Replication group partitioning

Patent metadata

Field	Value
Publication number	US-11157518-B2
Application number	US-201313969249-A
Country	US
Kind code	B2
Filing date	Aug 16, 2013
Priority date	Mar 13, 2013
Publication date	Oct 26, 2021
Grant date	Oct 26, 2021

Abstract

Systems for replication group partitioning include a workload profiling module configured to analyze historical workload data for a plurality of data elements to identify and categorize one or more transaction patterns; and a recommendation module configured to generate a recommended partitioning of the plurality of data elements into one or more replication groups, based on the one or more transaction patterns, that are optimized toward a partitioning goal.

First claim

What is claimed is:

1. A system for replication group partitioning, comprising: a workload profiling module configured to analyze historical workload data for a plurality of data elements to identify and categorize one or more transaction patterns, each transaction pattern corresponding to a particular group of transactions that updates the same data elements and having a corresponding peak throughput;
a recommendation module comprising a hardware processor configured to generate a recommended partitioning of the plurality of data elements into one or more replication groups, based on the peak throughputs of the one or more transaction patterns, that are optimized toward a partitioning goal; and
a change capture module configured to log transactions that involve the plurality of data elements in a first data center and to replicate the logged transactions in a second data center, where the replicated transactions are grouped according to the recommended partitioning.

2. The system of claim 1 , wherein the recommendation module is further configured to receive revisions to the recommended partitioning of the plurality of data elements from a user interface and to generate a new recommended partitioning based on the received revisions.

3. The system of claim 1 , wherein the plurality of data elements are tables or tablespaces in a database.

4. The system of claim 1 , wherein the transaction patterns correspond to a group of transactions that updates a given sat of data elements.

5. The system of claim 1 , wherein the partitioning goal comprises maintaining a predetermined affinity or separation between specific data elements.

6. The system of claim 1 , further comprising a replication monitoring module configured to monitor online workload changes, wherein the recommendation module is further configured to generate a new recommended partitioning based on said online workload changes to maintain an optimized partitioning goal.

7. The system of claim 1 , wherein the recommendation module is further configured to iteratively create partition groups by selecting data elements according to one or more selection criteria to generate the recommended partitioning.

8. The system of claim 1 , wherein the recommendation module is further configured to exhaustively evaluate every possible partitioning of the plurality of data elements to generate the recommended partitioning.

9. A non-transitory computer readable storage medium comprising a computer readable program, wherein the computer readable program when executed on a computer causes the computer to perform the steps of:
analyzing historical workload data for a plurality of data elements with a processor to identify and categorize one or more transaction patterns, each transaction pattern corresponding to a particular group of transactions that updates the same data elements and having a corresponding peak throughput;
generating a recommended partitioning of the plurality of data elements into one or more replication groups, based on the peak throughputs of the one or more transaction patterns, that are optimized toward a partitioning goal; and
logging transactions that involve the plurality of data elements in a first data center; and
replicating the logged transactions in a second data center, where the replicated transactions are grouped according to the recommended partitioning.

Assignees

• IBM

Inventors

• Bourbonnais Serge
• Fuh You-Chin
• Jin Yi
• Li Xiao
• Min Hong
• Zheng Miao

Classifications

• G06F16/278 Primary

  Data partitioning, e.g. horizontal or vertical partitioning · CPC title