Abstract: Computing resource management is improved during fast sorting using vector instructions. The process includes: determining a pivot value and a pivot position in a data set (e.g., by sampling with vectors and determining the sample median), determining whether moving data in the sampled portion may be avoided (e.g., if it is constant-valued or already sorted) and, leveraging that determination to possibly avoid unnecessary data movement, sorting the data set. Some examples further determine the microarchitecture version of the computing device performing the sorting and select an implementation of sorting instruction that is tuned for that microarchitecture version (e.g., based on the number of vector registers and motherboard cache configuration). Some examples leverage a soft 3-way quicksort by finding data elements adjacent to the pivot position that also have the pivot value and adding a partition boundary at the end of the set of same-valued data elements.

Abstract: Computing resource management is improved during fast sorting using vector instructions. The process includes: determining a pivot value and a pivot position in a data set (e.g., by sampling with vectors and determining the sample median), determining whether moving data in the sampled portion may be avoided (e.g., if it is constant-valued or already sorted) and, leveraging that determination to possibly avoid unnecessary data movement, sorting the data set. Some examples further determine the microarchitecture version of the computing device performing the sorting and select an implementation of sorting instruction that is tuned for that microarchitecture version (e.g., based on the number of vector registers and motherboard cache configuration). Some examples leverage a soft 3-way quicksort by finding data elements adjacent to the pivot position that also have the pivot value and adding a partition boundary at the end of the set of same-valued data elements.

Abstract: Database systems are subject to automatic troubleshooting. Query execution data for individual queries executed by a database system can be collected. The query execution data can be evaluated automatically in conjunction with one or more detection metrics to determine whether or not a potential performance issue exists. After detection of a potential issue, the query execution data can be analyzed further to determine one or more causes by matching query execution data to one or more patterns that encode candidate causes. Subsequently, a root cause amongst multiple causes is determined based on causal dependency. Corrective action associated with a root cause can also be determined and optionally applied automatically.

Abstract: Processing a database query. A method includes receiving a database query from a user. The database query includes one or more imperative functions. The one or more imperative functions are converted to one or more declarative query representations. The one or more declarative query representations include standardized relational operators included in a relational query language. Further, the one or more declarative query representations are optimizable by a query optimizer of the database. The database query is optimized at the query optimizer to create a query plan by evaluating any declarative query representation originally in the database query received from the user and the one or more declarative query representations.

Abstract: Database systems are subject to automatic troubleshooting. Query execution data for individual queries executed by a database system can be collected. The query execution data can be evaluated automatically in conjunction with one or more detection metrics to determine whether or not a potential performance issue exists. After detection of a potential issue, the query execution data can be analyzed further to determine one or more causes by matching query execution data to one or more patterns that encode candidate causes. Subsequently, a root cause amongst multiple causes is determined based on causal dependency. Corrective action associated with a root cause can also be determined and optionally applied automatically.

Abstract: Processing a database query. A method includes receiving a database query from a user. The database query includes one or more imperative functions. The one or more imperative functions are converted to one or more declarative query representations. The one or more declarative query representations include standardized relational operators included in a relational query language. Further, the one or more declarative query representations are optimizable by a query optimizer of the database. The database query is optimized at the query optimizer to create a query plan by evaluating any declarative query representation originally in the database query received from the user and the one or more declarative query representations.

Abstract: Mitigating problems related to the Halloween problem including where update operations potentially allow the record to be visited more than once during the operation. A method includes accessing an instance of a data store operation statement. The instance of the data store operation statement is executed causing an update or delete to an old version of data store record or creation of a data store record resulting in a new version of the data store record in the case of an update or creation of a data store record and a deleted version of the data store record in the case of a delete in the data store. The instance of the data store operation statement is correlated with the new version of the data store record or the deleted version of the data store record.

Abstract: The maintaining of a database that is distributed across a front database and one or more cloud databases. Rather than have the front and remote portions of the database be fixed, a data distribution manager evaluates databases segments following a data distribution policy to identify one or more database segments that are to be transferred to or from the one or more cloud databases from or to the front database. In response, the data distribution manager causes the identified database segment to be moved. Accordingly, the data that is in the front database (and the data that is stored in the cloud) may vary over time.

Abstract: Mitigating problems related to the Halloween problem including where update operations potentially allow the record to be visited more than once during the operation. A method includes accessing an instance of a data store operation statement. The instance of the data store operation statement is executed causing an update or delete to an old version of data store record or creation of a data store record resulting in a new version of the data store record in the case of an update or creation of a data store record and a deleted version of the data store record in the case of a delete in the data store. The instance of the data store operation statement is correlated with the new version of the data store record or the deleted version of the data store record.

Abstract: Mitigating problems related to the Halloween problem including where update operations potentially allow the record to be visited more than once during the operation. A method includes accessing an instance of a data store operation statement. The instance of the data store operation statement is executed causing an update or delete to an old version of data store record or creation of a data store record resulting in a new version of the data store record in the case of an update or creation of a data store record and a deleted version of the data store record in the case of a delete in the data store. The instance of the data store operation statement is correlated with the new version of the data store record or the deleted version of the data store record.

Abstract: Mitigating problems related to the Halloween problem including where update operations potentially allow the record to be visited more than once during the operation. A method includes accessing an instance of a data store operation statement. The instance of the data store operation statement is executed causing an update or delete to an old version of data store record or creation of a data store record resulting in a new version of the data store record in the case of an update or creation of a data store record and a deleted version of the data store record in the case of a delete in the data store. The instance of the data store operation statement is correlated with the new version of the data store record or the deleted version of the data store record.

Abstract: Mitigating problems related to the Halloween problem including where update operations potentially allow the record to be visited more than once during the operation. A method includes accessing an instance of a data store operation statement. The instance of the data store operation statement is executed causing an update or delete to an old version of data store record or creation of a data store record resulting in a new version of the data store record in the case of an update or creation of a data store record and a deleted version of the data store record in the case of a delete in the data store. The instance of the data store operation statement is correlated with the new version of the data store record or the deleted version of the data store record.

Abstract: Embodiments are directed to providing distributed database service upgrades of database server instances in a computer cluster using multiple database server instances and to monitoring and maintaining a distributed database service during upgrade. In an embodiment, each computer system in a computer cluster instantiates at least two different database server instances on each of the nodes in the cluster. The first database server instances are configured to operate using a current distributed database version and the second instances are configured to operate using a new, updated distributed database service version. The computer system receives an indication that the distributed database service is to be upgraded. Then, based on the received indication, the computer system migrates database replicas from the first database server instances to the second database server instances which operate the new, updated service version, substantially without user-visible downtime.

Abstract: Embodiments are directed to providing distributed database service upgrades of database server instances in a computer cluster using multiple database server instances and to monitoring and maintaining a distributed database service during upgrade. In an embodiment, each computer system in a computer cluster instantiates at least two different database server instances on each of the nodes in the cluster. The first database server instances are configured to operate using a current distributed database version and the second instances are configured to operate using a new, updated distributed database service version. The computer system receives an indication that the distributed database service is to be upgraded. Then, based on the received indication, the computer system migrates database replicas from the first database server instances to the second database server instances which operate the new, updated service version, substantially without user-visible downtime.

Abstract: Mitigating problems related to the Halloween problem including where update operations potentially allow the record to be visited more than once during the operation. A method includes accessing an instance of a data store operation statement. The instance of the data store operation statement is executed causing an update or delete to an old version of data store record or creation of a data store record resulting in a new version of the data store record in the case of an update or creation of a data store record and a deleted version of the data store record in the case of a delete in the data store. The instance of the data store operation statement is correlated with the new version of the data store record or the deleted version of the data store record.