Configurable data windows for archived relations

• US 10,025,825 B2
• Filed: 12/17/2015
• Issued: 07/17/2018
• Est. Priority Date: 09/28/2012
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Deshmukh, Unmesh Anil, Srinivasan, Anand, Bishnoi, Sandeep
• Primary Examiner(s): Khong, Alexander
• Application Number:
  US14/973,377
• Publication Number:
  US 20160103882A1
• Time in Patent Office: 943 Days
• CPC Class Codes: B29D 2030/0682 Inner liners tubeless tyres with impervious liner or coating on the tyre B60C5/14
• CPC Class Codes: B29D 30/0681 Parts of pneumatic tyres; accessories, auxiliary operations
• CPC Class Codes: B32B 27/08 of synthetic resin
• CPC Class Codes: B32B 27/34 comprising polyamides
• CPC Class Codes: B60C 1/0008 Compositions of the inner liner
• CPC Class Codes: B60C 5/14 with impervious liner or coating on the inner wall of the tyre
• CPC Class Codes: G06F 16/242 Query formulation
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/2453 Query optimisation
• CPC Class Codes: G06F 16/24535 of sub-queries or views
• CPC Class Codes: G06F 16/24539 using cached or materialised query results
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/24553 of query operations
• CPC Class Codes: G06F 16/2456 Join operations
• CPC Class Codes: G06F 16/24568 Data stream processing; Continuous queries
• CPC Class Codes: G06F 16/2474 Sequence data queries, e.g. querying versioned data
• CPC Class Codes: G06F 16/2477 Temporal data queries
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/25 Integrating or interfacing systems involving database management systems
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/9024 : Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/951 : Indexing; Web crawling techniques

Abstract

Techniques for managing configurable data windows for streams and/or archived relations are provided. In some examples, a query that identifies at least a data stream or an archived relation may be identified. Additionally, a configurable window size may be identified. In some instances, the query may be initialized with at least a portion of the historical data based at least in part on the window size. Further, the query may be evaluated based at least in part on the data stream and the portion of the historical data.

Claims

1. A system, comprising:

a memory storing a plurality of instructions; and

one or more processors that access the memory, wherein the one or more processors execute the plurality of instructions to at least;

identify a continuous query that identifies a data stream;

identify a configurable window size;

generate a data object to include at least a portion of historical data and that is configured to receive incoming events of the identified data stream that are within a window that corresponds to the configurable window size, the historical data associated with the identified data stream;

initialize the continuous query with the data object to process at least the portion of the historical data based at least in part on the window size; and

evaluate the data object using the continuous query based at least in part on the data stream and the portion of the historical data.

2. The system of claim 1, wherein the continuous query processes incoming real-time business event data of the data stream.

3. The system of claim 1, wherein the evaluation of the data object includes at least applying the continuous query against the data stream based at least in part on the configurable window size.

4. The system of claim 1, wherein the data stream comprises a relation configured with a second configurable window size for referencing a portion of the data stream within a second window with a size based at least in part on a second configurable window size.

5. The system of claim 4, wherein the second window moves along the data stream with time.

6. The system of claim 1, wherein the historical data is not included in the data stream at a time after initializing the query.

7. The system of claim 1, wherein the historical data comprises business event data from the data stream at a time before initializing the query.

8. The system of claim 1, wherein evaluating the data object includes at least forming a query graph with nodes representing operators of the continuous query.

9. The system of claim 8, wherein at least one of the nodes identifies the window size.

10. The system of claim 8, wherein the one or more processors are further configured to execute the plurality of instructions to at least traverse the query graph topographically from a source.

11. The system of claim 10, wherein the one or more processors further execute the plurality of instructions to at least initialize the continuous query with at least the historical data at a lowest stateful operator identified in the query graph.

12. A computer-readable memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising:

instructions that cause the one or more processors to receive a continuous query configured to process data objects, the continuous query including at least one of an identifier of the data stream or an identifier of historical data;

instructions that cause the one or more processors to generate a query graph based at least in part on the continuous query;

instructions that cause the one or more processors to receive a window size from an entity associated with the data stream;

instructions that cause the one or more processors to generate a data object to include at least a portion of the historical data and that is configured to receive incoming events of the data steam that are within a window that corresponds to the window size;

instructions that cause the one or more processors to initialize the continuous query with the data object to process at least the portion of the historical data based at least in part on the window size; and

instructions that cause the one or more processors to evaluate the data object using the continuous query based at least in part on the historical data.

13. The computer-readable memory of claim 12, wherein the data object is identified by a data definition language annotation of the continuous query that identifies at least one of a location of the historical data, a source of the data object, operation information associated with processing the continuous query, one or more relevant columns of a database corresponding to the historical data, or a location for providing the output of the continuous query.

14. The computer-readable memory of claim 12, wherein the plurality of instructions further comprise instructions that cause the one or more processors to determine an amount of historical data with which to initialize the continuous query based at least in part on the window size.

15. The computer-readable memory of claim 12, wherein the continuous query is initialized with the at least a portion of the historical data at a stateful operator of the query graph while traversing the query graph topographically from a source of the query graph.

16. A computer-implemented method, comprising:

receiving a continuous query configured to process data objects associated with business event data, the continuous query including an identifier of historical data associated with the stream;

generating a data object to include at least a portion of the historical data and that is configured to receive incoming events of the steam that are within a window that corresponds to the window size;

receiving a window size configured to identify a bounded range of the data object to process;

initializing the continuous query with the data object to process at least the portion of the historical data at the identified stateful operator based at least in part on the window size; and

evaluating the data object using the continuous query based at least in part on the window size.

19. The computer-implemented method of claim 16, further comprising determining an optimal amount of the historical data with which to initialize the continuous query based at least in part on the window size.

20. The computer-implemented method of claim 16, further comprising:

generating a query graph based at least in part on the continuous query, the query graph including at least a node representing an operator of the continuous query; and

traversing the query graph topographically from a source node of the query graph to identify a stateful operator.

17. The computer-implemented method of 16, wherein the historical data includes business event data from a point in time of the data object prior to initializing the continuous query.

18. The computer-implemented method of 16, further comprising determining an optimal amount of the historical data with which to initialize the continuous query based at least in part on the operator of the continuous query.