Abstract: Query execution on compressed in-memory data includes receiving, at a processor of an instance of a distributed in-memory database, a query for data from a table stored in the distributed in-memory database as compressed table data, obtaining results data responsive to the query from the table, and outputting the results data for presentation to a user. Obtaining results data includes allocating memory to identify allocated memory for decompressing the compressed table data, obtaining uncompressed table data by decompressing the compressed table data into the allocated memory, and obtaining the results data from the uncompressed table data. The allocated memory is deallocated in response to obtaining the results data. Compressing a table to form compressed table data is also described.

Abstract: The subject technology receives a query, the query referencing a unified representation for structured type data and semi-structured type data, the unified representation being provided in storage and in memory during query processing, the unified representation comprising a set of structured type fields that include a set of semi-structured typed fields that enables type safety and enforcement for the set of structured type fields, and flexibility for the set of semi-structured typed fields in a same column, the unified representation in storage including type information for the semi-structured type data as part of the semi-structured type data, the unified representation being utilized for structured type data and semi-structured type data. The subject technology processes the query using the unified representation stored in the memory, the unified representation providing performance parity between structured type data and semi-structured type data.

Abstract: A first replay log is replayed to generate a first replay result. Replaying the first replay log includes replacing, in the first replay result, a first value of a first field included in a first command in the first replay log with a first hash value responsive to a determination that the first field is not utilized as a condition in at least one command included in the first replay log. A second replay log is replayed to generate a second replay result. The first replay result and the second replay result are compared to verify that the first replay log and the second replay log are equivalent.

Abstract: A distributed database that includes multiple database instances receives a data-query that includes an aggregation clause on a first column of a table. The table is partitioned into shards according to a sharding criterion based on the first column such that all rows having the same value for the first column are included in the same shard. The shards are distributed to the multiple database instances. Respective intermediate results are received from at least some of the database instances. Each intermediate result received from a respective database instance that includes a respective shard aggregates values of the first column in the respective shard. The respective intermediate results are combined to obtain a final result of the data-query. The final result is then output.

Abstract: Query execution on compressed in-memory data includes receiving, at a processor of an instance of a distributed in-memory database, a query for data from a table stored in the distributed in-memory database as compressed table data, obtaining results data responsive to the query from the table, and outputting the results data for presentation to a user. Obtaining results data includes allocating memory to identify allocated memory for decompressing the compressed table data, obtaining uncompressed table data by decompressing the compressed table data into the allocated memory, and obtaining the results data from the uncompressed table data. The allocated memory is deallocated in response to obtaining the results data. Compressing a table to form compressed table data is also described.

Abstract: Query planning in a distributed database that includes a table partitioned into shards according to a sharding criterion and distributed to database instances includes receiving a data-query. The data-query includes a “distinct count” clause on a first column and a “group by” clause on least a second column. A query plan is formulated to include respective instructions for converting, at at least some of the database instances, distinct values of the first column grouped by values of the second column into a count of the distinct values grouped by the values of the second column to obtain respective intermediate results; instructions for receiving the respective intermediate results from at least a subset of the at least some of the database instances; and instructions for concatenating the respective intermediate results using a summing operation to obtain the first “distinct count” of the first column grouped by the second column.

Abstract: Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.

Abstract: Query planning in a distributed database that includes a table partitioned into shards according to a sharding criterion and distributed to database instances includes receiving a data-query. The data-query includes a “distinct count” clause on a first column and a “group by” clause on least a second column. A query plan is formulated to include respective instructions for converting, at at least some of the database instances, distinct values of the first column grouped by values of the second column into a count of the distinct values grouped by the values of the second column to obtain respective intermediate results; instructions for receiving the respective intermediate results from at least a subset of the at least some of the database instances; and instructions for concatenating the respective intermediate results using a summing operation to obtain the first “distinct count” of the first column grouped by the second column.

Abstract: A first replay log is replayed to generate a first replay result. Replaying the first replay log includes replacing, in the first replay result, a first value of a first field included in a first command in the first replay log with a first hash value responsive to a determination that the first field is not utilized as a condition in at least one command included in the first replay log. A second replay log is replayed to generate a second replay result. The first replay result and the second replay result are compared to verify that the first replay log and the second replay log are equivalent.

Abstract: Querying a distributed database including a table sharded into shards distributed to database instances includes receiving a data-query that includes an aggregation clause on a first column and a grouping clause on a second column; obtaining and outputting results data. Obtaining the results data includes receiving, by a query coordinator, intermediate results data; and combining, by the query coordinator, the intermediate results to obtain the results data.

Abstract: Database replay log compaction verification includes identifying at least one replay log of a table that includes first database manipulation commands; obtaining a compacted replay log that includes second database manipulation commands that are insert commands, where an insert command includes a column and a corresponding value; replaying, to obtain a first replay result, the first database manipulation commands; replaying, to obtain a second replay result, the second database manipulation commands; and, responsive to one row of the first replay result not matching a corresponding row of the second replay result, sending a notification including a non-match. Replaying the first database manipulation commands includes identifying condition columns of the table; responsive to the condition columns not including the column, obtaining a row corresponding to the insert command, where the row includes a modified value of the corresponding value of the column; and adding the row to the first replay result.

Abstract: Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.

Abstract: State-sequence pathing in a low-latency data access and analysis system includes obtaining, by the low-latency data access and analysis system, predicate data responsive to a request for data expressed in previously obtained data expressing usage intent, obtaining, by the low-latency data access and analysis system, state-sequence pathing criteria identified with respect to the predicate data, obtaining, by the low-latency data access and analysis system, state-sequence path data in accordance with the predicate data and the state-sequence pathing criteria, wherein the state-sequence path data aggregates data representing multiple state-sequence paths, wherein a respective state-sequence path represents an ordered sequence of states of a system, wherein the states are represented individually by the predicate data, generating, by the low-latency data access and analysis system, state-sequence path visualization data for presenting a visualization of the state-sequence path data, and outputting, by the low-lat

Abstract: Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.

Abstract: Database replay log compaction verification includes identifying at least one replay log of a table that includes first database manipulation commands; obtaining a compacted replay log that includes second database manipulation commands that are insert commands, where an insert command includes a column and a corresponding value; replaying, to obtain a first replay result, the first database manipulation commands; replaying, to obtain a second replay result, the second database manipulation commands; and, responsive to one row of the first replay result not matching a corresponding row of the second replay result, sending a notification including a non-match. Replaying the first database manipulation commands includes identifying condition columns of the table; responsive to the condition columns not including the column, obtaining a row corresponding to the insert command, where the row includes a modified value of the corresponding value of the column; and adding the row to the first replay result.

Abstract: Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.

Abstract: Querying a distributed database including a table sharded into shards distributed to database instances includes receiving a data-query that includes an aggregation clause on a first column and a grouping clause on a second column; obtaining and outputting results data. Obtaining the results data includes receiving, by a query coordinator, intermediate results data; and combining, by the query coordinator, the intermediate results to obtain the results data.

Abstract: Data-query execution with distributed machine-language query management in a low-latency database analysis system may include obtaining, at a distributed in-memory database, a data-query expressing a request for data in a defined structured query language associated with the distributed in-memory database, automatically generating a high-level language query representing at least a portion of the data-query, obtaining a machine language query corresponding to the high-level language query, executing the machine language query to obtain results data, and outputting the results data. Obtaining the machine language query may include determining whether the machine language query is cached, and in response to a determination that the machine language query is unavailable, sending a request for the machine language query to a distributed machine-language-query management instance.

Abstract: Query execution on compressed in-memory data includes receiving, at a processor of an instance of a distributed in-memory database, a query for data from a table stored in the distributed in-memory database as compressed table data, obtaining results data responsive to the query from the table, and outputting the results data for presentation to a user. Obtaining results data includes allocating memory to identify allocated memory for decompressing the compressed table data, obtaining uncompressed table data by decompressing the compressed table data into the allocated memory, and obtaining the results data from the uncompressed table data. The allocated memory is deallocated in response to obtaining the results data. Compressing a table to form compressed table data is also described.

Abstract: Data-query execution with distributed machine-language query management in a low-latency database analysis system may include obtaining, at a distributed in-memory database, a data-query expressing a request for data in a defined structured query language associated with the distributed in-memory database, automatically generating a high-level language query representing at least a portion of the data-query, obtaining a machine language query corresponding to the high-level language query, executing the machine language query to obtain results data, and outputting the results data. Obtaining the machine language query may include determining whether the machine language query is cached, and in response to a determination that the machine language query is unavailable, sending a request for the machine language query to a distributed machine-language-query management instance.