Patent Publication Number: US-10769034-B2

Title: Caching DML statement context during asynchronous database system replication

Description:
TECHNICAL FIELD 
     The subject matter described herein relates to enhanced techniques for database recovery caching the context of SQL Data Manipulation Language (DML) statements during asynchronous database system replication. 
     BACKGROUND 
     Database systems are susceptible to failure for a variety of reasons including both software and hardware related issues. As a result, recovery logs that record various operations performed by such database systems have been adopted. These recovery logs record various actions performed by the database systems which can be later replayed, if needed, as part of a recovery operation. Further, as part of such recovery, certain operations which have not been fully executed can be reverted or, if fully executed, might clean up or reorganize the data space. 
     SUMMARY 
     Objects common across multiple database manipulation language (DML) redo log records for a same table partition of a database system (having a primary system and a secondary system) are cached during continuous replay of redo log records on the secondary database system. Subsequently, cached objects are reused as they are accessed during the sequential processing of DML redo records for the same table partition. Other variations can be implemented using a single database without the shipping of redo log records from a primary system to a secondary system. 
     Non-transitory computer program products (i.e., physically embodied computer program products) are also described that store instructions, which when executed by one or more data processors of one or more computing systems, cause at least one data processor to perform operations herein. Similarly, computer systems are also described that may include one or more data processors and memory coupled to the one or more data processors. The memory may temporarily or permanently store instructions that cause at least one processor to perform one or more of the operations described herein. In addition, methods can be implemented by one or more data processors either within a single computing system or distributed among two or more computing systems. Such computing systems can be connected and can exchange data and/or commands or other instructions or the like via one or more connections, including but not limited to a connection over a network (e.g., the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc. 
     The subject matter described herein provides many technical advantages. For example, the current subject matter provides more rapid database recovery that reduces memory impact while also consuming fewer processor resources. 
     The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  is a system diagram illustrating an example database system for use in connection with the current subject matter; 
         FIG. 2  is a system diagram illustrating an example database system that can support distribution of server components across multiple hosts for scalability and/or availability purposes for use in connection with the current subject matter; 
         FIG. 3  is a diagram illustrating an architecture for an index server for use in connection with the current subject matter; 
         FIG. 4  is a functional flow diagram illustrating an architecture to support load balancing between a primary database system and a secondary database system; 
         FIG. 5  is a functional flow diagram depicting one example solution to managing load balancing in a HA/DR system for use in connection with the current subject matter; and 
         FIG. 6  is a process flow diagram illustrating caching of objects for reuse during continuous replay of redo log records. 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     The current subject matter is directed to enhanced techniques for database recovery in which certain objects that can be used for replay of redo log records can be cached and reused thereby reducing the consumption of memory and processing resources and additionally decreasing the amount of time required for database recovery. 
       FIG. 1  is a diagram  100  illustrating a database system  105  that can be used to implement aspects of the current subject matter. The database system  105  can, for example, be an in-memory database in which all relevant data is kept in main memory so that read operations can be executed without disk I/O and in which disk storage is required to make any changes durables. The database system  105  can include a plurality of servers including, for example, one or more of an index server  110 , a name server  115 , and/or an application server  120 . The database system  105  can also include one or more of an extended store server  125 , a database deployment infrastructure (DDI) server  130 , a data provisioning server  135 , and/or a streaming cluster  140 . The database system  105  can be accessed by a plurality of remote clients  145 ,  150  via different protocols such as SQL/MDX (by way of the index server  110 ) and/or web-based protocols such as HTTP (by way of the application server  120 ). 
     The index server  110  can contain in-memory data stores and engines for processing data. The index server  110  can also be accessed by remote tools (via, for example, SQL queries), that can provide various development environment and administration tools. Additional details regarding an example implementation of the index server  110  is described and illustrated in connection with diagram  300  of  FIG. 3 . 
     The name server  115  can own information about the topology of the database system  105 . In a distributed database system, the name server  115  can know where various components are running and which data is located on which server. In a database system  105  with multiple database containers, the name server  115  can have information about existing database containers and it can also hosts the system database. For example, the name server  115  can manage the information about existing tenant databases. Unlike a name server  115  in a single-container system, the name server  115  in a database system  105  having multiple database containers does not store topology information such as the location of tables in a distributed database. In a multi-container database system  105  such database-level topology information can be stored as part of the catalogs of the tenant databases. 
     The application server  120  can enable native web applications used by one or more remote clients  150  accessing the database system  105  via a web protocol such as HTTP. The application server  120  can allow developers to write and run various database applications without the need to run an additional application server. The application server  120  can also used to run web-based tools  155  for administration, life-cycle management and development. Other administration and development tools  160  can directly access the index server  110  for, example, via SQL and other protocols. 
     The extended store server  125  can be part of a dynamic tiering option that can include a high-performance disk-based column store for very big data up to the petabyte range and beyond. Less frequently accessed data (for which is it non-optimal to maintain in main memory of the index server  110 ) can be put into the extended store server  125 . The dynamic tiering of the extended store server  125  allows for hosting of very large databases with a reduced cost of ownership as compared to conventional arrangements. 
     The DDI server  130  can be a separate server process that is part of a database deployment infrastructure (DDI). The DDI can be a layer of the database system  105  that simplifies the deployment of database objects using declarative design time artifacts. DDI can ensure a consistent deployment, for example by guaranteeing that multiple objects are deployed in the right sequence based on dependencies, and by implementing a transactional all-or-nothing deployment. 
     The data provisioning server  135  can provide enterprise information management and enable capabilities such as data provisioning in real time and batch mode, real-time data transformations, data quality functions, adapters for various types of remote sources, and an adapter SDK for developing additional adapters. 
     The streaming cluster  140  allows for various types of data streams (i.e., data feeds, etc.) to be utilized by the database system  105 . The streaming cluster  140  allows for both consumption of data streams and for complex event processing. 
       FIG. 2  is a diagram  200  illustrating a variation of the database system  105  that can support distribution of server components across multiple hosts for scalability and/or availability purposes. This database system  105  can, for example, be identified by a single system ID (SID) and it is perceived as one unit from the perspective of an administrator, who can install, update, start up, shut down, or backup the system as a whole. The different components of the database system  105  can share the same metadata, and requests from client applications  230  can be transparently dispatched to different servers  110   1-3 ,  120   1-3 , in the system, if required. 
     As is illustrated in  FIG. 2 , the distributed database system  105  can be installed on more than one host  210   1-3 . Each host  210   1-3  is a machine that can comprise at least one data processor (e.g., a CPU, etc.), memory, storage, a network interface, and an operation system and which executes part of the database system  105 . Each host  210   1-3  can execute a database instance  220   1-3  which comprises the set of components of the distributed database system  105  that are installed on one host  210   1-3 .  FIG. 2  shows a distributed system with three hosts, which each run a name server  110   1-3 , index server  120   1-3 , and so on (other components are omitted to simplify the illustration). 
       FIG. 3  is a diagram  300  illustrating an architecture for the index server  110  (which can, as indicated above, be one of many instances). A connection and session management component  302  can create and manage sessions and connections for the client applications  150 . For each session, a set of parameters can be maintained such as, for example, auto commit settings or the current transaction isolation level. 
     Requests from the client applications  150  can be processed and executed by way of a request processing and execution control component  310 . The database system  105  offers rich programming capabilities for running application-specific calculations inside the database system. In addition to SQL, MDX, and WIPE, the database system  105  can provide different programming languages for different use cases. SQLScript can be used to write database procedures and user defined functions that can be used in SQL statements. The L language is an imperative language, which can be used to implement operator logic that can be called by SQLScript procedures and for writing user-defined functions. 
     Once a session is established, client applications  150  typically use SQL statements to communicate with the index server  110  which can be handled by a SQL processor  312  within the request processing and execution control component  310 . Analytical applications can use the multidimensional query language MDX (MultiDimensional eXpressions) via an MDX processor  322 . For graph data, applications can use GEM (Graph Query and Manipulation) via a GEM processor  316 , a graph query and manipulation language. SQL statements and MDX queries can be sent over the same connection with the client application  150  using the same network communication protocol. GEM statements can be sent using a built-in SQL system procedure. 
     The index server  110  can include an authentication component  304  that can be invoked with a new connection with a client application  150  is established. Users can be authenticated either by the database system  105  itself (login with user and password) or authentication can be delegated to an external authentication provider. An authorization manager  306  can be invoked by other components of the database system  150  to check whether the user has the required privileges to execute the requested operations. 
     Each statement can processed in the context of a transaction. New sessions can be implicitly assigned to a new transaction. The index server  110  can include a transaction manager  344  that coordinates transactions, controls transactional isolation, and keeps track of running and closed transactions. When a transaction is committed or rolled back, the transaction manager  344  can inform the involved engines about this event so they can execute necessary actions. The transaction manager  344  can provide various types of concurrency control and it can cooperate with a persistence layer  346  to achieve atomic and durable transactions. 
     Incoming SQL requests from the client applications  150  can be e received by the SQL processor  312 . Data manipulation statements can be executed by the SQL processor  312  itself. Other types of requests can be delegated to the respective components. Data definition statements can be dispatched to a metadata manager  306 , transaction control statements can be forwarded to the transaction manager  344 , planning commands can be routed to a planning engine  318 , and task related commands can forwarded to a task manager  324  (which can be part of a larger task framework) Incoming MDX requests can be delegated to the MDX processor  322 . Procedure calls can be forwarded to the procedure processor  314 , which further dispatches the calls, for example to a calculation engine  326 , the GEM processor  316 , a repository  300 , or a DDI proxy  328 . 
     The index server  110  can also include a planning engine  318  that allows planning applications, for instance for financial planning, to execute basic planning operations in the database layer. One such basic operation is to create a new version of a data set as a copy of an existing one while applying filters and transformations. For example, planning data for a new year can be created as a copy of the data from the previous year. Another example for a planning operation is the disaggregation operation that distributes target values from higher to lower aggregation levels based on a distribution function. 
     The SQL processor  312  can include an enterprise performance management (EPM) runtime component  320  that can form part of a larger platform providing an infrastructure for developing and running enterprise performance management applications on the database system  105 . While the planning engine  318  can provide basic planning operations, the EPM platform provides a foundation for complete planning applications, based on by application-specific planning models managed in the database system  105 . 
     The calculation engine  326  can provide a common infrastructure that implements various features such as SQLScript, MDX, GEM, tasks, and planning operations. The SQLScript processor  312 , the MDX processor  322 , the planning engine  318 , the task manager  324 , and the GEM processor  316  can translate the different programming languages, query languages, and models into a common representation that is optimized and executed by the calculation engine  326 . The calculation engine  326  can implement those features using temporary results  340  which can be based, in part, on data within the relational stores  332 . 
     Metadata can be accessed via the metadata manager component  308 . Metadata, in this context, can comprise a variety of objects, such as definitions of relational tables, columns, views, indexes and procedures. Metadata of all these types can be stored in one common database catalog for all stores. The database catalog can be stored in tables in a row store  336  forming part of a group of relational stores  332 . Other aspects of the database system  105  including, for example, support and multi-version concurrency control can also be used for metadata management. In distributed systems, central metadata is shared across servers and the metadata manager  308  can coordinate or otherwise manage such sharing. 
     The relational stores  332  form the different data management components of the index server  110  and these relational stores can, for example, store data in main memory. The row store  336 , a column store  338 , and a federation component  334  are all relational data stores which can provide access to data organized in relational tables. The column store  338  can stores relational tables column-wise (i.e., in a column-oriented fashion, etc.). The column store  338  can also comprise text search and analysis capabilities, support for spatial data, and operators and storage for graph-structured data. With regard to graph-structured data, from an application viewpoint, the column store  338  could be viewed as a non-relational and schema-flexible in-memory data store for graph-structured data. However, technically such a graph store is not a separate physical data store. Instead it is built using the column store  338 , which can have a dedicated graph API. 
     The row store  336  can stores relational tables row-wise. When a table is created, the creator can specify whether it should be row or column-based. Tables can be migrated between the two storage formats. While certain SQL extensions are only available for one kind of table (such as the “merge” command for column tables), standard SQL can be used on all tables. The index server  110  also provides functionality to combine both kinds of tables in one statement (join, sub query, union). 
     The federation component  334  can be viewed as a virtual relational data store. The federation component  334  can provide access to remote data in external data source system(s)  354  through virtual tables, which can be used in SQL queries in a fashion similar to normal tables. 
     The database system  105  can include an integration of a non-relational data store  342  into the index server  110 . For example, the non-relational data store  342  can have data represented as networks of C++ objects, which can be persisted to disk. The non-relational data store  342  can be used, for example, for optimization and planning tasks that operate on large networks of data objects, for example in supply chain management. Unlike the row store  336  and the column store  338 , the non-relational data store  342  does not use relational tables; rather, objects can be directly stored in containers provided by the persistence layer  346 . Fixed size entry containers can be used to store objects of one class. Persisted objects can be loaded via their persisted object IDs, which can also be used to persist references between objects. In addition, access via in-memory indexes is supported. In that case, the objects need to contain search keys. The in-memory search index is created on first access. The non-relational data store  342  can be integrated with the transaction manager  344  to extends transaction management with sub-transactions, and to also provide a different locking protocol and implementation of multi version concurrency control. 
     An extended store is another relational store that can be used or otherwise form part of the database system  105 . The extended store can, for example, be a disk-based column store optimized for managing very big tables, which ones do not want to keep in memory (as with the relational stores  332 ). The extended store can run in an extended store server  125  separate from the index server  110 . The index server  110  can use the federation component  334  to send SQL statements to the extended store server  125 . 
     The persistence layer  346  is responsible for durability and atomicity of transactions. The persistence layer  346  can ensure that the database system  105  is restored to the most recent committed state after a restart and that transactions are either completely executed or completely undone. To achieve this goal in an efficient way, the persistence layer  346  can use a combination of write-ahead logs, undo and cleanup logs, shadow paging and savepoints. The persistence layer  346  can provide interfaces for writing and reading persisted data and it can also contain a logger component that manages a recovery log. Recovery log entries can be written in the persistence layer  352  (in recovery log volumes  352 ) explicitly by using a log interface or implicitly when using the virtual file abstraction. The recovery log volumes  352  can include redo logs which specify database operations to be replayed whereas data volume  350  contains undo logs which specify database operations to be undone as well as cleanup logs of committed operations which can be executed by a garbage collection process to reorganize the data area (e.g. free up space occupied by deleted data etc.). 
     The persistence layer  236  stores data in persistent disk storage  348  which, in turn, can include data volumes  350  and/or recovery log volumes  352  that can be organized in pages. Different page sizes can be supported, for example, between 4 k and 16M. Data can be loaded from the disk storage  348  and stored to disk page wise. For read and write access, pages can be loaded into a page buffer in memory. The page buffer need not have a minimum or maximum size, rather, all free memory not used for other things can be used for the page buffer. If the memory is needed elsewhere, least recently used pages can be removed from the cache. If a modified page is chosen to be removed, the page first needs to be persisted to disk storage  348 . While the pages and the page buffer are managed by the persistence layer  346 , the in-memory stores (i.e., the relational stores  332 ) can access data within loaded pages. 
     In many applications, data systems may be required to support operations on a 24/7 schedule, and data system providers may be required to guarantee a minimum amount of downtime, that is time during which a system is not able to fully support ongoing operations. When a system is required to ensure an agreed level of operational performance, it may be referred to as a high availability system (“HA”). One solution to guarantee substantially continuous uptime with no, or very little, downtime is to maintain one or more hot-standby systems. A hot-standby system, or a backup system, is a system that may be activated quickly in the event of a disruption causing one or more functions of a primary operational data system to fail. Such a disruption may be referred to as a disaster, and the process of restoring a data system to full operations may be referred to as disaster-recovery (“DR”). 
     A hot-standby system may be an exact replica of a primary operational system that is capable of providing all the functions provided by the primary operational system, or a hot-standby may be a system that is capable of providing a minimum amount of essential functionality during the time required to restore the primary operational data system. The time it takes after a disaster to restore full, or minimum, functionality of a data system, for example by bringing a hot-standby online, is referred to as recovery time. In an effort to minimize recovery time, and thereby downtime, a hot-standby system is typically in a state just short of fully operational. For example, a system architecture may be implemented in which all functional systems of the hot-standby are active and operational, and all system and data changes or updates occur in the primary operational system and the hot-standby at the exact same time. In such a case the only difference in the two systems may be that the primary is configured to respond to user requests and the secondary is not. In other hot-standby systems one or more functions may be disabled until mission critical systems of the hot-standby are observed to be operating normally, at which time the remaining functions may be brought online. 
     In many applications, data systems may be required to provide prompt responses to users and applications that rely on the data managed by the data system. Providers and designers of data systems may be required to guarantee a minimum average throughput over time, or an average maximum response time. The speed with which a data system responds to a request from a user or an application may be dependent on many factors, but all systems are limited in the number of requests they can handle in a given period of time. When a data system manages a relatively large amount of data, and supports a relatively large number of users or applications, during high workloads a request may be queued, buffered or rejected until sufficient system resources are available to complete the request. When this happens, average throughput goes down and average response time goes up. One solution to such a problem is to distribute the workload across multiple processing systems. This is known as load balancing. 
     One drawback to load balancing and HA systems is that they may require additional processing systems, which in turn have a high cost. It is often the case with certain data systems supporting critical functions of an organization that additional systems are needed to perform both load balancing and HA functionality to efficiently support continuous operations. Given the redundant nature of DR systems, they are often left undisturbed unless a disaster occurs. Thus, in some circumstances, it is desirable to implement and maintain a combination high availability/disaster recovery (HA/DR) system with load balancing that includes both a primary operational system and a hot-standby system, and potentially one or more tertiary systems. Such a combination system allows for load balancing of workload between the processing systems of both the primary operational system and the hot-standby system, without disrupting the ability of the HA/DR system to assume primary functionality in the event of a disaster. 
       FIG. 4  is a functional flow diagram illustrating an architecture  400  to support load balancing between a primary database system, or primary system  405   a  and a secondary database system, or secondary system  405   b , which serves as hot-standby to primary system  405   a . Each of the primary system  405   a  and the secondary system  405   b  may be a single instance system, similar to database system  105  depicted in  FIG. 1 , or each may be a distributed variation of database system  105  as depicted in  FIG. 2 . Such an architecture  400  may be useful in a high availability data system, or in a disaster recovery system, or in a combination HA/DR system. 
     Each of the primary system  405   a  and secondary system  405   b  may include a load balancing functionality. Such load balancing functionality may for example be contained within a distinct load balancing server  470   a  or  470   b . But, such load balancing functionality may be managed by any suitable processing system. For example, the application server  120  of the primary system may also manage the load balancing of requests issued to the application server of the primary system  405   a , sending requests to the secondary system  405   b  as necessary to maintain a well distributed workload. 
     As depicted in  FIG. 4 , each of the primary system  405   a  and the secondary system  405   b  includes a load balancing server  470   a  and  470   b  which respectively receive requests from user applications directed to the primary system  405   a  or the secondary system  405   b . Such request may come from either admin tools  460  or web-based tools  450 , or any other user application. Upon receiving a request a load balancing server, e.g.  470   a , determines how to distribute the workload. As depicted load balancing server  470   a  routes an SQL request  465  from admin tools  460  to the index server  110  of the primary system  405   a , while routing an HTTP request  455  from web-based tools  450  to the application server  120  of the secondary system  405   b.    
     Load balancing of resources between a primary system  405   a  and a secondary system  405   b  can give rise to a number of complicating issues. For example, if either of the requests  455 ,  465  requires writing to one or more data tables, or modifying a data table, then the two systems  405   a ,  405   b  will diverge. After many instances of write requests being distributed between the primary system  405   a  and the secondary system  405   b , the two systems would be substantially different, and likely unusable. In another example, an application request, e.g.  465 , may perform a write transaction that is followed by a read transaction, e.g.  455 , related to the data written by the write request  465 . If the write request is allocated to the primary system  405   a , the read request would obtain a different result depending on whether the subsequent read transaction is carried out by the primary system  405   a  or by the secondary system  405   b.    
     Load balancing in a HA/DR system, by distributing a portion of the workload of a primary data system to a hot-standby or backup system must be done in a way that does not disturb the principal purpose of the backup system, which is to substantially eliminate downtime in a high availability system by enabling quick and efficient recovery of operations. In other words, as a rule load balancing cannot break the hot-standby. Given this principal purpose, any solution that enables load balancing of workload between a primary system and a backup system must maintain the backup system in an identical, or nearly identical, state as the primary system. Such a solution should also avoid or prohibit any actions which may cause the state of the backup system to substantially diverge from the state of the primary system. In this way, in the event of a partial or total failure of the primary system due to disaster, the backup system can failover to a primary system mode with minimal or no impact to client applications. 
       FIG. 5  depicts one possible solution to managing load balancing in a HA/DR system  500 . HA/DR system  500  includes a primary system  505  and a secondary system  510  and is capable of load balancing between primary system  505  and secondary system  510  without interfering with the hot-standby functionality of the secondary system  510 . Each of primary system  505  and secondary system  510  may be single instance database systems similar to database system  105  depicted in  FIG. 1 , or a distributed variation of database system  105  as depicted in  FIG. 2 . Furthermore, each of primary system  505  and secondary system  510  may comprise less, more or all the functionality ascribed to index server  110 ,  300 , name server  115 , application server  120 , extended store server  125 , DDI server  130 , data provisioning server  135 , and stream cluster  140 . But, for simplicity of illustration HA/DR system  500  has been simplified to highlight certain functionality by merely distinguishing between processing control  555 ,  560  and a persistence layer  565 ,  570  of each respective system  505 ,  510 . 
     A collection of clients may each maintain an open connection to both the primary system  505  and the secondary system  525 . For example, client  515  maintains a read/write connection  520  to the primary system  505  and a read only connection  525  to the secondary system  510 . Alternatively, client  515  may maintain a read/write connection with each of the primary system  505  and the secondary system  510 , while processes within the secondary system  510  itself prohibit execution of any requests that require a write transaction upon the secondary system while it is in backup mode. Management of load balancing of the workload required by a client application executing at client  515  may be managed by the client  515  application itself. Alternatively, a client  515  application may submit a query request to the primary system  505 . A process control  555  load balancing process executing on processor  545  then may determine where the query should be executed and replies to the client  515  with instructions identifying which system the client  515  should issue the query to. 
     Primary system  505  may include an in-memory database in which substantially all actively used data may be kept and maintained in main memory  535  so that operations can be executed without disk I/O, which requires accessing disk storage. 
     Active operations of applications within processing control  555  may cause processor  545  to read and write data into main memory  535  or to disk in the persistence layer  565 . Processing control  555  applications can also cause processor  545  to generate transaction logs (e.g., redo log, undo log, cleanup log, etc.) for capturing data transactions upon the database, which processor  545  then persists in the log volumes  585  and data volumes  575  respectively. As substantially all actively used data may reside in-memory, processing control  555  may interact primarily with data held in main memory while only resorting to data volumes  575  for retrieving and writing less often used data. Additional processes within processing control  555  may be executed by processor  545  to ensure that in-memory data is persisted in persistence layer  565 , so that the data is available upon restart or recovery. 
     Primary system  505  may be the primary operational system for providing the various functionality necessary to support 24/7 operations for an organization. Secondary system  510  may be a hot-standby, ready to come online with minimal recovery time so as to minimize downtime. Secondary system  510  may be an identical physical system as primary system  505 , and may be configured in a substantially identical manner in order to enable the secondary system  510  to provide all the same functionality as primary system  505 . For example, processing control  560  may include all the same applications and functionality as processing control  555 , and persistence layer  570  may include data volumes  580  and log volumes  590  that are configured in an identical manner as data volumes  575  and log volumes  585  respectively. Secondary system  510  may also include an in-memory database kept and maintained primarily in main memory  540 . 
     Primary system  505  and secondary system  510  differ in that all requests, from client  515  or otherwise, that require a write transaction are executed only in primary system  505 . Primary system  505  and secondary system  510  further differ in that all write transactions are prohibited by the secondary system  510 . In order to propagate changes to the data or the underlying schema from the primary system  505  to the secondary system  510 , processor  545  also replicates  530  transaction logs directly to the process control  560  of the secondary system  510 . Process control  560  includes one or more applications that cause processor  550  to then replay the transaction logs replicated from the primary system  505 , thereby replaying the transactions at the secondary system  510 . As transaction logs are replayed, the various transactions executed at the primary system become reflected in the secondary system  510 . In order to ensure both the HA functionality and the load balancing functionality, replay of the transaction logs at the secondary system places data in main memory  540 , and also persists any data committed in the primary system to persistence layer  570  to be stored by data volumes  580 . Replay of the transaction logs at the secondary system  510  may also results in the transaction logs being persisted in log volumes  590 . 
     Transaction logs (e.g., redo logs, undo logs, cleanup logs, etc.) in the log volumes  585  may be replicated in different ways. Where maintaining a standby system in as close to the same state as the primary system is an important factor, logs may be replicated synchronously meaning that the primary system will not commit a transaction until the secondary successfully responds to the log replication. Such an arrangement slows performance of the primary system. Conversely, where performance of a primary system is a priority, logs may be replicated asynchronously, in which case the primary operation proceeds with committing transactions without waiting for a response. Various tradeoffs can be made between these two scenarios to achieve a proper level of performance while ensuring replication of critical data. 
     It will be appreciated from the detailed description above that such a secondary system in standby mode, such as secondary system  510 , can only be as current as its most recently replayed transaction logs. Transaction logs are replicated and replayed at the secondary system  510  only after a transaction executes in the primary system  505 . Secondary system  510 , therefore, is always slightly behind an associated primary system  515 . Also, there is no guarantee that a query routed to the primary system in a load balancing effort will be executed before, during or after a particular transaction log is replayed. Thus, the state of the primary system  505  and the state of the secondary system will rarely if ever be identical. But, by addressing certain concerns, secondary system  510  may be kept in a state substantially close to the same state as the primary system  505  such that the workload required by many operations can be supported by the secondary  510 . 
     With the HA/DR system  500 , an initial copy on the primary system  505  can be shipped to the secondary system  510  that can serve as a starting point, where both the primary system  505  and the secondary system  510  have identical data, before transaction log replay commences to synchronize all future changes from the primary system  505  to the secondary system  510 . 
     As noted above, the data of the primary system  505  (also referred to as the primary system data) can comprise data volumes  350 ,  575  comprising a data store together with undo and cleanup log and recovery log volumes  352 ,  590  comprising the recovery log. Other types of storage arrangements can be utilized depending on the desired configuration. The data store can comprise a snapshot of the corresponding database contents as of the last system savepoint. System savepoints (also known in the field of relational database servers as checkpoints) can be periodically or manually generated and provide a point at which the recovery log can be truncated. 
     The savepoint can, in some variations, include an undo log of transactions which were open in the savepoint and/or a cleanup log of transactions which were committed in the savepoint but not yet garbage collected (i.e., data which has been deleted by these transactions has been marked as deleted but has not been deleted in a physical manner to assure multiversion concurrency control). With conventional arrangements, the cleanup is executed before the replay of the transaction log is started (by one or more garbage collection processes that frees data which has been deleted by committed transactions, etc.). In case this cleanup log is large or consists of expensive operations it can take a considerable time to process it. This arrangements adds to the downtime of the database system as this is part of the system start up or system takeover phase. 
     The recovery log can comprise a log of all changes to the database contents (i.e., the database system  105 , the primary database  505  and/or the secondary database  510 , etc.) since the last system savepoint, such that when a database server is restarted, its latest state is restored by replaying the changes from the recovery log on top of the last system savepoint. Typically, in a relational database system, the previous recovery log is cleared whenever a system savepoint occurs, which then starts a new, empty recovery log that will be effective until the next system savepoint. While the recovery log is processed, a new cleanup log is generated which needs to be processed as soon as the commit is replayed to avoid a growing data area because of deleted but not garbage collected data. 
     As mentioned above, high availability demands on a database involves replicating statements that were executed on the primary system  505  onto the secondary system  510 . This replication is done by shipping log records from the primary system  505  and replaying them on the secondary system  510  using a process called continuous log replay. The secondary system  510  takes over when there is failure on the primary system  505  and queries are then redirected to the secondary system  510 . 
     With a configuration in which both the primary system  505  and the secondary system  510  are active, readers are also allowed on the secondary system  510  while the log records are being replayed from the primary system  505  to the secondary system  510 . The read only queries on the secondary system  510  get a consistent and minimally delayed snapshot of the primary system  505 . 
     Data Manipulation Language (DML) involves operations that insert, update and delete data. Data Definition Language (DDL) involve operations that modify database object existence, structure or metadata. 
     Each of the primary system  505  and the secondary system  510  can be an in-memory database where read queries are directly executed on in-memory stores. Each table partition has its own in-memory store. The in-memory store further has a delta and a main fragment which are separate logical memory stores. Delta fragment is write optimized and main is read optimized. Data is persisted on the file system through a combination of redo/undo log records and a database page based persistency mechanism. The database page based persistency mechanism is encapsulated using persistent containers. 
     The continuous replay of log records on the secondary system  510  can be processed using queues called recovery queues. Redo log records of a table partition can be placed and processed at a specific recovery queue. A recovery queue can contain redo log records for a subset of table partitions on the secondary system  510 . The count of table partitions assigned to a recovery queue can depend on the number of recovery queues and the number of table partitions. So a recovery queue that is assigned to two table partitions can have redo log records of the two partitions interleaved in the queue depending on the order in which it receives records during continuous log replay. Each redo log record in a particular recovery queue can be processed sequentially in a FIFO (first in first out) manner. 
     Performance of the replay of the log records on the secondary system  510  can sometimes cause delays. The longer the delay, the older a consistent snapshot the readers on the secondary system  510  will see. Also, this delay can have implications during take over if the secondary system  510  recovery queue takes time to process the queued log records before being activated as the new primary system  505 . 
     As part of replaying a log record, the DML operation corresponding to a log record is re-executed. There are several objects that are constructed as part of this process which are later destroyed at the end after this log record replay is completed. 
     Object creation and destruction involves heavy memory manager operations. As this process is repeated for each log record to be replayed, all the repeated construction and destruction of different objects becomes a very expensive operation and can create a bottleneck in a very high memory intensive workload environment. 
     To optimize the process of sharing objects, a per table partition context called the DML redo context can be created in the table partition&#39;s recovery queue which caches objects common across multiple log records for the same table partition. As redo log records of a single table partition can be processed sequentially in a single recovery queue, each subsequent redo log record of that table partition can reuse the cached object previously constructed. Such context can be used for replaying DML log records until a log record that can modify these object contents is encountered upon which the cached object is invalidated or destroyed (e.g., via a garbage collection process, etc.). 
     Examples of objects that are common across DML log records and can be cached include table metadata. In this regard, there are some table level metadata information such as table name that are needed during log replay process. If there are several log records operating on the same table, creating and destroying string objects to store these information may unnecessarily cause overhead on the memory manager thereby degrading performance. Other examples of objects that are common across DML log record include column level metadata information. Such column level metadata information can include processing of DML execution need access to various metadata information on columns. Reconstructing such information through persistent structures is very expensive operation. By caching these objects locally, the replay process can be sped up by avoiding execution of redundant code path. 
     There are other examples of objects in the DML redo context that can be used to track statistics for all the DML redo log records processed on that table partition. DML redo context can also be used as a message passing mechanism between different redo operations. For example, some DDL redo operations that span multiple DDL redo log records but may have DML redo log records interspersed, can have a flag set in the DML redo context when the first DDL redo log record is processed. Any DML redo that needs to process afterwards can skip acquiring redundant synchronization primitives. The last DDL redo log record of the DDL operation can unset this flag in the DML redo context. 
     Some examples of log records that can invalidate the cached object include DDL log records. By definition, DDL operations change the existence, structure or metadata of a database object. In some cases these operations will invalidate some or all objects in the DML context associated with that table partition. The following lists the general classification of DDL operations and how their replay during continuous log replay can affect the DML context objects. Drop, rename or move table partition: these operations completely invalidate the cached DML context as the table partition may no longer be associated with the recovery queue. In cases of rename and move table partition, the table partition still exists, but will usually be moved to another recovery queue, because recovery queue assignment can be based on name and location. It is possible, based on the number of queues and queue assignment logic that the table partition will remain in the current queue, but this is not predictable so the DML context will always be invalidated during these DDL operations. 
     Add, alter or remove columns: these operations invalidate only the column metadata objects associated with the DML Context. The other top-level metadata such as name can remain. 
     Alter partition metadata: these operations generally do not affect cached DML Context objects. 
     The framework provided herein is designed to be easily extensible in the need of caching additional items with further enhancements in redo process or when new performance optimizations are identified during log replay. Thus, by providing the framework to cache necessary information during log replay, performance is improved by reducing instructions executed in creating and destroying frequently needed information and by reducing interaction with a memory manager layer. 
       FIG. 6  is a process flow diagram  600  in which, at  610 , objects common across multiple database manipulation language (DML) redo log records for a same table partition of a database system (having a primary system and a secondary system) are cached during continuous replay of redo log records on the secondary database system. Subsequently, at  620 , cached objects are reused as they are accessed during the sequential processing of DML redo records for the same table partition. Other variations can be implemented using a single database without the shipping of redo log records from a primary system to a secondary system. 
     One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) computer hardware, firmware, software, and/or combinations thereof. These various aspects or features can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
     These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural language, an object-oriented programming language, a functional programming language, a logical programming language, and/or in assembly/machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and/or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example as would a processor cache or other random access memory associated with one or more physical processor cores. 
     To provide for interaction with a user, the subject matter described herein may be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) and/or a touch screen by which the user may provide input to the computer. Other kinds of devices may be used to provide for interaction with a user as well; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input. 
     In the descriptions above and in the claims, phrases such as “at least one of” or “one or more of” may occur followed by a conjunctive list of elements or features. The term “and/or” may also occur in a list of two or more elements or features. Unless otherwise implicitly or explicitly contradicted by the context in which it is used, such a phrase is intended to mean any of the listed elements or features individually or any of the recited elements or features in combination with any of the other recited elements or features. For example, the phrases “at least one of A and B;” “one or more of A and B;” and “A and/or B” are each intended to mean “A alone, B alone, or A and B together.” A similar interpretation is also intended for lists including three or more items. For example, the phrases “at least one of A, B, and C;” “one or more of A, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, B alone, C alone, A and B together, A and C together, B and C together, or A and B and C together.” In addition, use of the term “based on,” above and in the claims is intended to mean, “based at least in part on,” such that an unrecited feature or element is also permissible. 
     The subject matter described herein can be embodied in systems, apparatus, methods, and/or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and/or variations can be provided in addition to those set forth herein. For example, the implementations described above can be directed to various combinations and subcombinations of the disclosed features and/or combinations and subcombinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and/or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations may be within the scope of the following claims.