DB2 11 for z/OS Overview and Latest News Jeff Josten

DB2 11 for z/OS
Overview and Latest News
Jeff Josten
IBM Silicon Valley Lab
©
1 2015 IBM Corporation
© 2012 IBM Corporation
Please Note
• IBM’s statements regarding its plans, directions, and intent are subject to change or
withdrawal without notice at IBM’s sole discretion.
• Information regarding potential future products is intended to outline our general
product direction and it should not be relied on in making a purchasing decision.
• The information mentioned regarding potential future products is not a commitment,
promise, or legal obligation to deliver any material, code or functionality.
Information
about potential future products may not be incorporated into any
contract.
• The development, release, and timing of any future features or functionality described
for our products remains at our sole discretion.
Performance is based on measurements and projections using standard IBM benchmarks
in a controlled environment. The actual throughput or performance that any user will
experience will vary depending upon many factors, including considerations such as
the amount of multiprogramming in the user’s job stream, the I/O configuration, the
storage configuration, and the workload processed. Therefore, no assurance can be
given that an individual user will achieve results similar to those stated here.
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2 © 2015 IBM Corporation
Agenda
• Intro - What we are up to
• DB2 11
• Key Challenges in Database Technology
• Next wave of applications
• Analytics and OLTP integration
• Performance and scalability
• Continuous availability, security
• Autonomics and simplification
• Wrap up with Q&A
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© 2015 IBM Corporation
DB2 for z/OS strategic roadmap
Support the next wave of applications ….
Analytics
Real time analytics
integrated with OLTP
Mobile
Easy app
development,
Enterprise mobile
scale
Cloud
Self provisioning,
Multi-tenancy, Self
managing, guaranteed
SLAs
while enhancing the unique value of System z
Scalable
Reliable
Efficient
Secure
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© 2015 IBM Corporation
DB2 for z/OS Purpose
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© 2015 IBM Corporation
DB2 for z/OS: the Engine that Drives the World
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© 2015 IBM Corporation
DB2 11 for z/OS
Strong uptake out of the gate
 Over 150 customers*
 Faster migration success
 2x faster adoption
Out-of-the-box quality/stability
 68% fewer PMRs
 35% fewer APARs
Fastest SAP certification
in history!
DB2 10
 Withdraw from Marketing: July 6, 2015
 End of Service: Sept 30, 2017
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* As of Dec 2014
Latest product Information at World of DB2
© 2015 IBM Corporation
Licenses
Strong DB2 11 Adoption
Quarters post GA
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© 2015 IBM Corporation
DB2 11 APAR Trend
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DB2 11 PMR Trend
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© 2015 IBM Corporation
DB2 11: The Database for Enterprise OLTP and Analytics
Affordable for every workload with out-of-the-box savings
– Up to10% for complex OLTP
– Up to15% for update intensive batch
– Up to 40% for queries
Business critical analytics
GA
Oct. 25,
2014
– Expanded SQL, XML and analytics capabilities
– Hadoop integration, NoSQL/JSON support
– In-transaction real-time scoring
– Advanced QMF analytic capabilities with mobile support
Enhanced Resiliency and Continuous Availability
– Fewer planned outages, fewer REORGs, faster recovery
– Transparent archiving, access warm/cold data in single query
Simpler, faster upgrades for faster ROI
– 16x faster catalog migration
– No application changes required for DB2 upgrade
– Product quality and stability – raised the bar
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© 2015 IBM Corporation
DB2 11 Planning





Dual mode migration (CM, ENFM, NFM)
DB2 10 is the platform for migration
z/OS 1.13 or above. z10 or above.
No pre-V9 bound packages
DB2 Connect V10.5 FP2 is the recommended level
– This level is required to exploit most new features
– Any in-service level DB2 Connect supports V11
 Sysplex query parallelism support is removed
 DB2 11 Migration Planning Workshop (MPW)
– Free, 1-day education
– DB2 11 MPW Community on DeveloperWorks, http://ibm.co/IIJxw8
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© 2015 IBM Corporation
DB2 for z/OS Timeline
DB2 Cypress Planning
• DB2 11 NFM is the prereq for migration
• z/OS 2.1 or above. z196 hw or above
• No pre-V10 bound packages
• Get rid of 31-bit runtime for code simplification and performance
• More memory = more performance
201x
2013
2010
2007
2004
V9
DB2 10
DB2 Cypress
V10 EOS
DB2 11
9/2017
V9 EOS
4/2014
V8
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© 2015 IBM Corporation
Extend the Core Values of DB2 for z/OS
 Parallel Sysplex and DB2 data sharing
 Availability and scalability leadership
 Multi-site DR and continuous availability leadership
 High Performance
 In-memory database evolution
 Reliability, quality, security leadership
 System z hw/sw integration
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Impressive DB2 11 Performance Results!
Query
Batch
OLTP
XML
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DB2 11 query performance
 Results show 10-46% CPU saving for DB2 measured workloads
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Performance Improvements
no REBIND needed – Partial List
 DDF performance improvements
– Reduced SRB scheduling on tcp/ip receive using new CommServer capabilities
– Improved autocommit OLTP performance
 INSERT performance
– Latch contention reduction
– CPU reduction for Insert column processing and log record creation
– Data sharing LRSN spin avoidance
– Page fix/free avoidance in GBP write
 Automatic index pseudo delete cleanup
 IFI 306 filtering capabilities to improve Replication capture performance
 DGTT performance improvements
– Avoid incremental binds for reduced cpu overhead
 Utilities performance improvements
 Java stored procedures: multi threaded JVMs, 64-bit JVM – more efficient
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© 2015 IBM Corporation
Performance Improvements
REBIND required – Partial List
 Query transformation improvements – less expertise required for performant SQL
 Enhanced duplicate removal
– Lots of queries require duplicate removal: e.g. DISTINCT, GROUP BY, etc.
– Dup elimination via sorting can be expensive
– New techniques: Index duplicate removal, early out
 In-memory techniques
– In-memory, reusable workfile
– Sparse index (limited hash join support)
– Non-correlated subquery using MXDTCACH
– Correlated subquery caching
 Select list do-once
– Non column expressions in the select list can be executed once rather than per-row
 Column processing improvements
– Xproc (generated machine code) for column processing
 DPSI performance improvements
 Data de-compression optimizations
 Optimizer CPU and I/O cost balancing improvements
 DRDA package based continuous block fetch
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© 2015 IBM Corporation
Performance Improvements
Sysprog, DBA, or application effort required – Partial List
 Suppress-null indexes
– Index entries not created when all values for indexed columns are NULL
– Reduced index size, improved insert/update/delete performance, compatibility
with other DBMSes
– Improved utility and CREATE INDEX performance
 New PCTFREE FOR UPDATE attribute to reduce indirect references
 DGTT performance improvements
– Non logged DGTTs
 Extended optimization - selectivity overrides (filter factor hints)
– Improve optimizer’s ability to find the cheapest access path
– Collect filter factors for predicates in a Selectivity Profile
 Open dataset limit raised to 200K
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© 2015 IBM Corporation
DB2 11 enhancements applicable to analytics
 Improved predicate filtering – filtering rows earlier
 Sparse index (in-memory data cache)
 Index skipping and Early-out
 Page range performance improvements
 Sort / Workfile performance improvements
 DPSI query performance improvements
–DPSI can benefit from page range screening from join
–Improved DPSI Join Performance (using parallelism)
–Sort avoidance for DPSIs (also known as DPSI merge)
–Straw-model parallelism support for DPSI
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© 2015 IBM Corporation
Hardware Trends Leveraged by DB2 for z/OS
 Multi core, future slowing growth in single thread performance
– Higher n-ways, more parallelism bring potential latching bottlenecks, memory cache thrashing, …
– S/W techniques for single threaded performance growth
– Clustered systems for massive scale out and continuous availability
 Specialty engines (price / performance)
 Hybrid systems, accelerators
– Use cores that are more specialized to their purpose
– New performance opportunities
 Memory hierarchy design
– Higher CPU frequencies, n-ways make cache utilization a critical factor
– Translation lookaside buffer design, large System z page sizes
 Solid state disk ( and other disk related improvements)
– Performance, energy consumption, reliability benefits of HDD
 Large and rapidly growing main memory sizes along with falling prices
– Performance improvements through more memory use
– DB2 10 enables more persistent threads with RELEASE(DEALLOCATE)
– Larger buffer pools, in-memory workfiles, I/O avoidance
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© 2015 IBM Corporation
zHyperWrite for z/OS, DB2 and DS8870
 New zHyperWrite function for DB2, z/OS and DS8870 with GDPS or TPC-R HyperSwap
– Delivered year end 2014
– Leverages synergy of z/OS and DS8870 replication technologies
– Designed to accelerate DB2 Log Writes in Metro Mirror environment
• Benefits include:
– Improved DB2 transactional latency
– Log throughput improvement
– Additional headroom for growth
– Improved resilience for workload spikes
– Potential cost savings from workload consolidation
– DB2 commit response time reduced up to 40%
• Benefit percentage varies with distance
• Requires:
– zHyperWrite function in z/OS 2.1, with the PTF for APAR OA45662
– DB2 10 and DB2 11 with PTF for APAR PI25747
– IBM DS8870 Storage Subsystem MCL
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© 2015 IBM Corporation
Milliseconds
Average commit time
Single log page writes
Log replication at zero distance
0.8
0.6
0.4
0.2
0
No zHyperWrite
zHyperWrite
# Log CI Created
Per Commit
 When writing a single log CI, IBM zHyperWrite saves 300 microseconds
per Commit.
 40% reduction in Commit response time.
IBM Confidential
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© 2015 IBM Corporation
HyperWrite with DB2 Batch Updates
• A customer running 2.7 millions update (commit every 3 updates)
• 28% DB2 elapsed time improvement by reducing log write wait during update
commit, 26% Job elapsed time improvement.
HypweWrite - ON
CL2 CPU
-28%
Update Commit
Lock latch
Log write
Not account
HyperWrite - OFF
Others
0
24
1000
2000
3000
4000
Time (second)
5000
6000
© 2015 IBM Corporation
DB2 11 RAS and Usability Improvement Highlights
 Logging capacity and performance: RBA/LRSN optionally expands to 10 bytes
 BIND / DDL / Online REORG break-in for persistent threads
– Avoid having to shut down apps to get a REBIND through, e.g. for application upgrades
 More online schema changes
– Alter partitioning limit keys
– DROP column
– Point in time recovery support for deferred schema changes
 Autonomics improvements
– Automatic index pseudo delete cleanup
– Overflow row reduction
– Optimizer externalizes missing stats to enable automated RUNSTATS
 Data sharing improvements
–
–
–
–
Group buffer pool write-around
Restart light enhancements
Index split performance and other indexing improvements
Full LRSN spin avoidance
 Plan management improvements - APREUSE(WARN) support
 ACCESS DATABASE … MODE(STATS) option to externalize RTS statistics
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© 2015 IBM Corporation
Security Enhancements
 Remove inconsistencies between DB2 and RACF access
controls
– Automatic DB2 cache refresh when RACF changes are made
• Package auth cache, dynamic statement cache, user authentication cache
– Support BIND OWNER when using RACF exit
– Support auto REBIND using owner’s authid when
using RACF exit
– Dynamic SQL authorisation checking improvements
• Honor DYNAMICRULES(BIND) rules
 Bind plan option to ensure the program is
authorized to use the plan
– New PROGAUTH bind option
 Remove column masking restrictions for GROUP BY and
DISTINCT
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© 2015 IBM Corporation
Summary of Utilities Improvements
 Over 40 new enhancements!
 Availability
– Online data repartitioning
• REORG REBALANCE SHRLEVEL(CHANGE)
• Online ALTER of limit keys
– Online REORG availability improvements
• SWITCH phase reduction
• Improved drain processing
– Part level inline image copies for REORG
 Usability
– Online REORG automated mapping tables
– REORG delete unused PBG datasets
– System cloning improvements
 CPU reduction
– More zIIP offload for LOAD and RUNSTATS
 Performance
–
–
–
–
–
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Faster LOAD processing via increased parallelism
Inline statistics improvements, reduced need for RUNSTATS
Optimizer input to statistics collection
REORG option to avoid sorting data for clustering
Improved buffer pool efficiency
© 2015 IBM Corporation
DB2 for z/OS: Empowering the Future
 Simplification and autonomics
 Analytics and Hybrid Transaction and Analytics Processing (HTAP)
 Support the next wave of applications
 Mobile and Internet of Things (IoT)
 Cloud and developer self-service
 DevOps: continuous application delivery
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© 2015 IBM Corporation
Example benefit of DB2 11 performance & pseudo delete cleanup
 Websphere Portal internal measurement
–At day 1 – 18% CPU saving
• Due to various DB2 11 performance improvements
–At day 5 – 39% CPU saving
• Due to 93% reduction in pseudo-deleted index entries
WAS Portal Workload 5 Days Performance
0.0035
2500000
2000000
CPU time (sec)
0.0025
1500000
0.002
0.0015
1000000
0.001
500000
#of pseudo_deleted entries
0.003
V10 Total CPU time
V11 Total CPU time
0.0005
0
30
0
Day1
Day2
Day3
Day4
Day5
© 2015 IBM Corporation
Easier DB2 Version Upgrade
 Application Compatibility (APPLCOMPAT)
– New feature to ease DB2 version upgrades – avoid impact to applications
– New mechanism to identify applications affected by SQL changes in the new release
– Seamless mechanism to make changes at an application (package) level or system level
 Faster ENFM processing
– Lab measurement showed 18x faster in V11 vs. V10 using a large customer catalog
 Access path stability improvements
 Higher code quality stability levels
 SQL Capture/Replay tooling can help testing of DB2 version upgrades
 Migration Planning Workshops (MPW)
– See the DB2 11 MPW community in DeveloperWorks for latest info
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© 2015 IBM Corporation
DB2 Drivers – Panoramic View
PhP
Javascript
node.js
Python/Jython
Ruby/JRuby
Scala
Java
JDBC API
Zend
framework
adapters
node-odbc
SqlAlchemy/
Django Adapter
node.js
JSON
driver
Rails
Adapter
Lift
pureQuery
API
JSON API
Python interpreter
c
java
Ruby interpreter
c
Hibernate
java
JPA
DB2 CLI and ODBC driver
DB2 JCC JDBC driver
DB2
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© 2015 IBM Corporation
DB2 11 Expanded Application Capabilities
 Global variables
 SQLPL improvements: array data type, autonomous transactions
 Alias support for sequence objects
 Temporal data enhancements
– Support for views
– Special register support
– Integrated auditing support (planned)
 Transparent archive query
 SQL Grouping Sets, including Rollup, Cube
 Unicode column support for EBCDIC tables
 Hadoop access via table UDF
 JSON support
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© 2015 IBM Corporation
Transparent Archive Query
Cheaper storage
High performance,
availability storage
Current
data
■
■
■
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Archive
data
Applications can query current + archive with no SQL changes
■
By default, data is retrieved from base table only, as usual
■
Set a new global variable when archive data is desired
■
DB2 automatically converts SQL to UNION ALL via dynamic plan switching
technique (high performance)
Archiving process is user-controlled
Move_To_Archive global variable allows DELETEs to be automatically archived
© 2015 IBM Corporation
Mobile workloads Impact Systems
differently than web workloads.
 Increase in peak and off-peak transactions.
 Increased query or “read-only” transactions.
 Unanticipated spikes in workload due to
popular apps, features or special offers.
 Mobile applications often change more
frequently
 Mobile workloads require an agile, scalable
and robust DBMS like DB2 for z/OS
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© 2015 IBM Corporation
Mobile/Cloud integration simple and secure
z/OS Connect – Enabling Hybrid Cloud for z/OS
IBM WebSphere Liberty z/OS Connect – Shipped with WAS, CICS, and IMS
Unifies z/OS connectors – a common solutions for mobile, cloud, and web
Simplified integration – discover, manage, securely access z/OS assets
Batch,
WAS
Mobile-Optimized APIs
Cloud APIs
Cloud-based
Services
Enterprise
Systems
Integration
On-Premise Enterprise APIs
Systems of Engagement
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Enterprise
Applications
IBM
WebSphere
Liberty
z/OS Connect
Enterprise
Data
Enterprise
Transaction
Processing
CICS,
IMS
Systems of Record
© 2015 IBM Corporation
Coming: DB2 Adapter for z/OS Connect *
Config.xml
Deploy to
bundled
zOS Connect
Data Web Services
Test Client
Data Web Services
of Data Studio or RDz
WSDL
Database operations
ZOS Connect
artifacts
* Planned GA June, 2015
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Deploy to
remote
zOS Connect
z/OS
Connect
CICS
IMS
WAS
DB2
ZOS Connect on LPAR
© 2015 IBM Corporation
Cloud: Challenge for Fast Provisioning of a DB2 Environment
 Fast provisioning a DB2 environment as part of provisioning a software stack
– DB2 environment scope can be
• DB2 system
• Migrate to a new version of a DB2 system
• Database in an existing DB2 system
• Access to an existing database in an existing DB2 system
• Copy of an existing database
• ….
 zSystem is a highly virtualized shared environment and many people need to
be involved for the provisioning
– SME (division of responsibilities) for different aspects of system management like
storage, network, security, … and DB2 system management
– DB2 Installations are highly customized across different companies
 Easier DB2 migration or provisioning would greatly help sysprog’s today and can also
form the basis for cloud based solutions
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© 2015 IBM Corporation
z/OS Management Facility Can Help
 IBM z/OS Management Facility (z/OSMF) delivers on the IBM’s strategy for
mainframe simplification and modernization
 Is a free feature of z/OS
 Provides a modern browser based interface for managing the z/OS system.
 z/OSMF workflows can be customized once, executed multiple times
 Extended to simplify management of z/OS subsystems like DB2
 z/OSMF workflows can help DB2 staff to
– Migration multiple DB2 subsystems easily
– Deploy a new DB2 subsystem quickly
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© 2015 IBM Corporation
Coming Soon - Default DB2 Migration Workflow
 z/OS cloud provisioning demo planned for upcoming zBLC
 DB2 will generate the default workflow artifacts through an updated
DB2 migration CLIST
– Workflow definition file
– Set of new JCL templates as the z/OSMF artifacts used for DB2 11
migration
– Workflow variable file
 DB2 users can edit the sample workflow input variable file with the
input for a specific DB2 subsystem
 Workflow can be customized by adding/removing/changing steps
 zOSMF to support REST calls over workflows to enable cloud-based
solutions such as automated test/dev configuration
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© 2015 IBM Corporation
DevOps: Continuous delivery for the mainframe
New capabilities speed delivery of interdependent, multi-platform applications
DB2 Plugin, available
Dec. 2014
IBM UrbanCode
Deploy for z/OS
Deploy
Test Environment – RD&T
Application
under test
• Download build output from
artifact repository on z/OS
• Deploy to z/OS or RD&T to
test application changes
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•
Unified solution for continuous delivery of heterogeneous enterprise applications
•
Accelerate delivery and reduces cycle time to develop/test multi-tier applications across
heterogeneous environments and platforms
•
Reduce costs and eliminate delays for delivering mainframe applications
•
Minimize risk and improve productivity across disparate teams with cross-platform release planning
© 2015 IBM Corporation
Bring Analytics to the Data, Reduce ETL
System z Host:
Analytics:
Predictive &
Descriptive
Orchestration
System z
network
Converge analytics with data
and transaction environments
where data exists on the
platform, and actively
encourage the growth of data
hosted on System z.
network
DATA
Transaction
Data
Business
Rules
Customer Data
Account Data
Payment Data
Claims Data
WHY?
• System z already hosts a majority of the systems of record
data that feeds business analytics
System z Host:
System z
Key Characteristics
Orchestration
• Unparalleled, proven performance execution of combining
transactional data environments, analytics & rules
DATA
Analytics:
Descriptive &
Predictive
• Extensive logging for data and transaction governance
Transaction
Data
Business
Rules
• Existing comprehensive, tested HA / DR capabilities
Customer Data
• Cost savings from reduced systems, software & people
Account Data
• Best of breed security for sensitive data
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• Plug and play capacity extensions and migrations
enabling quick production application deployment with no
downtime
Payment Data
Claims Data
© 2015 IBM Corporation
In-Database Analytics in DB2 for z/OS
SPSS Modeler Server Scoring Adapter for z Systems
 Execute predictive models inside DB2 for
z/OS, with little data movement
– Scores executed 10-50x faster than
making calls to remote scoring engines
– Achieve huge scale of execution without
performance degradation
– Leverage historical and current transaction
data to produce most accurate results
– Plans to exploit z13 SIMD for higher
performance
– Exploits DB2 UDF enhancements
Co-locate analytics with data!
System z SPSS Scoring Performance
System z
models
Available System z business solutions
that use this technology:
 IBM System z Smarter Analytics for Banking
anti-fraud and anti-money laundering focus
 IBM Signature Solution
anti-fraud, waste and abuse for Healthcare and Insurance
 IBM Signature Solution
anti-fraud, waste and abuse for Tax
 IBM Signature Solution for Next Best Action
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Execute thousands of model scores
per second with very high scale
© 2015 IBM Corporation
DB2 Analytics Accelerator Roadmap
•
•
•
•
•
IDAA VNext
•Advanced Analytics
•HA/DR Support
Enhancing current capabilities
Enabling more query acceleration
Increasing transparency
Supporting new use cases
Utilizing PDA technology advantages
IDAA VNext
•In-database transformation
•Acceleration of Predictive
Analytics applications
•Online Schema change
IDAA V2.1 (11/2011
•Netezza acceleration
Consolidation and
unification of transactional
and analytical data stores
2015
IDAA V4.1 (11/2013)
•Static SQL
•Workload balance
IDAA V3.1 (11/2012)
•Incremental Update
•Storage Saver
Advanced
Analytics
Unified
Store
Modeling, batch
scoring, Hadoop,
Streams integration
ELT
Accelerator
2012/2013/2014
Storage Saver
Accelerating indatabase
transformation
IDAA V4.1 PTF 5
•In-database transformation
•Incremental Update Turbo
IDAA V4.1 PTF 4 (10/2014)
•Encryption of data at rest
•Call Home
ISAO V1
2011
Sophisticated
multi-temperature data
and archiving solution
Query
Accelerator
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© 2015 IBM Corporation
Operations and analytics coexistence: results
Thousands of complex, analytical
queries now integrated with
operational workload
First use case: periodic data
synchronization – End-ofBusiness Day data access
Second use case: (near-)
real time data access
LPAR 2
LPAR 1
Accelerator
Baseline
w/Analytics
Operational throughput
maintained with no additional
mainframe capacity
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Baseline
w/Analytics
Data kept in sync real-time
with minimal degradation in
transaction ITR (3%)
© 2015 IBM Corporation
DB2 11 Expanded Analytics Capabilities
 Significant query performance improvements
– Without accelerator
 SQL Grouping Sets, including Rollup, Cube
– Rollup is helpful in providing subtotaling along a hierarchical dimension such as time or
geography
– CUBE is helpful in queries that aggregate based on columns from multiple dimensions
 High performance SPSS in-database scoring via PACK/UNPACK (rolled back to v10)
 Hadoop access via table UDF
– UDFs shipped with BigInsights
– Uses new V11 generic table UDF capability
 JSON support
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© 2015 IBM Corporation
Relative performance
Upgrade to DB2 11 for z/OS to achieve more operational
analytics throughput for the same cost
3,326
Reports
per
Minute
4,267
Reports
per
Minute
DB2 10
DB2 11
zEC12
zEC12
IBM internal analytics workload (BI Day)
Workload consisted of 160,860 Cognos BI Day simple reports. Both tests used 10 CPs and ran at 100%
utilization. Results may vary based on customer workload profiles/characteristics.
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© 2015 IBM Corporation
DB2 11 and Analytics
 What makes DB2 11 enhancements Analytics specific?
–Analytics queries often involve
•
•
•
•
More tables in a query – joins, subqueries, table expressions etc
More complex expressions (in WHERE clause or select list)
More rows being processed when compared with OLTP
More rows joined, sorted etc
 Does DB2 11 only benefit analytics?
–No, performance improvements are relevant to all workloads
• Although greater opportunities for performance improvements exist in
more complex workloads
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© 2015 IBM Corporation
Integrating Big Data Analytics with DB2 for z/OS
•
Much of the world’s operational data
resides on z/OS

Unstructured data sources are growing
fast
Two significant needs:
1. Merge this data with trusted OLTP data from zEnterprise data sources
2. Integrate this data so that insights from Big Data sources can drive business actions
New V11
features enable
this
 Connectors to allow BigInsights to easily & efficiently access DB2 data
 DB2 is providing the connectors & the DB capability to allow DB2 apps to easily and efficiently access hadoop data sources
Integrate
IBM BigInsights
• New user-defined
functions and V11 generic
table UDF capability
• IMS and DB2 JDBC
connectors
Merge
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© 2015 IBM Corporation
InfoSphere BigInsights
for Linux on System z - 2.1.2
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Secure Perimeter
© 2015 IBM Corporation
InfoSphere System z Connector for Hadoop
Easy to use ingestion engine
 Native data collectors accessed via
graphical interface
 Light-weight; no programming required
 Multiple z/OS data sources
 Conversions handled automatically
 Streaming technology leverages USS (no
z/OS engines) with no DASD required for
staging
A secure pipe for data
 RACF integration – no need for separate or
special credentials
 Data streamed over secure channel using
hardware crypto
 Combining with BigInsights for Linux on
System z means data never leaves the box
Mainframe efficiencies
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© 2015 IBM Corporation
DB2 Cypress Themes
Performance
 Expanded in-memory processing
 CPU reductions expected for most workloads
 Faster insert and easier insert performance mgmt
DBA productivity, autonomics
 More schema and partition flexibility
 Stay ahead of mobile, internet-of-things:
Extreme scale tables, indexes
 Streamlined and less disruptive migrations
 Self-optimizing system
– More transparent SQL optimization
– Easier management, higher availability for very large tables/indexes
Application enablement
 Cloud-based data or database as-a-Service provisioning
 SQL improvements for improved analytics, application porting, and developer
productivity
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© 2015 IBM Corporation
Jeff Josten ([email protected])
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© 2015 IBM Corporation