to subsequently access the stripe. While the latch serializes access to the corresponding stripe, other processes may concurrently access other stripes within a stripe list or in different stripe lists. For example, while a first process is performing an area operation on a first stripe, one or more other processes may concurrently perform area operations on different stripes without waiting for the first process to release the latch on the first stripe. Thus, control blocks may be used to spread access hotness during concurrent workloads.
In-Memory Segment Layout
An in-memory segment includes a collection of in-memory extents. Because in-memory extents are allocated in an on-demand/as-needed basis, the in-memory extents for an in-memory segment may not be contiguous within volatile memory. In addition, the in-memory extents for a particular in-memory segment may come from a single stripe or may span multiple stripes within a particular stripe list. For example, an individual in-memory segment may have in-memory extents allocated from both stripe control blocks 1104a and 1104j. 
Each in-memory segment generally comprises segment metadata for managing and defining the in-memory segment. Referring to FIG. 12, for example, it is a block diagram depicting an example in-memory segment layout, according to an embodiment. Segment layout 1200 includes segment header 1202, extent map block 1204, extent entries 1206a and 1206b, and in-memory extents 1208a and 1208b. 
Segment header 1202 includes a plurality of parameter values that define characteristics of the corresponding in-memory segment. For example, the segment header may identify the number of in-memory extent map blocks allocated for the in-memory segment, the in-memory object to which the in-memory segment belongs, an address of the last in-memory extent map block, and/or a parameter value (e.g., a high water mark) to indicate a boundary between used and unused in-memory extents within the in-memory segment.
When the in-memory segment has an on-disk counterpart, segment header 1202 includes mapping data that maps the in-memory segment to the on-disk segment as described in further detail below. If there is no on-disk counterpart, then mapping data to a corresponding on-disk segment is not maintained within segment header 1202.
Extent map block 1204 includes an array of extent entries, where each entry includes a pointer that maps to a different in-memory extent allocated to the segment. For example, extent entry 1206a maps to in-memory extent 1208a, extent entry 1206b maps to in-memory extent 1208b, etc. According to an embodiment, each extent entry may include:

    
    
        a start address of the corresponding in-memory extent;
        a length of the corresponding in-memory extent;
        a freeness state of the corresponding in-memory extent (e.g., what percent is used to store data and what percent is free to store data);
        an identifier of the stripe list control block to which the corresponding in-memory extent belongs;
        an identifier of the stripe control block to which the corresponding in-memory extent belongs; and/or
        mapping data that maps the in-memory extent to corresponding on disk counterpart as described in further detail below.
    
    


In one embodiment, the in-memory segment metadata is maintained in a set of one or more hash tables within volatile memory. Different hash tables may be used for different classes of in-memory segment. For example, a first hash table may be used to store segment metadata for a data segment of a table, a second hash table may be used to store segment metadata for sorted projections, and a third hash table may be used to store data for undo segments. One or more values within the segment header may be used as a hash key to identify a particular segment within a hash table. For instance, the tablespace identifier, data object identifier, and/or relative data bock address of an associated on-disk segment may be used as a hash key. A hash function is applied to map the hash key to the location of the segment within the hash table. In another embodiment, the in-memory segment metadata is maintained in a linked list of in-memory segments. As in-memory segments are added or dropped, the linked list is updated to maintain a current list of in-memory segments that reside within in-memory area 1030.
Auxiliary in-Memory Maps and Query Rewrite
As previously mentioned, data stored within the in-memory object containers may also be stored in corresponding persistent object containers in a persistent format. For example, an individual in-memory extent may contain data corresponding to contiguous data blocks on disk. As another example, an in-memory segment may contain data corresponding to an on-disk segment. In such scenarios, the in-memory object containers store data for the same database object as the persistent object containers; however, the in-memory object containers store the data in a mirror format, whereas the persistent database object containers store the data in a persistent format. Thus, the organization of the data and the metadata is different between the in-memory object containers and the persistent object containers.
To track which database objects have both PF data and MF data, space management layer 1020 maintains an in-memory map that stores an association between in-memory object container and their corresponding on-disk object container. For example, the in-memory map may maintain an association between a page range of an on-disk segment and a corresponding in-memory extent. As another example, the in-memory map may maintain associations that indicate which in-memory segments are associated with which on-disk segments and vice versa.
According to one embodiment, mapping data is maintained within the metadata of the in-memory data object containers. For example, the segment header for an in-memory segment may include one or more of the following:
a tablespace identifier for the corresponding on-disk segment;
a data object identifier for the corresponding on-disk segment;
a data block address for the corresponding on-disk segment;
the number of on disk ranges being mapped by the in-memory segment; and/or
the number of on-disk blocks being mapped by the in-memory segment.
As another example, each extent entry within the array of extent entries may include one or more of the following:

    
    
        a starting data block address of the on-disk block range mapped by the in-memory extent;
        an ending data block address of the on-disk block range mapped by the in-memory extent; and/or
        an extent offset for the corresponding on-disk range.
    
    


According to one embodiment, space layer 1020 maintains the associations in a set of auxiliary tablespace extent maps that are separate from the in-memory object containers. The auxiliary tablespace extent maps are maintained on a per tablespace basis, with each auxiliary tablespace extent map mapping portions of the respective on-disk tablespace to corresponding in-memory extents where the data is located. For example, the mapping may maintain a row data block address to in-memory extent mapping that indicates, for each respective in-memory extent that maps a portion of the on-disk tablespace, the on-disk range of data blocks that are contained by the respective in-memory extent and a memory address for the in-memory extent. Thus, given a relative data block address and tablespace identifier, space management layer 1020 may search the auxiliary tablespace extent map to determine whether a particular on-disk range is mirrored in the MF data and to retrieve the corresponding in-memory extents that contain the MF data.
In-Memory Extent Pools
As indicated above, in-memory extents within an in-memory area may come from different memory pools that handle allocations in units of different sizes. According to one embodiment, the in-memory area is divided into two pools: a first pool that handles allocations according to a first allocation size, and a second pool that handles allocations according to a second allocation size. In other embodiments, additional pools beyond the first and second pool may be used that handle allocations in different sizes.
Each pool may handle allocations for different in-memory objects. According to an embodiment, a first pool handles allocations of in-memory extents for IMCUs that are hosted in the in-memory area, and a second pool handles allocations for in-memory extents that store segment metadata, SMUs, private journal segments, and shared journal segments.
Generally, the allocation size for in-memory extents that store IMCU data will be much larger than the allocation size for in-memory extents storing metadata. For instance, the first pool may handle allocations in units of one megabyte while the second pool may handle allocations in units of 64 kilobytes. However, the allocation sizes of the various pools may vary from implementation to implementation.
According to one embodiment, the in-memory area is not divided evenly between the two pools. Rather, a greater percentage of the in-memory area is reserved for one pool than another pool. For example, a greater percentage of the in-memory area may be reserved for IMCU extents, as the IMCUs will likely require more memory than segment metadata, SMU extents and journal segment extents. The amount of memory that is reserved for each pool may vary from implementation to implementation.
In-Memory Area Creation and Extension
According to an embodiment, the area operations supported by in-memory area space management services 1022 include operations for creating the in-memory area. Creation of the in-memory area may be initiated at database system startup or on-demand. To create the in-memory area, space management layer 1020 allocates memory from volatile memory 102 for the stripe list control blocks. An array of stripe list control block addresses is then populated with the address of each stripe list control block within the in-memory area. For each stripe list control block, space management layer 1020 allocates memory from volatile memory 102 for a set of stripes and updates the stripe list control block with pointers to the set of stripes and with the total space mapped by the corresponding stripe list. For each stripe within a stripe list, space management layer 1020 reserves space at the beginning of memory allocated for the stripe and adds a stripe control block. Space management layer 1020 then formats the stripe control block by populating the fields of the stripe control block with the appropriate values. For example, space management layer 1020 may add a back pointer to the parent stripe list control block, the starting address of the first in-memory extent in the stripe, the length of the stripe, the first free bit in the stripe, the number of free bits in the stripe, etc. The in-memory area is created once the stripe list control blocks and stripes control blocks have been successfully generated and formatted.
In some cases, it may be beneficial to extend an existing in-memory area. For example, if space management layer 1020 is unable to locate a free in-memory extent for allocation, then the in-memory area may be extended to add more in-memory extents and stripes. According to an embodiment, space management services 1022 supports an extend in-memory area operation for adding stripes and in-memory extents and increasing the size of the in-memory area. To add a new stripe, space management layer 1020 allocates additional memory from volatile memory 102 for the stripe. A stripe control block is added to the beginning of the allocated memory and formatted such as described above for the create in-memory area operation. The parent stripe list control block is updated by adding a pointer to the new stripe. Accordingly, the new stripe, including the new set of in-memory extents to which it is mapped, is added to the stripe list.
Space Search and Reclamation
According to an embodiment, in-memory area space management services 1022 supports space search and reclamation operations. In an area space search operation, space management layer 1020 searches a set of one or more stripe lists in the in-memory area for a free in-memory extent. A “free” in-memory extent in this context refers to one that is available for allocation to an in-memory segment. An in-memory extent is free if it is not currently allocated to an in-memory segment and has not been freed (or in the process of being freed) back to volatile memory 102. If space is not found within the set of stripe lists, then space management layer 1020 may perform an in-memory extension operation to add more in-memory extents.
To search a stripe list for an unallocated in-memory extent, space management layer 1020 searches the array of stripe control blocks to identify an available stripe with free space. Once the available stripe is identified, space management layer 1020 then searches the bitmap in the stripe control block of the available stripe to identify an available in-memory extent. Space management layer 1020 updates the bit corresponding to the in-memory extent within the bitmap to indicate that the in-memory extent has been allocated.
Space reclamation operations allow space allocated for the in-memory area to be de-allocated and reclaimed by volatile memory 102 for other uses. For a drop area operation, space management layer 1020 de-allocates memory for the entire in-memory area, including for all stripe lists, stripes, and in-memory extents. For a free space operation, space management layer 1020 de-allocates memory for a set of one or more stripes within the in-memory area rather than de-allocating memory for all stripes. During the free space operation, space management layer 1020 updates the stripe control block status to indicate that the stripe is being reclaimed. This prevents in-memory services from searching for free space in such stripes during a space search operation.
In-Memory Segment Creation and Extent Allocation
According to an embodiment, in-memory segment space management services 1024 support segment creation and extent allocation operations. When a database object is in-memory enabled, space management layer 1020 performs a segment creation operation to create a set of one or more in-memory segments for the database object. To create an in-memory segment, space management layer 1020 performs a space search operation as described above to locate a free in-memory extent. Once located, space management layer 1020 reserves at least a portion of the in-memory extent for the segment header. Space management layer 1020 then formats the segment header by adding the appropriate segment metadata. For example, space management layer 1020 may add segment identification data to the segment header that defines the in-memory segment and identifies the in-memory object to which the in-memory segment belongs. Space management layer 1020 may further add data that identifies a corresponding on-disk segment such as a tablespace identifier, data object identifier, and data block address for the corresponding on-disk segment.
After an in-memory segment has been created, in-memory extents may be allocated and registered with the in-memory segment. An extent allocation operation takes place when an in-memory segment is loaded or refreshed. To allocate an in-memory extent for an in-memory segment, space management layer 1020 performs a space search operation as described above to locate a free in-memory extent. Once located, space management layer 1020 registers the in-memory extent with extent map block for the in-memory segment. This process may be repeated to allocate as many in-memory extents needed to meet the amount of space requested by a space management client.
“Registering” the in-memory extent, as used herein, includes adding extent identification data to an in-memory segment. For example, the following information may be added to the extent map block of an in-memory segment during registration:

    
    
        the start address of the in-memory extent;
        the length of the in-memory extent;
        an identifier of the stripe list control block for the stripe list to which the in-memory extent belongs;
        an index of the stripe control block for the stripe to which the in-memory extent belongs; and/or
        mapping information that indicates an on-disk range that is mapped by the in-memory extent.
    
    


Once registered, the in-memory extent “belongs” to the in-memory segment and cannot be used by another in-memory segment until it is de-allocated.
In-Memory Segment Drop and Extent De-Allocation
According to an embodiment, in-memory segment space management services 1024 support segment drops and extent de-allocations. In-memory segment drops occur when a DDL drop or truncate statement (e.g., DROP TABLE or TRUNCATE TABLE) is received for the database object to which the in-memory segment corresponds. To drop the in-memory segment, space management layer 1020 performs an extent de-allocation operation for each in-memory extent that is registered to the in-memory segment. Thus, dropping the in-memory segment frees the previously allocated in-memory extents within in-memory area 1030 such that these in-memory extents may be reused by a different in-memory segment.
During an extent de-allocation operation, space management layer 1020 updates the bitmap in the stripe control block for the stripe to which the in-memory extent belongs to indicate that the in-memory extent is no longer allocated and is free for reuse by a different in-memory segment. To update the bitmap, the bit value corresponding to the in-memory extent is changed from a first value indicating the in-memory extent is allocated to a second bit value indicating that the in-memory extent is unallocated. Space management layer 1020 then updates the extent map block by deleting the extent entry for the in-memory extent.
In-Memory Segment Loads
According to an embodiment, a background loader interacts with space management layer 1020 to load an on-disk segment into an in-memory segment. During a load operation, the background loader divides the entire on-disk segment into chunks. For example, each chunk may correspond to a different on-disk range of data blocks within the on-disk segment where each on-disk range stores a different set of rows for a table in a persistent format. For each on-disk chunk, the background loader reads data from the on-disk chunk, converts the data from a persistent format to a mirror format, and requests variable sized in-memory chunks from space management layer 1020 that are bounded by the corresponding on-disk chunk range.
By dividing the on-disk segment into different chunks, different background processes may load the data into the in-memory segment in parallel. For example, a first background process may perform a first scan operation to read PF data from a first on-disk chunk while a second background process concurrently performs a second scan operation to read PF data from a second on-disk chunk. Similarly, the conversion of PF data to MF data and the memory loads may be performed by different background processes in parallel for the different on-disk chunks.
To convert the data from the persistent format to the mirror format during a load operation, the background loader creates an IMCU to store the data as described above. For example, the background loader may convert the data from a compressed row-major format to a compressed column-major format. Once the IMCU is created, the background loader requests n bytes of space from space management layer 1020 to store the IMCU within in-memory area 1030, where n bytes corresponds to the size of the IMCU.
During the load operation, space management layer 1020 creates an in-memory segment representing the entire on-disk segment. When space management layer 1020 receives a request to load data for an on-disk chunk, space management layer 1020 performs an in-memory extent allocation operation as described above to allocate a set of one or more in-memory extents for each on-disk chunk and register the set of in-memory extents within the in-memory segment. The background loader then loads the MF data into the set of one or more in-memory extents.
During the load operation, space management layer 1020 adds/updates the mapping data that maintains associations between the in-memory object containers and their corresponding on-disk counterparts. In one embodiment, space management layer 1020 adds, to the extent map block of the in-memory segment, the start data block address and the end data block address for the on-disk range that is mapped by the in-memory extent. In another embodiment, space management layer 1020 adds an on-disk range to in-memory extent mapping to the auxiliary tablespace map. Thus, given the location of PF data within persistent storage, the in-memory extent(s) that store the corresponding MF data may be located and retrieved.
In-Memory Segment Scans
As previously mentioned, a database server may determine which blocks contain data that needs to be scanned and whether to scan the blocks from the PF data or to obtain the data from the MF data. According to one embodiment, the database server uses the auxiliary tablespace extent map for queries trying to scan a given on-disk range to locate and the MF data from the appropriate in-memory object containers. For a query performing a lookup of a single row, for example, the database server determines the on-disk range of data blocks that contain data for the row. The database server then searches the auxiliary tablespace extent map to determine whether the on-disk range maps to one or more in-memory extents. If the on-disk range is mapped to the in-memory extents, then MF data for the row exists, and the memory address of the in-memory extents is determined from the auxiliary tablespace extent map. The database server then retrieves MF data from the in-memory extents without the need to scan the PF data from disk blocks in persistent storage.
In-Memory Segment Refresh
As previously mentioned, MF data becomes stales when the corresponding PF data is changed in a manner that is not reflected in the MF data. For example, a transaction may write a row update to an on-disk segment without writing the update to a corresponding in-memory segment. Thus, the data is not consistent between the on-disk segment and the in-memory segment after the update.
In order to maintain data consistency and integrity between an on-disk segment and the corresponding in-memory segment, a background processes refreshes the in-memory segment in a copy-on-write fashion, according to an embodiment. When the on-disk segment is changed, space management layer 1020 performs a segment refresh operation on the corresponding in-memory segment to load the new/changed MF data into the in-memory segment and discard the outdated MF data. For example, the refresh operation may be triggered by a row update, insertion, or other DML operation that alters the PF data.
To perform a refresh operation, space management layer 1020 determines which on-disk segment has been updated. Space management layer 1020 then performs a drop operation such as described above to drop the corresponding in-memory segment from in-memory area 1030. Once the stale in-memory segment is dropped, space management layer 1020 performs a segment create and load operation to re-allocate space from in-memory area 1030 for a new in-memory segment corresponding to the updated on-disk segment and load the new in-memory segment with the current data from the updated on-disk segment. Once refreshed, the database server may access MF data from the new segment during a scan operation while maintaining data consistency and integrity.
Parallel Query and Non-Uniform Memory Access Overview
Some RDBMSs support parallel query processing with respect to on-disk database objects such that query operations on the on-disk database objects are distributed across multiple processes in a multi-processor system. For example, a parallel query may divide a full table scan operation across different processes, with each process transferring a different set of rows within the table from disk to memory. The processes operate on the on-disk database object in parallel, which may significantly reduce overall query execution time.
According to techniques described herein, parallel query processing is extended to the in-memory objects described above. A parallel query on an in-memory object determines the in-memory chunks that belong to the in-memory object. The parallel query then assigns a set of processes and divides the query workload among these processes. The processes operate on the in-memory chunks in parallel to generate a series of result sets. An “in-memory chunk” in this context is a portion of the in-memory object that may be operated on and stored independently from other portions (e.g., chunks) of the in-memory object. For example, the in-memory chunk may correspond to an IMCU that is stored across a set of one or more in-memory extents such as described above. After generating the series of result sets for the individual in-memory chunks, the parallel query combines the result sets to obtain a final result set for the query operation.
In order to further optimize query execution, the in-memory chunks for an in-memory object are distributed across different nodes in a NUMA multiprocessor system, according to an embodiment. NUMA multiprocessor systems allow clustering of CPUs into groups where each group is assigned a local memory. A process running on a particular NUMA node can access the local memory at a much lower latency than accessing memory assigned to another NUMA node. By distributing the in-memory chunks across different NUMA nodes, memory access bottlenecks may be alleviated while the processes operate on the in-memory chunks in parallel.
In an embodiment, the database server maintains NUMA affinity information for the in-memory objects. Without the NUMA affinity information, the parallel query would be unaware of the NUMA location of individual in-memory chunks. As a result, the query workload would be distributed randomly across NUMA nodes, which could result in processes running on a particular NUMA node operating on in-memory chunks from remote memory of other NUMA nodes. With the NUMA affinity information the parallel query distributes query operations across NUMA nodes such that processes affined to an individual node operate on in-memory chunks in the local memory of the NUMA node and not in-memory chunks in remote memories belonging to other NUMA nodes. Thus, the processes may take full advantage of fast local memory accesses while operating on the in-memory chunks in parallel.
General NUMA Architecture
FIG. 13 is a block diagram illustrating a multiprocessor system that allows NUMA affinitized parallel queries on in-memory objects, according to an embodiment. Multiprocessor system 1300 represents a single database compute node where a database instance is run. For example, multiprocessor system 1300 may be a single-instance symmetric processing (SMP) compute node equipped with hundreds of processors. Multiprocessor system 1300 generally includes server processes 1302 and NUMA nodes 1304a to 1304i. 
Server processes 1302 comprise processes that are associated with the database server instance, including processes for distributing in-memory objects across NUMA nodes 1304a to 1304i and processes for performing parallel query processing. For example, server processes 1302 may include processes that are associated with the background loader, space management layer, and/or other components of an RDBMS. Processes for performing parallel query processing are herein referred to as “parallel server processes” and are described in further detail below. Server processes 1302 may be executed on one or more of NUMA nodes 1304a to 1304i, on a separate node, or some combination thereof
Each NUMA node in multiprocessor system 1300 comprises a cluster of processors that are assigned or otherwise associated with a memory. NUMA node 1304a includes CPUs 1306a to 1306j and memory 1310a, and NUMA node 1304i includes CPUs 1308a to 1308k and memory 1310l. A memory that is associated with a particular NUMA node is referred to as “local” with respect to the particular NUMA node and processes running on the particular NUMA node and “remote” with respect to other NUMA nodes and processes running on other NUMA nodes. For example, memory 1310a is a local memory for NUMA node 1304a and a remote memory for NUMA node 1304i, and memory 1310l is local to NUMA node 1304i and remote to NUMA node 1304a. 
Processors within multiprocessor system 1300 have different access priorities based on their NUMA node location. Processors have higher access priority to local memory (i.e., memory at the same NUMA node location) than to remote memory. For example, CPUs 1306a to 1306j have higher access priority to memory 1310a than to memory 1310l, and CPUs 1308a to 1308k have higher access priority to memory 1310l than memory 1310a. Such location-based access priority typically results in inter-node memory accesses having higher latencies than intra-node memory accesses.
The processor clusters for each NUMA node may be software or hardware based, depending on the particular implementation. In a software-based implementation, software is responsible for grouping processors and assigning each group to a local memory. In a hardware-based implementation, multiprocessor system 1300 includes physically different system buses at each NUMA node location. Processors that belong to the same NUMA node share the same memory bus, which connects to a memory controller for accessing local memory. The memory bus at one NUMA node location is not shared with processors that do not belong to the NUMA node. Rather, the memory controller for the local memory is connected to memory controllers for remote memories in other NUMA nodes via a separate high-speed interconnect. When a processor accesses data that resides in a remote memory, the data is transferred over the high-speed interconnect and not the system bus in the remote NUMA node. This arrangement reduces bottlenecks caused when several processors attempt to access memory via the same bus.
In some embodiments, each processor within multiprocessor system 1300 may have direct access to all input/output devices (I/O devices) and all memory areas within the multiprocessor system 1300. In addition or alternatively, each multiprocessor system may be controlled by a single operating system instance. In addition or alternatively, each processor may execute processes or threads that belong to or are otherwise associated with a single database server instance.
Stripe Allocation Across NUMA Nodes
As indicated above, in-memory area 1030 serves as a container for the in-memory objects on a database compute instance and includes a collection of stripes. According to an embodiment, in-memory area 1030, including the collection of stripes that form in-memory area 1030, is distributed and maintained across a plurality of NUMA nodes within multiprocessor system 1300. When space management layer 1020 performs an in-memory area creation or extension operation, for example, a set of one or more stripes is carved out of each of memories 1310a to 1310l to create or extend in-memory area 1030 across the plurality of NUMA nodes. Thus, each of NUMA nodes 1304a to 1304i maintains a different set of respective stripes that belong to in-memory area 1030.
According to one embodiment, space management layer 1020 evenly distributes the stripes of in-memory area 1030 across NUMA nodes 1304a to 1304i such that each NUMA node is represented by approximately the same number of stripes. For example, the stripes may be allocated in a round robin fashion starting with NUMA node 1304a. and ending with NUMA node 1304i and repeating until all the stripes have been allocated. Thus, the first stripe is allocated from memory 1310a, and the next set of stripes are allocated from any intervening nodes between NUMA node 1304a and NUMA node 1304i until NUMA node 1304i is reached. Once a stripe is allocated from memory 1310l for NUMA node 1304i, the stripe allocation cycles back to NUMA node 1304a, and the process repeats until there are no stripes left to distribute.
Maintaining Different in-Memory Extent Pools on NUMA Nodes
As indicated above, in-memory area 1030 may be divided into a plurality of pools, with each pool handling allocations in different allocation size units. For example, in-memory area 1030 may be divided into two pools, with memory managed as a set of stripes, including a first pool that handles allocations of in-memory extents according to a first allocation size (e.g., 1 MB), and a second pool that handles allocations of in-memory extents according to a second allocation size (e.g., 64 KB).
According to an embodiment, the plurality of memory pools are distributed across NUMA nodes 1304a to 1304i, with each NUMA node maintaining a set of stripes for each pool. With two pools, for example, in-memory extents from both the first pool and second pool are carved out of memory 1310a. Similarly, in-memory extents from both the first pool and second pool are carved out of the local memories of other NUMA nodes, including memory 1310l. Thus, each NUMA node may handle memory allocation in units of varying allocation sizes.
NUMA-Aware in-Memory Chunk Allocation
According to an embodiment, the in-memory chunks that belong to an in-memory object are distributed across a plurality of NUMA nodes. Referring to FIG. 14, it depicts a block diagram of a set of NUMA nodes that maintain different in-memory chunks for an in-memory object. In-memory object 1402 is sharded across a set of NUMA nodes, including NUMA node 1404, NUMA node 1406, and NUMA node 1408. Each of these NUMA nodes maintains multiple in-memory chunks that belong to in-memory object 1402. NUMA node 1404 maintains in-memory chunks 1410, 1416, and 1422, NUMA node 1406 maintains in-memory chunks 1412, 1418, and 1424, and NUMA node 1408 maintains in-memory chunks 1414 and 1420.
The manner in which the in-memory chunks are distributed across the plurality of NUMA nodes may vary from implementation to implementation and may depend on whether the in-memory object is associated with a corresponding on-disk object or whether the in-memory object is purely in-memory without any corresponding on-disk object. A database server instance may select the policy to apply based on whether an in-memory object has a corresponding on-disk counterpart or is purely in-memory. As an example, if a database table that resides on-disk is in-memory enabled, the database server instance may employ a policy that selects NUMA node locations based on page range, such as described in further detail below. By selecting the NUMA node location based on page range, refreshes and reloads of the in-memory chunk result in allocations from the same NUMA node. If the database server instance determines that the in-memory object does not have a corresponding on-disk counterpart, such as may be the case with views or other objects that are not materialized, the database server may employ a policy that evenly distributes the in-memory chunks across the NUMA nodes such that each NUMA node has substantially the same number of in-memory chunks.
In one embodiment, a database server instance allocates in-memory extents in a round-robin fashion such that the load is uniformly balanced across NUMA nodes. For example assume that in-memory object 1402 corresponds to an in-memory segment that is purely in memory, and each of in-memory chunks 1410 to 1424 corresponds to a different in-memory extent. When loading the in-memory object, an in-memory extent is allocated from local memory on NUMA node 1404 for in-memory chunk 1410. Next, an in-memory extent is allocated from local memory on NUMA node 1406 for in-memory chunk 1412, followed by an in-memory extent from local memory on NUMA node 1408 for in-memory chunk 1414. This process repeats in a round robin fashion until all in-memory chunks for in-memory object 1402 have been distributed. Thus, in-memory extents for in-memory chunks 1410 to 1424 are allocated in sequential order. A round-robin distribution allows a purely in-memory segment to be effectively load balanced across the plurality of NUMA nodes.
When an in-memory object references an on-disk database object, NUMA nodes are assigned to an in-memory chunk based, at least in part, on the on-disk page range that maps to the in-memory chunk. According to an embodiment, a hash function is applied to the page addresses of each on-disk chunk to compute a NUMA node location for a corresponding in-memory chunk. For example, assume that each of in-memory chunks 1410 to 1424 corresponds to an on-disk chunk. Applying the hash function to the on-disk page addresses corresponding to in-memory chunks 1410, 1416, and 1422 maps these chunks to NUMA node 1404. Similarly applying the hash function maps in-memory chunks 1412, 1418, and 1424 to NUMA node 1406 and in-memory chunks 1414 and 1420 to NUMA node 1408 based on the page addresses for the corresponding on-disk objects.
According to an embodiment, the background loader is responsible for determining how to distribute in-memory chunks across the plurality of NUMA nodes. As discussed above, if an on-disk segment is enabled to be in-memory, the background loader creates a corresponding in-memory segment. Depending on the on-disk data to be loaded in-memory, the background loader divides the entire on-disk segment into chunks and then, for each chunk, requests variable sized in-memory chunks from in-memory area 1030 to host data in IMCUs bounded by the on-disk chunk range. If NUMA is enabled, then, before the background loader requests space from space management layer 1020, the background loader maps the on-disk chunk to an individual NUMA node. For example, the background loader may apply a hash function to the page addresses of each on-disk chunk to map the on-disk chunk to a particular NUMA node location. The background loader passes this NUMA affinity information to space management layer 1020 when making space requests for the on-disk chunks during the loading process. Space management layer 1020 allocates in-memory extents to store the MF data from the appropriate NUMA node location and registers the in-memory extents within the in-memory segment.
In the case where a hash function is used to assign NUMA nodes to in-memory chunks, a policy-based approach may be applied to guarantee that a certain range of on-disk data is mapped to the same NUMA node. For example, the hash function may be chosen such that contiguous range (e.g., 8 MB) of page addresses from an on-disk segment map to the same NUMA node (herein referred to as the “target” NUMA node). Once the on-disk chunk range is mapped to the target NUMA node, the background loader sends a space request for the corresponding chunk to space management layer 1020, which meets the space request with stripes belonging to the target NUMA node. In the end of the load, the entire segment is distributed across NUMA nodes in chunk size units determined by the policy (e.g., 8 MB). By using such a policy, parallel queries may take advantage of page coalescing, merging adjacent stripes mapped to the on-disk chunk range, for faster throughputs during query processing.
NUMA Affinity Mappings
As previously mentioned, the RDBMS maintains auxiliary mapping data to track which database objects have both PF data and MF data, according to an embodiment. When NUMA is enabled, the auxiliary mapping data maintains NUMA affinity information in addition to the association between the on-disk range and the corresponding in-memory chunk. Thus, for any given on-disk range, the database server may determine whether there is a corresponding in-memory chunk and, if so, the NUMA node location where the in-memory chunk resides.
Referring to FIG. 15, it is a table illustrating an auxiliary map that includes NUMA affinity information, according to an embodiment. Table 1500 includes on-disk range column 1502, in-memory chunk identification column 1504, and NUMA affinity column 1506. On-disk range column 1502 identifies an on-disk page range that maps to the corresponding in-memory chunk identified by in-memory chunk identification column 1504. NUMA affinity column 1506 identifies the NUMA node location where the in-memory chunk resides. For example, on-disk blocks 1 to 100 map to in-memory chunk 1410, which resides in local memory on NUMA node 1404. Similarly, on-disk blocks 101 to 200 map to in-memory chunk 1412 and NUMA node 1406, on disk blocks 201 to 300 map to in-memory chunk 1414 and NUMA node 1408, etc.
In addition or as an alternative to maintaining NUMA affinity information in the auxiliary maps, the NUMA affinity information may be maintained within the in-memory segment. According to one embodiment, for each in-memory extent in the in-memory segment, extent map block 1204 includes a field that identifies the NUMA affinity of the corresponding in-memory extent. For example, assume that in-memory object 1402 is an in-memory segment and that each of in-memory chunks 1410 to 1424 corresponds to a different in-memory extent for in-memory object 1402. Upon allocation, each of in-memory chunks 1410 to 1424 is registered with in-memory object 1402, adding a corresponding entry to the extent map block. When registering the in-memory extent, the NUMA affinity information is added that identifies the NUMA node from which the in-memory extent was allocated. Thus, the extent entries for in-memory chunks 1410, 1416, and 1422 map to NUMA node 1404, the extent entries for in-memory chunks 1412, 1418, and 1424 map to NUMA node 1406, and the extent entries for in-memory chunks 1414 and 1420 map to NUMA node 1408.
NUMA-Aware Parallel Query Processing of in-Memory Chunks
A parallel query includes a plurality of processes that divide the workload of a query. For example, the parallel query processes may include a coordinator process for coordinating execution of a query operation and a set of execution processes for operating on the underlying database data. According to one embodiment, the coordinator process uses NUMA affinity information to divide and distribute the query workload amongst execution processes that are assigned to different NUMA nodes. Specifically, the coordinator process assigns execution processes on a particular NUMA node to in-memory chunks that belong to the same NUMA node. Each execution process operates on the database data per the coordinator's assignment to generate and output a respective result set. The coordinator collates or otherwise combines the result sets to generate a final result for the requested operation.
According to an embodiment, the coordinator process uses the auxiliary maps to determine how to divide and distribute the query workload during query execution. When a parallel query is imitated on an in-memory enabled database object, the coordinator process searches the auxiliary map to determine the NUMA node location for each in-memory chunk that belongs to the in-memory object and that is required to process the query. Based on this search, the coordinator process determines which in-memory chunks belong to the same cluster. A “cluster” in this context is a group of in-memory chunks that belong to the same in-memory object and reside on the same NUMA node. For example, in-memory chunks 1410, 1416, and 1422 belong to a first cluster, in-memory chunks 1412, 1418, and 1424 belong to a second cluster, and in-memory chunks 1414 and 1420 belong to a third cluster.
Once the clusters are determined, the coordinator process assigns a set of one or more execution processes running on a particular NUMA node to the cluster corresponding to the particular NUMA node. Continuing with the preceding example, the coordinator process distributes the three clusters to processes according to NUMA node location. Thus, the coordinator assigns a first set of execution processes running on NUMA node 1404 to operate on in-memory chunks 1410, 1416, and 1422, a second set of execution processes running on NUMA node 1406 to operate on in-memory chunks 1412, 1418, and 1424, and a third set of execution processes running on NUMA node 1408 to in-memory chunks 1414 and 1420.
Based on the coordinator assignments, each of the sets of execution processes accesses and returns row subsets (or other results) from the corresponding NUMA node's local memory. In one embodiment, execution processes only operate on in-memory chunks that reside in the local memory of the NUMA node and do not operate on in-memory chunks that reside in remote memory. For example, an execution process running on NUMA node 1404 may process in-memory chunk 1412 to generate a first result. Once complete, the execution process may operate on in-memory chunk 1418 to generate a second result since in-memory chunk 1418 also resides in local memory. However, the process does not operate on in-memory chunks 1414, 1418, 1420, and 1424 since these in-memory chunks reside in remote memory with respect to NUMA node 1404.
According to one embodiment, execution processes running both across different NUMA nodes and within the same NUMA node operate in parallel. For example, a first execution process running on CPU 1306a may operate on a first in-memory chunk in memory 1310a. While the first execution process is operating on the first in-memory chunk, a second execution process running on CPU 1306j may operate on a second in-memory chunk in memory 1310a and/or a set of one or more processes running on CPUs 1308a to 1308k may operate on one or more in-memory chunks residing in memory 1310l. Thus, parallel execution may happen on both an intra-node and inter-node basis to improve query processing times.
In some instances, parallel query processing may be distributed across both in-memory chunks and on-disk chunks. For example, some portions of a database object may reside in memory in a mirror format, while other portions may reside only on disk in a persistent format. When a parallel query is initiated on an in-memory enabled database object, according to one embodiment, the coordinator process first reads a table of on-disk pages that constitute the database object. For each on-disk page range, the coordinator uses the auxiliary map to check which portions of the database object are in memory and which portions are not in memory. For the portions that are in memory, parallel processing is performed as described above such that all execution processes operate only on data from their local memory without operating on in-memory chunks in remote memory. For the portions that are on-disk, the coordinator assigns a set of execution processes to scan the data from disk.
The query operations that are performed by the parallel query processes may vary from implementation to implementation and based on the query. Example query operations that may be performed may include, without limitation, scanning, filtering, sort, and/or aggregation operations. For instance, each execution process may scan for rows that satisfy a particular predicate, sort rows based on a key value, or aggregate column values for a particular table. The coordinator process receives one or more result sets from each NUMA node and combines the result sets into a final result. The manner in which the coordinator process combines the result sets depends on the particular query operation. With a scan or filter operation, for example, combining the result sets may involve collating the returned rows into a final table. With a sort operation, the coordinator may perform a final sort of the result sets. As indicated above, these operations may be distributed across NUMA nodes such that processes affined to an individual NUMA node only operate on data in the local memory of the NUMA node.

## Claims

1. A method comprising:
storing, by a database server instance that is executing on a particular machine, a plurality of in-memory chunks that contain data from an object;
wherein the plurality of in-memory chunks include a first in-memory chunk and a second in-memory chunk;
wherein the first in-memory chunk includes a first portion of the object;
wherein the second in-memory chunk includes a second portion of the object;
wherein the first portion of the object is different than the second portion of the object;
wherein each in-memory chunk of the plurality of in-memory chunks is stored in a corresponding memory area within volatile memory of the particular machine;
wherein the particular machine includes a plurality of compute nodes;
wherein the plurality of computer nodes includes a first compute node and a second computer node;
wherein each compute node of the plurality of compute nodes has direct access to all memory areas within the volatile memory of the particular machine;
wherein each compute node, of the plurality of compute nodes, has a local memory area, within the volatile memory of the particular machine, that the compute node is able to access more efficiently than other areas of the volatile memory of the particular machine;
wherein storing the plurality of in-memory chunks includes:
assigning, by the database server instance, each in-memory chunk of the plurality of in-memory chunks to one or more compute nodes of the plurality of compute nodes; and
causing each in-memory chunk of the plurality of in-memory chunks to be loaded into one or more local memory areas of the one or more compute nodes to which the in-memory chunk is assigned;

wherein the step of assigning each in-memory chunk includes:
assigning the first in-memory chunk to the first compute node; and
assigning the second in-memory chunk to the second compute node;

storing, by the database server instance, an in-memory map that indicates the one or more compute nodes to which each of the plurality of chunks is assigned.

2. The method of claim 1,
wherein the object corresponds to an on-disk object;
wherein the first in-memory chunk stores data from a first on-disk chunk of the on-disk object; and
wherein the second in-memory chunk stores data from a second on-disk chunk of the on-disk object.

3. The method of claim 2, wherein the step of assigning each memory chunk comprises:
selecting, to store the first in-memory chunk in volatile memory, the first compute node from a plurality of compute nodes by applying a hash function to a first page address of the first on-disk chunk;
selecting, to store the second in-memory chunk in volatile memory, the second compute node from a plurality of compute nodes by applying the hash function to a second page address of the second on-disk chunk.

4. The method of claim 2, wherein the in-memory map indicates that the first in-memory chunk corresponds to the first on-disk chunk and that the second in-memory chunk corresponds to the second on-disk chunk.

5. The method of claim 2, wherein the first in-memory chunk stores data from the first on-disk chunk in a mirror format and the second in-memory chunk stores data from the second on-disk chunk in the mirror format.

6. The method of claim 1, wherein the step of assigning each memory chunk comprises selecting the first compute node from a plurality of compute nodes and the second compute node from the plurality of compute nodes using a round-robin distribution wherein the plurality of in-memory chunks are evenly distributed across the plurality of compute nodes.

7. The method of claim 1, further comprising:
allocating a plurality of stripes from volatile memory;
wherein the step of allocating the plurality of stripes from volatile memory includes:
allocating, from a first memory area that is local to the first compute node, a first set of stripes;
allocating, from a second memory area that is local to the second compute node, a second set of stripes;

wherein each stripe in the first set of stripes includes a contiguous chunk of memory of the first memory area;
wherein each stripe in the second set of stripes includes a contiguous chunk of memory of the second memory area.

8. The method of claim 7, wherein the plurality of stripes are allocated across a plurality of compute nodes in a round-robin distribution such that each compute node of the plurality of compute nodes has approximately the same number of stripes.

9. The method of claim 7, further comprising:
sending, by the database server instance to the first compute node, a first request to allocate space for the first in-memory chunk;
sending, by the database server instance to the second compute node, a second request to allocate space for the second in-memory chunk;
wherein the first compute node satisfies the first request using at least one stripe from the first set of stripes;
wherein the second compute node satisfies the second request using at least one stripe from the second set of stripes.

10. The method of claim 1 wherein the first compute node and the second compute node are non-uniform memory access (NUMA) nodes; wherein a first memory controller that controls access to a first memory area of the first compute node is connected to a second memory controller that controls access to a second memory area of the second compute node.

11. One or more non-transitory computer-readable media storing instructions, wherein the instructions include:
instructions which, when executed by one or more hardware processors, cause storing, by a database server instance that is executing on a particular machine, a plurality of in-memory chunks that contain data from an object;
wherein the plurality of in-memory chunks include a first in-memory chunk and a second in-memory chunk;
wherein the first in-memory chunk includes a first portion of the object;
wherein the second in-memory chunk includes a second portion of the object;
wherein the first portion of the object is different than the second portion of the object;
wherein each in-memory chunk of the plurality of in-memory chunks is stored in a corresponding memory area within volatile memory of the particular machine;
wherein the particular machine includes a plurality of compute nodes;
wherein the plurality of computer nodes includes a first compute node and a second computer node;
wherein each compute node of the plurality of compute nodes has direct access to all memory areas within the volatile memory of the particular machine;
wherein each compute node, of the plurality of compute nodes, has a local memory area, within the volatile memory of the particular machine, that the compute node is able to access more efficiently than other areas of the volatile memory of the particular machine;
wherein instruction for storing the plurality of in-memory chunks include:
instructions which, when executed by one or more hardware processors, cause assigning, by the database server instance, each in-memory chunk of the plurality of in-memory chunks to one or more compute nodes of the plurality of compute nodes; and
instructions which, when executed by one or more hardware processors, cause loading each in-memory chunk of the plurality of in-memory chunks into one or more local memory areas of the one or more compute nodes to which the in-memory chunk is assigned;

wherein the step of assigning each in-memory chunk includes:
assigning the first in-memory chunk to the first compute node; and
assigning the second in-memory chunk to the second compute node;

storing, by the database server instance, an in-memory map that indicates the one or more compute nodes to which each of the plurality of chunks is assigned.

12. The one or more non-transitory computer-readable media of claim 11,
wherein the object corresponds to an on-disk object;
wherein the first in-memory chunk stores data from a first on-disk chunk of the on-disk object; and
wherein the second in-memory chunk stores data from a second on-disk chunk of the on-disk object.

13. The one or more non-transitory computer-readable media of claim 12, wherein the instructions for assigning each memory chunk include instructions for:
selecting, to store the first in-memory chunk in volatile memory, the first compute node from a plurality of compute nodes by applying a hash function to a first page address of the first on-disk chunk;
selecting, to store the second in-memory chunk in volatile memory, the second compute node from a plurality of compute nodes by applying the hash function to a second page address of the second on-disk chunk.

14. The one or more non-transitory computer-readable media of claim 12, wherein the in-memory map indicates that the first in-memory chunk corresponds to the first on-disk chunk and that the second in-memory chunk corresponds to the second on-disk chunk.

15. The one or more non-transitory computer-readable media of claim 12, wherein the first in-memory chunk stores data from the first on-disk chunk in a mirror format and the second in-memory chunk stores data from the second on-disk chunk in the mirror format.

16. The one or more non-transitory computer-readable media of claim 1, wherein instructions for assigning each memory chunk comprise instructions for selecting the first compute node from a plurality of compute nodes and the second compute node from the plurality of compute nodes using a round-robin distribution wherein the plurality of in-memory chunks are evenly distributed across the plurality of compute nodes.

17. The one or more non-transitory computer-readable media of claim 11, wherein non-transitory computer-readable media further stores instructions including:
instructions which, when executed by one or more hardware processors, cause allocating a plurality of stripes from volatile memory;
wherein the step of allocating the plurality of stripes from volatile memory includes:
instructions which, when executed by one or more hardware processors, cause allocating, from a first memory area that is local to the first compute node, a first set of stripes;
instructions which, when executed by one or more hardware processors, cause allocating, from a second memory area that is local to the second compute node, a second set of stripes;
wherein each stripe in the first set of stripes includes a contiguous chunk of memory of the first memory area;
wherein each stripe in the second set of stripes includes a contiguous chunk of memory of the second memory area.

18. The one or more non-transitory computer-readable media of claim 17, wherein the plurality of stripes are allocated across a plurality of compute nodes in a round-robin distribution such that each compute node of the plurality of compute nodes has approximately the same number of stripes.

19. The one or more non-transitory computer-readable media of claim 17, wherein non-transitory computer-readable media further stores instructions including:
instructions which, when executed by one or more hardware processors, cause sending, by the database server instance to the first compute node, a first request to allocate space for the first in-memory chunk;
instructions which, when executed by one or more hardware processors, cause sending, by the database server instance to the second compute node, a second request to allocate space for the second in-memory chunk;
wherein the first compute node satisfies the first request using at least one stripe from the first set of stripes;
wherein the second compute node satisfies the second request using at least one stripe from the second set of stripes.

20. The one or more non-transitory computer-readable media of claim 11 wherein the first compute node and the second compute node are non-uniform memory access (NUMA) nodes; wherein a first memory controller that controls access to a first memory area of the first compute node is connected to a second memory controller that controls access to a second memory area of the second compute node.