Oracle International Corporation

Robert Hanckel

Siva Ravada

Jayanta Banerjee

Garret Frederick Swart

Parthiban Thilagar

Lory Dean Molesky

Rongzheng Yan

Chen Cao

Manjari D Yalavarthy

Techniques are described herein for analyzing data streams in conjunction with relational database data in a DBMS. A database dictionary defines one or more columns for an external table and a data source for said external table that comprises an external message stream. Executing a query that targets the external table includes generating one or more rows from the external table, each row corresponding to a message in the external message stream that was retrieved from the external message stream after a message stream offset.

Integrating Kafka data-in-motion with data-at-rest tables

Systems, methods, and other embodiments associated with logging event messages in a computing grid using a distributed memory are described. In one embodiment, a method includes storing, in a distributed memory, a batch of log messages received from one of a plurality of handlers. Storing the batch in the distributed memory includes storing the batch in one of a plurality of memories that are part of the distributed memory and storing the batch with a group of batches from the plurality of handlers. The example method may also include generating, after a predefined period of time has elapsed, a formal log from the group of batches. The predefined period of time is an amount of time after a reporting period within which batches from the reporting period are expected to arrive in the distributed memory. The formal log includes sorted messages from the group of batches.

Batch logging in a distributed memory

A method and system to creating query-level access to an external distributed file system by identifying a location of one or more external data residing on the external distributed file system, creating a query specifying an external table within a database engine having one or more location files, wherein the location files identify metadata operations for accessing and processing the one or more external data, defining metadata operations for accessing and processing the one or more external data, wherein the processing that produces one or more result files occurs at the external distributed file system, and executing the query at the database engine to create the external table, the external table comprising the one or more location files identifying the metadata directives for processing query-level requests on the one or more external data stored on the external distributed file system.

Query-level access to external petabyte-scale distributed file systems

Systems, methods, and other embodiments associated with intelligently processing log messages are described. In one embodiment, a computer-implemented method includes analyzing, by a logging appliance that includes at least hardware, communications received from a plurality of handlers to determine whether at least one of the communications indicates an error has been encountered by one of a plurality of components associated with the plurality of handlers. The method includes scheduling a subset of the plurality of handlers to provide reports that include a detailed set of log messages in response to detecting the error. The subset of the plurality of handlers includes handlers that are associated with the error.

Logging incident manager

A system to implement query-level access by a database engine to an external distributed file system by identifying a results file location of one or more results files on the external distributed file system, and storing the results file locations in external table files on the database engine for subsequent use during retrieval of data from the results files. The database engine serves to process queries where the query specifies the external table (which in turn references locations of the results files). Execution of the query streams data from the external distributed file system into the database engine. The data from the external distributed file system is not stored in the external table files on the database engine; rather, the external table files specify a location of code or operational directives which, when executed, streams results from the external distributed file system to at least one parallel query engine.

Systems, methods, and other embodiments associated with intelligently gathering log messages are described. In one embodiment, a computer-implemented method includes collecting, by a logging handler associated with a component, log messages according to two different granularities for logging messages. The two different granularities include a fine granularity and a coarse granularity. Collecting the log messages according to the fine granularity includes storing log messages in an incident buffer. The method includes, in response to detecting an error at the component, (i) freezing the incident buffer to preserve log messages present in the incident buffer, and (ii) communicating log messages that are collected according to the coarse granularity that are stored in a regular buffer to a logging appliance.

Fine and coarse granularity logging handler

Techniques for history enabling a table in a database system so that past versions of rows of the history-enabled table are available for temporal querying. The table is history enabled by adding a start time column to the table and creating a history table for the history-enabled table. The start time field's value in a row of the history-enabled table indicates when the contents of the row last changed. The rows of the history table are copies of rows of the history-enabled table that have been deleted or updated. The rows include end time fields whose values indicate when the row was updated or deleted. A history table's row thus indicates a period in which the history table's row was in the history-enabled table. Temporal queries are performed on a view which is the union of the history-enabled table and the history table.

Database system that provides for history-enabled tables

Techniques for history enabling a table in a database system so that past versions of rows of the history-enabled table are available for temporal querying. The table is history enabled by adding a start time column to the table and creating a history table for the history-enabled table. The history table's rows are copies of rows of the history-enabled table that have changed and include start time and end time fields whose values indicate a period in which the history table's row was in the history-enabled table. Temporal queries are performed on a view which is the union of the history-enabled table and the history table. The temporal queries are speeded up by period of time indexes in which the leaves are grouped based on time period size, identifiers are assigned to the groups, and the keys of the index include the group identifiers.

Techniques for implementing indexes on columns in database tables whose values specify periods of time

Interval-based adjustments to data are applied by a relational database engine. The engine stores raw, unadjusted data values in a separate table from adjustment values. The adjustment values are dynamically updated in response to a new adjustment interval. The adjustments are applied to the raw data during retrieval from the database. This results in an efficient and transparent adjustment to data.

Interval-based adjustment data includes computing an adjustment value from the data for a pending adjustment in response to retrieval of an adjusted data value from a database

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Amherst, NH, United States of America

Robert Hanckel