Abstract: Storage level query processing may be implemented for processing database queries. Nodes that can access a database may perform parallel processing for at least a portion of a database query. An indication may be received that specifies parallel processing for the database query. The nodes can then be caused to perform the portion of the query as part of providing a result in response to the database query instead of a node, such as a query engine node, that received the database query.

Abstract: A database system may include a database service and a separate distributed storage service. The database service (or a database engine head node thereof) may be responsible for query parsing, optimization, and execution, transactionality, and consistency, while the storage service may be responsible for generating data pages from redo log records and for durability of those data pages. For example, in response to a write request directed to a particular data page, the database engine head node may generate a redo log record and send it, but not the data page, to a storage service node. The storage service node may store the redo log record and return a write acknowledgement to the database service prior to applying the redo log record. The server node may apply the redo log record and other redo log records to a previously stored version of the data page to create a current version.

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: A service provider network implements seamless scaling via proxy replay of session state. Upon a trigger, such as a determination to scale a server, a scaled server may be spun up and an identifier of the scaled server provided to a first (existing) server. The first server sends the identification of the second server, and session state information for each of the connections between the first server and the request router, to the request router. For each of the connections, the request router establishes a new connection between the request router and the second (scaled) server, and replays the session state information for the connection to the second server. The request router then routes traffic between each existing client connection (e.g., the same existing client connection which carried traffic delivered to the first server) and the corresponding new connection to the second server.

Abstract: Using a graph representation of join history may be performed to distribute database data. Join history may be collected, captured, or tracked which describes the history of join operations between columns of different tables in a database. A graph representation of the join history may be generated. The graph representation may indicate a likelihood of different joins that may be performed between the columns of the tables of a database. An evaluation of the join history may be performed to identify columns for tables in the database to distribute the data of the tables amongst multiple storage locations according to the identified columns.

Abstract: A database system may include a database service and a separate distributed storage service. The database service (or a database engine head node thereof) may be responsible for query parsing, optimization, and execution, transactionality, and consistency, while the storage service may be responsible for generating data pages from redo log records and for durability of those data pages. For example, in response to a write request directed to a particular data page, the database engine head node may generate a redo log record and send it, but not the data page, to a storage service node. The storage service node may store the redo log record and return a write acknowledgement to the database service prior to applying the redo log record. The server node may apply the redo log record and other redo log records to a previously stored version of the data page to create a current version.

Abstract: Proxy-based scaling may be performed for databases. A proxy may be implemented for a database that can establish a connection between the proxy and a database engine to perform a database queries received from a client at the proxy. A scaling event may be detected for the database responsive to which the proxy may establish a connection with a new database engine which may, in some embodiments, have different capabilities or resources that address the features or criteria that triggered the scaling event. Session state may be copied from the database engine to the new database engine so that the new database engine may be able to provide access to the database on behalf of requests received from the client through the proxy.

Abstract: Data to be stored in a data block for a columnar database table may be compressed according to a multi-level compression scheme. Data to be stored in the data block may be received. The data may be compressed according a column-specific compression technique to produce compressed data. The compressed data may then be compressed according to a second compression technique different than the column-specific compression technique to produce multi-level compressed data. The multi-level compressed data may be stored in the data block. When reading from the data block, multi-level compressed data may be decompressed according to the column-specific compression technique and the default compression technique applied to the data.

Abstract: Extract, Transform, Load (ETL) processing may be initiated by detected events. A trigger event may be associated with an ETL process apply one or more transformations to a source data object. The trigger event may be detected for the ETL process and evaluated with respect to one or more execution conditions for the ETL process. If the execution conditions for the ETL process are satisfied, then the ETL process may be executed. At least some of the source data object may be obtained, the one or more transformations of the ETL process may be applied, and one or more transformed data objects may be stored.

Abstract: A distributed data warehouse system maintains data blocks on behalf of clients, and stores primary and secondary copies of data blocks on different disks or nodes in a cluster. The data warehouse system may back up data blocks in a key-value backup storage system. In response to a query targeting a data block previously stored in the cluster, the data warehouse system may determine whether a consistent, uncorrupted copy of the data block is available in the cluster (e.g., by applying a consistency check). If not (e.g., if a disk or node failed), the data warehouse system may automatically initiate an operation to restore the data block from the backup storage system, using a unique identifier of the data block to access a backup copy. The target data may be returned in a query response prior to restoring primary and secondary copies of the data block in the cluster.

Abstract: Code may be dynamically routed to computing resources for execution. Code may be received for execution on behalf of a client. Execution criteria for the code may be determined and computing resources that satisfy the execution criteria may be identified. The identified computing resources may then be procured for executing the code and then the code may be routed to the procured computing resources for execution. Permissions or authorization to execute the code may be shared to ensure that computing resources executing the code have the same permissions or authorization when executing the code.

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 stateful cluster may implement scaling of the stateful cluster while maintaining access to the state of the stateful cluster. A scaling event for a stateful cluster may be detected, and in response the stateful cluster may be adjusted to include a different number of nodes. The state of the cluster may then be logically distributed among the different number of nodes according to a monotone distribution scheme. The adjusted node may then service access requests according to the monotone distribution scheme. Prior to making the adjusted storage cluster available for servicing access requests, the nodes from the original cluster may still service access requests for state.

Abstract: History for data objects may be maintained to detect data events. An indication of an Extract, Transform, Load (ETL) process applied to one or more source data objects to generate one or more transformed data objects may be received. History for the source data objects may be updated to include the transformed data objects and the ETL process that generated the transformed data objects. An evaluation of the update may be performed to determine whether an event associated with the data lineage is triggered. If the event is triggered, a notification of the event may be sent to one or more subscribers for the event.

Abstract: Specified performance attributes may be used to configure machine learning transformations for ETL jobs. Performance attributes for a machine learning pipeline that applies a model to as part of a transformation for an ETL job may be used to configure a parameter in a stage of the machine learning pipeline. The configured stage may then be used when training the model. The trained machine learning pipeline may then be applied as part of a transformation operation included in an ETL job performed by the ETL system.

Abstract: Proxy-based scaling may be performed for databases. A proxy may be implemented for a database that can establish a connection between the proxy and a database engine to perform a database queries received from a client at the proxy. A scaling event may be detected for the database responsive to which the proxy may establish a connection with a new database engine which may, in some embodiments, have different capabilities or resources that address the features or criteria that triggered the scaling event. Session state may be copied from the database engine to the new database engine so that the new database engine may be able to provide access to the database on behalf of requests received from the client through the proxy.

Abstract: A multi-column index is generated based on an interleaving of data bits for selectivity for efficient processing of data in a relational database system. Two or more columns may be identified for inclusion in the multi-column index for a relational database table. Based, at least in part, on the interleaving of data bits for selectivity from the identified columns, a multi-column index is generated for the relational database table that provides a respective index value for each entry in the relational database table. The entries of the relational database table may then be stored according to the index values of the multi-column index.

Abstract: Online restore operations for a database engine can be performed. A request to restore a database to a previous state can be received. Previously stored content of the database, such as snapshot stored prior to the previous state, can be identified along with log records describing changes to be made to the content prior to the previous state. State information in a query engine can be updated based on the previously stored content and log records so that queries can be processed based on the state information at the database restore to the previous state.

Abstract: Data transformation workflows may be generated to transform data objects. A source data schema for a data object and a target data format or target data schema for a data object may be identified. A comparison of the source data schema and the target data format or schema may be made to determine what transformations can be performed to transform the data object into the target data format or schema. Code to execute the transformation operations may then be generated. The code may be stored for subsequent modification or execution.

Abstract: Code may be dynamically routed to computing resources for execution. Code may be received for execution on behalf of a client. Execution criteria for the code may be determined and computing resources that satisfy the execution criteria may be identified. The identified computing resources may then be procured for executing the code and then the code may be routed to the procured computing resources for execution. Permissions or authorization to execute the code may be shared to ensure that computing resources executing the code have the same permissions or authorization when executing the code.