Patent Publication Number: US-9418091-B2

Title: Database operations on a columnar table database

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims priority under 35 U.S.C. §119 to Chinese Patent Application 201310403089.1, filed Sep. 6, 2013, titled “DATABASE OPERATIONS ON A COLUMNAR TABLE DATABASE,” which is incorporated herein by reference in its entirety. 
     TECHNICAL FIELD 
     This description relates to systems and techniques for performing database operations on a database organized in columnar tables. 
     BACKGROUND 
     A database may be organized in different formats. For example, a database with support for a two-dimensional table may be organized in a row table format or it may be organized in column table (also referred to as a columnar table) format. For example, in a database that is formatted in a row table, the storage sequence of the data is row by row. In a database that is formatted in a column table, the storage sequence is column by column. A database formatted as a column table may have advantages over a database formatted as a row table. For example, a columnar table may provide advantages when the database includes huge amounts of data and the data needs to be aggregated and analyzed. A columnar table also may provide other advantages over other types of tables. 
     Despite the advantages of a columnar table, there may be shortcomings in the columnar table where improvement is desirable. For example, some database operations may create and consume more memory than desirable and create additional processing load. Improvements may be sought to limit the memory consumption of a columnar table and to increase processing efficiency for the columnar table. 
     SUMMARY 
     According to one general aspect, a computer system includes at least one processor and at least one memory operably coupled to the at least one processor. The memory includes a memory pool and a database partitioned into multiple fragments. Each of the fragments is allocated a block of memory from the memory pool and the fragments store compressed data in a columnar table format. A database operation is applied in a compressed format to the compressed data in at least one of the fragments. 
     Implementations may include one or more of the following features. For example, the database operation may include an insert operation, where the insert operation causes appending of new data in compressed format to a last row in one of the fragments. The memory may include a change log that is configured to store uncompressed data and the database operation may include a read operation. The read operation causes reading the compressed data from at least one of the fragments, decompressing the compressed data, reading the change log for associated data and combining the decompressed data from the fragment with the associated data from the change log. The memory may include a change log that is configured to store uncompressed data and the database operation may include an update operation. The update operation causes locating a corresponding row of data in one of the fragments, if a memory space in the fragment is sufficient to include one or more updated values in the data in compressed format, compressing the updated values and replacing the updated values in the data and if the memory space in the fragment is not sufficient to include the updated values in the data in compressed format, recording the updated values in the change log in an uncompressed format. The database may be configured to compress the data in the change log and merge the compressed data from the change log with the compressed data in the fragments. The database may include a compression engine that is configured to compress data using one of multiple compression schemes. The compression engine may use a dictionary encoding scheme to compress the data stored in the fragments. The database may be an in-memory database. 
     In another general aspect, a method includes partitioning a database into multiple fragments, where each of the fragments is allocated a block of memory from a memory pool. Compressed data is stored in each of the fragments in a columnar table format. A database operation is applied in a compressed format to the compressed data in at least one of the fragments. 
     Implementations may include one or more of the following features. The database operation may include an insert operation and the method may further include, responsive to the insert operation, appending new data in compressed format to a last row in one of the fragments. 
     The database operation may include a read operation and the method may further include, responsive to the read operation, reading the compressed data from at least one of the fragments, decompressing the compressed data, reading a change log for associated data, where the change log is configured to store uncompressed data and combining the decompressed data from the fragment with the associated data from the change log. The database operation may include an update operation and the method may further include, responsive to the update operation, locating a corresponding row of data in one of the fragments, if a memory space in the fragment is sufficient to include one or more updated values in the data in compressed format, compressing the updated values and replacing the updated values in the data and if the memory space in the fragment is not sufficient to include the updated values in the data in compressed format, recording the updated values in a change log in an uncompressed format, wherein the change log is configured to store uncompressed data. The method may further include compressing the data in the change log and merging the compressed data from the change log with the compressed data in the fragments. The method may further include compressing data using one of a plurality of compression schemes. The compression schemes may include a dictionary encoding scheme. 
     In another general aspect, a computer program product is, tangibly embodied on a non-transitory computer-readable storage medium and includes instructions that, when executed, are configured to partition a database into multiple fragments, where each of the fragments is allocated a block of memory from a memory pool, store compressed data in each of the fragments in a columnar table format and apply a database operation in a compressed format to the compressed data in at least one of the fragments. 
     Implementations may include one or more of the following features. For example, the database operation may include an insert operation and the instructions, when executed, may be further configured to, responsive to the insert operation, append new data in compressed format to a last row in one of the fragments. The database operation may include a read operation and the instructions, when executed, may be further configured to, responsive to the read operation, read the compressed data from at least one of the fragments, decompress of the compressed data, read a change log for associated data, where the change log is configured to store uncompressed data and combine the decompressed data from the fragment with the associated data from the change log. The database operation may include an update operation and the instructions, when executed, may be further configured to, responsive to the update operation, locate a corresponding row of data in one of the fragments, if a memory space in the fragment is sufficient to include one or more updated values in the data in compressed format, compress the updated values and replace the updated values in the data and if the memory space in the fragment is not sufficient to include the updated values in the data in compressed format, record the updated values in a change log in an uncompressed format, where the change log is configured to store uncompressed data. The instructions, when executed, may be further configured to compress the data in the change log and merge the compressed data from the change log with the compressed data in the fragments. 
     The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a database system. 
         FIG. 2  is a flowchart illustrating example operations of the system of  FIG. 1 . 
         FIG. 3  is an example illustration of a memory pool allocated between multiple fragments. 
         FIG. 4  is an example illustration of a fragment organized in a columnar format. 
         FIG. 5  is an example illustration of a dictionary encoding scheme. 
         FIG. 6  is an example illustration of a prefix encoding scheme. 
         FIG. 7  is an example illustration of a run length encoding scheme. 
         FIG. 8  is an example illustration of a cluster encoding scheme. 
         FIG. 9  is an example illustration of a sparse encoding scheme. 
         FIG. 10  is an example illustration of an indirect encoding scheme. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is an example block diagram of a database system  100 . The database system  100  includes at least one memory  102 . The memory  102  may be a non-volatile memory or other type of memory that is configured to store large amounts of data. The memory  102  may include non-volatile memory such as, for example, flash memory. The memory  102  also may include volatile memory such as, for example, random access memory (RAM). Although the memory  102  is illustrated as at least one memory, the memory  102  may be made up of multiple memory modules or arrays of memory modules. The memory  102  may include memory resources to provide a memory pool, where the memory pool may be divided and allocated to support various systems resident in the memory  102 . 
     The data stored in the memory  102  may be organized into a database  104 . The database  104  may be referred to as an in-memory database. As discussed above, the memory  102  may include a memory pool that may be used and allocated as needed to support the database  104 . Also, as discussed above, the memory  102  may include multiple memory modules or memory arrays that may be configured to work in concert with one another to support the database  104  and database operations. 
     In one example implementation, the database  104  may be organized to store data in a column table format, also referred to as a columnar table. Data stored in a column format may be optimized for data aggregation and subsequent analysis on the aggregated data. 
     Each record in the database  104  may be identified by a unique identifier. A column of unique identifiers may be used to identify a particular row in the columnar table. For example, each record may be identified by a value identifier. The value identifier also may be referred to as a column identifier (ID). 
     Database operations may be performed or applied to the database  104 . Database operations may include read operations, delete operations, insert operations, update operations and other types of database operations. Database operations will be explained in more detail below. 
     Queries may be received by the database  104  in the form of a database operation. For example, a query may be received in order to read data from the database as part of a read operation. 
     In one example implementation, the database  104  may be partitioned or divided into multiple fragments  106 . Each of the fragments  106  may store a portion of the data organized in the database  104 . Each of the multiple fragments  106  may be organized in a same or similar manner. For example, if the database  104  is organized in a column format, each of the partitioned fragments  106  may be organized in the same column format. Similarly, each record in a fragment may be identified by a unique identifier such as a column ID or value identifier as described above. 
     In one example implementation, each of the fragments  106  stores a different portion of the database  104 . Each of the fragments  106  may be allocated a block of memory from the memory (or memory pool)  102 . The blocks of memory allocated to the fragments may be of different sizes. In some cases, one fragment may be allocated a larger block of memory than another fragment. The allocation of the blocks of memory may be automatically configured based on a determined need for each of the fragments. In other implementations, the allocation of the blocks of memory may be manually configured by a database operator. 
     Database operations may be performed on each of the fragments  106 . The database operations may be performed independent of database operations performed on other fragments. That is, by dividing the database  104  into multiple fragments  106 , database operations performed on one of the fragments may not affect or be affected by database operations performed on the other fragments. Said another way, database operations may be limited to performance on a subset of the total fragments, including performing the operations on a single fragment. 
     In other example implementations, some of the fragments  106  may store the same data as other fragments to provide redundancy in the case of a failure in the database or to perform maintenance operations on the database. 
     Data stored in the fragments  106  in a column format may be stored as either uncompressed data  108   a  or compressed data  108   b . A fragment  106  may include both uncompressed data  108   a  and compressed data  108   b . A fragment  106  also may include only uncompressed data  108   a  or only compressed data  108   b . When a fragment includes compressed data  108   b , the data may be compressed according to one or more different compression schemes, as discussed in more detail below. 
     For a fragment  106  containing only uncompressed data  108   a , all of the data may be stored in the fragment without the use of a change log to store some of the data. In this manner, the database operations may be performed only on the fragment  106  and not on any other tables, including change logs. 
     The following examples are provided for database operations performed on fragments containing uncompressed data  108   a . In the provided examples, a column ID containing a value may be used to identify a particular record in the fragment  106 . In one example implementation, a read operation may be performed. For a read operation on a fragment  106  containing uncompressed data  108   a , the data is simply read from the fragment  106   a  containing the data. In contrast, read operations performed on other traditional column formatted databases on uncompressed data typically involved more steps. For example, a read operation on a traditional column formatted database may have required both a read of a main storage table and also a read from a delta storage table. However, in this example implementation, a read from a delta storage may no longer required because the data is all located in a single place, namely the fragment itself. 
     In another example implementation, a delete operation may be performed. In the example of a delete operation, a user may desire to delete one or more records in the database  104 . When a delete operation is received, the database  104  will mark a delete identity (or identifier or other marker) on a record that is designated for deletion in one of the fragments  106 . When a query to read data is received, the database will skip any record that has been identified or marked as deleted. 
     In another example implementation, an update operation may be performed. In the example of an update operation, it may be desired to update a record in the database  104  with a new value for that particular record. The database  104  first processes the update operation by identifying the corresponding column ID value for the row that contains the value to be updated. Once the corresponding row is identified, a key-value pair in the record is replaced with the updated value identified in the update operation. In this manner, the update operation is a simple process to identify the correct location for the value to be updated and to replace the current value with the updated value. 
     In another example implementation, an insert operation may be performed. In the example of an insert operation, a new record is to be inserted into the database  104 . For the new record to be inserted, it is assigned a column ID and appended after a last row of the database  104 . Thus, the new record becomes a last row in the database  104  (or fragment  106  if fragmented). The data is inserted directly into the database  104  and not into a delta merge storage table. In contrast, in prior traditional column table databases, multiple tables were required to perform certain types of database operations, including an insert operation. Data would not be inserted into the main storage but instead was inserted into a delta merge storage table. The use of both a main storage table and a delta storage table consumed more memory. Because the insert operation previously inserted data into a second table, an impact occurred on read operations in prior systems, since the read operation needed to read from both the main storage and the delta merge table. 
     Table 1 below provides an example pseudo code for performing database operations on a database  104  (or fragment  106 ) with uncompressed data. For example, the pseudo code in Table 1 may be applied to database  104  containing uncompressed data  108   a . 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Pseudo Code 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                   
                 IF operation-type = READ 
               
               
                   
                     Read data from storage 
               
               
                   
                     Filter deleted data 
               
               
                   
                 ELSE IF operation-type = DELETE 
               
               
                   
                     Find corresponding row and mark deleted identity 
               
               
                   
                 ELSE IF operation-type = UPDATE 
               
               
                   
                     Find corresponding row and replace the key-value pair 
               
               
                   
                 ELSE IF operation-type = INSERT 
               
               
                   
                     Append the result into the last row 
               
               
                   
                   
               
            
           
         
       
     
     When performing database operations on a database containing uncompressed data  108   a , the database  104  may or may not be divided into multiple fragments  106 . The database  104  may not be divided into multiple fragments because the operations can be performed directly onto the database without a need for creating a separate delta merge table to perform any of the operations. 
     The pseudo code described in Table 1 provides code for read operations, delete operations, update operations and insert operations. The pseudo code in Table 1 follows the description of these operations as described above. For instance, if the operation type is a read operation, then data is read directly from the storage (or directly from database  104 ) while filtering any data that has been marked for deletion. 
     If the operation type is a delete operation, then the corresponding row is identified and marked for deletion. When a row is marked for deletion, the row may not be removed permanently from the database. Instead, the row marked for deletion may be skipped when performing other operations, such as read operations. 
     If the operation is an update operation, the database identifies the corresponding row and replaces the key-value pair with an updated value identified in the update operation. If the operation is an insert operation, the database appends the new row directly to the database  104 . As discussed above, the new row may be appended as the last row in the database  104 . In this manner, new data that is inserted is inserted directly into the database  104  and not into a separate delta merge table. 
     While the pseudo code in Table 1 and other description above may apply to the situation of uncompressed data, database operations performed on compressed data, such as compressed data  108   b , may be slightly more complex. As discussed above, the database  104  may first be partitioned into multiple fragments  106 , with each of the fragments  106  including compressed data  108   b . In this manner, database operations that are dependent on a memory space allocated to the fragment  106  may affect only the particular fragment and not the entire database. Thus, database operations that exceed the memory space of the fragment may affect that fragment and not any other fragments. In this manner, these types of changes are felt on a smaller scale rather than on a global scale for the entire database. 
     The database  104  may include a compression engine  114  that may be configured to perform compression and decompression operations on the data stored and to be stored in the database  104  (and fragments  106 ). The compression engine  114  may be configured to perform compression on the data using one of multiple different types of compression schemes. For example, the compression engine  114  may be configured to use a dictionary encoding compression scheme, a prefix encoding compression scheme, a run length encoding compression scheme, a cluster encoding compression scheme, a sparse encoding compression scheme, an indirect encoding compression scheme, and/or combinations of various schemes or other types of encoding compression schemes. The compression schemes may work to allow the database to function in a more efficient and space saving manner. 
     Database operations may be performed on fragments  106  containing compressed data  108   b  in the following manner. For certain operations, a change log or change logs  110  may be used. In some implementations, the change log stores uncompressed data  112 . In other example implementations, the change log  110  may include data that uses only a basic compression scheme which may be less complex and not as efficient as the compression schemes used in the fragments  106 . 
     In some example implementations, each of the multiple fragments  106  may have its own associated change log  110 . In other example implementations, some of the fragments  106  may share a change log  110 . In other example implementations, a single change log  110  may be used for all of the fragments  106 . 
     For a delete operation performed on the compressed data  108   b  in one of the fragments  106 , the database identifies a corresponding row in the fragment  106  and marks the row with a delete identifier. The delete operation performed on the compressed data  108   b  is the same as the delete operation performed on the uncompressed data  108   a.    
     For a read operation performed on the compressed data  108   b  in one of the fragments  106 , the database executes the read operation by reading the data from each fragment that may contain data associated with the operation. The compressed data  108   b  may be decompressed using, for example, the compression engine  114 . The database may query the change log  110  for any associated data contained in the change log  110 . The decompressed data from the fragments  106  and the data from the change log  110  may be combined and returned as part of the read operation. The read operation also may filter out any data that has been marked for deletion so that the results of the read operation do not include data that has been marked as deleted. 
     For an insert operation on the compressed data  108   b  in one of the fragments  106 , the database  104  executes the insert operation by compressing the data using the compression engine  114  and then appending the compressed data directly into the fragment  106 . The compressed new data may be appended after the last row in a particular fragment  106 . In this manner, insert operations are performed directly on the fragments  106  containing the compressed data. Unlike previous database systems containing compressed data, a delta merge table is not used for insert operations because the insert operation is performed directly on the main data table (i.e., the fragment  106 ). In this manner, the size of the change log is reduced because insert operations are not performed on the change log  110  but are instead performed on the fragment  106  in a compressed format. 
     The compression engine  114  may compress new data to be inserted using one of multiple compression schemes. The compression scheme selected by the compression engine  114  may match the compression scheme used in a particular fragment  106 . The compression engine  114  may select any of the compression schemes that a particular memory space allocation would accommodate for the particular fragment. 
     In some example implementations, data may be inserted into one of the fragments  106  in a batch mode. If there are batch data to be inserted, the data insert may occur at a same time. The database may determine whether to insert the batch data directly into the fragment by appending it at an end of the fragment or may determine whether to insert the batch data into the change log  110 . Prior to inserting the batch data directly into the fragment  106 , the compression engine  114  would compress the batch data and then the database  104  would append the newly compressed data to a last row of the fragment  106 . The database  104  may determine or estimate an execution effort prior to selecting where to insert the batch data. 
     For an update operation on compressed data  108   b  in one of the fragments  106 , the database executes the update operation by first finding a corresponding row containing the value that needs to be updated. The database  104  then determines if the memory space is sufficient to accommodate the updated value. If the memory space is sufficient to include the updated value in a compressed format, the database  104  causes the compression engine  114  to compress the new value. Then, the new compressed value is used to replace the key-value pair directly in the fragment  106 . In this manner, update operations are performed directly on the compressed data  108   b  in the fragment  106 . If the database  104  determines that the local memory space for the fragment  106  is not sufficient, then the updated value is recorded in the change log  110  in an uncompressed format as part of the uncompressed data  112 . A subsequent read operation for this record containing the updated value would be performed by reading both the record in the fragment  106  in the compressed format and the record corresponding from the change log  110  containing the updated value in the uncompressed format. 
     The process of performing an update operation directly on the fragment  106  in a compressed format, without directly adding it to a delta merge table, is more efficient and can save more space than creating a large delta merge table as in previous typical database systems. In the example implementations described above regarding database operations on the compressed data, the update operation may be the only operation that causes data to be added to the change log  110 . An insert operation (other than potentially a batch insert) may not cause data to be added to the change log  110 . Instead, the data from an insert operation is compressed and inserted directly into the fragment  106 . 
     For example, the compressed data for an update operation may be one size (e.g., 2 bytes), but the space allocated may only allow for one byte. In this situation, since the compressed value is larger than the allocated space, then the update operation is performed on the change log  110 . In other situations, when the space in the fragment  106  is sufficient to accommodate the compressed value in the update operation, then the update operation is performed directly on the fragment  106  without using the change log  110 . 
     Table 2 below provides an example pseudo code for performing database operations on a database with compressed data. For example, Table 2 pseudo code may be applied to database  104  containing compressed data  108   b . 
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Pseudo Code 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
            
               
                 IF operation-type = READ 
               
               
                     Read data from each fragment 
               
               
                     Decompress the data 
               
               
                     Query change log in the fragment and combine the data 
               
               
                     Filter deleted data 
               
               
                 ELSE IF operation-type = DELETE 
               
               
                     Find corresponding row in fragment and mark deleted identity 
               
               
                 ELSE IF operation-type = UPDATE 
               
               
                     Find corresponding row 
               
               
                     IF local memory space is enough THEN 
               
               
                       Compress new value 
               
               
                       Replace the key-value pair directly 
               
               
                     ELSE 
               
               
                       Record this change into change log 
               
               
                       Merge it asynchronously during system idle time 
               
               
                     END 
               
               
                 ELSE IF operation-type = INSERT 
               
               
                     Compress input data 
               
               
                 Append the result after the last row 
               
               
                   
               
            
           
         
       
     
     The example pseudo code listed in Table 2 above describes an algorithm for processing compressed data by first attempting to perform the database operation directly on one or more of the fragments  106 . The pseudo code in Table 2 provides for database operations including read operations, delete operations, update operations, and insert operations. 
     For a read operation, the pseudo code instructs the database  104  to read the data from each fragment  106 . The data in each fragment  106  is in a compressed format. The database  104  then decompresses the compressed data and queries the change log  110  in the fragment for any related data. The database  104  combines the data from the fragment  106  and data from the change log  110 . Any data that has been marked for deletion is filtered and not included in the result that is returned as part of the read operation. 
     For a delete operation, the pseudo code instructs the database  104  to find the corresponding row in the fragment  106  and mark the row with a delete identity (or identifier). In this manner, as described above, the row will remain in the fragment, but will not be included in the results from other database operations. The row marked for deletion will be filtered out of other results. 
     For an update operation, the pseudo code in Table 2 instructs the database  104  to find the corresponding row in the fragment. The database  104  determines if there is enough local memory space within the fragment  106  to add the update directly into the fragment  106 . If there is a local enough local memory space, then the new value in the update operation is compressed by the compression engine  114  and is used to replace the key-value pair directly in the row in the fragment  106 . If the database  104  determines the local memory space is not enough, then the update value is recorded into the change log  110  as part of the uncompressed data  112 . 
     At some time in the future, the change log  110  may be merged into the fragment  106 . For example, the change log  110  may be merged asynchronously with the fragment  106  during an idle or lesser used time. 
     For an insert operation, the pseudo code in Table 2 instructs the database  104  to compress the new input data using the compression engine  114 . The compressed new data is then appended after the last row directly in one of the fragments  106 . 
     As discussed above with respect to the pseudo code in Table 1, batch data may be treated either by compressing the data in a batch mode and appending results after the last row directly in the fragment  106  or, the database  104  may determine that it is more efficient to insert the new data into the change log  110  and at a later time merged change log  110  with the fragment  106 . 
     In the example of  FIG. 1 , the database system  100  is illustrated as being executed by at least one computing device  134 , which is illustrated as including at least one processor  134 A and a computer readable storage medium  134 B. The computing device  134  may host the database system  100 , where the computing device  134  may be a server or other computing device capable of hosting such a database system  100 . The computing device  134  may include multiple computing devices, such as, multiple servers, that are operably coupled and configured to host the database system  100  across the multiple computing devices. The computing device  134  may be networked to other computing devices (not shown) such that the systems on the computing device  134  may send and receive information across a network (not shown), such as the Internet, a wide area network and/or a local area network. 
     Thus, the at least one processor  134 A may represent two or more processors executing in parallel, and the computer-readable storage medium  134 B may represent virtually any non-transitory medium that may be used to store instructions for executing database  104 , and related data. Further, the at least one computing device  134  may represent two or more computing devices, which may be in communication with one another. In some implementations, each of the fragments  106  may be associated with a processor  134 A such as in a multi-processor core environment. 
     The database  104  described in  FIG. 1  may use multi-version concurrency control (MVCC) to ensure consistent read operations. Also, MVCC may be used to implement different transaction isolation levels. With multi-version concurrency control, concurrent read operation may be presented a consistent view of the database  104  without blocking concurrent write operations (e.g., insert operations). In some example implementations, a timestamp or other similar mechanism may be associated with each version of the database. The timestamp information may be used to determine which versions are visible for particular transactions. Deletion operations may be implemented by inserting a deletion version or by some other mechanism. 
     In one example implementation, a temporal fragment with a timestamp may be use to support and the MVCC. Prior to a transaction being committed to the database, the temporal fragment may not replace the original fragment in the storage. In this manner, multiple data versions will exist in the database for different transactions and connections. Even though tables may not be duplicated, the system can still guarantee the isolation level of the transaction. 
     Using the database  104  as described above with respect to  FIG. 1 , the database system can achieve better performance and use less memory compared to prior database systems. The database system  100  may reduce lots of unnecessary merges of a delta merge table and a main table and may be better applied when faced with limited memory resources. 
       FIG. 2  is a flowchart  200  illustrating example operations of the system  100  of  FIG. 1 . In the example of  FIG. 2 , operations  202 - 206  are illustrated as separate, sequential operations. However, it may be appreciated that, in alternative embodiments, two or more of the operations  202 - 206  may be executed in a partially or completely overlapping or parallel manner, and/or in an iterative, nested, looped, or branched manner. Moreover, in any such implementations, additional or alternative operations may be included, while, in other implementations, one or more operations may be omitted. 
     As illustrated in  FIG. 2 , process  200  includes partitioning a database into a plurality of fragments, where each of the fragments is allocated a block of memory from a memory pool ( 202 ). For example, as described, the database  104  may be partitioned into a plurality of fragments  106 . Each of the fragments  106  may be allocated a block of memory from the memory pool  102 . 
     Referring also to  FIG. 3 , an example allocation of the memory pool  302  is illustrated. The memory pool  302  is allocated among three fragments  306   a - 306   c . The size of the fragments  306   a - 306   c  may be selected, either automatically or manually, to ensure sufficient size to accommodate when new data is inserted and/or updated into the fragment without having to move and reallocate data. As discussed above, the fragments  306   a - 306   c  are used so that database operations may be performed on each of the individual fragments instead of applying be database operations to the database as an undivided whole. 
     In this example implementation, a row ID range and fragment start address may be used to map each of the fragments into separate fragments. In this example, the length of each fragment  306   a - 306   c  is 8. The memory consumption for each item (cell) is the same, so the fragment start address may be calculated using this information. 
     In an example memory pool, many fragments may be pre-allocated. Additional fragments may be allocated is there is a need for more fragments. The additional fragments may be provided from among the pre-allocated fragments in the memory pool. 
     Referring back to  FIG. 2 , compressed data is stored in each of the fragments in a columnar table format ( 204 ). For example, as discussed, compressed data  108   b  may be stored in each of the fragments  106 . Similarly, referring back to  FIG. 3 , compressed data may be stored in each of the fragments  306   a - 306   c . The compressed data may be stored in the fragment using one of multiple different types of compression schemes. 
     The database operation may be applied in a compressed format to the compressed data in at least one of the fragments ( 206 ). For example, as discussed, a database operation may be performed using a compressed format of the data referred to in the database operation and applied to the compressed data  108   b  in one of the fragments  106 . In some cases, a database operation may be performed across multiple different fragments  106 . In each of the fragments  106 , the database operation is first attempted to be applied directly to the fragment with the data from the database operation in a compressed format. For some database operations, if the memory allocated to a particular fragment is not sufficient to enable the database operation to be performed in a compressed format, then a change log  110  may be used. This may be the situation for an update operation where the database determines the memory space for the fragment cannot fit the compressed updated value. 
       FIG. 4  illustrates an example fragment  406 . Fragment  406  may correspond to one of the multiple fragments  106 , as described above with respect to  FIG. 1 . As illustrated in  FIG. 4 , the fragment  406  may be labelled as table T 1 . The fragment  406  may be organized in a column format. In this example, the fragment  406  includes three columns: a column ID  436 , a column description  438 , and a column size  440 . Each of the different columns includes different types of values. For example, the column ID column  436  includes a identifier for each of the rows. The identifier may be used to locate a particular record in the fragment  406 . The column ID also may correspond to a column ID value contained in a change table. In this manner, read operations may be performed against the fragment  406  using the column ID as well as a change log containing the same column ID value. 
     As discussed above, database operations are performed and applied in a compressed format to the compressed data in the fragments. With respect to  FIG. 4 , for example, an insert operation may be applied directly to the fragment  406  by appending the inserts of the new data after a last row in the fragment  406 . In this example, an insert operation may append the last row  442  after the last row in the column. 
     For an update operation, the update operation may be applied directly to the fragment  406 . For example, an update operation may specify to change the column size value in row  3  ( 444 ) from “large” to “small.” If the fragment  406  is in an uncompressed format, the change may be made directly by replacing the value “large” with the value “small.” If the data is in a compressed format, the database may first determine whether there is enough memory space in the fragment  406  to accommodate the change. If there is enough space, then the new value is compressed and inserted directly into the table by replacing the value “large” with the value “small” in a compressed format. If there is not enough space, then the new value is added instead to a change log having a same column ID as the column ID in the fragment  406 . A subsequent read operation would read from both the fragment  406  and the corresponding change log in order to return the correct values in response to the read operation. 
     For a delete operation, the row to be deleted is marked with a delete identifier. For example, if row  1  is to be deleted ( 446 ), then the row is marked with an identifier such that database operations will filter out row  1  as being deleted. 
       FIGS. 5-10  illustrate example encoding schemes that may be used to compress the data in the database  104 . The encoding schemes may be used to compress the data in each of the partitioned fragments  106 . As discussed above, a compression engine  114  may be used to compress the data and perform database operations to apply a compressed format of new or updated values directly to the existing compressed data in the fragment. 
     For example,  FIG. 5  illustrates an example dictionary encoding scheme  500 . The database operations described above with respect to  FIGS. 1 and 2  may be applied using the dictionary encoding scheme  500 . In the dictionary encoding scheme, a dictionary table is used where each entry in the dictionary includes a value ID. Then, in the fragments  106 , the value ID is inserted in place of the actual name. Any time the value is referred to a lookup may be performed by looking into the dictionary using the value ID. For insert operations using a dictionary encoding scheme, the new entry is appended as described above after a last row in the fragment. Prior to appending the new record, the entry is compressed according to the dictionary encoding scheme. For an update operation, the same process is applied as describe above with respect to the pseudo code described in Table 2. When it calls for compression of the updated value, the value would be compressed using the dictionary encoding scheme including adding a new entry to the dictionary table, should it be necessary. 
       FIG. 6  illustrates an example prefix encoding compression scheme  600 . In the prefix encoding scheme, if the column starts with a long sequence of the same value V, the sequence is replaced by storing the value once, together with the number of occurrences. For data read operations, the reads would read the data from the fragment and any change log. For data insert operations, new data would be compressed according to the prefix encoding compression scheme and appended to the end of the fragment. For the data delete operation, for example, if an item is to be deleted, the database system can mark an identifier on the row ID so it would not affect the compression results. 
     For an update operation, the same algorithm as described above in the pseudo code of Table 2 is followed. If the update value has sufficient memory space, then the update value is compressed according to the prefix encoding scheme and inserted directly into the record in compressed format. If the updated value does not have enough memory space, the updated value would not be compressed and instead would be inserted into a change log. In this example shown, the memory space may not be large enough to hold the new longer data. Instead of affecting the entire database  104 , the updated value only affects the particular fragment and the change log associated with it. At a later time, a merge may be performed to reallocate the memory for the fragment to accommodate the larger size of the newer data contained in the change log. Then, the change log may be merged asynchronously with the fragment. 
       FIG. 7 . Illustrates an example run length encoding scheme  700 . In a run length encoding scheme, the sequences of the same value are replaced with a single instance along with its start position. This example variant of run length encoding was chosen, as it speeds up access to storing the number of occurrences with each value. As discussed above with the other encoding schemes, insert operations may be appended to the fragment after compressing the new data according to the run length encoding scheme. 
       FIG. 8  illustrates an example cluster encoding scheme  800 . Cluster encoding partitions the sequence into N blocks of fixed size (1024 elements). If a cluster contains only occurrences of a single value, the cluster is replaced by a single occurrence of that value. A bit vector length N indicates which clusters were replaced by a single value. 
     The cluster encoding is similar to prefix encoding in the handling of database operations. An update operation may break the compression result. The pseudo code described in Table 2 would be used to perform an update operation. 
       FIG. 9  illustrates an example sparse encoding compression scheme  900  and  FIG. 10  illustrates an example in direct encoding compression scheme  1000 . As discussed above, the pseudo code in Table 2 would be applied to a fragment using one or more of these compression schemes. 
     As can be seen from the examples of  FIGS. 5-10 , the compression scheme used in the fragment does not matter because any database operation that breaks the memory space allocated for the particular scheme is limited to one fragment and does not affect the entire database. In this manner, memory and CPU resources may be saved. 
     Implementations of the various techniques described herein may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Implementations may be implemented as a computer program product, i.e., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. A computer program, such as the computer program(s) described above, can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. 
     Method steps may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Method steps also may be performed by, and an apparatus may be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). 
     Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. Elements of a computer may include at least one processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer also may include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory may be supplemented by, or incorporated in special purpose logic circuitry. 
     To provide for interaction with a user, implementations may be implemented on a computer having a display device, e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. 
     Implementations may be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation, or any combination of such back-end, middleware, or front-end components. Components may be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN) and a wide area network (WAN), e.g., the Internet. 
     While certain features of the described implementations have been illustrated as described herein, many modifications, substitutions, changes and equivalents will now occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the scope of the embodiments.