This document outlines the architecture and service offerings of Treasure Data, a cloud-based big data analytics platform founded by Japanese entrepreneurs. It details the company's use of AWS products for data collection, storage, and analytics, emphasizing efficiency and scalability. The presentation highlights the importance of real-time data processing and the integration of open-source tools like Fluentd for log collecting.

1. Treasure Data
The architecture of data analytics PaaS on AWS
Masahiro Nakagawa
JAWS Days: 2013/03/16
Friday, April 5, 13

2. Who are you?
 Masahiro Nakagawa
• @repeatedly / masa@treasure-data.com
 Treasure Data, Inc.
• Senior Software Engineer, since 2012/11
 Open Source projects
• D Programming Language
• MessagePack: D, Python, etc...
• Fluentd: Core, mongo, etc...
• etc...
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3. Introduction to
Treasure Data
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4. Company Overview
 Silicon Valley-based Company
• All Founders are Japanese
• Hironobu Yoshikawa
• Kazuki Ohta
• Sadayuki Furuhashi
 OSS Enthusiasts
• MessagePack, Fluentd, etc.
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5. Investors
 Bill Tai
 Naren Gupta - Nexus Ventures, Director of Redhat, TIBCO
 Othman Laraki - Former VP Growth at Twitter
 James Lindenbaum, Adam Wiggins, Orion Henry - Heroku
Founders
 Anand Babu Periasamy, Hitesh Chellani - Gluster Founders
 Yukihiro “Matz” Matsumoto - Creator of Ruby
 Dan Scheinman - Director of Arista Networks
 Jerry Yang - Founder of Yahoo!
 + 10 more people
• and....
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6. Treasure Data = Cloud + Big Data
Cloud Big Data-as-a-Service
Database-as-a-service
Enterprise
Lightweight RDBMS Traditional
RDBMS Data Warehouse
DB2
On-Premise
$34B $10B
market market
1Bil entry Data Volume
Or 10TB
© 2012 Forrester Research, Inc. Reproduction Prohibited 6
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7. Why Cloud? ‘Time’ is Money
Ideal
Customer Expectation
Value
Obsolete
over time
Reality
(On-Premise)
Upgrade
HW/SW Selection, PoC, Deploy...
Time
Sign-up or PO
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8. Big Data Adoption Stages
Optimization What’s the best?
Predictive Analysis What’s a trend? Analytics
Statistical Analysis Treasure Data’s FOCUS
Why?
Alerts Error?(80% of needs)
Drill Down Query Where exactly?
Reporting
Ad-hoc Reports Where?
Standard Reports What happened?
Intelligence Sophistication
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9. Full Stack Support for Big Data Reporting
Our best-in-class architecture Data from almost any source
and operations team ensure the can be securely and reliably
integrity and availability of your uploaded using td-agent in
data. streaming or batch mode.
Our SQL, REST, JDBC, ODBC You can store gigabytes to
and command-line interfaces petabytes of data efficiently and
support all major query tools securely in our cloud-based
and approaches. columnar datastore.
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10. Vision: Single Analytics Platform for the World
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11. 11
Our Customers – Fortune Global 500 leaders and
start-ups including:
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12. Treasure Data’s
Service Architecture
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13. Treasure Data = Collect + Store + Query
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14. Example in AdTech: MobFox
1. Europe’s largest independent mobile ad exchange.
2. 20 billion imps/month (circa Jan. 2013)
3. Serving ads for 15,000+ mobile apps (circa Jan. 2013)
4. Needed Big Data Analytics infrastructure ASAP.
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15. Two Weeks From Start to Finish!
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16. Used AWS Products (1)
 RDS
• Store user information, job status, etc...
• Store metadata of our columnar database
• Queue of worker (perfectqueue / perfectsched)
 EC2
• API servers
• Hadoop clusters
• Job workers
• Using Chef to deploy
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17. Used AWS Products (2)
 ELB
• Load balancing of API servers
• Load balancing of td-agents
 S3
• Columnar storage built on top of S3
• MessagePack columnar format
• realtime / archive storage
• Our Result feature supports S3 output.
No EMR, SQS and other products !
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18. Architecture Breakdown
Data Collection Data Store/Analytics Connectivity
• Increasing variety of • Remaining complexity in • Required to ensure
data sources both traditional DWH connectivity with
• No single data schema and Hadoop (very slow existing BI/visualization/
• Lack of streaming data time to market) apps by JDBC, REST
collection method • Challenges in scaling and ODBC.
• 60% of Big Data project data volume and • Output ot other services,
resource consumed expanding cost. e.g. S3, RDBMS, etc.
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19. 1) Data Collection
 60% of BI project resource is consumed here
 Most ‘underestimated’ and ‘unsexy’ but MOST important
 Fluentd: OSS lightweight but robust Log Collector
• http://fluentd.org/
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20. Fluentd
the missing log collector
fluentd.org
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21. In short
 Open sourced log collector written in Ruby
 Using rubygems ecosystem for plugins
It’s like syslogd, but
uses JSON for log messages
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22. Time 2012-02-04 01:33:51
Apache Tag apache.log
Record {
"host": "127.0.0.1",
tail "method": "GET",
"path": "/",
write ...
}
insert
127.0.0.1
127.0.0.1
127.0.0.1
-
-
-
-
-
-
[11/Dec/2012:07:26:27]
[11/Dec/2012:07:26:30]
[11/Dec/2012:07:26:32]
"GET
"GET
"GET
/
/
/
...
...
...
Fluentd
127.0.0.1 - - [11/Dec/2012:07:26:40] "GET / ...
127.0.0.1 - - [11/Dec/2012:07:27:01] "GET / ...
...
event
buffering
Mongo
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23. Architecture
Pluggable Pluggable Pluggable
Input Buffer Output
> Forward > Memory > Forward
> HTTP > File > File
> File tail > Amazon S3
> dstat > MongoDB
> ... > ...
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24. Before Fluentd
Server1 Server2 Server3
Application Application Application
･･･ ･･･ ･･･
High Latency!
must wait for a day...
Fluent
Log Server
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25. After Fluentd
Server1 Server2 Server3
Application Application Application
Fluentd ･･･ Fluentd ･･･ Fluentd ･･･
In streaming!
Fluentd Fluentd
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26. Access logs Alerting
Apache Nagios
App logs Analysis
Frontend MongoDB
Backend
MySQL
System logs Hadoop
syslogd
filter / buffer / routing
Archiving
Databases Amazon S3
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27. td-agent
 Open sourced distribution package of fluentd
 ETL part of Treasure Data
 Including useful components
• ruby, jemalloc, fluentd
• 3rd party gems: td, mongo, webhdfs, etc...
• td plugin is for Treasure Data
 http://packages.treasure-data.com/
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28. Treasure Data Service Architecture
This!
Apache
App Treasure Data
td-agent columnar data
App RDBMS warehouse
Other data sources
MAPREDUCE JOBS
HIVE, PIG (to be supported)
td-command
Query
Query
Processing
API
JDBC, REST Cluster
User BI apps
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29. AWS plugins
 S3
 SNS
 SQS
 DynamoDB
 foward-aws
 RDS http://fluentd.org/plugin/
 RedShift
 CloudWatch
 Yet Another Cloud Watch
 CloudWatch Lite
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30. 2) Data Store / Analytics - Columnar Storage
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31. Treasure Data Service Processing Flow
Worker
Frontend
Job Queue Hadoop
Hadoop
Applications push
metrics to Fluentd
sums up data minutes
(via local Fluentd) Fluentd Fluentd (partial aggregation)
Treasure
Librato Metrics
Data
for historical analysis for realtime analysis
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32. Friday, April 5, 13

33. Structure of Columnar Storages
import bulk import SELECT ...
Import Storage Bulk Import Storage
Realtime Storage Archive Storage
merge (every 1 hour)
23c82b0ba3405d4c15aa85d2190e 2013-03-15 00:23:00 912ec80
6d7b1482412ab14f0332b8aee119 2013-03-16 00:01:00 277a259
8a7bc848b2791b8fd603c719e54f ...
0e3d402b17638477c9a7977e7dab
...
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34. Query Language
Query Execution
Columnar Data
Object Storage
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35. 1/4: Compile SQL into MapReduce
SQL Statement
SELECT COUNT(DISTINCT ip) FROM tbl;
Hive
SQL - to - MapReduce
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36. 2/4: MapReduce is executed in parallel
SELECT COUNT(DISTINCT ip) FROM tbl;
cc2.8xlarge cluster compute instance (up to 100 nodes * 32 threads)
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37. 3/4: Columnar Data Access
SELECT COUNT(DISTINCT ip) FROM tbl;
10Gbps Network
Read ONLY the Required Part of Data
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38. 4/4: Object-based Storage
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39. Data first, Schema later
SELECT 54 (int) “test” (string) 120 (int) NULL
Schema user:int name:string value:int host:int
Raw data（JSON） {“user”:54, “name”:”test”, “value”:”120”, “host”:”local”}
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40. 3) Connectivity
REST API
td-command
Query
Query
Query API
Processing
JDBC, ODBC Driver Cluster
BI apps
Web App
Treasure Data
Result MySQL Columnar Storage
S3
…
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41. Multi-Tenancy
 All customers share the Hadoop clusters (Multi Data Centers)
 Resource Sharing (Burst Cores), Rapid Improvement, Ease of Upgrade
Job Submission
+ Plan Change
Local FairScheduler
datacenter A
Local FairScheduler
Global
datacenter B
Scheduler
Local FairScheduler
datacenter C On-Demand
Resouce Allocation
Local FairScheduler
datacenter D
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42. Conclusion
 Treasure Data
• Cloud based Big-data analytics platform
• Provide Machete for Big data reporting
 Big Data processing
• Collect / Store / Analytics / Visualization
Our focus!
 Our used AWS products
• EC2, S3, RDS, ELB
• Building Treasure Data specific systems on AWS
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43. Big Data for the Rest of Us
www.treasure-data.com | @TreasureData
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