Abstract: Consistent External Table Access maintains transactional consistency for queries that access external tables stored in a DBFS. This ability is achieved by bypassing the OS. One or more database processes executing a query that access an external table stored in a DBFS access the database-file table like other database tables in the DBMS that can be accessed to execute a query. Based on metadata stored in the DBMS regarding how an external table is stored in a DBFS, a DBMS is able to marshal database processes that access database-file tables directly to execute a query.

Abstract: A storage system communicatively coupled to a database management system (DBMS performs storage-side scanning of data sources that are not stored in native database storage format of the DBMS. Data sources for external tables are accessible in a storage system referred to as a distributed data access system (DDAS), e.g. a Hadoop Distributed File System. To execute a query that references an external table, a DBMS first generates an execution plan. The DDAS supplies the DBMS with information that specifies each portion of the data source, and specifies which data node to use to access the portion. The DBMS sends a request for each portion to the respective data node, requesting that the data node generate rows from data in the portion. The request may specify scanning criteria, specifying one or more columns to project and/or filter on, and code modules for the data node to execute to generate records.

Abstract: Techniques are provided for managing cached data objects in a mixed workload environment. In an embodiment, a database system receives request to access a target data object. The database system determines whether the request to access the target data object is associated with a first type of workload or a second type of workload. In response to determining that the request is associated with the first type of workload, the target data object replaces a least recently used data object in a cache. In response to determining that the request is associated with the second type of workload, the target data object is cached based on an associated access-level value.

Abstract: A storage system communicatively coupled to a DBMS performs storage-side scanning of data sources that are not stored in the native database storage format of the DBMS. Data sources for external tables are accessible in a storage system referred to herein as a distributed data access system, e.g. a Hadoop Distributed File System. To execute a query that references an external table, a DBMS first generates an execution plan. The distributed data access system supplies the DBMS with information that specifies each portion of the data source, and specifies which data node to use to access the portion. The DBMS sends a request for each portion to the respective data node, the request requesting that the data node generate rows from data in the portion. The request may specify scanning criteria, specifying one or more columns to project and/or filter on. The request may also specify code modules for the data node to execute to generate rows or records and columns.

Abstract: In a consumer-producer distributed model of computing, consumers request data from producers and perform computing work on the data received from the producers, thereby transforming the data. At least some of the computing work performed by a consumer on data provided by the producer is commutable between the consumer and producer, that is, either the producer as well as the consumer can perform the computing work on the data. A producer redistributes commutable based on the work load of the producer and consumer as well as other factors.

Abstract: Techniques are provided for managing cached data objects in a mixed workload environment. In an embodiment, a database system receives request to access a target data object. The database system determines whether the request to access the target data object is associated with a first type of workload or a second type of workload. In response to determining that the request is associated with the first type of workload, the target data object replaces a least recently used data object in a cache. In response to determining that the request is associated with the second type of workload, the target data object is cached based on an associated access-level value.

Abstract: In a consumer-producer distributed model of computing, consumers request data from producers and perform computing work on the data received from the producers, thereby transforming the data. At least some of the computing work performed by a consumer on data provided by the producer is commutable between the consumer and producer, that is, either the producer as well as the consumer can perform the computing work on the data. A producer redistributes commutable based on the work load of the producer and consumer as well as other factors.

Abstract: A method and apparatus for creating a target database on a target platform based on a source database on a source platform is provided. A target database is created on the target platform based on data, metadata (for example, data in the system tablespace and control files), and external objects (for example, password files and externally stored tables) comprised within the source database. Data and metadata stored in tablespaces of the source database are converted to the format compatible with the target platform. The conversion of the data and metadata may be performed either at the source database or the target database. Redo information and undo information of the source database are not copied from the source database to the target database. Certain files, e.g., a password files, and links to externally stored object may be recreated on the target database.

Abstract: A mechanism is described for constructing work granules, where the work granules are constructed independently of whether two or more of the work granules are assigned to operate on a logical storage unit that contains a portion of the same row. A database system maintains transaction control data for data blocks in a manner that avoids self-deadlocks for slaves that follow a row collision protocol. A row collision protocol is a set of rules or steps that slaves follow to ensure that only one subtransaction in a distributed transaction updates a spanning row. Thus, for a particular spanning row, a single subtransaction modifies all the row pieces of the spanning row in any data block containing any row pieces of the spanning row. Consequently, a given data block may be affected by multiple subtransactions in a distributed transaction, one subtransaction affecting a row piece in the data block, another subtransaction affecting other rows in the data block.