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All (that I know) About Exadata!
Prasad Chitta
Agenda
• Technology Trends
– Software Defined Everything vs Engineered Appliances
• Exadata X3 Specifications
– Compute, Storage, Network & Software
• Architectural Considerations
– Resource Management, Performance, High Availability
• Exadata Specific Features Explained
– Smart Scan, Storage Indexes, HCC, Smart Flash Cache
• Exadata Specific Recommendations
– And results from a Practical Implementations
• Finally, Some Criticism
Technology Trends
• Software Defined
Everything…
– (some times) Open
source
– Virtualization
– SDN
– Highly Customizable
– May involve multiple
vendors
– Google like…
• Engineered Systems
– Hardware aware
software
– Software aware
hardware
– Optimized (for a
workload)
– One vendor for complete
ownership
– Apple like….
Exadata Specifications
Welcome to the Oracle’s
Engineered Database
Appliance with intelligent
Exadata storage and
Infiniband Connectivity….
From: http://www.oracle.com/technetwork/server-storage/engineered-
systems/exadata/dbmachine-x3-twp-1867467.pdf
Architectural Considerations
• Exadata comes in its own predefined size /
Capacity
– “capacity planning” is really “resource
management”
– Suited for consolidation
– Instance Caging, DBRM and IORM
• One database with multiple schemas?
• Multiple databases?
• Mixed, multi-workload consolidation?
Architectural Considerations
• Performance
– Smart Scans or Query Offloading
– Storage Indexes
– Hybrid Columnar Compression (HCC)
– Smart flash cache
• High Availability
– X3-2 is built in RAC capabilities for local failover
– A “DR” is still needed….
Traditional Scan Processing
• With traditional
storage, all database
intelligence resides in the
database hosts
• Very large percentage of
data returned from
storage is discarded by
database servers
• Discarded data consumes
valuable resources, and
impacts the performance
of other workloads
I/Os Executed:
1 terabyte of data
returned to hosts

DB Host reduces terabyte
of data to 1000 customer
names that are returned to
client

Rows Returned

SELECT
customer_name
FROM calls
WHERE amount >
200;

Table Extents
Identified

I/Os Issued
Exadata Smart Scan Processing
• Only the relevant columns
– customer_name
and required rows
– where amount>200
are are returned to hosts
• CPU consumed by predicate
evaluation is offloaded
• Moving scan processing off
the database host frees host
CPU cycles and eliminates
massive amounts of
unproductive messaging
– Returns the needle, not the
entire hay stack

2MB of data returned
to server

Rows Returned

Smart Scan
Constructed And Sent
To Cells

Smart Scan identifies
rows and columns
within terabyte table
that match request

Consolidated
Result Set Built
From All Cells

SELECT
customer_name
FROM calls
WHERE amount >
200;
Storage Index explained….
• A –ve index built automatically
http://www.oracle.com/technetwork/issue-archive/2011/11-may/o31exadata-354069.html
select avg(amt) from sales where
cust_level = 3
EHCC explained….
• Hybrid Columnar Compression
– Row major
– Column major
– Hybrid / Bank (compression unit ‘CU’) major
http://www.oracle.com/technetwork/middleware/bi-foundation/ehcc-twp-131254.pdf
Flash Cache – The OLTP acceleration..
• Flash Cache for Objects
– ALTER TABLE customers STORAGE
(CELL_FLASH_CACHE KEEP)
• Flash Logging
– log_file_sync events?
http://www.oracle.com/technetwork/server-storage/engineered-systems/exadata/exadata-
smart-flash-cache-366203.pdf
Workload Management
Recommendations
• Separate the database instances that are processing
completely separate subject areas that do not need linking.
E.g., APP1 and APP2 need not share the same database.
• Separate DEV/QA Environments from the production
instances
Run Multiple
Databases
• Use different services and server pools within a given instance
to isolate different services for different workloads
• Each service uniquely identifies the type of workload and can
be tied to a server pool if needed.
Design Services to
isolate Load,
Transform &
Reporting Streams
• Use as many as qualifiers to associate a user session to
resource consumer groups like Service, User, Client User,
Client program, module, action etc.
• Design simple high level plans using mgmt_p1, mgmt_p2
parameters
DBRM and IORM
design to allocate
and limit resources
within and across
databases
http://www.oracle.com/technetwork/database/features/availability/exadata-consolidation-
522500.pdf
SQL Recommendations
1. Smart scan: Use suitable selection and projection on the SQL queries with operators that can be cell offload.
Take the free flash course -
http://apex.oracle.com/pls/apex/f?p=44785:24:0:::24:P24_CONTENT_ID,P24_PREV_PAGE:5827,1 Determine if
_serial_direct_read=TRUE will help your session.
2. Avoid concurrent reads and updates to the same table blocks. When blocks are not current, smart scan cannot
happen.
3. Storage indexes: Use an ordered load of tables where possible for exploiting the storage indexes (read more on
http://www.oracle.com/technetwork/issue-archive/2011/11-may/o31exadata-354069.html )
4. Consider creating indexes only when the data accessed is < 1% of the total rows in the table.
5. Avoid having LOB columns along with the other columns in the table.
6. Consider Hybrid columnar compression to the tables that are always truncated and loaded. This will help the
tables with > 255 columns also to be offloaded.
7. Partition large tables either using range partitions or hash partitions.
8. Use direct read and write wherever possible.
9. Avoid row-by-row operations and use bulk operations.
10. Avoid updates / deletes to the data when possible. Consider achieving the result by copying the data using
CATS (Create table as select) in parallel and nologging mode.
A Practical Implementation
APP_NAME
Superdome
(in Mins)
Exadata
(in Mins)
Improvement Over
Superdome
(in Mins)
%
Improvement
APP1 117.53 1.78333 115.75 98%
App2 157.08 8.55 148.53 95%
APP3 48.92 3.01 45.91 94%
App4 68.27 5.4 62.87 92%
APP5 276.00 24.48 251.52 91%
App6 146.15 13.28 132.87 91%
APP7 129.73 12.07 117.66 91%
App8 33.35 3.78 29.57 89%
APP9 113.00 13.00 100.00 88%
App10 101.48 12.02 89.46 88%
APP11 6.23 0.77 5.46 88%
App12 198.00 25.74 172.26 87%
APP13 18.77 2.50 16.27 87%
App14 55.83 7.92 47.91 86%
APP15 16.85 2.68 14.17 84%
App16 66.58 11.98 54.60 82%
APP17 59.98 13.08 46.90 78%
App18 44.70 9.79 34.91 78%
APP19 132.03 32.56 99.47 75%
App20 5.13 1.27 3.86 75%
APP21 108.32 30.60 77.72 72%
App22 166.95 64.46 102.49 61%
APP23 110 46.30 63.70 58%
App24 60.17 26.02 34.15 57%
App25 8 3.81 4.19 52%
App26 4.28 2.29 1.99 47%
App27 15.88 11.15 4.73 30%
Another Large Implementation…
1
Hour
Staging Atomic Delivery
Staging Atomic Delivery
13 18.5 7
6 17 5
1
Hour
2
Hour
Scheduling
Changes Scheduling
Changes
Date
1
0.5
Hour
Index,
Parallelism
Changes
36.5
28
[Start + 4Months]
[Start]
4 +1
Hour
Code
Changes
1
Hour
Code
Changes
2
Hour
New
Changes
Date
2
Date
3
Date
2
Date
3
Date
3
Date
4
Date
5
Index
Changes
1500 Joba
Some Criticism…
• Software based acceleration is not guaranteed to
work….
• It is still Oracle…
• Does “Flash Cache” in Exadata really improve
performance?
• etc..,
• But, if you really want a OLTP + DW database
appliance based consolidation solution,
EXADATA is here to stay!
LinkedIn: http://www.linkedin.com/in/prasadchitta
Blog: http://technofunctionalconsulting.blogspot.com
http://www.oracle.com/us/products/database/exadata/overview/index.html
http://www.oracle.com/technetwork/server-storage/engineered-
systems/exadata/index.html
And
Google “exadata” for latest updates! All the best!!

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All (that i know) about exadata external

  • 1. All (that I know) About Exadata! Prasad Chitta
  • 2. Agenda • Technology Trends – Software Defined Everything vs Engineered Appliances • Exadata X3 Specifications – Compute, Storage, Network & Software • Architectural Considerations – Resource Management, Performance, High Availability • Exadata Specific Features Explained – Smart Scan, Storage Indexes, HCC, Smart Flash Cache • Exadata Specific Recommendations – And results from a Practical Implementations • Finally, Some Criticism
  • 3. Technology Trends • Software Defined Everything… – (some times) Open source – Virtualization – SDN – Highly Customizable – May involve multiple vendors – Google like… • Engineered Systems – Hardware aware software – Software aware hardware – Optimized (for a workload) – One vendor for complete ownership – Apple like….
  • 4. Exadata Specifications Welcome to the Oracle’s Engineered Database Appliance with intelligent Exadata storage and Infiniband Connectivity…. From: http://www.oracle.com/technetwork/server-storage/engineered- systems/exadata/dbmachine-x3-twp-1867467.pdf
  • 5. Architectural Considerations • Exadata comes in its own predefined size / Capacity – “capacity planning” is really “resource management” – Suited for consolidation – Instance Caging, DBRM and IORM • One database with multiple schemas? • Multiple databases? • Mixed, multi-workload consolidation?
  • 6. Architectural Considerations • Performance – Smart Scans or Query Offloading – Storage Indexes – Hybrid Columnar Compression (HCC) – Smart flash cache • High Availability – X3-2 is built in RAC capabilities for local failover – A “DR” is still needed….
  • 7. Traditional Scan Processing • With traditional storage, all database intelligence resides in the database hosts • Very large percentage of data returned from storage is discarded by database servers • Discarded data consumes valuable resources, and impacts the performance of other workloads I/Os Executed: 1 terabyte of data returned to hosts  DB Host reduces terabyte of data to 1000 customer names that are returned to client  Rows Returned  SELECT customer_name FROM calls WHERE amount > 200;  Table Extents Identified  I/Os Issued
  • 8. Exadata Smart Scan Processing • Only the relevant columns – customer_name and required rows – where amount>200 are are returned to hosts • CPU consumed by predicate evaluation is offloaded • Moving scan processing off the database host frees host CPU cycles and eliminates massive amounts of unproductive messaging – Returns the needle, not the entire hay stack  2MB of data returned to server  Rows Returned  Smart Scan Constructed And Sent To Cells  Smart Scan identifies rows and columns within terabyte table that match request  Consolidated Result Set Built From All Cells  SELECT customer_name FROM calls WHERE amount > 200;
  • 9. Storage Index explained…. • A –ve index built automatically http://www.oracle.com/technetwork/issue-archive/2011/11-may/o31exadata-354069.html select avg(amt) from sales where cust_level = 3
  • 10. EHCC explained…. • Hybrid Columnar Compression – Row major – Column major – Hybrid / Bank (compression unit ‘CU’) major http://www.oracle.com/technetwork/middleware/bi-foundation/ehcc-twp-131254.pdf
  • 11. Flash Cache – The OLTP acceleration.. • Flash Cache for Objects – ALTER TABLE customers STORAGE (CELL_FLASH_CACHE KEEP) • Flash Logging – log_file_sync events? http://www.oracle.com/technetwork/server-storage/engineered-systems/exadata/exadata- smart-flash-cache-366203.pdf
  • 12. Workload Management Recommendations • Separate the database instances that are processing completely separate subject areas that do not need linking. E.g., APP1 and APP2 need not share the same database. • Separate DEV/QA Environments from the production instances Run Multiple Databases • Use different services and server pools within a given instance to isolate different services for different workloads • Each service uniquely identifies the type of workload and can be tied to a server pool if needed. Design Services to isolate Load, Transform & Reporting Streams • Use as many as qualifiers to associate a user session to resource consumer groups like Service, User, Client User, Client program, module, action etc. • Design simple high level plans using mgmt_p1, mgmt_p2 parameters DBRM and IORM design to allocate and limit resources within and across databases http://www.oracle.com/technetwork/database/features/availability/exadata-consolidation- 522500.pdf
  • 13. SQL Recommendations 1. Smart scan: Use suitable selection and projection on the SQL queries with operators that can be cell offload. Take the free flash course - http://apex.oracle.com/pls/apex/f?p=44785:24:0:::24:P24_CONTENT_ID,P24_PREV_PAGE:5827,1 Determine if _serial_direct_read=TRUE will help your session. 2. Avoid concurrent reads and updates to the same table blocks. When blocks are not current, smart scan cannot happen. 3. Storage indexes: Use an ordered load of tables where possible for exploiting the storage indexes (read more on http://www.oracle.com/technetwork/issue-archive/2011/11-may/o31exadata-354069.html ) 4. Consider creating indexes only when the data accessed is < 1% of the total rows in the table. 5. Avoid having LOB columns along with the other columns in the table. 6. Consider Hybrid columnar compression to the tables that are always truncated and loaded. This will help the tables with > 255 columns also to be offloaded. 7. Partition large tables either using range partitions or hash partitions. 8. Use direct read and write wherever possible. 9. Avoid row-by-row operations and use bulk operations. 10. Avoid updates / deletes to the data when possible. Consider achieving the result by copying the data using CATS (Create table as select) in parallel and nologging mode.
  • 14. A Practical Implementation APP_NAME Superdome (in Mins) Exadata (in Mins) Improvement Over Superdome (in Mins) % Improvement APP1 117.53 1.78333 115.75 98% App2 157.08 8.55 148.53 95% APP3 48.92 3.01 45.91 94% App4 68.27 5.4 62.87 92% APP5 276.00 24.48 251.52 91% App6 146.15 13.28 132.87 91% APP7 129.73 12.07 117.66 91% App8 33.35 3.78 29.57 89% APP9 113.00 13.00 100.00 88% App10 101.48 12.02 89.46 88% APP11 6.23 0.77 5.46 88% App12 198.00 25.74 172.26 87% APP13 18.77 2.50 16.27 87% App14 55.83 7.92 47.91 86% APP15 16.85 2.68 14.17 84% App16 66.58 11.98 54.60 82% APP17 59.98 13.08 46.90 78% App18 44.70 9.79 34.91 78% APP19 132.03 32.56 99.47 75% App20 5.13 1.27 3.86 75% APP21 108.32 30.60 77.72 72% App22 166.95 64.46 102.49 61% APP23 110 46.30 63.70 58% App24 60.17 26.02 34.15 57% App25 8 3.81 4.19 52% App26 4.28 2.29 1.99 47% App27 15.88 11.15 4.73 30%
  • 15. Another Large Implementation… 1 Hour Staging Atomic Delivery Staging Atomic Delivery 13 18.5 7 6 17 5 1 Hour 2 Hour Scheduling Changes Scheduling Changes Date 1 0.5 Hour Index, Parallelism Changes 36.5 28 [Start + 4Months] [Start] 4 +1 Hour Code Changes 1 Hour Code Changes 2 Hour New Changes Date 2 Date 3 Date 2 Date 3 Date 3 Date 4 Date 5 Index Changes 1500 Joba
  • 16. Some Criticism… • Software based acceleration is not guaranteed to work…. • It is still Oracle… • Does “Flash Cache” in Exadata really improve performance? • etc.., • But, if you really want a OLTP + DW database appliance based consolidation solution, EXADATA is here to stay!