Session slide at db tech showcase 2012 How Rakuten Reduced Database Management Spending by 90% through Clustrix implementation - About Rakuten - Rakuten database environment and operational issues - What is Clustrix? - Clustrix verification results and implementation effectiveness - Summary

1. 楽天事例紹介: Clustrix導入による
DB管理コストの削減
How Rakuten Reduced Database
Management Spending by 90% through
Clustrix implementation
October 17th, 2012
Ryutaro Yada (矢田 龍太郎)
Database Platform Group
Global Infrastructure Development Dept.
Rakuten, Inc.

2. Introduction
 Ryutaro Yada
 First employed by Rakuten in 2008
 Present job
 Development of a platform database to support Rakuten
 Testing and discussion of new techniques and new architecture in view of having it
adopted for use.
 Previous functions
 Promotion of Oracle business with specified customer
 Establish collaborative network with Oracle, develop and verify new solutions, etc..
 LinkedIn profile: http://www.linkedin.com/pub/ryutaro-yada/32/368/4b0
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3. Agenda
 About Rakuten
 Rakuten database environment and
operational issues
 What is Clustrix?
 Clustrix verification results and
implementation effectiveness
 Summary
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4. Introduction to Rakuten
 About 3000 employees: (Group approx. 7000)
 Market / more than 40 services provided including travel
 More than 120,000 contracted firms; more than 80,000,000
registered products
 Group distribution total: 3.2 trillion yen (2011)
Rakuten market

5. Rakuten Global Expansion
Our Goal is to become the No. 1 Internet Service in the World
LS(UK)
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Taiwan ★
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*To be open soon ★
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★ Ichiba (EC)
★ ★ Travel ★ Performance marketing
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6. Rakuten’s Global Position
 Rakuten is aiming to be the world’s largest internet firm.
 Firm and highly flexible infrastructure is required to achieve this
goal
Retail / auction site global ranking 2011 based on unique (no. of) visitors
300000
250000
200000
150000
100000
50000
0
Amazon e-Bay Alibaba Apple Rakuten Walmart
Source： comScore Media Metrics

7. Rakuten Database
 Breakdown according to the number of databases:
approx. 80% MySQL (more than 1100)
 More than 350 MySQL database servers
 MySQL has the largest share
Oracle PostgreSQL Teraddata
 No. of databases according
Informix
to actual environment
RDBMS
 Same number of databases
for each STG and DEV
MySQL
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8. MySQL Database Issue (1)
 Data Sharding Operations
 Required for functionality scaling
 Instance/database/table splitting, data redistribution
 Correction of application code, control of database access
 Data Protection, HA Securing
 Replication cannot realize zero data loss at failure
 Switch back/switch over management takes a lot of effort
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9. MySQL Database Issue (2)
 Online Maintainability
 Schema modification and index addition, rebuild
 Lock, access concentration
 Number of Units Tends to Increase
 Load distribution slave, redundant configuration of slave
 Tendency for preparations on an individual service basis (service level
differences, maintenance adjustment diversion)
 CPU efficiency decreases; increases in data center costs
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10. Clustrix Characteristics
What is Clustrix?
 Appliance-style database server
 Cluster database
 NewSQL = LegacySQL + NoSQL
 LegacySQL: SQL access, transaction consistency
 NoSQL: Scalability, high performance
 Fault-tolerance function
 MySQL compatibility
 Usually access is through MySQL protocol
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11. Clustrix Provision Model
 2 Models
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12. Looking at Clustrix
SSD
Infiniband
 Low latency
 High performance
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13. Clustrix Operation
 Distributed arrangement on the physical layer
 Redundancy protection, auto rebalance
 Parallel query execution
SQL
SQL SQL SQL  Query, not data, is
migrated (this
concept differs
from Oracle RAC)
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14. TPC-C Benchmark Result
13

15. GUI
14

16. Useful Command Interface
15

17. Clustrix Implementation Cases
Rakuten is the first case in Japan
Numerous foreign cases
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18. Verification Points
Performance
Scalability
Fault-tolerance verification
Online schema modification
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19. OLTP Performance Results (1)
Insert
45000
40000
35000
30000
25000
(ops/sec)
20000
15000
10000
5000
0
p3 p12 p24 p48 p96 p192
Single Throughput 4014.703409 8350.801098 10022.32827 10448.25479 10520.08066 10213.98278
Clx 3 nodesThroughput 6301.603599 18530.31626 26182.77331 30021.30841 27581.92104 24401.28904
Clx 4 nodes Throughput 6090.513193 20584.42 30544.8252 38545.21774 36837.10176 33221.72529
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20. OLTP (2)
Update
25000
20000
15000
(ops/sec)
10000
5000
0
p3 p12 p24 p48 p96 p192
Single Throughput 3854.243586 8018.593249 12186.20793 13385.77834 13395.06587 11538.29668
Clx 3 nodes Throughput 3377.359372 10741.77417 16505.79652 16964.01107 16189.88416 15379.62683
Clx 4 nodes Throughput 3682.99886 12679.26966 19737.63815 22232.7357 21568.39318 21303.72872
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21. OLTP (3)
Read
80000
70000
60000
50000
(ops/sec) 40000
30000
20000
10000
0
p3 p12 p24 p48 p96 p192
Single Throughput 6134.386466 26773.71202 44388.78477 56144.27281 57926.62433 49362.51106
Clx 3 nodes Throughput 5050.230295 17380.6806 27803.82494 39693.75633 49822.34026 56847.77879
Clx 4 nodes Throughput 5959.900064 20794.2083 34743.31655 54382.96419 70302.27313 76000.59175
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22. OLTP (4)
Mix
40000
35000
30000
25000
(ops/sec) 20000
15000
10000
5000
0
p3 p12 p24 p48 p96 p192
Single Throughput 3976.841546 8587.218158 11632.64122 12946.2536 13122.33748 12769.45794
Clx 3 nodes Throughput 3113.109431 12940.99191 21264.63309 26759.75291 25976.26625 25334.18054
Clx 4 nodes Throughput 5150.537999 15220.8469 25601.67909 34647.41616 34697.737 30804.09949
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23. Complex and Heavy SQL Comparison
Clustrix IA with SSD SPARC with SAN
J) Count+GroupBy+OrderBy+Limit 1.9s (3.4s) 2.1s (8.5s) 3.4s (409.32s)
K) Count+GroupBy+OrderBy+Limit 0.7s (1.13s) 5.9s (7.49s) 13.0s (39.41s)
L) 2000 of IN+GroupBy 3.8s (8.97s) 106.5s (103.77s) 193.0s (321.68s)
M) Case+OrderBy 31.0s (45.66s) 47.3s (60.9s) 22 90.5s (112.24s)

24. Example of Performance Improvements
 Example improvements regarding a particular service
 Before: 116.8ms
 After: 21.4ms
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25. Fault-Tolerance Inspection
Failure Test Items Downtime
1 Front network (port1) No
2 Front network (port2) No
3 Internal network (primary) < 12s
4 Internal network (standby) No
Front SW1 Front SW2
5 MySQL instance < 4s
6 Node OS < 4s 1
Online data disk 11
7 < 5s 2
(SSD) failure
Log/work data disk
8 No DB DB DB DB
(SATA) failure 5,6 7,8
9 4 12
Infiniband switch (primary) < 12s
3
10 Infiniband switch (standby) No
11 Front network (port1&2) < 18s 10
9
Internal network
12 < 12s Infiniband SW1 Infiniband SW2
(primary & standby)
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26. Time Required for Online Maintenance
Table Rows and Size
Small Medium Large
Row 50,000 500,000 5,000,000
Size (byte) 113,639,424 1,063,190,528 10,696,130,560
Implementation Time
Small Medium Large
Create Column 1.6s 13.5 149.8
Create Index 1.6s 13.0s 172.7s
Drop Column 1.5s 13.8s 125.5s
Drop Index 0.5s 0.5s 0.5s
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27. Impacts During Online Scheme Modification
 No impact on access performance in areas other than those subject to work operations
 Some impact on performance of access to table being subject to work operations (taking
periods with little impacts, such as night service, into consideration)
 Online execution –
5 million
cases, total tables
10G
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28. Clustrix Implementation Impacts Release from Sharding (1)
Before
……
DB DB DB DB
……
No more sharding!
After
DB DB DB DB
…… +

29. Clustrix Implementation Impacts Release from Sharding (2)
 No need for correction of application
 No need for DB distribution
 Sharding production costs reduction (over 90%) for
both application engineer and DBA
 In case of large-scale
as-i
s sharding
project, actual
production costs
DBA
compared to
to-be
APP
original
0 2 4 6 8 10 12 14
m an-m onth
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30. Clustrix Implementation Impacts Cost Reductions due to Consolidation (1)
 Sufficient performance scalability
 Fault-tolerance ready for mission critical
 No data loss
 High online maintainability that doesn’t affect other services
 Possibility of consolidation to Clustrix of existing MySQL
database
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31. Clustrix Implementation Impacts Cost Reductions due to Consolidation (2)
 Consolidation of all existing MySQL within Clustrix
 Number of servers will be reduced to 10%
 Monthly system costs will be reduced to 40%
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32. Back-up Structure
Clustrix
DB DB DB
…
Node 1 Node 2 Node 3
Replication
Slave as first
backup
Backup by mysqldump
MySQL
DB
NFS
NAS
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33. Data Migration Procedure
 Replication to DEV for verification
 Replication to PRO for migration
 Conversion of application access point to PRO
MySQL
DB
Replication
Replication
Clustrix DEV
Clustrix PRO DB DB DB
DB DB DB
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34. Other Advantages of Clustrix
 Auto-Defrag
 Cordial Support Service
 Advice regarding structure
 Troubleshooting
 Tuning advice
 Etc.
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35. Operational Issues Resolved with Clustrix
 Data sharding operations  Unnecessary, operational
cost reduction
 Data protection, HA securing  Possible
 Online maintenance  Possible
 Tendency for large number of units  Consolidation possible
 Cost reduction
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36. Clustrix at Rakuten
 An important database platform
 Provided as Database-as-a-Service
 No lead-time
 Usage volume rate structure
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