Processing data from multiple sources

Patent metadata

Field	Value
Publication number	US-9607073-B2
Application number	US-201414255579-A
Country	US
Kind code	B2
Filing date	Apr 17, 2014
Priority date	Apr 17, 2014
Publication date	Mar 28, 2017
Grant date	Mar 28, 2017

Abstract

In a first aspect, a method includes, at a node of a Hadoop cluster, the node storing a first portion of data in HDFS data storage, executing a first instance of a data processing engine capable of receiving data from a data source external to the Hadoop cluster, receiving a computer-executable program by the data processing engine, executing at least part of the program by the first instance of the data processing engine, receiving, by the data processing engine, a second portion of data from the external data source, storing the second portion of data other than in HDFS storage, and performing, by the data processing engine, a data processing operation identified by the program using at least the first portion of data and the second portion of data.

First claim

What is claimed is:

1. A method including;
at a node of a Hadoop duster, the node storing a first portion of data in HDFS data storage:
executing a first instance of a data processing engine capable of receiving data from a data source external to the Hadoop cluster;
receiving a computer-executable program by the data processing engine, the computer-executable program including a dataflow graph capable of being executed by a graph execution engine of the data processing engine;
executing at least part of the computer-executable program by the first instance of the data processing engine;
receiving, by the data processing engine, a second portion of data from the external data source;
storing the second portion of data other than in HDFS storage; and
performing, by the data processing engine, a data processing operation identified by the computer-executable program using at least the first portion of data and the second portion of data,
the dataflow graph including a) at least one component representing the Hadoop cluster, b) at least one component representing the external data source, and c) at least one link that represents at least one dataflow associated with the operation to be performed on the at least the first portion of data and the second portion of data.

2. The method of claim 1 in which the Hadoop duster includes nodes each executing an instance of the data processing engine, the instances of the data processing engine running concurrently to perform the data processing operation together in parallel on a) a first body of data that includes the first portion of data, the first body of data also including other portions of data being processed by other nodes of the Hadoop duster, and b) a second body of data that includes the second portion of data, the second body of data being stored in a format native to a relational database system, and the second body of data being divided into portions that each can be stored in volatile memory of the nodes of the Hadoop cluster.

3. The method of claim 1 in which at least one component of the dataflow graph is connected to a link representing a flow of data from the Hadoop cluster, and wherein the at least one component is connected to a link representing a flow of data from the external data source of the second portion of data.

4. The method of claim 1 in which the data processing engine does not implement a MapReduce programming model.

5. The method of claim 1 , in which the second portion of data is stored in volatile memory.

6. The method of claim 1 including
receiving a database query, the database query including at least one operation to be performed on data received from at least one source of data that includes the Hadoop cluster; and
the computer program includes components representing operations corresponding to the database query, wherein the computer program includes at least one component representing the at least one source of data and at least one link that represents at least one dataflow associated with the operation to be performed on data received from at least one source of data.

7. The method of claim 1 in which the second portion of data was chosen based on characteristics of the first portion of data.

8. The method of claim 1 in which the second portion of data includes a subset of rows of a relational database, and the second portion of data includes a subset of columns of the relational database.

9. The method of claim 1 in which the second portion of data is distinct from a third portion of data received at a second node of the Hadoop cluster from the external data source.

10. The method of claim 1 including communicating with an instance of at least part of the computer-executable program that is being executed by a second instance of the data processing engine that is outside of the Hadoop cluster.

11. The method of claim 1 including executing at least part of the computer-executable program by a second instance of the data processing engine outside of the Hadoop cluster.

12. A non-transitory computer-readable storage device including instructions for causing a node of a Hadoop cluster storing a first portion of data in HDFS data storage to carry out operations including:
executing a first instance of a data processing engine capable of receiving data from a data source external to the Hadoop cluster;
receiving a program by the data processing engine, the program including a dataflow graph capable of being executed by a graph execution engine of the data processing engine;
executing at least part of the program by the first instance of the data processing engine;
receiving, by the data processing engine, a second portion of data from the external data source;
storing the second portion of data other than in HDFS storage; and
performing, by the data processing engine, a data processing operation identified by the program using at least the first portion of data and the second portion of data,
the dataflow graph including a) at least one component representing the Hadoop cluster, b) at least one component representing the external data source, and c) at least one link that represents at least one dataflow associated with the operation to be performed on the at least the first portion of data and the second portion of data.

13. The non-transitory computer-readable storage device of claim 12 in which the Hadoop cluster includes nodes each executing an instance of the data processing engine, the instances of the data processing engine running concurrently to perform the data processing operation together in parallel on a) a first body of data that includes the first portion of data, the first body of data also including other portions of data being processed by other nodes of the Hadoop cluster, and b) a second body of data that includes the second portion of data, the second body of data being stored in a format native to a relational database system, and the second body of data being divided into portions that each can be stored in volatile memory of the nodes of the Hadoop cluster.

14. The non-transitory computer-readable storage device of claim 12 in which at least one component of the dataflow graph is connected to a link representing a flow of data from the Hadoop cluster, and wherein the at least one component is connected to a link representing a flow of data from the external data source of the second portion of data.

15. The non-transitory computer-readable storage device of claim 12 in which the data processing engine does not implement a MapReduce programming model.

16. The non-transitory computer-readable storage device of claim 12 , in which the second portion of data is stored in volatile memory.

17. The non-transitory computer-readable storage device of claim 12 , the operations including
receiving a database query, the database query including at least one operation to be performed on data received from at least one source of data that includes the Hadoop cluster; and
the computer program including components representing operations corresponding to the database query, wherein the computer program includes at least one component representing the at least one source of data and at least one link that represents at least one dataflow associated with the operation to be performed on data received from at least one source of data.

18. The non-transitory computer-readable storage device of claim 12 in which the second portion of data was chosen based on characteristics of the first portion of data.

19. The non-transitory com

Assignees

• Ab Initio Technology Llc

Inventors

• Schechter Ian
• Wakeling Tim
• Wollrath Ann M

Classifications

• G06F16/284

  Relational databases · CPC title

• G06F16/13

  File access structures, e.g. distributed indices (arrangements of input from, or output to, record carriers G06F3/06) · CPC title

• G06F16/9024

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title

• G06F16/254

  Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses · CPC title

• G06F16/285

  Clustering or classification · CPC title