2. Agenda
1. Who We Are
2. The Scalability Problem
3. Scale Up vs. Scale Out
4. Customer ROI/Case Studies
5. Q & A
(please type questions directly into the GoToWebinar side panel)
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3. Who We Are
Presenters: Paul Campaniello,
VP of Global Marketing
25 year technology veteran with
marketing experience at Mendix,
Lumigent, Savantis and Precise.
Doron Levari, Founder
A technologist and long-time
veteran of the database industry.
Prior to founding ScaleBase, Doron
was CEO to Aluna.
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4. Pain Points – The Scalability Problem
• Thousands of new online and mobile
apps launching every day
• Demand climbs for these apps and
databases can’t keep up
• App must provide uninterrupted
access and availability
• Database performance and
scalability is critical
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5. Big Data = Big Scaling Needs
Big Data = Transactions + Interactions + Observations
Sensors/RFID/Devices Mobile Web User Generated Content Spatial & GPS Coordinates
BIG DATA
Petabytes User Click Stream Sentiment Social Interactions & Feeds
Web Logs Dynamic Pricing Search Marketing
WEB
Offer History A/B Testing Affiliate Networks
Terabytes External
Demographics
Segmentation Customer Touches
CRM
Business Data
Offer Details Support Contacts Feeds
Gigabytes
HD Video, Audio, Images
Behavioral
ERP
Purchase Detail
Targeting Speech to Text
Purchase Record
Product/Service Logs
Payment Record Dynamic
Funnels
SMS/MMS
Megabytes
Increasing Data Variety and Complexity
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The 451 Group & Teradata
6. Scalability Pain
Infrastructure
Cost $
Large You just lost
Capital customers
Expenditure
Predicted
Demand
Opportunity Traditional
Cost Hardware
Actual
Demand
Dynamic
Scaling
time
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7. Scale Up
http://forge.mysql.com/wiki/Top10SQLPerformanceTips
innodb_buffer_pool_size
Instance query_cache_size
Tuning
EXPLAIN
SSD SQL Tuning Indexes
SELECT *
DISTINCT vs. GROUP BY
Hardware
Partitioning
Upscale
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8. Partitioning Performance
• See excellent presentation by Giuseppe Maxia from 2010
– http://www.slideshare.net/datacharmer/partitions-performance-with-
mysql-51-and-55
Engine No Partitions Partitions
InnoDB 4min 30s 13.19s
MyISAM 25.03s 4.45s
• Keeps data objects at their sweet spot
• Helps fit indexes in RAM
• Distributes sessions load over disks
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9. Scaling Up Hardware
• Usually DB gets the strongest servers
• However – there is a limit to how much performance
improvement can be derived from increasing hardware
• Some data:
http://www.mysqlperformanceblog.com/2011/01/26/modeling-innodb-
scalability-on-multi-core-servers/
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10. Scale Up Pros & Cons
Pros Cons
May result in major performance Tedious, never ending…
improvements
Mostly transparent to the application SQL modifications are not always an option
HW upscale is easy Expensive
Requires unique skill set
Requires downtime
Limited. At one (near) point – the database engine
itself becomes the bottleneck
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11. The Database Engine is the Bottleneck...
• Every write operation is At Least 4 write operations inside the DB:
– Data segment
– Index segment
– Undo segment
– Transaction log
• And Multiple Activities in the DB engine memory:
– Buffer management
– Locking
– Thread locks/semaphores
– Recovery tasks
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12. The Database Engine is the Bottleneck
• Every write operation is At Least 4 write operations inside the DB:
– Data segment
– Index segment
– Undo segment
Now multiply
– Transaction log
by 10TB and
• And Multiple Activities in the DB engine memory:
10,000
– Buffer management concurrent
– Locking sessions
– Thread locks/semaphores
– Recovery tasks
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13. Scale Out (two methods)
Read
Write
Read/Write
1
Splitting
Replication
Data Distribution
2
(sharding)
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14. Read/Write Splitting
• Write to master, read from (1 or more) slaves
• Good for scaling reads
– Although big data is still big data
• Not good for scaling writes
• Many issues:
– A-synchronous replication’s lag – read might not be up to date
– A “query my update” inside a transaction will always be out of date
– Adhere transactions isolation with stickiness?
– Requires code changes
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15. Data Distribution (sharding)
• If done right and all the way:
– The ultimate scaling machine
– Provides significant performance improvements
– The only way to really improve read and also writes
• However if done in-house, (and not done properly), it can cause:
– Substantial development efforts
– Silos of data with no merging
http://www.scalebase.com/don’t-ever-ever-write-your-own-sharding-code
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16. Scale Out Features and Benefits
Feature Benefit
Automatic data distribution (sharding) Scale data-, read-, write- intensive applications
Great performance of cross-db queries & maintenance
Parallel query execution
commands
Support of sophisticated cross-db queries, even with ORDER
Query result aggregation
BY, GROUP BY, LIMIT, Aggregate functions…
Flexibility: no need to over-provision
Online data redistribution
No downtime
Read/Write splitting Easily scale read-intensive applications
Replication lag-based routing Improves data consistency and isolation
Read stickiness after writes Ensure consistent and isolated database operation
100% compatible MySQL proxy Applications unmodified
Standard MySQL tools and interfaces
MySQL databases untouched Data is safe within MySQL InnoDB/MyISAM/any
Data distribution review and analysis Optimization of data distribution policy
Data consistency verifier Validate system-wide data consistency
Real-time monitoring and alerts Simplify management, reduce TCO
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17. Scale Out Provides Immediate & Tangible Value
Application Server Database A Standby A
Application Server Database B Standby B
Database C Standby C
BI
Database D Standby D
Management
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18. Typical Scale Out (ScaleBase) Deployment
Application Server Database A Standby A
ScaleBase
Central Management
Application Server Database B Standby B
ScaleBase
Data Traffic Manager
Database C Standby C
BI
Database D Standby D
Management
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19. Scaling Out Achieves Unlimited Scalability
160000
140000
120000
100000
Throughput
84000
80000 Throughput (TPM)
Total DB Size (MB)
60000 60000 # Connections
48000
40000
36000
24000 2500
20000 2000
12000 1500 1500
6000 1000
0 500 500
1 2 4 6 8 10 14
Number of Databases
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20. Summary
• Database scalability is a significant problem
– Growing trends such as Big Data and mobile only compound it
• Scale Up helps somewhat, but has limitations
• Scale Out provides a longer term and more cost effective solution
• ScaleBase has an effective scale out solution with a proven ROI
– ScaleBase improves performance and requires NO changes to your
existing infrastructure
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21. Questions (please enter directly into the GTW side panel)
617.630.2800
www.ScaleBase.com
doron.levari@scalebase.com
paul.campaniello@scalebase.com
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