Treasure Data simplifies event analytics for the complex digital world. Our customers send us 1,000,000 events per second and issue 30,000+ Presto queries everyday to understand their customers better. One of the challenges is designing a cloud database with zero downtime to support a global customer base. We have achieved this goal by developing several open-source technologies; Fluentd and Embulk enable seamless log collection from stream/batch sources, and with MessagePack we can provide an extensible columnar store that accommodates future schema changes. Finally, Presto allows us to serve a wide variety of data processing our customers perform on our service. In this talk, I will present an overview of our system, and how our customers keep using Presto while collecting and extending their data set.

1. Designing An Evolving
Database Service with Presto
Taro L. Saito
leo@tresaure-data.com
Oct 6th, 2015.
Presto Meetup @ Boston

2. Presto Usage at Treasure Data
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• 100~ customers are actively using Presto
• 30,000~ Presto queries every day
• Importing 1,000,000~ records / sec.
Import Export
Store Analyze with
Presto/Hive

3. Mobile and Web Sources
Mobile SDKs
JavaScript SDK
(web access logs)
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4. Stream Sources
Streaming
Apache Logs
nginx logs
syslog
JSON logs
…
4
JSON

5. Existing Data Sources
Bulk Import
Data files (CSV, TSV, etc.)
MySQL
PostgreSQL
Oracle
…
5

6. Embedded Devices
• Collect data from Embedded linux, serial devices, MQTT, XBee Radio, etc.
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7. Import data, now.
7

8. Treasure Data Architecture
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LogLogLogLogLogLog
1-hour
partition1-hour
partition1-hour
partition
Hadoop
MapReduce
2015-09-29 01:00:00
2015-09-29 02:00:00
2015-09-29 03:00:00
Real-Time
Storage
Archive
Storage
time column-based partitioning
…
Hive Presto
Log
many small log files log merge job
LogLogLogLogLog
Distributed SQL Query Engine
S3 (AWS)
Rick CS (IDCF)
Columnar Format

9. • JSON data
• {“time”: 1412380700, “user”:1}
• Additional Column
• {“time”: 1412381000, “user”:2, “status”:200}
• Type Escalation (int -> string)
• {“time”: 1412390000, “user”:”U01”, “status”:200}
• MessagePack
• A fast and compact JSON-like format
• Auto type conversion
• Table schema <=> MessagePack types
Extensible Columnar Store
9

10. Use Cases

11. E-COMMERCE
BEFORE
AFTER
Biggest Mobile Shopping
WISH.COM
• Reduced costs
• Scalability
• Single data warehouse11

12. GAMING
BEFORE
AFTER
Daily Upload Delay of 1-2 days
2500+ servers
Real-time
Real-time
2500+ servers
1 Billion records/day
• Reduced TCO
• Real-time collection
• Real-time access to KPIs
Top 10 globally; 40M+ users
x 20
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13. AD TECH
Publishers’ Dashboard Advertisers’ Dashboard
• 800 B/month
• Live in 2 weeks with 1 engineer!
• 300% growth
Europe’s largest mobile ad-exchange
More than 50 billion impressions/month
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14. LOYALTY
Aggregation
E-Commerce
Marketing Campaigns;
Promotions
• Customer Segmentation
• A/B Testing
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15. Challenges
• Handle Huge Query Result Output
• SELECT */ CREATE TABLE AS /INSERT INTO
• Parallel Result Upload to S3
• Bypass JSON result generation at the coordinator
• td-presto connector
• Accesses MessagePack based columnar store
• Handle S3 access retry / pipelining
• Future:
• Better query plan visualization
• Quickly find the performance bottleneck and memory consuming tasks
• Storing intermediate query results to disks
• Process large joins, query resource limitation
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16. Extensible Schema
SQL via Hive, Presto
Unlimited Users, Queries
Enterprise Apps
Enterprise Apps Data Science
Tools
REST API
Ingestion: Streaming, Bulk
BI Tools
treasuredata.com/request_demo