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: Placement decisions may be made to place data in a multi-tenant cache. Usage of multi-tenant cache nodes for performing access requests may be obtained. Usage prediction techniques may be applied to the usage to determine placement decisions for data amongst the multi-tenant cache nodes. Placement actions for the data amongst at the multi-tenant cache nodes may be performed according to the placement decisions.

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: Dynamic generation of data catalogs may be implemented for accessing data sets in different storage locations. Data sets may be accessed in order to extract portions of data. Structure recognition techniques may be applied to the extracted data in order to determine structural information for the data sets. The structural information may then be stored as part of a data catalog for the data sets. Requests to access the data catalog from different clients may be received and the requested structural data supplied so that the clients may access different data sets utilizing the supplied structural data. Data catalogs may be updated as changes to data sets are made.

Abstract: A tree-based format may be implemented for data stored in a data store. A table may be maintained across one or multiple storage nodes in storage slabs. Storage slabs may be mapped to different nodes of a tree. Each node of the tree may be assigned a different range of distribution scheme values which identify what portions of the table are stored in the storage slab. Storage slabs mapped to child nodes in the tree may be assigned portions of the range of distribution scheme values assigned to a parent. Storage nodes may be added or removed for storing the table. Storage slabs may be moved from one storage node to another in order to accommodate the addition or removal of storage nodes.

Abstract: Placement decisions may be made to place data in a multi-tenant cache. Usage of multi-tenant cache nodes for performing access requests may be obtained. Usage prediction techniques may be applied to the usage to determine placement decisions for data amongst the multi-tenant cache nodes. Placement actions for the data amongst at the multi-tenant cache nodes may be performed according to the placement decisions.

Abstract: Tiered storage may be implemented for processing data. Data processors may maintain some of a data set, including user data and metadata describing the user data, locally. The data set is also maintained a data store remote to the data processor. When processing requests are received, a determination is made as to whether the local portions of the data set can execute the processing request or one or more additional portions of the data set are needed from the remote data store. If additional portions of the data set are needed, then a request may be sent to the data store for the additional portions. Once received, the data processor may execute the processing request utilizing the additional portions. Portions of the data set maintained locally at the data processor may be selected and flushed from local storage to the remote data store.

Abstract: A system such as a database management system determines a rule to impose sorted order upon a column without applying a sorting algorithm to the column and stores the rule to be applied to sort the column. In an embodiment, a permutation is the rule. The column, in an embodiment, is a column of a database table. In some cases, the rule is used to efficiently impose a correct sorted order on a subset of unsorted columns without the use of a sorting algorithm. In some cases, the rule is generated based at least in part on another column that is sorted using a sorting algorithm such as quicksort.

Abstract: The configuration of computing resources for executing queries may be selected. A comparison of the configuration of computing resources that executed previous queries may be made to select the configuration of computing resources for a received query. A historical query execution model maybe applied, in some embodiments, to determine a resource configuration for computing resources to execute a query. The computing resources may be selected from available computing resources according to the determined resource configuration.

Abstract: Late encoding binding can be applied to data added to a data set according to the encoding history for the data set. When a request to add data is received, an evaluation of the encoding history can determine whether a currently specified encoding for the data can be changed. If changeable, a different encoding scheme for the data may be identified and the data added to the data set according to the different encoding scheme.

Abstract: Optimized query plans may be generated independent of the query engine that performs the optimized query plan. A request to generate an optimized query plan for a query may be received and a type of engine for performing the query may be identified. An initial plan may be generated in an engine-specific format for the type of engine that is translated into an optimization plan format. An analysis of the initial plan optimization plan format may be performed to generate an optimized query plan. The optimized query plan may be translated into the engine-specific format and sent in response to the request for the optimized query plan.

Abstract: The capabilities of individual nodes that process a query may be evaluated to perform query planning. An operation of a plan to perform a query can be identified for assignment to a node based on the capabilities of nodes to perform the query operation. A node may be selected based on the difference between the selected node's capability to perform the query operation and other nodes capabilities to perform the operation. Capability-based selection can be performed at runtime, in some embodiments, in order to make query operation assignments based on further information obtained as a result of performing prior operations in the query.

Abstract: Dynamic generation of data catalogs may be implemented for accessing data sets in different storage locations. Data sets may be accessed in order to extract portions of data. Structure recognition techniques may be applied to the extracted data in order to determine structural information for the data sets. The structural information may then be stored as part of a data catalog for the data sets. Requests to access the data catalog from different clients may be received and the requested structural data supplied so that the clients may access different data sets utilizing the supplied structural data. Data catalogs may be updated as changes to data sets are made.

Abstract: Cardinality values can be derived from an approximate quantile summary. An approximate quantile summary can be generated for a column of a database table at data ingestion, data update, upon request, in response to a query, and in various other scenarios. When a query is received that includes a predicate directed to the column of the approximate quantile summary, a cardinality value may be derived from the boundary values of one or more quantiles that include the predicate. The cardinality value may then be used to select a query plan. The query may be performed according to the selected query plan.

Abstract: Tiered storage may be implemented for processing data. Data processors may maintain some of a data set, including user data and metadata describing the user data, locally. The data set is also maintained a data store remote to the data processor. When processing requests are received, a determination is made as to whether the local portions of the data set can execute the processing request or one or more additional portions of the data set are needed from the remote data store. If additional portions of the data set are needed, then a request may be sent to the data store for the additional portions. Once received, the data processor may execute the processing request utilizing the additional portions. Portions of the data set maintained locally at the data processor may be selected and flushed from local storage to the remote data store.

Abstract: The configuration of computing resources for executing queries may be selected. A comparison of the configuration of computing resources that executed previous queries may be made to select the configuration of computing resources for a received query. A historical query execution model maybe applied, in some embodiments, to determine a resource configuration for computing resources to execute a query. The computing resources may be selected from available computing resources according to the determined resource configuration.

Abstract: Dynamic generation of data catalogs may be implemented for accessing data sets in different storage locations. Data sets may be accessed in order to extract portions of data. Structure recognition techniques may be applied to the extracted data in order to determine structural information for the data sets. The structural information may then be stored as part of a data catalog for the data sets. Requests to access the data catalog from different clients may be received and the requested structural data supplied so that the clients may access different data sets utilizing the supplied structural data. Data catalogs may be updated as changes to data sets are made.

Abstract: Techniques for multi-tenant storage for analytics with push down filtering are described. A multi-tenant storage service can include resources can be grouped into racks, with each rack providing a distinct endpoint to which client services may submit queries. Each rack may include interface nodes and storage nodes. The interface nodes can preprocess queries that are received by splitting them into chunks to be executed by the storage nodes. Each storage node includes a field programmable gate array (FPGA) and a CPU. The CPU can receive the operations and convert the operations into instructions that can be executed by the FPGA. The instructions may include pointers to data and operations for the FPGA to perform on the data. The FPGA can process the data stream and return the results of the processing which are returned via the interface node.

Abstract: Tiered storage may be implemented for processing data. Data processors may maintain some of a data set, including user data and metadata describing the user data, locally. The data set is also maintained a data store remote to the data processor. When processing requests are received, a determination is made as to whether the local portions of the data set can execute the processing request or one or more additional portions of the data set are needed from the remote data store. If additional portions of the data set are needed, then a request may be sent to the data store for the additional portions. Once received, the data processor may execute the processing request utilizing the additional portions. Portions of the data set maintained locally at the data processor may be selected and flushed from local storage to the remote data store.

Abstract: The configuration of computing resources for executing queries may be selected. A comparison of the configuration of computing resources that executed previous queries may be made to select the configuration of computing resources for a received query. A historical query execution model maybe applied, in some embodiments, to determine a resource configuration for computing resources to execute a query. The computing resources may be selected from available computing resources according to the determined resource configuration.