Abstract: Idle periods may be for management actions at processing clusters for managed databases. A leader node of a processing cluster for a managed database may monitor a network endpoint at a proxy service associated with a database managed by the database service. An idle period for the database may be detected. A management action for the processing cluster may be determined to be performed during the detected idle period. The leader node may cause the determined management action to be performed.

Abstract: Database query processing may be scaled using additional processing clusters. A database query is received at a processing cluster. A determination is made as to whether additional processing clusters will be used to process the database query. Operations to cause compute nodes of the processing cluster to instruct operations at the additional processing clusters are included in a plan generated to perform database queries determined to use additional processing clusters. The plan is executed to be perform the database query causing compute nodes of the processing cluster to send instructions to corresponding additional processing clusters in order to generate and return a response to the database query.

Abstract: Dynamic plan interpretation may be implemented to perform queries. A query is received at a database system. A plan to perform the query is generated. For at least a portion of the plan to perform the query, a domain-specific language may be applied to interpret and execute the portion of the plan to perform the query using one or more previously compiled executors instead of generating and compiling code to execute the portion of the plan. Returning a result of the query according to the execution of the plan.

Abstract: Burst performance of a database query may be determined according to a size of the database query. A query to a database may be received. A size may be determined for the query. If the size is less than a size threshold assigned to a first query engine, then the query may be performed at the first query engine. If the size is greater than or equal to the size threshold assigned to the first query engine, then the query may be performed at a second query engine.

Abstract: A primary query engine may receive a query and determine whether the query is eligible for performance at a secondary query engine. If eligible, the primary query engine may evaluate the availability of the first query engine to perform the query. The first query engine may determine whether to assign the query to the primary query engine or to the secondary query according to availability evaluation. For queries assigned to the secondary query engine, the primary query engine may send a request to the secondary query engine to being processing of the query.

Abstract: Compiled portions of code generated to perform a query plan at a query engine may be shared with other query engines. A data store, separate from the query engines, may store compiled portions of query code generated for different queries. If a query engine does not have a locally stored compiled portion of query code, then the separate data store may be accessed in order to obtain a compiled portion of query code, allowing reuse of compiled query code across different queries engines for queries directed to different databases.

Abstract: Online restore may be performed between databases with different topologies while applying a custom data distribution. A request to restore a database into a different topology of nodes may be received. A plan to move different portions of the database from a current topology to the new topology made using a general distribution scheme. The plan may be performed to move the different portions of the database into the new topology and the database made available for access using the new topology. A background process may be applied to modify the distribution of the database at the new topology to match a custom distribution scheme that was implemented at the current topology.

Abstract: Compiled portions of code generated to perform a query plan at a query engine may be shared with other query engines. A data store, separate from the query engines, may store compiled portions of query code generated for different queries. If a query engine does not have a locally stored compiled portion of query code, then the separate data store may be accessed in order to obtain a compiled portion of query code, allowing reuse of compiled query code across different queries engines for queries directed to different databases.

Abstract: Database systems may dynamically management concurrency levels for performing queries. A query may be received at a database system and a memory usage for the query may be predicted. A determination may be made as to whether available memory is enough to satisfy the predicted memory usage for the query. If the available memory is enough to satisfy the predicted memory usage for the query, then an increase in a concurrency level for performing queries at the database system may be made. The query may be allowed to execute concurrently with other queries according to the increased concurrency level.

Abstract: Context dependent execution time prediction may be applied to redirect queries to additional query processing resources. A query to a database may be received at a first query engine. A prediction model for executing queries at the first query engine may be applied to determine predicted query execution time for the first query engine. A prediction model for executing queries at a second query engine may also be applied to determine predicted query execution time for the second query engine. One of the query engines may be selected to perform the query based on a comparison of the predicted query execution times.

Abstract: Scaling of query processing resources for efficient utilization and performance is implemented for a database service. A query is received via a network endpoint associated with a database managed by a database service. Respective response times predicted for the query using different query processing configurations available to perform the query are determined. Those query processing configurations with response times that exceed a variability threshold determined for the query may be excluded. A remaining query processing configuration may then be selected to perform the query.

Abstract: Idle periods may be for management actions at processing clusters for managed databases. A leader node of a processing cluster for a managed database may monitor a network endpoint at a proxy service associated with a database managed by the database service. An idle period for the database may be detected. A management action for the processing cluster may be determined to be performed during the detected idle period. The leader node may cause the determined management action to be performed.

Abstract: Scaling of query processing resources for efficient utilization and performance is implemented for a database service. A query is received via a network endpoint associated with a database managed by a database service. Respective response times predicted for the query using different query processing configurations available to perform the query are determined. Those query processing configurations with response times that exceed a variability threshold determined for the query may be excluded. A remaining query processing configuration may then be selected to perform the query.

Abstract: Online restore may be performed between databases with different topologies while applying a custom data distribution. A request to restore a database into a different topology of nodes may be received. A plan to move different portions of the database from a current topology to the new topology made using a general distribution scheme. The plan may be performed to move the different portions of the database into the new topology and the database made available for access using the new topology. A background process may be applied to modify the distribution of the database at the new topology to match a custom distribution scheme that was implemented at the current topology.

Abstract: Performance measures are predicted for queries to prioritize query performance at a database system. A trained machine learning model for the database system may be applied to a query to determine a predicted performance measure for the query. The predicted performance measure may be compared with other predicted performance measures for other waiting queries to determine a priority for executing the query.

Abstract: Context dependent execution time prediction may be applied to redirect queries to additional query processing resources. A query to a database may be received at a first query engine. A prediction model for executing queries at the first query engine may be applied to determine predicted query execution time for the first query engine. A prediction model for executing queries at a second query engine may also be applied to determine predicted query execution time for the second query engine. One of the query engines may be selected to perform the query based on a comparison of the predicted query execution times.

Abstract: A primary query engine may receive a query and determine whether the query is eligible for performance at a secondary query engine. If eligible, the primary query engine may evaluate the availability of the first query engine to perform the query. The first query engine may determine whether to assign the query to the primary query engine or to the secondary query according to availability evaluation. For queries assigned to the secondary query engine, the primary query engine may send a request to the secondary query engine to being processing of the query.

Abstract: Context dependent execution time prediction may be applied to redirect queries to additional query processing resources. A query to a database may be received at a first query engine. A prediction model for executing queries at the first query engine may be applied to determine predicted query execution time for the first query engine. A prediction model for executing queries at a second query engine may also be applied to determine predicted query execution time for the second query engine. One of the query engines may be selected to perform the query based on a comparison of the predicted query execution times.

Abstract: A primary query engine may receive a query and determine whether the query is eligible for performance at a secondary query engine. If eligible, the primary query engine may evaluate the availability of the first query engine to perform the query. The first query engine may determine whether to assign the query to the primary query engine or to the secondary query according to availability evaluation. For queries assigned to the secondary query engine, the primary query engine may send a request to the secondary query engine to being processing of the query.

Abstract: A method includes encoding, by an encoding engine, consecutive sections of a received data stream that includes a stream of values. The encoding includes identifying a minimum value in a section of the stream. The encoding includes determining, for each value in the section of the stream, respective differences with the minimum value. An encoded version of the section includes the minimum value and a mask value. The mask value is combined with respective portions of the respective differences to generate the respective differences of each value in the section. The encoded version of the section further includes the respective portions of the respective differences.