Oracle International Corporation

Sanket Hase

Vivekanandhan Raja

Vineet Marwah

Amit Ganesh

Tirthankar Lahiri

Atrayee Mullick

Niloy Mukherjee

Sukhada Pendse

Jesse Kamp

Allison Holloway

Michael J Gleeson

Shuang Su

Juan R Loaiza

Shasank Kisan Chavan

Vasudha Krishnaswamy

Teck Hua Lee

Krishna Kunchithapadam

Kothanda Umamageswaran

Neil MacNaughton

Kartik Kulkarni

Xiaoming He

J William Lee

Michael P Stewart

Sujatha Muthulingam

Muthu Olagappan

Michael John Skarpelos

Vipin Gokhale

Ankita Kumar

Solmaz Kolahi

Maheswaran Venkatachalam

Allison L Holloway

Techniques for non-disruptive versioning of in-memory units in a database are provided. A database server generates and maintains a first IMU that reflects changes made to a mirrored-data-set up to a first snapshot time, and a second IMU that reflects changes made to the mirrored-data-set up to a second snapshot time. During a first period, the database server responds to updates to first data items in the mirrored data by storing first staleness metadata that indicates that the copies of the first data items in the first IMU are stale. During a second period, the database server responds to updates to second data items in the mirrored data by storing second staleness metadata that indicates that the copies of the second data items in the second IMU are stale. The database server responds to a request by accessing the first IMU or the second IMU.

Versioning and non-disruptive servicing of in-memory units in a database

Techniques are described for maintaining coherency of a portion of a database object mirrored in a particular node of a database. The techniques involve maintaining invalidation logs which identify transactions that have committed to a database. Based on the invalidation logs, the particular node generates invalid-row ID metadata which identifies, for each system change number, one or more rows that are not transactionally consistent with data stored in the database object as of said system change number.

Enhancing consistent read performance for in-memory databases

Techniques are described for maintaining coherency of a portion of a database object populated in the volatile memories of multiple nodes in a database cluster. The techniques involve maintaining a local invalidation bitmap for which identifies block-level invalidity data and item-level invalidity data. In response to detecting a patch-up triggering event, a particular node identifies, in the block-level invalidity data, blocks that have been marked as invalid. For each block that has been marked as invalid, the node identifies specific items that have changed and marks the items as invalid in the item-level invalidity data. The node then updates the block-level invalidity data to indicate that the blocks are no longer invalid.

Patch-up operations on invalidity data

A minimum value (MV) is computed for start timestamps that each correspond to an uncommitted transaction. In an embodiment, the MV is computed for a pluggable database that is open on at least first and second instances of a database. The MV is computed for the first instance as of a first current timestamp (CT). The MV and the first CT are communicated to a second instance that has a second CT. If the first and second CTs are equal, the second instance store the MV. If the first CT is bigger, the second CT also becomes equal to the first CT. If the first CT is smaller, the MV is discarded, and the first CT becomes equal to the second CT. In an embodiment, if the MV remains unchanged for a predetermined time period, a start timestamp corresponding to the MV is advanced to a current or future timestamp.

Efficient determination of committed changes

Techniques are provided for maintaining and using row-level and column-level invalidation information for a copy of data items stored in-memory via a row-bitmap and column-bitmap. The row-bitmap and column-bitmap can be used to determine whether a database query can be processed using in-memory data or not. The row-bitmap and column-bitmap can thus improve the frequency with which database queries can be processed using in-memory data.

Column level invalidation for in-memory database

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.

Mechanism to run OLTP workload on in-memory database under memory pressure

Techniques are described for maintaining coherency of a portion of a database object populated in the volatile memories of multiple nodes in a database cluster. The techniques involve maintaining a local invalidation bitmap for chunks of data stored in memory in each particular node in the cluster by tracking locks granted by a lock manager. During a pre-loading operation, each given node requests a set of shared locks associated with the chunks of data to be store in the given node's memory. When a request to release one of these shared locks occurs, the in-memory copy of those data items may be invalidated in the node releasing its shared lock.

Maintaining cross-node coherence of an in-memory database object in a multi-node database cluster

Mirroring, in memory, data from disk to improve query performance

Techniques are herein described for loading a portion of a database object into volatile memory without blocking database manipulation language transactions. The techniques involve invalidating data items loaded from blocks affected by a transaction, referred to as a straddling transaction that started before the load time and committed after the load time. Identifying these straddling transactions involves reviewing one or more transaction lists associated with the set of data items loaded in memory. The transaction list may be read in reverse temporal order of commit to identify a transaction meeting the criteria of starting before the load start, not committing before the load time, and affecting a data item loaded in memory.

Loading and reloading an in-memory copy of a database object without blocking concurrent updates to the database object

To prioritize repopulation of in-memory compression units (IMCU), a database server compresses, into an IMCU, a plurality of data units from a database table. In response to changes to any of the plurality of data units within the database table, the database server performs the steps of: (a) invalidating corresponding data units in the IMCU; (b) incrementing an invalidity counter of the IMCU that reflects how many data units within the IMCU have been invalidated; (c) receiving a data request that targets one or more of the plurality of data units of the database table; (d) in response to receiving the data request, incrementing an access counter of the IMCU; and (e) determining a priority for repopulating the IMCU based, at least in part, on the invalidity counter and the access counter.

Prioritizing repopulation of in-memory compression units

Techniques are provided for using bitmaps to indicate which items, in a set of items, are invalid. The bitmaps include an 'active' bitmap and one or more 'temporal clones'. The active bitmap indicates which items in the set are currently valid. The temporal clones are outdated versions of the active bitmap that indicate which items in the set were invalid at previously points in time. Temporal clones may not be very different from each other. Therefore, temporal clones may be efficiently compressed. For example, a bitmap may be selected as a &#x201c;base bitmap&#x201d;, and one or more other bitmaps are encoded using delta encoding. Run length encoding may then be applied to further compress the bitmap information. These bitmaps may then be used to determine which items are valid relative to past-version requests.

Temporal clones to identify valid items from a set of items

Techniques are provided for granular load and refresh of columnar data. In an embodiment, a particular data object that contains particular data formatted different from column-major format is maintained, the particular data including first data and second data. First and second data objects contain the first and second data, respectively, organized in the column-major format. In response to changes being committed to the first data in the particular data object, invalidating one or more rows of the first data object. In response to a number of invalidated rows of the first data object exceeding a threshold, automatically performing a refresh operation on the first data object independent of any refresh operation on the second data object.

Granular creation and refresh of columnar data

Growing community of inventors

Mountain View, CA, United States of America

Sanket Hase