Abstract: A database system stores both compressed and uncompressed data in a row-based database system. The database system converts a representation of data involving a set of rows of a source database table, each row comprising multiple values, into a representation involving a single row stored in a target database table, each column of the row comprising arrays of values from the set. The database system may perform type-specific compression of data when storing in the target database table. Accordingly, the database system may apply different compression schemes for different columns or sets of values obtained from the source database table and may group or order selected rows or store additional summary information to improve query performance to the target database table. The database system allows users to query data stored in compressed form. The database system combines compressed and uncompressed data at query time for efficient database analytics.

Abstract: A database system stores both compressed and uncompressed data in a row-based database system. The database system converts a representation of data involving a set of rows of a source database table, each row comprising multiple values, into a representation involving a single row stored in a target database table, each column of the row comprising arrays of values from the set. The database system may perform type-specific compression of data when storing in the target database table. Accordingly, the database system may apply different compression schemes for different columns or sets of values obtained from the source database table and may group or order selected rows or store additional summary information to improve query performance to the target database table. The database system allows users to query data stored in compressed form. The database system combines compressed and uncompressed data at query time for efficient database analytics.

Abstract: Materialized views are maintained for time-series databases, to enable faster analytics over large data sets and to downsample data for storage savings. These materialized views are the result of computations on existing tables, such as aggregation, approximation, sampling, filtering, statistical estimates, and more. A materialized view is created on a base table, and continuously maintained, such that as changes are made to the base table, the materialized view is correspondingly updated. The database system records metadata identifying regions of the base table that have already been materialized. Subsequently, these invalidated regions are rematerialized. Queries ensure up-to-date results by combining data from the materialized view with results calculated at query time on the base table. Invalidation records and completion thresholds are used to determine which results should use records from the materialized table and which should be computed from records from the base table.

Abstract: A database system stores both compressed and uncompressed data in a row-based database system. The database system converts a representation of data involving a set of rows of a source database table, each row comprising multiple values, into a representation involving a single row stored in a target database table, each column of the row comprising arrays of values from the set. The database system may perform type-specific compression of data when storing in the target database table. Accordingly, the database system may apply different compression schemes for different columns or sets of values obtained from the source database table and may group or order selected rows or store additional summary information to improve query performance to the target database table. The database system allows users to query data stored in compressed form. The database system combines compressed and uncompressed data at query time for efficient database analytics.

Abstract: Materialized views are maintained for time-series databases, to enable faster analytics over large data sets and to downsample data for storage savings. These materialized views are the result of computations on existing tables, such as aggregation, approximation, sampling, filtering, statistical estimates, and more. A materialized view is created on a base table, and continuously maintained, such that as changes are made to the base table, the materialized view is correspondingly updated, either synchronously or asynchronously. The database system records metadata identifying regions of the base table that have already been materialized. Subsequently, these invalidated regions are rematerialized. Queries ensure up-to-date results by combining data from the materialized view with results calculated at query time on the base table. Invalidation records and completion thresholds are used to determine which results should use records from the materialized table and which from records from the base table.

Abstract: A database system stores both compressed and uncompressed data in a row-based database system. The database system converts a representation of data involving a set of rows of a source database table, each row comprising multiple values, into a representation involving a single row stored in a target database table, each column of the row comprising arrays of values from the set. The database system may perform type-specific compression of data when storing in the target database table. Accordingly, the database system may apply different compression schemes for different columns or sets of values obtained from the source database table and may group or order selected rows or store additional summary information to improve query performance to the target database table. The database system allows users to query data stored in compressed form. The database system combines compressed and uncompressed data at query time for efficient database analytics.

Abstract: A database system stores both compressed and uncompressed data in a row-based database system. The database system converts a representation of data involving a set of rows of a source database table, each row comprising multiple values, into a representation involving a single row stored in a target database table, each column of the row comprising arrays of values from the set. The database system may perform type-specific compression of data when storing in the target database table. Accordingly, the database system may apply different compression schemes for different columns or sets of values obtained from the source database table and may group or order selected rows or store additional summary information to improve query performance to the target database table. The database system allows users to query data stored in compressed form. The database system combines compressed and uncompressed data at query time for efficient database analytics.

Abstract: A database system stores both compressed and uncompressed data in a row-based database system. The database system converts a representation of data involving a set of rows of a source database table, each row comprising multiple values, into a representation involving a single row stored in a target database table, each column of the row comprising arrays of values from the set. The database system may perform type-specific compression of data when storing in the target database table. Accordingly, the database system may apply different compression schemes for different columns or sets of values obtained from the source database table and may group or order selected rows or store additional summary information to improve query performance to the target database table. The database system allows users to query data stored in compressed form. The database system combines compressed and uncompressed data at query time for efficient database analytics.

Abstract: A database system stores both compressed and uncompressed data in a row-based database system. The database system converts a representation of data involving a set of rows of a source database table, each row comprising multiple values, into a representation involving a single row stored in a target database table, each column of the row comprising arrays of values from the set. The database system may perform type-specific compression of data when storing in the target database table. Accordingly, the database system may apply different compression schemes for different columns or sets of values obtained from the source database table and may group or order selected rows or store additional summary information to improve query performance to the target database table. The database system allows users to query data stored in compressed form. The database system combines compressed and uncompressed data at query time for efficient database analytics.

Abstract: Materialized views are maintained for time-series databases, to enable faster analytics over large data sets and to downsample data for storage savings. These materialized views are the result of computations on existing tables, such as aggregation, approximation, sampling, filtering, statistical estimates, and more. A materialized view is created on a base table, and continuously maintained, such that as changes are made to the base table, the materialized view is correspondingly updated. The database system records metadata identifying regions of the base table that have already been materialized. Subsequently, these invalidated regions are rematerialized. Queries ensure up-to-date results by combining data from the materialized view with results calculated at query time on the base table. Invalidation records and completion thresholds are used to determine which results should use records from the materialized table and which should be computed from records from the base table.

Abstract: Materialized views are maintained for time-series databases, to enable faster analytics over large data sets and to downsample data for storage savings. These materialized views are the result of computations on existing tables, such as aggregation, approximation, sampling, filtering, statistical estimates, and more. A materialized view is created on a base table, and continuously maintained, such that as changes are made to the base table, the materialized view is correspondingly updated, either synchronously or asynchronously. The database system records metadata identifying regions of the base table that have already been materialized. Subsequently, these invalidated regions are rematerialized. Queries ensure up-to-date results by combining data from the materialized view with results calculated at query time on the base table. Invalidation records and completion thresholds are used to determine which results should use records from the materialized table and which from records from the base table.

Abstract: Disclosed are solutions for improving Internet video streaming. A first number is determined based on one or more parameters, including network conditions. A second number is then determined corresponding to a number of video segments that is greater than or equal in size to a third number determined based on a bandwidth-delay product of the network to a remote machine. The second number of video segments is then requested in a pipelined fashion. Pipelined requests are stopped when a predetermined size of the video has been requested that is greater than or equal to the first number. Alternatively, a request is sent to the remote machine to send a portion of the video, where the size of the portion of the video is equal to the first number or equal to the size of video remaining if less than the first number. Combined with pipelining, the approach achieves near-optimal throughput and fast bitrate adaptation, regardless of control plane algorithm.

Abstract: A database system stores data as hypertables that represent partitioned database tables. Each hypertable comprises chunks of data that may be distributed across multiple locations, each location comprising at least a storage device. The database system provides an interface that allows database queries seamlessly to hypertables as well as standard tables. The database system dynamically creates chunks as records are added to a hypertable. The database system defines a new partitioning strategy if the storage configuration of the database system is changed by adding new locations or removing existing locations. The records added to the hypertable before the storage configuration was changed continue to be stored as chunks distributed according to the previous partitioning policy.

Abstract: Disclosed are solutions for improving Internet video streaming. A first number is determined based on one or more parameters, including network conditions. A second number is then determined corresponding to a number of video segments that is greater than or equal in size to a third number determined based on a bandwidth-delay product of the network to a remote machine. The second number of video segments is then requested in a pipelined fashion. Pipelined requests are stopped when a predetermined size of the video has been requested that is greater than or equal to the first number. Alternatively, a request is sent to the remote machine to send a portion of the video, where the size of the portion of the video is equal to the first number or equal to the size of video remaining if less than the first number. Combined with pipelining, the approach achieves near-optimal throughput and fast bitrate adaptation, regardless of control plane algorithm.

Abstract: A database system stores data as hypertables that represent partitioned database tables. Each hypertable comprises chunks of data that may be distributed across multiple locations, each location comprising at least a storage device. The database system provides an interface that allows database queries seamlessly to hypertables as well as standard tables. The database system dynamically creates chunks as records are added to a hypertable. The database system defines a new partitioning strategy if the storage configuration of the database system is changed by adding new locations or removing existing locations. The records added to the hypertable before the storage configuration was changed continue to be stored as chunks distributed according to the previous partitioning policy.

Abstract: A database system stores data as hypertables that represent partitioned database tables. Each hypertable comprises chunks of data that may be distributed across multiple locations, each location comprising at least a storage device. The database system provides an interface that allows database queries seamlessly to hypertables as well as standard tables. The database system dynamically creates chunks as records are added to a hypertable. The database system defines a new partitioning strategy if the storage configuration of the database system is changed by adding new locations or removing existing locations. The records added to the hypertable before the storage configuration was changed continue to be stored as chunks distributed according to the previous partitioning policy.

Abstract: A database system stores data as hypertables that represent partitioned database tables. Each hypertable comprises chunks of data that may be distributed across multiple locations, each location comprising at least a storage device. The database system provides an interface that allows database queries seamlessly to hypertables as well as standard tables. The database system dynamically creates chunks as records are added to a hypertable. The database system defines a new partitioning strategy if the storage configuration of the database system is changed by adding new locations or removing existing locations. The records added to the hypertable before the storage configuration was changed continue to be stored as chunks distributed according to the previous partitioning policy.

Abstract: A database system stores data as hypertables that represent partitioned database tables. Each hypertable comprises chunks of data that may be distributed across multiple locations, each location comprising at least a storage device. The database system provides an interface that allows database queries seamlessly to hypertables as well as standard tables. The database system dynamically creates chunks as records are added to a hypertable. The database system defines a new partitioning strategy if the storage configuration of the database system is changed by adding new locations or removing existing locations. The records added to the hypertable before the storage configuration was changed continue to be stored as chunks distributed according to the previous partitioning policy.

Abstract: A database system stores data as hypertables that represent partitioned database tables. Each hypertable comprises chunks of data that may be distributed across multiple locations, each location comprising at least a storage device. The database system provides an interface that allows database queries seamlessly to hypertables as well as standard tables. The database system dynamically creates chunks as records are added to a hypertable. The database system defines a new partitioning strategy if the storage configuration of the database system is changed by adding new locations or removing existing locations. The records added to the hypertable before the storage configuration was changed continue to be stored as chunks distributed according to the previous partitioning policy.