Abstract: The subject technology generates, by a compute service manager, a schema hash value for a new schema version associated with a new schema version value, the schema hash value based on determining a sum of hash values of a set of attributes of value columns, the set of attributes comprises a column identifier, and a logical type of a column. The subject technology stores a mapping of the schema hash value to the new schema version value for a table in a metadata database. The subject technology stores a new schema entry based on the schema hash value, the new schema version value, and a new column for the table in the metadata database, the metadata database storing multiple entries for different schema versions, each entry including a particular schema hash value for mapping to a corresponding schema version from the different schema versions.

Abstract: The subject technology serializes, by at least one hardware processor, non-primary key data of column-organized data into compressed serialized value data that is in a row-organized sequence, the compressed serialized value data compressed using at least one bitmap, the non-primary key data comprising a schema identifier, the column-organized data being stored in a columnar database system, the column-organized data comprising primary key data and the non-primary key data. The subject technology stores the compressed serialized value data in a key-value data store of a key-value database system, the key-value database system processing key-value data in a key-value format. The subject technology receives a query by the columnar database system. The subject technology deserializes a portion of the compressed serialized value data that corresponds to the query. The subject technology processes the query using the columnar database system.

Abstract: A distributed database system can implement a column-based database system and a row-based database system for processing data. The row-based database system can store data organized into key value pairs, and data to be processed by the row-based database system is converted to a key-value format compressing keys that correspond to values. The distributed database system can perform serialization and compression in converting the data to the key-value format for efficient data storage performance. The distributed database system can unpack portions of the converted serialized compressed data in response to queries that process a portion of serialized compressed data without unpacking the entire converted dataset.

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: The subject technology receives a statement to perform an operation to add a new column into a table. The subject technology generates a schema hash value for a new schema version associated with a new schema version value. The subject technology stores a mapping of the schema hash value to the new schema version value for the table in a metadata database. The subject technology stores a new schema entry based on the schema hash value, the new schema version value, and the new column for the table in the metadata database. The subject technology performs an operation to add the new column to the table.

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: A distributed database system can implement a column-based database system and a row-based database system for processing data. The row-based database system can store data organized into key value pairs, and data to be processed by the row-based database system is converted to a key-value format compressing keys that correspond to values. The distributed database system can perform serialization and compression in converting the data to the key-value format for efficient data storage performance. The distributed database system can unpack portions of the converted serialized compressed data in response to queries that process a portion of serialized compressed data without unpacking the entire converted dataset.

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: Database change test system that includes an SQL performance analyzer (SPA) to efficiently test execute workload set of queries on a production or test database management system (DBMS) and report to a user the impact of database changes, is provided. Techniques are described that limit the resource consumption of test execution of workload set of queries, especially to enable such test execution on a production DBMS. A method and apparatus for storing in persistent storage query test profile that specifies query test restrictions and execution plan parameters, which indicate how to generate execution plan operators for query execution plans; storing a workload set of queries in persistent storage; establishing a session with a database management systems; retrieving the query test profile; configuring the session according to the test profile; receiving user input requesting to execute the workload set; and executing the queries according to the query test profile, is also provided.

Abstract: Approaches, techniques, and mechanisms are disclosed for ensuring that a database command is executed according to a query plan that has been verified to be actually optimal. Except in rare circumstances, a database server does not execute a query plan unless it is first verified by the database server. The database server receives a request to execute a database command. The database server determines an unverified plan is the best plan for satisfying the request. Rather than risk the unknown behavior of an unverified plan, the database server instead satisfies the request according to a verified plan. Subsequently—for example as part of a scheduled job—the database server executes the unverified plan to determine performance statistics. Based at least on the performance statistics, the database server determines whether or not to verify the unverified plan. Techniques for concurrent and optimistic verifications are also disclosed.

Abstract: Embedded Performance Monitoring is an “out-of-box” approach for performance monitoring of a DBMS. Performance monitoring of a DBMS is achieved through use of a browser to access the DBMS once a DBMS is installed and configured. The approach exploits a DBMS configured with the native capability to provide performance monitoring data and software via a browser. To retrieve and use the performance monitoring data from the DBMS, a browser executes software downloaded to the browser from the DBMS using web-based technologies.

Abstract: High-risk database statements are identified. A DBMS performs a series of experiments by generating sets of statistics describing data over which particular database statements run. The DBMS submits each of these sets of statistics to a query optimizer, which returns information for an execution plan that the optimizer would use to implement the statements given the submitted set of data statistics. If the DBMS determines that the optimizer changes the established execution plan for the database statements for any of the submitted sets of statistics, the DBMS performs one or more actions to mitigate the risk of changing execution plans, such as automatically tuning the database statements or the entire workload, and/or sending information about the database statements to a database administrator. Techniques are also described for calculating diagnostic values based on the results of such experiments, which quantify the sensitivity of the execution plans to changes in data statistics.

Abstract: Techniques are provided for a fully-automated process for tuning database query language statements that selects database query language statements for tuning, tunes the database query language statements and generates tuning recommendations, tests the tuning recommendations, and determines whether to implement the tuning recommendations based on the test results. The fully-automated tuning process may also automatically implement certain tuning recommendations and monitor the performance of the database query language statements for which tuning recommendations have been implemented.

Abstract: Techniques for generating and processing composite active reports are provided. An active report is a report that can be displayed and interacted with at a client device even though the client device is not connected to a database from which data for the report originates. A composite active report is an active report that includes multiple reports embedded in the same file. Each report in a composite active report may be separately compressed to allow a client device to decompress a report on demand. A composite active report may include, for each report indicated in the composite active report, executable identification data that is used to retrieve, from a remote source, an executable that is used to generate, based on report data of the report, display data, which is displayed on a computer display of a client device.

Abstract: Techniques for automatically recommending parallel execution of a SQL statement. In one set of embodiments, a first determination can be made regarding whether a SQL statement can be executed in parallel. Further, a second determination can be made regarding whether executing the SQL statement in parallel is faster than executing the statement in serial by a predetermined factor. If the first determination and second determination are positive (i.e., the statement can be executed in parallel and parallel execution is faster by the predetermined factor), a recommendation can be provided indicating that the SQL statement should be executed in parallel. In some embodiments, the recommendation can include a report specifying the degree of performance improvement gained from parallel execution, additional system resources consumed by parallel execution, and other statistics pertaining to the recommended parallel execution plan.

Abstract: Database change test system that includes an SQL performance analyzer (SPA) to efficiently test execute workload set of queries on a production or test database management system (DBMS) and report to a user the impact of database changes, is provided. Techniques are described that limit the resource consumption of test execution of workload set of queries, especially to enable such test execution on a production DBMS. A method and apparatus for storing in persistent storage query test profile that specifies query test restrictions and execution plan parameters, which indicate how to generate execution plan operators for query execution plans; storing a workload set of queries in persistent storage; establishing a session with a database management systems; retrieving the query test profile; configuring the session according to the test profile; receiving user input requesting to execute the workload set; and executing the queries according to the query test profile, is also provided.

Abstract: Embedded Performance Monitoring is an “out-of-box” approach for performance monitoring of a DBMS. Performance monitoring of a DBMS is achieved through use of a browser to access the DBMS once a DBMS is installed and configured. The approach exploits a DBMS configured with the native capability to provide performance monitoring data and software via a browser. To retrieve and use the performance monitoring data from the DBMS, a browser executes software downloaded to the browser from the DBMS using web-based technologies.

Abstract: Approaches, techniques, and mechanisms are disclosed for maintaining a set of baseline query plans for a database command. Except in rare circumstances, a database server may only execute a command according to a baseline plan, even if the database server predicts that a different plan has a lower cost. The set of baseline plans are plans that, for one reason or another, have been determined to provide acceptable actual performance in at least one execution context. When the database server receives a request to execute a particular command, the database server, if possible, always executes the command according to the lowest predicted cost baseline plan. The database server may evolve the plan baseline to include additional plans by generating and testing new plans in response to new requests to execute the database command, or as part of a query optimization or tuning process.

Abstract: High-risk database statements are identified. A DBMS performs a series of experiments by generating sets of statistics describing data over which particular database statements run. The DBMS submits each of these sets of statistics to a query optimizer, which returns information for an execution plan that the optimizer would use to implement the statements given the submitted set of data statistics. If the DBMS determines that the optimizer changes the established execution plan for the database statements for any of the submitted sets of statistics, the DBMS performs one or more actions to mitigate the risk of changing execution plans, such as automatically tuning the database statements or the entire workload, and/or sending information about the database statements to a database administrator. Techniques are also described for calculating diagnostic values based on the results of such experiments, which quantify the sensitivity of the execution plans to changes in data statistics.

Abstract: Approaches, techniques, and mechanisms are disclosed for maintaining a set of baseline query plans for a database command. Except in rare circumstances, a database server may only execute a command according to a baseline plan, even if the database server predicts that a different plan has a lower cost. The set of baseline plans are plans that, for one reason or another, have been determined to provide acceptable actual performance in at least one execution context. When the database server receives a request to execute a particular command, the database server, if possible, always executes the command according to the lowest predicted cost baseline plan. The database server may evolve the plan baseline to include additional plans by generating and testing new plans in response to new requests to execute the database command, or as part of a query optimization or tuning process.