Oracle International Corporation

Chinmayi Krishnappa

Shasank Kisan Chavan

Amit Ganesh

Vineet Marwah

Weiwei Gong

Allison Holloway

Teck Hua Lee

Dennis Lui

Jim Kearney

Vicente Hernandez

An efficient join processing technique is provided that improves multi-level hash join performance by decomposing join operations into a set of opcodes that describe any complex hash join, pipelining execution of these opcodes across join levels, and sharing join operand metadata across opcodes. Complex multi-level joins are easier to describe and execute when decomposed into opcodes. The join technique decomposes multi-level join operations into a minimal set of opcodes such that the join work at each node of the multi-level join can be fully described as an execution of a sequence of opcodes. Operand metadata is shared across the opcodes of all join levels that reference the operand, thereby obviating the need to copy or transmit rows between the join nodes.

Efficient opcode-driven pipelined execution of multi-level hash joins

Described are improved systems, computer program products, and methods for adaptively provisioning an ordered sequence in a clustered database environment. The approach includes identifying a cached list of sequence numbers. A request for one or more sequence numbers in a database environment may be received. A determination may be made to decide whether the request pertains to an ordered sequence. The one or more sequence numbers may be adaptively provisioned to fulfill the request based at least in part upon an independent cache implementation or upon a shared cache implementation.

Methods, systems, and computer program products for adaptively provisioning a sequence in a clustered database environment

For join acceleration, a computer stores local encoding dictionaries (ED), including a build ED that contains a plurality of distinct build dictionary codes (DC) and a probe ED that contains a plurality of distinct probe DCs that is not identical to the plurality of distinct build DCs (BDC). Build data rows (DR) that contain a build key that contains BDCs from the plurality of distinct BDCs is stored. Probe DRs that contain a probe key that contains probe DCs from the plurality of distinct probe DCs is stored. A request for a relational join of the build DRs with the probe DRs is received. The BDCs from the build key and the probe DCs from the probe key are transcoded to global DCs (GDC) of a global ED. Based on GDCs for the build key, a build array whose offsets are respective GDCs of the global ED is populated. Based on GDCs for the probe key, offsets of the build array are accessed. A response to the request for the relational join that is based on accessing offsets of the build array is sent.

Technique for fast join processing of dictionary encoded key columns in relational database systems

Techniques are described herein for hybrid parallelization of in-memory table scans. Work for an in-memory scan is divided into granules based on a degree of parallelism. The granules are assigned to one or more processes. The work for each granule is further parallelized by dividing the work granule into one or more tasks. The tasks are assigned to one or more threads, the number of which can be dynamically adjusted.

Efficient hybrid parallelization for in-memory scans

Techniques are provided for maintaining data persistently in one format, but making that data available to a database server in more than one format. For example, one of the formats in which the data is made available for query processing is based on the on-disk format, while another of the formats in which the data is made available for query processing is independent of the on-disk format. Data that is in the format that is independent of the disk format may be maintained exclusively in volatile memory to reduce the overhead associated with keeping the data in sync with the on-disk format copies of the data. Selection of data to be maintained in the volatile memory may be based on various factors. Once selected the data may also be compressed to save space in the volatile memory. The compression level may depend on one or more factors that are evaluated for the selected data. The factors for the selection and compression level of data may be periodically evaluated, and based on the evaluation, the selected data may be removed from the volatile memory or its compression level changed accordingly.

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San Mateo, CA, United States of America

Chinmayi Krishnappa