How to Improve Planning Data Collections Performance Oracle ASCP Collection Process Insight

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1 Solution Highlights 1. Why do we need Data Collection? 2. Definitions How to Improve Planning Data Collections Performance Oracle ASCP Collection Process Insight 3. Overview of Data Collection Process 4. How Refresh collection Snapshots work and use of MLOG$ tables? 5. Performance of Data Collection Process About the Author This whitepaper is written by Shailesh Kumar Gupta, a Value Chain Planning (VCP) Senior Consultant with Rapidflow Apps. In addition to MBA in Supply Chain Management, Shailesh brings solid consulting background in Oracle Manufacturing and VCP product suites. About the Company Rapidflow Apps was founded with the sole objective of providing high quality, innovative and practical solutions to the real world business problems of our clients in the areas of Supply Chain Planning, Product Lifecycle Management, Master Data Management and Business Intelligence. Please send us your comments/feedback at feedback@rapidflowapps.com For more information on ASCP data collection and plan performance issues, please feel free to contact us at info@rapidflowapps.com. Rapidflow Apps bring years of experience in handling very large data volumes and has drastically improved planning processes performance at many clients using Oracle Advanced Supply Chain Planning(ASCP) and Inventory Optimization(IO).

2 Executive Summary Most of the companies using Oracle Advanced Planning Suite face performance issue with data collection. To improve performance of data collection it is important to understand the process of data collection. This document provides an insight to data collection process and then discusses some of the techniques to improve data collection performance. Contents of this white paper are as follows: Why do we need Data Collection? Definitions Overview of Data Collection Process How Refresh collection Snapshots work and use of MLOG$ tables? Performance of Data Collection Process Why do we need Data Collection? Oracle planning engine uses fairly complex algorithms to suggest solutions to a business scenario. These functions give best results when dealing with a static data, where as in real world scenarios the transactional data can be highly dynamic. Thus, it may become infeasible for the planning engine to handle this type of dynamic data. So, to deal with this situation static data is provided to planning engine. This is done through data collection process which captures snapshot of all the transactional data that is relevant to planning and stores in database table so that it can be used by planning engine. Definitions Applications Data Store (ADS): The set of source data tables in each transaction instance that contain data relevant to planning. Data Pull: The data collection process consists of the Data Pull and the Operational Data Store (ODS) Load. The collection process lets you collect across several Oracle application versions. It supports several configurations. The two types of collections process are standard and continuous. Operational Data Store (ODS): The planning data tables in the planning server that act as destination for the collected data from each of the data sources (both ADS and Legacy). Planning Data Store (PDS): The outputs of the planning process. The PDS resides in the same data tables Data Collection Process White Paper Page 2 of 6

3 as the ODS. However, PDS data are marked with plan IDs that show which plans they correspond to, while ODS data are marked with plan ID = -1. Pull program: Collects the data from the ADS and stores the data into the staging tables. This pull program is a registered AOL concurrent program that could be scheduled and launched by a system administrator. If you are using a legacy program, you must write your own pull program. ODS Load: A PL/SQL program which performs the data transform and moves the data from the staging tables to the ODS. This collection program is a registered AOL concurrent program that could be scheduled and launched by the system administrator. Snapshots: Snapshots are the set of tables and materialized which are refreshed by Refresh Collection Snapshots request. They store the data that is relevant to planning in source instance. MLOG$ Tables: An MLOG$ table is the Snapshot Log table created against a base table. A row is inserted in to the table each time a change (insert/update/delete) is made to the base table. Overview of Data Collection Process Data collection process starts with the launch of Planning Data Collection Request. This request triggers Planning data pull Request. Planning Data Pull launches a spawned program called Refresh Collection Snapshots. Refresh collection snapshots gathers data from source instance (ADS) and stores that data in materialized views or snapshot tables. These objects are present at source instance. After all data has been gathered in the snapshots, Planning Data Pull Workers are launched which collects data from snapshots and populates planning staging tables in the Planning Server. After data is staged at staging tables all the data is populated to relevant planning tables in ODS by Planning ODS Load process with the help of ODS Load Workers. This data in ODS is characterized by plan_id = -1. Same tables are also used by ASCP plans but in that case data is characterized by plan_id = plan_id of the plan. Source Instance Planning Data Collection Planning Instance ADS Planning Data Pull Refresh Collection Snapshots Snapshots Planning Data Pull Workers Staging Tables Planning ODS Load ODS Load workers ODS PDS Data Collection Process White Paper Page 3 of 6

4 How Refresh collection Snapshots work and use of MLOG$ tables? Every table in ADS that stores transactional data to be used in planning process has a snapshot associated with it. Each snapshot has log table i.e. MLOG$ associated with it. For each change made in base table, a row is inserted in MLOG$. When refresh collection snapshot is launched the programs looks for the rows present in MLOG$ and for each row in MLOG$ the program inserts a row in snapshots from corresponding base tables. All the details regarding data to be inserted like base table name, changes made etc. are present in MLOG$ table. Refresh Collection Snapshots can be run in three modes. a) Fast- In this mode programs looks at the MLOG$ table and inserts a row in respective snapshot corresponding to the row in MLOG$. After the data is inserted program deletes the row from MLOG$. b) Complete- In this mode program by passes the MLOG$ and inserts all the data in snapshots directly from base tables and program deletes all the data from MLOG$. c) Automatic- In this mode programs looks at the MLOG$ table and inserts a row in respective snapshot corresponding to the row in MLOG$. After the data is inserted program truncates the MLOG$. Performance of Data Collection Process Size of MLOG$ govern performance of Data collection. Thus, every method that reduces size of MLOG$ improves the performance of Data Collection Process. Various methods to improve data collection performance are: a) Run Refresh Collection Snapshot once before Data Collection is launched. Reason: Usually refresh collection snapshot is run in fast mode. This mode works on incremental basis i.e it creates snapshot of the data that was created or updated after the last successful snapshot. Thus, running this request before collection is launched will ensure that there is lesser data to be refreshed at time of collection, hence the performance of collection will improve. Pros: Improves Data Collection performance. Cons: No disadvantages. b) Run Gather Schema Statistics regularly Reason: When large changes to the data or database take place, the system performance may get slow. Gather Schema Statistics gathers information about the tables and the data in the tables so that it can determine the best approach for querying the data in the tables. So, if we are experiencing Data Collection Process White Paper Page 4 of 6

5 poor performance then try running this process as it will usually help significantly improve performance. Pros: Apart from performance of Data Collection process it also improves performance of other programs. Cons: No disadvantages. c) Run Refresh Collection Snapshot in Automatic mode on periodic basis, weekly or monthly. Reason: When Refresh is performed in fast or complete mode then the RDBMS deletes the rows from MLOGs while running Refresh in Automatic mode truncates the MLOGs. When there are many rows being inserted in the MLOG on a regular basis, then the delete performed by the RDBMS will not recover space and the size of the MLOG can grow. Data collections in Automatic Refresh mode truncate the MLOG and then perform a Complete Refresh of the Snapshot. Pros: In addition to improving performance of Refresh Collection Snapshots, it also recovers database storage space. Cons: While dealing with very high volumes sometimes it is found that after executing Automatic Refresh performance of Refresh Snapshots is not improved or degenerates quickly. Therefore, if the Automatic Refresh does not resolve your performance issues with Refresh Collection Snapshots, we recommend the advanced Strategy for handling MLOGs. Data Collection Process White Paper Page 5 of 6

6 2010 Rapidflow Apps Inc. All rights reserved. No part of this document may be reproduced in any form by any means, nor may it be distributed without the permission of Rapidflow Apps Inc., nor may it be resold or distributed by any entity other than Rapidflow Apps Inc., without prior written authorization of Rapidflow Apps Inc. THIS DOCUMENT IS PROVIDED AS-IS WITHOUT WARRANTY OF ANY KIND. ALL EXPRESS OR IMPLIED REPRESENTATIONS, CONDITIONS AND WARRANTIES, INCLUDING ANY IMPLIED WARRANTY OF MERCHANTIBILITY OR FITNESS FOR A PARTICULAR PURPOSE, ARE DISCLAIMED, EXCEPT TO THE EXTENT THAT SUCH DISCLAIMERS ARE DETERMINED TO BE ILLEGAL. Data Collection Process White Paper Page 6 of 6