2. Hadoop and Cloudera
Managing Petabytes with Open Source
Jeff Hammerbacher
Chief Scientist and Vice President of Products, Cloudera
December 3, 2009
Thursday, December 3, 2009
3. My Background
Thanks for Asking
▪ hammer@cloudera.com
▪ Studied Mathematics at Harvard
▪ Worked as a Quant on Wall Street
▪ Conceived, built, and led Data team at Facebook
▪ Nearly 30 amazing engineers and data scientists
▪ Several open source projects and research papers
▪ Founder of Cloudera
▪ Vice President of Products and Chief Scientist (other titles)
▪ Also, check out the book “Beautiful Data”
Thursday, December 3, 2009
4. Presentation Outline
▪ What is Hadoop?
▪ HDFS
▪ MapReduce
▪ Hive, Pig, Avro, Zookeeper, and friends
▪ Solving big data problems with Hadoop at Facebook and Yahoo!
▪ Short history of Facebook’s Data team
▪ Some stats about Hadoop use at Yahoo!
▪ What we’re building at Cloudera
▪ Questions and Discussion
▪ Including some questions Ajay sent me
Thursday, December 3, 2009
5. What is Hadoop?
▪ Apache Software Foundation project, mostly written in Java
▪ Inspired by Google infrastructure
▪ Software for programming warehouse-scale computers (WSCs)
▪ Hundreds of production deployments
▪ Project structure
▪ Hadoop Distributed File System (HDFS)
▪ Hadoop MapReduce
▪ Hadoop Common
▪ Other subprojects
▪ Avro, HBase, Hive, Pig, Zookeeper
Thursday, December 3, 2009
6. Anatomy of a Hadoop Cluster
▪ Commodity servers
▪ 1 RU, 2 x 4 core CPU, 8 GB RAM, 4 x 1 TB SATA, 2 x 1 gE NIC
▪ Typically arranged in 2 level architecture
▪
Commodity
40 nodes per rack Hardware Cluster
▪ Inexpensive to acquire and maintain
•! Typically in 2 level architecture
–! Nodes are commodity Linux PCs
Thursday, December 3, 2009 –! 40 nodes/rack
7. HDFS
▪ Pool commodity servers into a single hierarchical namespace
▪ Break files into 128 MB blocks and replicate blocks
▪ Designed for large files written once but read many times
▪ Files are append-only via a single writer
▪ Two major daemons: NameNode and DataNode
▪ NameNode manages file system metadata
▪ DataNode manages data using local filesystem
▪ HDFS manages checksumming, replication, and compression
▪ Throughput scales nearly linearly with node cluster size
Thursday, December 3, 2009
9. Hadoop MapReduce
▪ Fault tolerant execution layer and API for parallel data processing
▪ Can target multiple storage systems
▪ Key/value data model
▪ Two major daemons: JobTracker and TaskTracker
▪ Many client interfaces
▪ Java
▪ C++
▪ Streaming
▪ Pig
▪ SQL (Hive)
Thursday, December 3, 2009
10. MapReduce
MapReduce pushes work out to the data
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11. Hadoop Subprojects
▪ Avro
▪ Cross-language framework for RPC and serialization
▪ HBase
▪ Table storage on top of HDFS, modeled after Google’s BigTable
▪ Hive
▪ SQL interface to structured data stored in HDFS
▪ Pig
▪ Language for data flow programming; also Owl, Zebra, SQL
▪ Zookeeper
▪ Coordination service for distributed systems
Thursday, December 3, 2009
12. Hadoop Community Support
▪ 185+ contributors to the open source code base
▪ ~60 engineers at Yahoo!, ~15 at Facebook, ~15 at Cloudera
▪ Over 500 (paid!) attendees at Hadoop World NYC
▪ Three books (O’Reilly, Apress, Manning)
▪ Training videos free online
▪ Regular user group meetups in many cities
▪ New York Meetup group has 238 members
▪ University courses across the world
▪ Growing consultant and systems integrator expertise
▪ Commercial training, certification, and support from Cloudera
Thursday, December 3, 2009
13. Hadoop Project Mechanics
▪ Trademark owned by ASF; Apache 2.0 license for code
▪ Rigorous unit, smoke, performance, and system tests
▪ Release cycle of 9 months
▪ Last major release: 0.20.0 on April 22, 2009
▪ 0.21.0 will be last release before 1.0; nearly complete
▪ Subprojects on different release cycles
▪ Releases put to a vote according to Apache guidelines
▪ Releases made available as tarballs on Apache and mirrors
▪ Cloudera packages a distribution for many platforms
▪ RPM and Debian packages; AMI for Amazon’s EC2
Thursday, December 3, 2009
14. Hadoop at Facebook
Early 2006: The First Research Scientist
▪ Source data living on horizontally partitioned MySQL tier
▪ Intensive historical analysis difficult
▪ No way to assess impact of changes to the site
▪ First try: Python scripts pull data into MySQL
▪ Second try: Python scripts pull data into Oracle
▪ ...and then we turned on impression logging
Thursday, December 3, 2009
15. Facebook Data Infrastructure
2007
Scribe Tier MySQL Tier
Data Collection
Server
Oracle Database
Server
Thursday, December 3, 2009
16. Facebook Data Infrastructure
2008
Scribe Tier MySQL Tier
Hadoop Tier
Oracle RAC Servers
Thursday, December 3, 2009
17. Major Data Team Workloads
▪ Data collection
▪ server logs
▪ application databases
▪ web crawls
▪ Thousands of multi-stage processing pipelines
▪ Summaries consumed by external users
▪ Summaries for internal reporting
▪ Ad optimization pipeline
▪ Experimentation platform pipeline
▪ Ad hoc analyses
Thursday, December 3, 2009
18. Workload Statistics
Facebook 2009
▪ Largest cluster running Hive: 4,800 cores, 5.5 PB of storage
▪ 4 TB of compressed new data added per day
▪ 135TB of compressed data scanned per day
▪ 7,500+ Hive jobs on per day
▪ 80K compute hours per day
▪ Around 200 people per month run Hive jobs
(data from Ashish Thusoo’s Hadoop World NYC presentation)
Thursday, December 3, 2009
19. Hadoop at Yahoo!
▪ Jan 2006: Hired Doug Cutting
▪ Apr 2006: Sorted 1.9 TB on 188 nodes in 47 hours
▪ Apr 2008: Sorted 1 TB on 910 nodes in 209 seconds
▪ Aug 2008: Deployed 4,000 node Hadoop cluster
▪ May 2009: Sorted 1 TB on 1,460 nodes in 62 seconds
▪ Sorted 1 PB on 3,658 nodes in 16.25 hours
▪ Other data points
▪ Over 25,000 nodes running Hadoop across 17 clusters
▪ Hundreds of thousands of jobs per day from over 600 users
▪ 82 PB of data
Thursday, December 3, 2009
20. Hadoop at Cloudera
Cloudera’s Distribution for Hadoop
▪ Open source distribution of Apache Hadoop for enterprise use
▪ Includes HDFS, MapReduce, Pig, Hive, and ZooKeeper
▪ Ensures cross-subproject compatibility
▪ Adds backported patches and customer-specific patches
▪ Adds Cloudera utilities like MRUnit and Sqoop
▪ Better integration with daemon administration utilities
▪ Follows the Filesystem Hierarchy Standard (FHS) for file layout
▪ Tools for automatically generating a configuration
▪ Packaged as RPM, DEB, AMI, or tarball
Thursday, December 3, 2009
21. Hadoop at Cloudera
Training and Certification
▪ Free online training
▪ Basic, Intermediate (including Hive and Pig), and Advanced
▪ Includes a virtual machine with software and exercises
▪ Live training sessions
▪ One live session per month somewhere in the world
▪ If you have a large group, we may come to you
▪ Certification
▪ Exams for Developers, Administrators, and Managers
▪ Administered online or in person
Thursday, December 3, 2009
22. Hadoop at Cloudera
Services and Support
▪ Professional Services
▪ Get Hadoop up and running in your environment
▪ Optimize an existing Hadoop infrastructure
▪ Design new algorithms to make the most of your data
▪ Support
▪ Unlimited questions for Cloudera’s technical team
▪ Access to our Knowledge Base
▪ Help prioritize feature development for CDH
▪ Early access to upcoming Cloudera software products
Thursday, December 3, 2009
23. Hadoop at Cloudera
Commercial Software
▪ General thesis: build commercially-licensed software products
which complement CDH for data management and analysis
▪ Current products
▪ Cloudera Desktop
▪ Extensible user interface for users of Cloudera software
▪ Upcoming products
▪ Talk to me in private
Thursday, December 3, 2009
24. Cloudera Desktop
Big Data can be Beautiful
Thursday, December 3, 2009
25. Gemini-Specific Questions
▪ Scala
▪ ScalaNLP’s SMR, Jonhnny Weslley’s SHadoop
▪ Jeff Hodges’s Componentize
▪ Compliance/regulatory domain
▪ Security roadmap
▪ HADOOP-4487
▪ HIVE-842
▪ Real-time BI
▪ MapReduce Online Prototype (MOP)
▪ Talk to me in private
Thursday, December 3, 2009
26. (c) 2009 Cloudera, Inc. or its licensors. "Cloudera" is a registered trademark of Cloudera, Inc.. All rights reserved. 1.0
Thursday, December 3, 2009
