Kevin Shuma, VP of Product Management at CA Technologies, presented on how CA Datacom Presspack can reduce the total cost of ownership for CA Datacom environments. Presspack provides data compression for database tables, which reduces disk space usage and I/O. The presentation covered the benefits of compression, new online compression capabilities in TA24, benchmark results showing improved performance with compression, and recommendations for related sessions.

1. World®
’16
Reducing TCO with Datacom
Compression (Presspack)
Kevin Shuma, VP Product Management
CA Technologies
MFX94S
MAINFRAME AND WORKLOAD AUTOMATION

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© 2016 CA. All rights reserved. All trademarks referenced herein belong to their respective companies.
The content provided in this CA World 2016 presentation is intended for informational purposes only and does not form any type of
warranty. The information provided by a CA partner and/or CA customer has not been reviewed for accuracy by CA.
For Informational Purposes Only
Terms of this Presentation

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Abstract
Come to this session to learn how the CA Datacom® Presspack
can reduce the total cost of ownership for CA Datacom®
environments.
CA Datacom® Presspack is an optional component of the CA
Datacom® product family and provides high performance
compression for data rows stored in CA Datacom®/DB database
tables.
Kevin Shuma
CA Technologies
VP, Product
Management

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Agenda
WHAT ARE THE BENEFITS/COSTS OF COMPRESSION?
NEW ONLINE COMPRESS ABILITY – TA24
EXAMPLE OF ONLINE COMPRESS
BENCHMARK IN OUR LAB
SUMMARY OF FINDINGS
1
2
3
4
5

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What are the benefits of compression?
§ A compressed data block typically has significantly more rows
which means
– Less blocks on DASD to store the same number of rows
– Better data buffer reuse which reduces IO and improves performance
– Less memory required to place tables in memory (MRDF)
– Quicker utility processing for backups and loads (less IO)
Why should I consider compression?

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What are the benefits of compression?
§ Can make data less “readable” by non CA Datacom® utilities
like TSO Browse
§ Slowing DASD growth and multiple extent management
Some other Benefits of Using Compression:

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What are the costs of compression?
§ Requires a data access outage to implement
§ Not easily removed or changed
§ Hard to switch from one vendor to another
§ Uses expensive general purpose processor (GP CPU)
instructions to save cheap DASD
Why I Should Avoid Using Compression

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Using zIIP for Compression
§ zIIP processors
– Can be used for in-memory processing like compression
– Can only be used for code you own
§ Cannot be used to process imbedded user/vendor code
– Example: Non-CA compression code imbedded in MUF
§ Datacom compression runs on the zIIP processor(s) while
reducing the IOs which run on General Purpose processor(s)
– Effectively trading inexpensive zIIP CPU for expensive GP CPU
Tipping the Balance for Using Compression

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New Online Compress Ability – TA24
§ Provides the user the ability to alter a table’s compression
algorithm without interrupting data access
§ Avoids a costly data access outage to implement or change
compression
§ Compression/decompression activity runs in background in
MUF
– Utilizes zIIP processors for compression processing
Capabilities Without Interrupting User Access

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New Online Compress Ability – TA24
§ Fully restart-able across failures (lack of space, MUF or LPAR)
– The partially compressed data remains accessible
– Once the failure issue is resolved, the Online Compress will be
restarted and run to completion
§ Process can be stopped (REQABORT)
– Allows user to reverse or change compression setting
– New process will run to completion and reset all rows to new selection
Fully Restart-able and Recoverable

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New Online Compress – Data Blocks
§ When decompressing a table
– Rows may grow and additional data blocks will be used
§ When compressing a table
– Rows will shrink making more space available in each data block
– Space immediately available for use by new requests
§ To complete the process a online reorganization is
recommended
– OLREORG REFGROUP=0 provides best result (DSOP 4/5)
§ May require a data area extension if not enough empty blocks to get rolling
Data Blocks in-use Will be Affected

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Example of Online Compress
§ Sample table containing 1 million rows
§ Uncompressed data rows fill 62K data
blocks
§ After compression 62K data blocks still
in use but each block has significant
free space
§ After OLREORG REFGROUP=00 data
blocks are defragmented
Come to Resource Center for a Live Demo
Before
62K Full Data Blocks
After TA24 62K
Partially Data
Blocks
After OLREORG 24.5K
Full Data Blocks

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DBUTLTY DATASP Report (Before TA24)
Shows Data Blocks, Data Bytes and Data Rows “IN-USE”
1M rows stored in 98% of the available bytes and 99% of the available data blocks in
the 5,250 track dataset
62K Data Blocks

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Use DDUPDATE to Update CA Datacom® Datadictionary™
and Trigger Online Compression
One million rows
compressed in 2
minutes (elapsed)

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DBUTLTY DATASP Report (After TA24)
Shows Data Bytes Significantly Less, but Data Blocks and Rows the Same
1M rows stored in 39% of the available bytes and 99% of the available data blocks in
the 5,250 track dataset
62K Partially full
Data Blocks

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Use DBUTLTY Online Reorganization To
One million rows
reorganized and
defragmented in 5
minutes (elapsed)

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DBUTLTY DATASP Report (After TA24/OLREORG)
Shows Number of Blocks in-use Dropped Dramatically (Reduced by 60%)
Same number of rows stored in 39% of the bytes and 40% of the data blocks in the
5,250 track dataset
24.5K Full Data Blocks

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Comparing the Processing Before/After TA24 + OLREORG
Sequential Program Processing All 1M Rows
Logical work
is exactly
the same
(Requests,
Logical
reads and
Writes).
Physical
reads
reduced by
64%.

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Lab Benchmark Comparing CPU/IO Consumption
READ 1M Rows Each - 10 Consecutive Processes
Logical work
is exactly
the same
(Requests,
Logical
reads and
Writes).
Small
buffer
pool
Large
buffer
pool
Uncompressed
DB Compress
Presspack Weak
Presspack Strong
Presspack Custom
Uncompressed
DB Compress
Presspack Weak
Presspack Strong
Presspack Custom
Reduced Elapsed, CPU and IO
Much better reduction with large buffer pools

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Lab Benchmark Comparing CPU/IO Consumption
READ 1M Rows Each - 10 Consecutive Processes
Large
buffer
pool
w/MRDF
Uncompressed
DB Compress
Presspack Weak
Presspack Strong
Presspack Custom
Logical work
is exactly
the same
(Requests,
Logical
reads and
Writes).
Reduced Elapsed, CPU and IO
Best reduction with large buffer pools and MRDF covered

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Lab Benchmark Comparing CPU/IO Consumption
Update 100K of 1M Rows Read - 10 Consecutive Processes
Large
buffer
pool
Large
buffer
pool
w/MRDF
Uncompressed
DB Compress
Presspack Weak
Presspack Strong
Presspack Custom
Uncompressed
DB Compress
Presspack Weak
Presspack Strong
Presspack Custom
Logical work
is exactly
the same
(Requests,
Logical
reads and
Writes).
Reduced Elapsed, CPU and IO
Best reduction with large buffer pools and MRDF covered

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Small
buffers
Large
Buffers
w/MRDF
CPU/IO Consumption Without a zIIP Processor
READ 1M Rows Each - 10 Consecutive Processes
Logical work
is exactly
the same
(Requests,
Logical
reads and
Writes).
Reduced IO
CPU and Elapsed differences depend on buffering
Best reduction with large buffer pools and MRDF covered

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What if There is a 3rd Party Compression
MRDF
w/Presspack
MRDF w/3rd
party
JOEPACK Do not use 3rd party
compression and zIIP
(SMPTASK x,x,x,SRB )

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Summary
§ Online Compression (TA24) removes several of the key drawbacks
of compression
– No longer requires a data access outage to implement
– Can be easily removed or changed
– Easily switch from 3rd party vendor to CA
§ When compression (Presspack) combined with zIIP (SRB)
processing, the TCO tips in favor of Presspack (CA) compression
– Uses cheaper zIIP (CPU) instructions and saves DASD, IO and memory

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One Extra Benefit
Uncompressed or Light Compress Data is Readable
Uncompressed
DB
Compress
and
Presspack
Weak
Readable

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One Extra Benefit
Strong and Custom Compression Can Make Data Unreadable
Presspack
Strong
Presspack
Custom
Not
Readable

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Recommended Sessions
SESSION # TITLE DATE/TIME
MFX92S Strategy and Vision for CA Datacom and CA Ideal™ 11/16/2016 at 12:45 pm
MFX93S What’s new in CA Datacom 11/16/2016 at 1:45 pm
MFX94S Reducing TCO with Datacom Compression (Presspack) 11/16/2016 at 3:00 pm
MFX95SA Modernizing the CA Datacom data – SQL Access Part I 11/16/2016 at 3:45 pm
MFX95SB Modernizing the CA Datacom data – SQL Access Part II 11/16/2016 at 4:30 pm
MFX96S CA Datacom/AD Installation/Upgrade and Maintenance 11/17/2016 at 12:45 pm
MFX97S
CA Datacom/AD Report Utilization, and
information gather for Support
11/17/2016 at 1:45 pm
MFX98S
Using CA SYSVIEW® to monitor and measure your CA
Datacom environment – AD and DB
11/17/2016 at 3:00 pm
MFX99S Birds of a Feather/Stump the Techie! 11/17/2016 at 3:45 pm

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Questions?

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Mainframe and Workload Automation
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