Abstract: A method for optimizing a database query includes receiving a database query from a user requesting a database to conditionally return one or more rows stored at the database and characterizing one or more user filters. Each user filter filters rows from the database. The method includes determining that a security filter restricts the user from accessing one or more rows of the database. For each respective user filter, the method includes classifying the respective user filter as a safe user filter incapable of reporting an error or an unsafe user filter capable of reporting an error. The method also includes determining a filter execution order of the one or more user filters and the security filter based on the classified one or more user filters and executing, using the determined filter execution order, the one or more user filters and the security filter.

Abstract: An example method includes receiving a data load request requesting loading and partitioning of an unknown quantity of user data for storage at a data storage system. The user data including a partitioning key; a total data size of the user data; a plurality of rows, each row of the plurality of rows associated with a value defined by the partitioning key; and one or more columns. The method also includes identifying one or more storage constraints for the data storage system. The method further includes, after receiving the user data, determining a plurality of partitioning quantiles defining respective ranges of values of the partitioning key based on the user data and the one or more storage constraints for the data storage system; and range partitioning each row of the user data into files based on the value associated with the row defined by the partitioning key, and the respective ranges of the values of the partitioning key defined by the plurality of partitioning quantiles.

Abstract: A method for execution-time dynamic range partitioning includes receiving user data including a partitioning key and a clustering key. The user data includes a respective number of total rows defining a total data size for the user data. The method also includes identifying storage constraints for the data storage system. The storage constraints include a target file size and a target number of rows per file. The method further includes determining a plurality of split points for the user data based on the storage constraints. The method also includes generating partitioning quantiles from the plurality of split points that define a range between each split point of the plurality of split points. The method further includes range partitioning each row of the user data into files using the partitioning quantiles.

Abstract: A method for execution-time dynamic range partitioning includes receiving user data including a partitioning key and a clustering key. The user data includes a respective number of total rows defining a total data size for the user data. The method also includes identifying storage constraints for the data storage system. The storage constraints include a target file size and a target number of rows per file. The method further includes determining a plurality of split points for the user data based on the storage constraints. The method also includes generating partitioning quantiles from the plurality of split points that define a range between each split point of the plurality of split points. The method further includes range partitioning each row of the user data into files using the partitioning quantiles.

Abstract: Architecture that includes an index creation algorithm that utilizes available resources and dynamically adjusts to successfully scale with increased resources and be able to do so for any data distribution. The resources can be processing resources, memory, and/or input/output, for example. A finer level of granularity, called a segment, is utilized to process tuples in a partition while creating an index. The segment also aligns with compression techniques for the index. By choosing an appropriate size for a segment and using load balancing the overall time for index creation can be reduced. Each segment can then be processed by a single thread thereby limiting segment skew. Skew is further limited by breaking down the work done by a thread into parallelizable stages.

Abstract: A currently operating computing cluster that has multiple nodes storing cluster data may be scaled. A cluster scaling request may be received for the current cluster indicating a change in a number or type of nodes in the current cluster. In response to receiving the cluster scaling request, a new cluster may be created as indicated in the cluster scaling request, a copy of the cluster data from the current cluster to the nodes in the new cluster may be initiated, a network endpoint for the current cluster may be moved to the new cluster, and the current cluster may be disabled. The current cluster may, in some embodiments, respond to read access requests during the copy of the cluster data.

Abstract: Architecture that includes an index creation algorithm that utilizes available resources and dynamically adjusts to successfully scale with increased resources and be able to do so for any data distribution. The resources can be processing resources, memory, and/or input/output, for example. A finer level of granularity, called a segment, is utilized to process tuples in a partition while creating an index. The segment also aligns with compression techniques for the index. By choosing an appropriate size for a segment and using load balancing the overall time for index creation can be reduced. Each segment can then be processed by a single thread thereby limiting segment skew. Skew is further limited by breaking down the work done by a thread into parallelizable stages.

Abstract: A query optimizer may receive a query (e.g., from a source that generated the query). Input that specifies both a query hint string and a hint may be received to a hint specification interface. The hint may be applied to the query, from outside the query, to optimize a query execution plan. Applying the hint may be based, at least in part, on a query hint string. For example, which query block is associated with the query hint string may be determined. Upon such a determination, the hint may be applied to the determined query block.

Abstract: Architecture that includes an index creation algorithm that utilizes available resources and dynamically adjusts to successfully scale with increased resources and be able to do so for any data distribution. The resources can be processing resources, memory, and/or input/output, for example. A finer level of granularity, called a segment, is utilized to process tuples in a partition while creating an index. The segment also aligns with compression techniques for the index. By choosing an appropriate size for a segment and using load balancing the overall time for index creation can be reduced. Each segment can then be processed by a single thread thereby limiting segment skew. Skew is further limited by breaking down the work done by a thread into parallelizable stages.

Abstract: Architecture that includes an index creation algorithm that utilizes available resources and dynamically adjusts to successfully scale with increased resources and be able to do so for any data distribution. The resources can be processing resources, memory, and/or input/output, for example. A finer level of granularity, called a segment, is utilized to process tuples in a partition while creating an index. The segment also aligns with compression techniques for the index. By choosing an appropriate size for a segment and using load balancing the overall time for index creation can be reduced. Each segment can then be processed by a single thread thereby limiting segment skew. Skew is further limited by breaking down the work done by a thread into parallelizable stages.

Abstract: A data normalization system is described herein that represents multiple data types that are common within database systems in a normalized form that can be processed uniformly to achieve faster processing of data on superscalar CPU architectures. The data normalization system includes changes to internal data representations of a database system as well as functional processing changes that leverage normalized internal data representations for a high density of independently executable CPU instructions. Because most data in a database is small, a majority of data can be represented by the normalized format. Thus, the data normalization system allows for fast superscalar processing in a database system in a variety of common cases, while maintaining compatibility with existing data sets.

Abstract: A data normalization system is described herein that represents multiple data types that are common within database systems in a normalized form that can be processed uniformly to achieve faster processing of data on superscalar CPU architectures. The data normalization system includes changes to internal data representations of a database system as well as functional processing changes that leverage normalized internal data representations for a high density of independently executable CPU instructions. Because most data in a database is small, a majority of data can be represented by the normalized format. Thus, the data normalization system allows for fast superscalar processing in a database system in a variety of common cases, while maintaining compatibility with existing data sets.

Abstract: A data normalization system is described herein that represents multiple data types that are common within database systems in a normalized form that can be processed uniformly to achieve faster processing of data on superscalar CPU architectures. The data normalization system includes changes to internal data representations of a database system as well as functional processing changes that leverage normalized internal data representations for a high density of independently executable CPU instructions. Because most data in a database is small, a majority of data can be represented by the normalized format. Thus, the data normalization system allows for fast superscalar processing in a database system in a variety of common cases, while maintaining compatibility with existing data sets.

Abstract: Architecture that includes an index creation algorithm that utilizes available resources and dynamically adjusts to successfully scale with increased resources and be able to do so for any data distribution. The resources can be processing resources, memory, and/or input/output, for example. A finer level of granularity, called a segment, is utilized to process tuples in a partition while creating an index. The segment also aligns with compression techniques for the index. By choosing an appropriate size for a segment and using load balancing the overall time for index creation can be reduced. Each segment can then be processed by a single thread thereby limiting segment skew. Skew is further limited by breaking down the work done by a thread into parallelizable stages.

Abstract: A distributed data warehouse system may maintain data blocks on behalf of clients in multiple clusters in a data store. Each cluster may include a single leader node and multiple compute nodes, each including multiple disks storing data. The warehouse system may store primary and secondary copies of each data block on different disks or nodes in a cluster. Each node may include a data structure that maintains metadata about each data block stored on the node, including its unique identifier. The warehouse system may back up data blocks in a remote key-value backup storage system with high durability. A streaming restore operation may be used to retrieve data blocks from backup storage using their unique identifiers as keys. The warehouse system may service incoming queries (and may satisfy some queries by retrieving data from backup storage on an as-needed basis) prior to completion of the restore operation.

Abstract: A query optimizer may receive a query (e.g., from a source that generated the query). Input that specifies both a query hint string and a hint may be received to a hint specification interface. The hint may be applied to the query, from outside the query, to optimize a query execution plan. Applying the hint may be based, at least in part, on a query hint string. For example, which query block is associated with the query hint string may be determined. Upon such a determination, the hint may be applied to the determined query block.

Abstract: A query optimizer may receive a query (e.g., from a source that generated the query). Input that specifies both a query hint string and a hint may be received to a hint specification interface. The hint may be applied to the query, from outside the query, to optimize a query execution plan. Applying the hint may be based, at least in part, on a query hint string. For example, which query block is associated with the query hint string may be determined. Upon such a determination, the hint may be applied to the determined query block.

Abstract: A data normalization system is described herein that represents multiple data types that are common within database systems in a normalized form that can be processed uniformly to achieve faster processing of data on superscalar CPU architectures. The data normalization system includes changes to internal data representations of a database system as well as functional processing changes that leverage normalized internal data representations for a high density of independently executable CPU instructions. Because most data in a database is small, a majority of data can be represented by the normalized format. Thus, the data normalization system allows for fast superscalar processing in a database system in a variety of common cases, while maintaining compatibility with existing data sets.

Abstract: Architecture that includes an index creation algorithm that utilizes available resources and dynamically adjusts to successfully scale with increased resources and be able to do so for any data distribution. The resources can be processing resources, memory, and/or input/output, for example. A finer level of granularity, called a segment, is utilized to process tuples in a partition while creating an index. The segment also aligns with compression techniques for the index. By choosing an appropriate size for a segment and using load balancing the overall time for index creation can be reduced. Each segment can then be processed by a single thread thereby limiting segment skew. Skew is further limited by breaking down the work done by a thread into parallelizable stages.

Abstract: A data normalization system is described herein that represents multiple data types that are common within database systems in a normalized form that can be processed uniformly to achieve faster processing of data on superscalar CPU architectures. The data normalization system includes changes to internal data representations of a database system as well as functional processing changes that leverage normalized internal data representations for a high density of independently executable CPU instructions. Because most data in a database is small, a majority of data can be represented by the normalized format. Thus, the data normalization system allows for fast superscalar processing in a database system in a variety of common cases, while maintaining compatibility with existing data sets.