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This webinar examines best practices around scaling MySQL databases.
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Webinar Methods and challenges to Scale out a MySQL Database
October 23, 2012
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Agenda
1. Who We Are 2. The Scalability Problem
3. Methods and challenges to scale out a MySQL DB
4. Customer ROI/Case Studies
5. Q & A
(please type questions directly into the GoToWebinar side panel)
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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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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Big Data = Big Scaling Needs
The 451 Group & Teradata
Big Data = Transactions + Interactions + Observations
BIG
DA
TA
ER
P
CR
M
WE
B
Petabytes
Terabytes
Gigabytes
Megabytes
Increasing Data Variety and Complexity
Purchase Detail
Purchase Record
Payment Record
Segmentation
Offer Details
Customer Touches
Support Contacts
Web Logs
Offer History A/B Testing
Dynamic Pricing
Affiliate Networks
Search Marketing
Behavioral
Targeting
Dynamic
Funnels
Sensors/RFID/Devices
User Click Stream
Mobile Web
Sentiment
User Generated Content
Social Interactions & Feeds
Spatial & GPS Coordinates
External
Demographics
Business Data
Feeds
HD Video, Audio, Images
Speech to Text
Product/Service Logs
SMS/MMS
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Scalability Pain
You just lost
customers
Infrastructure Cost $
time
Large
Capital
Expenditure
Opportunity
Cost
Predicted Demand
Traditional Hardware
Actual Demand
Dynamic Scaling
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Methods and Challenges to Scale Out MySQL
• August 16 and September 20, 2012
– Scaling MySQL: ScaleUp versus Scale Out: This webinar will
examine best practices around scaling MySQL databases
• Today
– Methods and challenges to Scale out MySQL
• November 15, 2012
– Catch 22 of read-write splitting
• December 13, 2012
– Automated data distribution
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Scale Up Pros & Cons
Pros Cons
May result in major performance improvements
Tedious, never ending…
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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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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• 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
The Database Engine is the Bottleneck
Now multiply
by 10TB accessed by
10000 concurrent
sessions
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Scale Out (two methods)
Write
Read
Replication
Read/Write Splitting
Automatic Data Distribution
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2
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Read/Write Splitting
• Good for scaling high session-volume reads
• Limited in scaling high data-volume reads
– Replication of data-volume is costly
– Queries balanced to the slave, still meet big data
• Limited in for scaling writes
• Home-grown implementations have drawbacks
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Scale Out Features and Benefits
Feature Benefit
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
Real-time monitoring and alerts Simplify management, reduce TCO
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Automatic Data Distribution
• The ultimate way to scale
• Provides significant performance improvements
• The only way to really improve read and also writes
• Good for scaling high session-volume reads and writes
• Good for scaling high data-volume reads and writes
• Home-grown implementations have drawbacks
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Scale Out Features and Benefits
Feature Benefit
Parallel query execution Great performance of cross-db queries & maintenance commands
Query result aggregation Support of sophisticated cross-db queries, even with ORDER BY, GROUP BY, LIMIT, Aggregate functions…
Online data redistribution Flexibility: no need to over-provision No downtime
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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Scale Out Provides Immediate & Tangible Value
Application Server
BI
Management
Application Server
Database A Standby A
Database B Standby B
Database C Standby C
Database D Standby D
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Typical Scale Out (ScaleBase) Deployment
Database B
Database C
Database D
Database A Standby A
Standby B
Standby C
Standby D
ScaleBase
Data Traffic Manager
Application Server
BI
Management
Application Server
ScaleBase
Central Management
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Choose Your Scale-out Path
# of concurrent sessions
Dat
abas
e S
ize
1 DB?
Good for me!
Data Distribution (Reads and writes)
Read/Write Splitting (Reads)
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Scaling Out Achieves Unlimited Scalability
6000 12000
24000
36000
48000
60000
84000
500 500 1000
1500 1500 2000
2500
0
20000
40000
60000
80000
100000
120000
140000
160000
1 2 4 6 8 10 14
Thro
ugh
pu
t
Number of Databases
Throughput (TPM)
Total DB Size (MB)
# Connections
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Detailed Scale Out Case Studies
Nokia
• Device Apps App
• Availability
• Scalability
• Geo-clustering
• 100 Apps
• 300 MySQL DB
Solar Edge
• Next Gen Monitoring App
• Massive Scale
• Monitors real time data from thousands of distributed systems
Mozilla
• New Product/ Next Gen App/ AppStore
• Scalability
• Geo-sharding
AppDynamics
• Next gen APM company
• Scalability for the Netflix implementation
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Summary
• Database scalability is a significant problem
– App explosion, Big Data and mobile compound it
• Scale Up helps somewhat, but 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
• Choose your scale-out path....
– ScaleBase is a platform that enables
you to start with R/W splitting and
grow into data distribution
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Questions (please enter directly into the GTW side panel)
617.630.2800
www.ScaleBase.com
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Thank You
