Salesforce Com, Inc.

Salesforce, Inc.

Rohitashva Mathur

Jesse Yarbro Collins

Prem Veeramani

Arup Dutta

Awais Tariq

A database entry may be stored in a container in a database table corresponding with a partition key. The partition key may be determined by applying one or more partition rules to one or more data values associated with the database entry. The database entry may be an instance of one of a plurality of data object definitions associated with database entries in the database. Each of the data object definitions may identify a respective one or more data fields included within an instance of the data object definition.

Elastic data partitioning of a database

Disclosed herein are system, method, and computer program product embodiments for a query processing system. An embodiment operates by receiving a query associated with a table. An ordered index with entries that include key values that uniquely identifies a corresponding record in the table is determined. A last key value is initialized to store a minimum value for a data type of the key value. The query is executed as a union-all of a plurality of queries. While a count of selected records is less than or equal to a threshold quantity and at least one entry remains in the ordered index, an ordered next entry with a key value subsequent to the last key value in the index is identified, the corresponding record is selected from the table, the last key value is updated, the query is executed, and the count is incremented.

Maintaining data across query executions of a long-running query

A seed value assigned to a database table is determined. Numerically ordered invocation numbers are determined. Unordered pseudorandom numbers are generated based on the seed value and the invocation numbers to index temporally ordered data changes to the database table. It is determined whether the total number of yet-to-be-saved pseudorandom numbers reaches a maximum total number threshold. If so, the yet-to-be-saved pseudorandom numbers and a sequence of corresponding invocation numbers are saved in a sync table.

Efficient production and consumption for data changes in a database under high concurrency

A request for performing a data storing operation directed to a database table that comprises a plurality of table columns is received. Columnar compression metadata is accessed to identify one or more table columns in the database table, each of the one or more table columns being designated to store compressed columnar values. The columnar compression metadata is used to apply one or more columnar compression methods to generate, from one or more uncompressed columnar values received with the request for the data storing operation, one or more compressed columnar values to be persisted in the one or more table columns in the database table. A database statement is executed to persist the one or more compressed columnar values in the one or more table columns in the database table.

Column data compression schemes for scaling writes and reads on database systems

In a multitenant data platform architecture, one or more supporting data tables are used to write and store tenant data responsive to data write requests. Based on the contextual data returning an indication of a data corruption in one or more supporting data structures, an action associated with the data transaction thread is performed. A log of data corruptions and corresponding call stack trace data may be generated. The data transaction thread may be allowed to continue to execute statements that modify data tables, or the data transaction thread may be terminated. Data corruptions may be compensated for by nullifying data changes caused by corruption causing call sites. Verification methods may be used to ensure correctness of data within a transaction thread.

Platform for handling data corruptions

Described are mechanisms for creating and utilizing a specialized index to improve the efficiency of various types of leading wildcard searches without having to unduly burden the system with write requests. The mechanisms may identify particular fields to be included in one or more types of specialized indexes that are accessed in response to particular types of wildcard queries including various combinations of leading, trailing, and intermediate wildcard operators. In addition, the mechanism may perform various pre-processing by an application when creating such indexes to conserve resources of an underlying database system. The mechanisms may also execute such wildcard queries in a manner that appears to be synchronous to a user while maintaining such indexes asynchronously for efficiency purposes.

Efficient database searching for queries using wildcards

A database query message may be received at a database system via a network. The database query message may include one or more parameters for retrieving data from a database based on a designated database record insertion timestamp. A database query may be executed against the database system to retrieve one or more database records based on a database record insertion timestamp range determined based on the designated database record insertion timestamp and including a plurality of database record insertion timestamp values.

Index contention under high concurrency in a database system

A query to retrieve database values for a first subset of table columns in a database table is determined. Runtime column selection variables are set up to identify the first subset of table columns for data retrieval, and are dynamically bound to a prepared query statement with single-column query statements joined by interstitial union operators. Each single-column query statement in the prepared query statement specifies a single-column query directed to a corresponding table column with a corresponding predicate to be bound at runtime dynamically with a corresponding portion of the one or more runtime column selection variables. The predicate is evaluated to be true or false in dependence on whether the corresponding table column is in the first subset of table columns. The prepared query statement is executed to retrieve database values for the first subset of table columns in the database table.

Precompiled SQL queries that allow for dynamic selection of columns

A plurality of database access requests are received at a database system within a designated database access scope. The plurality of database access requests may include a first request to read from a first database table and a second request to write to a second database table. The second request may occur during or after the receipt of the first request. A database access dependency rule linking the first database table and the second database table may be created. The database access dependency rule may impose a protected database access framework when executing a subsequent write request to the second database table when the first database table is updated. The protected database access framework may store one or more values associated with the subsequent write request in a staging table that are capable of being used to undo the subsequent write request.

Change-protected database system

A request to update an original data value in a first row in a database table in a database system. An updated data value is written to a second row in a staging table in the database system. The updated data value corresponds with the original data value. The first row includes a database table key, which is also included in the second row. The original data value in the database table is replaced with a corresponding replacement value, which is determined based on a value replacement update function that takes as input the updated data value. The staging table maintains a record value for reversing the update to the database table.

In a multitenant data platform architecture, one or more supporting data tables are used to efficiently retrieve data responsive to data retrieval requests. Programmatic code markers associated with contextual data about a data transaction thread are identified. The contextual data is examined using the one or more programmatic code markers invoked by the database in the data transaction thread at runtime. Based on the contextual data returning an indication of a data corruption in one or more supporting data structures, an action associated with the data transaction thread is performed. A log of data corruptions and corresponding call stack trace data may be generated. The data transaction thread may be allowed to continue to execute statements that modify data tables, or the data transaction thread may be terminated.

Runtime detector for data corruptions

A database query is received at a first time when a database is in a first state. The database query is analyzed to determine whether it has one or more characteristics that fall within a historical top range of database queries. If the database query falls within the historical top range of database queries the query is stored for later query tuning activity. The query is executed against the database in the first state. Query tuning recommendations are generated for stored database queries that fall within the historical top range of database queries. A subsequent request is received at a second time when a database is in a second state. The subsequent database query is executed with at least one of the query tuning recommendations against the database in the second state.

Background processing to provide automated database query tuning

A method for processing database package connections and updates has been developed. First, an execution request for a database package is received. A connection context is then established which can execute the execution request. The database package is determined whether or not it is valid with proper package updates prior to establishing the connection. If the database package is determined to not be valid, change packages are retrieved for the database package. The valid change packages are then compiled for the database package and the connection request is executed for the updated database package with the connection context.

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Walnut Creek, CA, United States of America

Rohitashva Mathur