Oracle International Corporation

Facebook, Inc.

Namit Jain

Shailendra Mishra

Anand Madihalli Srinivasan

Nipun Agarwal

Ravi Murthy

Eric Sedlar

Sam Idicula

Syam Sundar Pannala

Hoyong Park

Krishnan Meiyyappan

Neerja Bhatt

Vikram Kapoor

Kapil Laxmikant Surlaker

Sanjay Kaluskar

Wei Min Wang

Bhagat V Nainani

Rakesh Komuravelli

Juan R Loaiza

Carol L Colrain

Kumar Rajamani

Parul Jain

Sandeep Bishnoi

Hakan Jakobsson

Graham Wood

Asha Tarachandani

Thuvan Hoang

Fei Ge

Rajit S Kambo

Mohit Thatte

Subramanian Muralidhar

Amit Ganesh

Sivasankaran Chandrasekar

Bipul Sinha

Debashish Chatterjee

Srinivas Vemuri

Paul A Reilly

Archna Kalra Johnson

James Stenoish

Ellen Batbouta

Sambavi Muthukrishnan

Bhushan Mandhani

Sriram Samu

Raghotham Sathyanarayana Murthy

Syam Pannala

Techniques for detecting patterns in one or more data or event streams. A pattern to be detected may be specified using a regular expression. Events received in a data stream are processed during runtime to detect occurrences of the specified pattern in the data stream. In one embodiment, a pattern type or class is determined for the specified pattern and pattern matching is performed using a technique selected based upon the type or class determined for the specified pattern.

Framework for supporting regular expression-based pattern matching in data streams

A mechanism is used to improve the efficiency of performing resource-to-path resolution. A hierarchical link structure contains entries and/or records ('link entries') that each defines a parent-child link within an information hierarchy. A certain linking entry may have data that refers to another link entry representing a link to the parent&#x2014;a link in which the parent is a child. This data allows resource-to-path resolutions to be performed more efficiently.

Efficient processing of path related operations on data organized hierarchically in an RDBMS

Techniques for reducing the memory used for processing events received in a data stream are provided. This may be achieved by reducing the memory required for storing tuples. A method for processing a data stream includes receiving a tuple and determining a tuple specification that defines a layout of the tuple. The layout identifies one or more data types that are included in the tuple. A tuple class corresponding to the tuple specification may be determined. A tuple object based on the tuple class is instantiated, and during runtime of the processing system. The tuple object is stored in a memory.

Framework for dynamically generating tuple and page classes

A method system and program for performing a query in which context and information including opcodes accompanies an execution plan for the query. The opcodes determine operations to retrieve a set of bind variables. The execution plan and context information shared by a plurality of application programs.

Compile time opcodes for efficient runtime interpretation of variables for database queries and DML statements

Techniques for detecting patterns in one or more data streams. A pattern to be detected may be specified using a regular expression. Events received in a data stream are processed during runtime to detect occurrences of the specified pattern in the data stream.

Techniques for performing regular expression-based pattern matching in data streams

A data stream management system (DSMS) is designed to support a new user-defined function, by creating and using at least two structures as follows. A first structure ('metadata entry') is created in response to a command for creation of the new function, and maps a single instance of a class to the function's name. A second structure is created with creation of an operator on receipt of each new continuous query that uses the new function. The second structure ('operator specific data structure') contains a path to the newly-created instance, which path is obtained by looking up the first structure. Additional second structures are created on receipt of additional continuous queries which use the new function, but all second structures contain the same path. All continuous queries use the same instance. Repeated use of a single instance to compile and execute multiple queries eliminates repeated instantiation of the same function.

Support for user defined functions in a data stream management system

Techniques for logging data pertaining to the operation of a data stream processing server. In one set of embodiments, logging configuration information can be received specifying a functional area of a data stream processing server to be logged. Based on the logging configuration information, logging can be dynamically enabled for objects associated with the functional area that are instantiated by the data stream processing server, and logging can be dynamically disabled for objects associated with the functional area that are discarded (or no longer used) by the data stream processing server. In another set of embodiments, a tool can be provided for visualizing the data logged by the data stream processing server.

Logging framework for a data stream processing server

A computer is programmed to accept a command for creation of a new aggregation defined by a user to process data incrementally, one tuple at a time. One or more incremental function(s) in a set of instructions written by the user to implement the new aggregation maintain(s) locally any information that is to be passed between successive invocations, to support computing the aggregation for a given set of tuples as a whole. The user writes a set of instructions to perform the aggregation incrementally, including a plus function which is repeatedly invoked, only once, for each addition to a window of a message. The user also writes a minus function to be invoked with the message, to return the value of incremental aggregation over the window after removal of the message. In such embodiments, the computer does not maintain copies of messages in the window for use by aggregation function(s).

Support for incrementally processing user defined aggregations in a data stream management system

Techniques for detecting patterns in data streams. A pattern to be detected may be specified using a regular expression. Events received in data streams are then processed during runtime to detect occurrences of the pattern specified by the regular expression in the data stream. If the pattern to be matched belongs to a certain predetermined class of patterns, then a pattern matching technique that is customized for that class of patterns is used during the runtime events processing.

Techniques for matching a certain class of regular expression-based patterns in data streams

Log visualization tool for a data stream processing server

Techniques for facilitating communication between one or more client applications and an event processing server. In one set of embodiments, a framework is provided that enables a client application to communicate with an event processing server via a standard database connectivity API, such as Java Database Connectivity (JDBC).

Standardized database connectivity support for an event processing server

Techniques for managing memory usage in a processing system are provided. This may be achieved by receiving a data stream including multiple tuples and determining a query plan that was generated for a continuous query applied to the multiple tuples in the data stream. The query plan may include one or more operators. Before scheduling an operator in the query plan, it is determined when an eviction is to be performed based a level of free memory of the processing system. An eviction candidate is determined and a page associated with the eviction candidate is evicted from the memory to a persistent storage.

Infrastructure for spilling pages to a persistent store

In one embodiment, a system for distributed event processing includes a first event processor and a second event processor. The first event processor receives event data associated with an event stream. The second event processor receives a query associated with the event stream. A request to the first event processor is generated by the second event processor based on the query for a first portion of event data associated with the event stream. The second event processor receives the first portion of event data from the first event processor. The second event processor then generates an event based on the first portion of event data.

Methods and systems for querying event streams using multiple event processors

Standardized database connectivity support for an event processing server in an embedded context

A new continuous query to a data stream management system (DSMS) may use several operators that are equivalent to operators currently being used by continuous queries that have been previously registered in the DSMS. To determine equivalence of operators, the DSMS checks at least the function and the data input to the operators. On finding equivalence, the DSMS modifies a global plan being executed, to use at least an existing subtree of operators during execution of the new continuous query, to generate a modified plan. The DSMS is also programmed to cause each relation source operator (which outputs a relation) to propagate a current state of the relation to each operator newly coupled to the relational operator. After propagation of current state to newly-coupled operators, each operator in the modified plan processes any new data and supplies the result to all operators coupled thereto, including newly-coupled operators and existing operators.

Dynamically sharing a subtree of operators in a data stream management system operating on existing queries

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Santa Clara, CA, United States of America

Namit Jain