情報処理学会 Exciting Coding! Treasure Data

www.treasuredata.com

Treasure Data Exciting Coding! Nov 2013

Presented by

Masahiro Nakagawa Senior Software Engineer

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•  Masahiro Nakagawa –  @repeatedly

–  [email protected] or d@

•  Treasure Data, Inc –  Senior Software Engineer

•  Fluentd / Client libraries / etc...

–  Since 2012/11

•  Open Source projects –  D Programming Language

–  MessagePack: D, Python, etc…

–  Fluentd: Core, Mongo, Logger, etc…

–  Etc…

Who are you

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Board Meeting Presentation August 15th, 2013 - 3:30PM PDT

Presented by

Hironobu Yoshikawa – CEO Kazuki Ohta – CTO Rich Ghiossi – VP, Marketing Keith Goldstein – VP, Sales Kengo Hirouchi – Director, Japan Ankush Rustagi – Director, Marketing

Company & Service Introduction

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•  Founded 2011 in Mountain View, CA –  The first cloud service for the entire data pipeline

–  Including: Acquisition, Storage, & Analysis

•  Provide a “Cloud Data Service” –  Fast Time to Value

–  Cloud Flexibility and Economics

–  Simple and Well Supported

•  Treasure Data has over 100+ customers in production –  Incl. Fortune 500 companies

–  500+ Billion new records / month

–  Around 2 Trillion records under management

–  Variety of use cases and verticals

Company Background

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The Treasure Data Team Hiro Yoshikawa – CEO Open source business veteran

Kaz Ohta – CTO Founder of world’s largest Hadoop Group

Jeff Yuan – Director, Engineering LinkedIn, MIT / Michale Stonrebrraker Lab

Keith Goldstein – VP Sales & Bus Dev VP of Bus Dev from Tibco and Talend

Rich Ghiossi – VP Marketing VP of Marketing from ParAccel

Notable Investors Othman Laraki Ex-VP of Growth at Twitter

Jerry Yang Founder of Yahoo!

Yukihiro “Matz” Matusmoto Creator of “Ruby” programming language

James Lindenbaum Founder of Heroku

•  Lots of companies today produce Big Data by having “New Data Sources” (Sensor, Weblog, etc) –  But few have the resources to build a

Big Data Analytics system

•  60-70% of a company’s Big Data time & budget consumed by: –  Infrastructure setup & Maintenance –  Building Collection & Storage Flows –  Hiring/Training Hadoop Expertise

•  On average, it takes 6 months to get a Hadoop environment into production

Problem Statement

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Treasure Data’s Focus

(80% of the needs)

BI Tools Tableau, Metric Insights,

QlikView, Excel, etc.

Treasure Data Service: Overview

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Web logs

App logs

Sensor

CRM

ERP

RDBMS

Streaming Log !Collector (JSON)!

Treasure Agent

Parallel Upload from CSV, MySQL, etc.!

Bulk Import

Treasure Data Cloud

Flexible, Scalable, Columnar Storage!

REST API, SQL, Pig, JDBC / ODBC!

BI Connectivity

REST API, SQL, Pig!

Result Push Dashboards

Custom App, Local DB, FTP Server, etc.

Time to Value Economy & Flexibility Simple & Supported

Acquire Analyze Store

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Our Value Propositions •  Faster time to value

On-demand cloud infrastructure & versatile streaming data collection agent –  Instantly provision a fully tuned & managed infrastructure –  Go live into production on average in 14 days (collection, analytics, & BI)

•  Cloud flexibility and economics Fraction of the cost of traditional solutions by leveraging cloud storage and processing, which scales to meet your needs –  Leverage the cost-advantage of the cloud –  Leverage the elasticity of the cloud – scale on demand –  Predictable monthly subscription fee –  No upfront costs & no long-term commitment

•  Simple and well supported We are passionate about simplicity, and customer support excellence –  Focus your time on analyzing your data –  Rely on us to keep your data secure & online –  We love making customers successful & building long-term relationships

Initial Setup & Onboarding – Two Weeks

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1. Data Collection 2. Data Storage

3. Data Analysis 4. Service & Support

•  Setup, tuning, and monitoring of Treasure Agent

•  Embed Treasure Agent code into applications

•  Basic log templates (register, pay, login, etc.)

•  Basic KPI queries (DAU, MAU, ARPU, etc.)

•  Setup dashboards with basic KPIs

•  Training on creating customized reports and ad-hoc querying

•  Assigned a dedicated technical account manager

•  Real-time support via email, online chat, and call

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Solutions Accelerators

Treasure Data Platform

Out-of-the Box Reporting

Configured Treasure Agent

Solution Components: -  Treasure Data Platform -  Event Collection

Template -  Pre-configured

Treasure Agent Configuration

-  BI Dashboard with KPIs

…

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- Vision - Single Analytics Platform for the World



Presented by


Treasure Data Platform Architecture Overview

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Treasure Data Cloud

Data Acquisition – Streaming Capture

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# Application Code ... ... # Post event to Treasure Data TD.event.post('access', {:uid=>123}) ... ...

Treasure Data Library Java, Ruby, PHP, Perl, Python, Scala,

Node.js

Application Server

Treasure Agent (local)

•  Automatic Micro-batching

•  Local buffering Fall-back

•  Network Tolerance

Open-Sourced as Fluentd Project ( http://fluentd.org/ )

Data Acquisition – Bulk Loader

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Treasure Data Cloud

RDBMS App SaaS

FTP

CSV, TSV, JSON, MessagePack, Apache, regex, MySQL, FTP

Bulk Loader

Prepare Upload Perform Commit

Data Storage

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Treasure Data Cloud

•  Stored “schema-less” as JSON –  Schema can be applied/updated

AFTER storage

•  Compressed & columnar format –  For higher query performance

•  Optimized for time-based filtering

•  Quickly scale-up processing power –  WITHOUT reloading/redistributing the data

time v 13841604

00 {“ip”:”135.52.211.23”, “code”:”0”}

1384162200

{“ip”:”45.25.38.156”, “code”:”-1”}

1384164000

{“ip”:”97.12.76.55”, “code”:”99”}

time ip : string code : int 138416040

0 135.52.211.23 0

1384162200

45.25.38.156 -1

1384164000

97.12.76.55 99

Default (schema-less)

Schema applied

SELECT v[‘ip’] as ip, v[‘code’] as code …

SELECT ip, code …

~30% Faster

Data Analysis

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Treasure Data Cloud

Scripted Processing (Pig): - DataFu (LinkedIn) - Piggybank (Apache)

Heavy Lifting SQL (Hive): - Hive’s Built-in UDFs - TD Added Functions:

- Time Functions - First, Last, Rank - Sessionize

JDBC Connectivity: -  Custom Java Apps -  Standards-based -  BI Tool Integration

Tableau ODBC connector - Leverages Impala

Push Query Results: - MySQL, PostgreSQL - Google Spreadsheet - Web, FTP, S3 - Leftronic, Indicee - Treasure Data Table

Interactive SQL Treasure Query Accelerator (Impala)

Scheduled Jobs - SQL, Pig Scripts - Data Pushes REST API



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Treasure Data General Use Cases

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A case: “14 Days” from Signup to Success

1.  Europe’s largest mobile ad exchange.

2.  Serving >60 billion imps/month for >30,000 mobile apps (Q4 2013)

3.  Immediate need of analytics infrastructure: ASAP!

4.  With TD, MobFox got into production only in 14 days, by one engineer.

"Time is the most precious asset in our fast-moving business, and Treasure Data saved us a lot of it." Julian Zehetmayr, CEO & Founder

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A case: “Replace” in-house Hadoop to TD

1.  Global “Hulu” - Online Video Service with millions of users

2.  Video contents are distributed to over 150 languages.

3.  Had hard time maintaining Hadoop cluster

4.  With TD, Viki deprecated their in-house Hadoop cluster and use engineer for core businesses.

Before

After

“Treasure Data has always given us thorough and timely support peppered with insightful tips to make the best use of their service." Huy Nguyen, Software Engineer

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A case: Treasure Data with BI Tool (Tableau)

1.  World’s largest android application market

2.  Serving >3 billion app downloads for >100 million users

3.  Only one engineer managing the data infrastructure

4.  With TD, the data engineer can focus on analyzing data with existing BI tool

"I will recommend Treasure Data to my friends in a heartbeat because it benefits all three stakeholders: Operations, Engineering and Business." Simon Dong, Principal Architect - Data Engineering



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Treasure Data Platform Fluentd Overview

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•  Open sourced log collector written in Ruby –  Easy to use, reliable and well performance –  Streaming event processing

•  Using rubygems ecosystem to distribute plugins

What is Fluentd?

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Fluentdthe missing log collector

fluentd.org

Data processing pipeline

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Collect Store Process Visualize

Data source

Reporting Monitoring

Data processing pipeline

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Collect Store Process Visualize

Data source

Reporting Monitoring

Important but no defacto

middleware!

Fluentd general example

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tail

insert

eventbuffering

127.0.0.1 - - [11/Dec/2012:07:26:27] "GET / ...127.0.0.1 - - [11/Dec/2012:07:26:30] "GET / ...127.0.0.1 - - [11/Dec/2012:07:26:32] "GET / ...127.0.0.1 - - [11/Dec/2012:07:26:40] "GET / ...127.0.0.1 - - [11/Dec/2012:07:27:01] "GET / ...

...

Fluentd

Web Server

2012-02-04 01:33:51apache.log

{ "host": "127.0.0.1", "method": "GET", ...}

Pluggable Architecture

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Buffer Output

Input

> Forward> HTTP> File tail> dstat> ...

> Forward> File> MongoDB> ...

> File> Memory

Engine

Output

> rewrite> ...

Pluggable Pluggable

Resolve your requirement by writing plugin

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Nagios

MongoDB

Hadoop

Alerting

Amazon S3

Analysis

Archiving

MySQL

Apache

Frontend

Access logs

syslogd

App logs

System logs

Backend

Databasesfilter / buffer / routing

•  Open sourced distribution package of Fluentd –  ETL part of Treasure Data –  deb / rpm / homebrew

•  Including useful components –  Ruby, jemalloc, fluentd –  3rd party gems: td, mongo, webhdfs, etc… –  Init script

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Treasure Agent (td-agent)

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http://packages.treasure-data.com/

Fluentd users

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Treasure Data Platform Backend Overview

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•  RDS –  Store user information, job, status, etc… –  Queue Worker / Scheduler

•  EC2 –  API Server, Hadoop Cluster, Job Worker / Scheduler

•  S3 –  Columnar storage

•  Realtime / Archive storage • MessagePack columnar

•  ELB

AWS components

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Plazma(Hadoop, Storage, Queue and Workers)

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FrontendQueue

WorkerHadoop

Fluentd

Applications push metrics to Fluentd(via local Fluentd)

Librato Metricsfor realtime analysis

Treasure Data

for historical analysis

Fluentd sums up data minutes(partial aggregation)

Hadoop



Presented by


Treasure Data Development Philosophy

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•  TD prefers engineers, who are contributing to the OSS products –  MessagePack, Fluentd, ZeroMQ, Hadoop,

MongoDB, Angular.js, Huahin, D-Lang, etc. –  https://github.com/treasure-data?tab=members

•  Reasons –  Fixing & Improving the other people’s code is

crucial for the distributed team. –  TD’s engineering workflow is really similar with

OSS product workflow. –  A+ OSS engineers will bring another A+ OSS

engineer!

Open-Source Culture

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•  OSS Everything on the Client Side –  http://github.com/treasure-data/ –  http://fluentd.org/

•  TD is helping the world to collect more data in an analytics-ready format

•  2000+ companies (e.g. Nintendo, SlideShare/LinkedIn) are using as OSS product. 3-4% of the users are TD’s customer.

•  We also leverage other OSS products as much as possible.

•  Closed Source on the Cloud Side –  The core value must be a proprietary to sustain as a

business. –  The components can be OSS, but the most of the system will

remain proprietary to create the value chain.

OSS v.s. Proprietary

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•  Solving the Customer Pain is the #1 Priority –  Developers directly provide the support for customers, and spending

30%-40% of the development time to talk with customers –  Developers are the BEST person to come up with the solution. –  # of code lines != value

•  Suffering Oriented Development

–  First, make it possible –  Then, make it beautiful –  Then, make it fast

•  The Largest Customer Pain is NOT always applicable to other customers. –  Need to be brave to say NO. NO. NO. NO. NO….

•  TD doesn’t have 1-year Product Roadmap. Having 3-months roadmap accelerates the development, and other teams (marketing / sales), too.

How to decide Product Roadmap?

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•  13 Engineers as of Nov. 2013 –  5 Engineers in Tokyo, Japan –  8 Engineers in Mountain View, USA –  40% of the whole company

•  Asynchronous Communication –  Use async communication tools as much as possible:

Chat, JIRA, Email, Github, etc. –  Use video conferencing for weekly sync-up

•  English is the primary communication language –  If you cannot speak English, your value is nearly zero at

Treasure Data engineering team.

Distributed Team (International)

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•  Predictable Deployment Cycle –  Weekly Deployment

•  Continuous Deployment didn’t fit into B2B SaaS application, our customers want predictability of the changes.

•  As a distributed team, it’s hard to track the every changes + deployment status.

–  Track every changes on JIRA, and QA engineer is responsible for the deployment too.

•  Continuous Deployment for Staging –  Single branch, always automatically deployed to the staging

environment –  Monitoring is a continuous testing

•  On-Call Alert Schedule, based on the Timezone –  No need to get up around 3am

Distributed Team (Deployment)

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•  Use Cloud Services as Much as Possible –  Don’t hire people, use cloud services. –  Out source everything, except your core value. –  Developers tend to forget his own cost. If you spend 1-hour, it

already costs around $50 as a company.

•  Examples –  EC2 (IaaS) –  CopperEgg (Infrastructure Monitoring) –  NewRelic (Application Performance Management) –  Hosted Chef (Configuration Management) –  Librato Metrics (Application Metrics) –  Pager Duty (Alerting) –  Logentries (Log Search) –  CircleCI, TravisCI (Continuous Integration) –  HipChat, JIRA, Confluence (Development Tool) –  Etc….

Leverage Cloud Services

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Treasure Data Conclusion

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•  Treasure Data, Inc –  Cloud based Data Service for the world –  Customer oriented development

•  Our Unique Products and Culture –  Fluend / Plazma (backend) –  OSS enthusiast

•  Use Cloud or not? –  Cloud leverages an idea but not differentiator –  Focus own vision!

Key points

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情報処理学会 Exciting Coding! Treasure Data

Technology

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