The document discusses various options for scaling MySQL databases to handle increasing load. It begins with simple options like upgrading MySQL versions, adding caching layers, and read/write splitting. More complex and reliable options include using MySQL replication, cloud hosting, MySQL Cluster, and columnar storage engines. Scaling to very large "big data" workloads may involve using NoSQL technologies, Hadoop, and data partitioning/sharding. The key challenges discussed are defining business and technical requirements, planning for high availability, and managing increased complexity.

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2. MySQL Scaling:
Drinking from the fire hose,
never being down & serving
billions
Dave Stokes
MySQL Community Manager
David.Stokes@Oracle.com @Stoker
Slideshare.net/davestokes/presentations
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3. You want to get a bigger, badder MySQL -BUT what are the costs???
 Budget
 Complexity
 Application
 Opportunity
 Sanity
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4. Simple Stuff
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5. Option 0
Run the Latest, Greatest MySQL
MySQL 5.5 was 20% faster than 5.1! 5.6 is another 10-15%
--Make sure your config file matches your hardware
--ANALYSE TABLE
--Check for SLOW QUERIES, QUERIES w/o INDEXES
--TUNE QUERIES
This is the least complex, less upsetting, and usually lowest budget cost option
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6. Option 0.1
Add in a Memcached Layer
Cache for quick access
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Session Data
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Sports Scores
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Opens up NoSQL access
to InnoDB later
This is the second least complex, less upsetting, and usually lowest budget
cost option but does make you change your application to become 'cache
aware'.
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7. What database service
Levels do you Need to
Provide?
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8. UpTime versus Response Time versus Access
Things to be engineered/negotiated upfront
Up Time
– How much down time can you afford?
Response Time
– How long is too long to wait?
Access
– How many do you need to serve simultaneously
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9. Simple Level 1
Read/Write Splitting
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10. Use MySQL Replication to Split Read/Writes
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11. Read/Write Splitting
Most Common Scaling Technique
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More complex environment
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Application Changes
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Easier with PHP & Java
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Low opportunity and sanity costs
Less risk of one server stopping operations
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12. Read/Write Splitting—Easy Way
Use MySQL Utilities to copy master, set up failover
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mysqlserverclone
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mysqlfailover
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When master dies, most up to date slave (GTID) promoted
automatically
Mysqlrplcheck & mysqlrplshow
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13. Simple Level 2
Moderately Easy, Better
Availability
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14. Distributed Replicated
Block Device (DRBD)
High Availability
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More complex environment
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Proven, supported
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Please practice fail over
and back
Easy to manage but
not HA panacea
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15. CLOUD
High Availability, Low Cost (maybe)
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Slightly more complex environment
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Proven, supported
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Best for ephemeral data, may not make upper management
comfortable (or you)
Easy to manage but
not HA panacea
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16. Complex Level N^N
Never, Ever Down
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17. MySQL Cluster
For 99.999% uptime, upper bound on response time
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Extremely Complex
No single point of
failure = lots of $$
hardware
Not general
purpose MySQL
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Auto sharding
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Add/Subtract nodes at will
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Replicate between
geographically separated
locations
Need a smart staff

18. MySQL Cluster Shards Data Automatically
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Single or multiple data node
failures do not bring down a
cluster
Easy to add/remove data
nodes

19. MySQL Cluster Topology
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Nodes
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Data
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MGMT – startup/add node
Mysqld
Can be engineered for no
single point of failure
Can replicate across
data centers
Expensive on all fronts
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20. Big Data
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21. Ask ten DBAs to define 'Big Data' and You Will
Get At Least Eleven Answers
.BIG as massive amounts
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Define MASSIVE
BIG as lots of eyes
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How many simultaneous users
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BIG as in fast
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Define fast
BIG as all of the other three!!!

22. The Easiest Way to Get Speed
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NoSQL/Memcached to InnoDB/NBD
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9x faster by skipping parser, optimizer
Key/Value pair access
and SQL access of same data
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23. Easiest Way to Gain Size
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Columnar Storage Engines for MySQL
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Not InnoDB
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Unique features, quirks
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Need > 26,000,000,000 rows to start seeing 'sweet spot'
Use new server, don't scrimp
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24. Calpont
InfiniDB
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Column-oriented
MySQL front end
Multi threaded
High speed data loading
Automatic partitioning
Enterprise Features
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Partition Drop
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UDF’s
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Physical compression
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MPP, multi-node scale-out
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Distributed, Shared Nothing data cache
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Auto Increment
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Centralized database and operating system
monitoring
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Notifications and Alerts
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INSERT INTO...SELECT FROM...
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Views on InfiniDB Tables
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25. Infobright
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Similar Features
to Calpont but
different implementation
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26. Hadoop-- getting data from MySQL to Hadoop
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80% of Hadoop Installations feed from MySQl
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Hadoop is made up of several components
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Twisty little maze, all alike
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Use Apache Bigtop
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Tested for interoperability
Cloudera & Horton Works have great tutorials
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Kinda 'SQL-ish'
Squoop and MySQL Hadoop Applier to move data
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27. Hadoop Applier
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Infobright

28. Partitioning and Sharding /
Dividing and Conquering
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Partitions built-in to MySQL
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Nice management tool
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Fast drops
Sharding
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Rolling quarters/months
–
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No change to application but
higher complexity, low
opportunity cost
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Much more complex
application and operations
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Not off shelf
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Hard to re-engineer
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Works

29. Hard Part – Thinking
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30. Goals – What are you trying to
accomplish
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Define problem completely
Who produces, who consumes
data
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What does end solution
look/feel like?
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Negotiate performance rates
Plan for failure, catastrophe
Design for long life
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Platforms
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Speed
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Access areas
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Security
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Serenity

31. Where Else to Seek Info
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MySQL Connect Conference
Sept. in San Francisco
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Hadoop
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Same time as OOW
Weekdays!
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Planet.mysql.com and
forums.mysql.com
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Horton Works and Cloudera
both have wonderful tutorials
Apache Bigtop project

32. MySQL Training
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MySQL Marinate
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Online Virtual Class run by
Boston MySQL Users Group
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1 chapter/week
Free
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Oracle and other vendors
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Dozens of book
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Online

33. David.Stokes@Oracle.com
@Stoker
slideshare.net/davestokes
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