Abstract: The subject technology receives, by a first set of decoupled compute instances of a distributed database, a plurality of transactional queries against a transactional processing database stored on a first set of decoupled storage instances, at least one of the first set of decoupled compute instances bypassing a compiler of the distributed database to process the plurality of transactional queries using locally cached data. The subject technology receives at least one select query for processing on an aggregated analytical query database of the distributed database. The subject technology generates, by the compiler of the distributed database, a query plan for execution of the at least one select query using a second set of decoupled compute instances. The subject technology generates, by the second set of decoupled compute instances, an aggregated analytical query result dataset for the at least one select query according to the query plan.

Abstract: A hybrid network-based database system for handling OLTP and OLAP queries using decoupled compute and storage devices. A set of decoupled compute instances perform transactions on an OLTP database, and the data is replicated to an OLAP database, which is managed by another set of decoupled compute instances. Further, in response to queries, the database system can retrieve data from the OLTP and OLAP database for merging and processing according to the query.

Abstract: Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.

Abstract: Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.

Abstract: A hybrid network-based database system for handling OLTP and OLAP queries using decoupled compute and storage devices. A set of decoupled compute instances perform transactions on an OLTP database, and the data is replicated to an OLAP database, which is managed by another set of decoupled compute instances. Further, in response to queries, the database system can retrieve data from the OLTP and OLAP database for merging and processing according to the query.

Abstract: Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.

Abstract: Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.

Abstract: Systems, methods, and other embodiments associated with continuous capture of replayable workload for a database are described. In one embodiment, a method includes capturing workload units being executed on a production database and storing the captured workload units in a circular workload buffer. The circular workload buffer is configured to write newest captured workload units over oldest workload units in the workload buffer when the workload buffer becomes full. The method includes, in response to detecting an event, exporting contents of the workload buffer to offline memory. The exporting includes at least one I/O operation. In one embodiment, an advisor feature guides set up of the continuous capture feature by providing historical workload data and making recommendations regarding an amount of memory that should be allocated to the workload buffer.

Abstract: Systems, methods, and other embodiments associated with continuous capture of replayable workload for a database are described. In one embodiment, a method includes capturing workload units being executed on a production database and storing the captured workload units in a circular workload buffer. The circular workload buffer is configured to write newest captured workload units over oldest workload units in the workload buffer when the workload buffer becomes full. The method includes, in response to detecting an event, exporting contents of the workload buffer to offline memory. The exporting includes at least one I/O operation. In one embodiment, an advisor feature guides set up of the continuous capture feature by providing historical workload data and making recommendations regarding an amount of memory that should be allocated to the workload buffer.

Abstract: Techniques for leveraging frequent patterns identified in a captured workload are provided. In one approach, multiple frequent patterns detected in a captured workload may be ordered by frequency to determine, for example, which patterns should be targeted for optimization. In another approach, a model of a captured workload is created, where the model comprises nodes that represent templates (which in turn correspond to requests) and edges that represent transitions between templates. The model is used to create an artificial workload, such as a workload that is twice as large as the originally-captured workload. The model may also be edited before creating the artificial workload. In another approach, workload models are compared to identify errors, regressions, or security issues. In another approach, an artificial workload is created for an application that is not yet deployed and then executed to determine whether the artificial workload or the originally-captured workload executed faster.

Abstract: A method for replaying a simulation workload is provided. The simulation workload identifies a plurality of workload parts. Each workload part is at least a portion of a respective captured workload. In an embodiment, a first workload part is a portion of a first workload executed relative to a first capture database server and a second workload part is a portion of a second workload executed relative to a second capture database server. A portion of the first workload part and a portion of the second workload part may be replayed concurrently.

Abstract: Described herein are methods for determining patterns based on requests received by a server. Based on the determined patterns, insight into the types of requests received by the server can be gained. Additionally, performance statistics and query statistics can be aggregated in a useful way. For example, performance statistics may be summarized for each determined pattern. One technique for determining patterns includes determining a sequence of template identifiers identifying templates that correspond to sub-sequences of requests in a sequence of server requests. A model may be created based on the sequence of template identifiers. Based on the model, template patterns may be determined. Template patterns may further be grouped into pattern clusters.

Abstract: A method and apparatus for capturing database system workload and replaying that workload in a database system is provided. According to one aspect, in order to subject a test database system to the same workload to which the production database system actually would be subjected, a database server in the production system captures and records workload that the database server receives from external entities. This captured workload is processed. Processes external to a database server in the test database system send the processed workload to that database server. As a result, the test database system is subjected to the same workload to which the production database system originally was subjected. The foregoing technique permits a database administrator to determine how the production database system will fare if the difference that is present in the test database system is introduced into the production database system.

Abstract: A method for replaying a simulation workload is provided. The simulation workload identifies a plurality of workload parts. Each workload part is at least a portion of a respective captured workload. In an embodiment, a first workload part is a portion of a first workload executed relative to a first capture database server and a second workload part is a portion of a second workload executed relative to a second capture database server. A portion of the first workload part and a portion of the second workload part may be replayed concurrently.

Abstract: Described herein are methods for determining patterns based on requests received by a server. Based on the determined patterns, insight into the types of requests received by the server can be gained. Additionally, performance statistics and query statistics can be aggregated in a useful way. For example, performance statistics may be summarized for each determined pattern. One technique for determining patterns includes determining a sequence of template identifiers identifying templates that correspond to sub-sequences of requests in a sequence of server requests. A model may be created based on the sequence of template identifiers. Based on the model, template patterns may be determined. Template patterns may further be grouped into pattern clusters.

Abstract: A method, apparatus, and computer-readable medium is provided for replaying captured workload data in a database environment with connection pools. A replay system includes a dispatcher that manages a connection pool. In one aspect, the dispatcher selects available connections for sending database commands that are ready to be executed against a database, but prevents from being sent, or holds, commands that are not ready to be executed against the database. Commands may be stored at the dispatcher until the database advances to a state that allows the commands to be executed. In another aspect, the dispatcher may use an available connection to send a command to a database instance even though the command is not ready to be executed, as long as at least a threshold number or percentage of connections remains available.

Abstract: A database system providing transactionally consistent replay of application workloads. In a database system, a method providing transactionally consistent replay of application workloads comprises the steps of: during a replay phase in which a plurality of application requests that were previously submitted to an application server system during a capture phase are being replayed, receiving, at a database server, a database request to execute a database command against a database. The database request specifies an application request identifier that identifies a particular application request of the plurality of application requests. The particular application request caused the database command to be executed previously during the capture phase. In response to receiving the database request, determining, based on the application request identifier in the database request, whether to delay execution of the database command at least until a particular replay-phase database transaction has been committed.

Abstract: An application workload capture and replay system with a transactionally consistent application workload replay feature is provided. More particularly, the feature includes capture-phase components for capturing and recording a real application workload submitted to a production web application system and includes replay-phase components for replaying the captured application workload against a test web application system in a transactionally consistent manner. The feature provides guarantees about the order of database transactions that are caused when the workload is replayed such that there is a consistency between the replay-phase order of the database transactions and the order of those transactions that occurred when the workload was captured. These consistency guarantees facilitate a faithful reproduction of database changes observed in the production web application system in the test web application system using a captured real application workload.

Abstract: A method, apparatus, and computer readable medium for preserving data dependency during replay of database commands without strictly preserving a global ordering of the database commands is provided. A capture process captures a workload of database commands executed by a production system. The captured workload includes object identifiers that identify database objects that were referenced directly or indirectly during execution of the database commands by the production system. The captured workload also includes an indication of whether the database objects were potentially read or written during execution of the commands. The workload is processed to determine that an object accessed during execution of a command was previously modified during execution of one or more other commands. A replay process on a test database system prevents the command from being replayed until after the one or more other commands have been replayed to modify the object.

Abstract: An application workload capture and replay system with a transactionally consistent application workload replay feature is provided. More particularly, the feature includes capture-phase components for capturing and recording a real application workload submitted to a production web application system and includes replay-phase components for replaying the captured application workload against a test web application system in a transactionally consistent manner. The feature provides guarantees about the order of database transactions that are caused when the workload is replayed such that there is a consistency between the replay-phase order of the database transactions and the order of those transactions that occurred when the workload was captured. These consistency guarantees facilitate a faithful reproduction of database changes observed in the production web application system in the test web application system using a captured real application workload.