Hadoop @ eBay: Past, Present and Future

Presentation on theme: "Hadoop @ eBay: Past, Present and Future"— Presentation transcript:

Slide1
Hadoop @ eBay: Past, Present and Future
Ryan HennigHadoop Platform TeamSlide2
ABOUT MESlide3
RYAN HENNIG
Born and raised in Seattle, WAStudied Computer Science at University of Washington in Seattle
Worked on Microsoft SQL Server 2006 – 2012Shipped SQL Server 2008, 2008 R2, 2012
Joined eBay Hadoop team in early 2012
- Based in Bellevue, suburb of Seattle
COMPUTE AND DATA INFRASTRUCTURE
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AGENDA
Past: Growth of Hadoop at eBayPresent
: Hadoop Use Cases, Operations ToolsFuture: Hadoop 2.0Slide5
HADOOP AT EBAY:
PASTGrowth of Hadoop at eBay
Adventures in ForkingPartnership with HortonworksSlide6
HADOOP EVOLUTION @ eBay
HADOOP AT EBAY: PAST
6
2007
Single digit nodes
2010
Shared cluster
100s nodes
1000s + core
PB
CDH2
2011
Shared clusters
1000s node
10,000+ core
10s PB
Wilma (0.20)
2012
Shared clusters
1000s node
10,000+ core
10s PB
Argon (0.22)
2013
Shared clusters

4k
+ node
40
,000+ core
50s PBHDP 1.x
2009
Search
10s
- nodesSlide7
ADVENTURES IN FORKING
2007-2010: eBay runs shared clusters on Cloudera Distribution of Hadoop2010-2012: eBay runs shared clusters on custom Hadoop versions
2010: Wilma (based on 0.20)2011: Argon (based on 0.22)2012: Custom branch abandoned
Lessons Learned
Forking a fast-changing open source project is difficult and risky
Balancing Development and operations needs
Development team size
Facebook had 100
eBay had 15Coordination with open source community = lots of overhead
Divergence from open source: Push changes early and often
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EBAY AND HORTONWORKS
2012: eBay enters partnership with HortonWorks
GoalsFocus on eBay-specific development internallyLeverage HortonWorks
expertise for general Hadoop Development
Avoid source code divergence
by making open source contribution a priority
Benefits to
HortonWorks
Credibility enhanced by having a well-known customerAbility to test at large scale
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HADOOP AT EBAY:
PRESENTShared and Dedicated Clusters
Job DistributionUse Case ExampleseBay Data Platform OverviewSlide11
SHARED AND DEDICATED CLUSTERS
Shared
clusters10s of PB and 10s of thousands of slots per clusterUsed primarily for analytics of user behavior and inventory
Mix of production and ad-hoc jobs
Mix of MR, Hive, PIG, Cascading etc.
Hadoop and
HBase
security
enabled
Dedicated clustersVery specific use cases like Index BuildingTight SLAs for jobs (in order of minutes)
Immediate revenue impact
Usually smaller than our shared clusters, but still big (100s of nodes…)
HADOOP AT
EBAY: PRESENT
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JOB DISTRIBUTION BY TYPE
HADOOP AT EBAY: PRESENT
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USE CASE EXAMPLES
Cassini, eBay’s new search
engine:Use MR to build full and incremental near-real-time indexesRaw Data is stored in HBase
for efficient updates and random read
Strong
SLAs: < 10 minutes
Run on dedicated clusters
Related and similar Items recommendations:
Use transactional data, click stream data, search index, etc.
Production MR jobs on a shared cluster
Analytics dashboard:
Run Mobius MR jobs to join click stream data and transactional data
Store summary data in
HBase
Web application to query
HBase
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HADOOP OPERATIONS
LDAP Integration
- All users stored in Active Directory, accessed via LDAP - Access to MapReduce Queues granted via
MapReduce
queues

- Batch users: shared by a group of users
Security

- Kerberos as implemented by Microsoft Active Directory - One domain for users, another for service/server principals

- Batch
users authenticated via
keytabs
, not passwords
Misc

- 10’s of slave nodes are broken at any given time

- Often need to add several racks of machines at a time
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HADOOP OPERATIONS
Team has Development and Operations Responsibilities
2 Huge shared clusters1800+ users, exponential growthAbout 10 Hadoop developers
Recently: operations work moved to dedicated team
Developed several tools to manage operations
Hadoop Management Console
: user-facing web app
ldap
-admin
: swiss-army knife style tool for
hadoop
admins
Puppet
: for adding machines to the clusters, many racks at a time
Decom
/
Recom
scripts
: automatic detection, repair, decommission, and
recommission
of slave nodes
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HADOOP MANAGEMENT CONSOLE
Custom Web application built on Ruby on RailsSelf-service tools are continually added to reduce support load
User ManagementAccess RequestsGroup MembershipBatch User Management
New Requests
Sudoer
management
Dataset Management
Explore Datasets
Request New dataset transfer between Teradata and Hadoop
Metadata toolsEach dataset is stored in custom XML formatCode Generation: Hive Tables, Java POJOs
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ldap-admin
Command-line tool written in Ruby
Swiss-army knife tool, features added on demand for support issuesOften used features:Add a user to a group
View key details for LDAP users and groups
List all users, batch users,
hadoop
groups
Reset batch user passwords and
keytabs
Show/add/remove sudoers for a batch accountRun user diagnostics: check permissions,
keytabs
,
etc
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HADOOP AT EBAY:
FUTUREHDFS Federation
YARNNew ScenariosStorage and Operational EfficiencySlide26
HDFS HA and Federation
HDFS High-Availability for ReliabilityNameNode in Hadoop 1.0 is a Single Point of Failure
Automated failover to hot standby Depends on ZooKeeper
HDFS Federation for
Scalability and Isolation
Hadoop 1.0: Single
NameNode
service
“Secondary NameNode” is not for failoverStorage scales horizontally, but Namespace scales vertically
No isolation for different tenants or applicationsHadoop 2.0: HDFS Federation
Partition the HDFS Namespace
Many independent
NameNodes
Allows direct access to Block Storage w/o going through HDFS interface
HADOOP AT
EBAY: FUTURE
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HDFS HA
HADOOP AT EBAY: FUTURE
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HDFS HA
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HDFS HA
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HDFS Federation
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Horizontal Scalability of HDFS Namespace
Multiple independent
NameNodes
serving a
subtree
of the
NameSpace
Example: NN1 provides /users, NN2 provides /reportsSlide31
YARN
31Hadoop 1.0:
MapReduce
JobTracker

and
TaskTracker
servicesHandles Resource Management, Job Execution
Hadoop 2.0: YARNRefactoring
Responsiblities
of
JobTracker
and
TaskTracker
into more general platform
Global
ResourceManager
Cluster-wide resource managements
Per-application
ApplicationMaster
Application-specific job controlSlide32
YARN
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YARN
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YARN
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YARN
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New Scenarios
Iterative QueryStinger (Hive), Impala, etc
Rapid Data exploration and analysisGraph DatabasesTitanDB, Giraph

Billions of vertices and edges
Complex Graph Traversals
Applications: PayPal fraud detection, Social Graph Analysis
Real-Time Processing
Storm (Twitter), Apache S4
Reinforcement Learning, Monitoring
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Efficiency and Reliability
Storage EfficiencyHDFS introduces a 3x storage cost for its replicas
HDFS-RAID: more reliability for 1.5x storage costReed-SolomonLocally Repairable Codes (Project Xorbas
)
Tradeoff: the cost of repairing lost data is much higher
Operational Efficiency
More automation
More self-service tools
Better Monitoring
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Open Source
HMC MetadataLong term goal: standardize on open source technologies (
HCatalog)Short term: explore what should be open sourcedHadoop Management ConsoleHadoop Access Request Automation
Batch user creation and management
Metadata management
Code generation of dataset to Hive tables and Java POJOs
l
dap_admin
toolsVery useful but tightly coupled to eBay’s LDAP configuration
Willing to open source if there is interest
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THANK YOU
Questions?

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Hadoop @ eBay: Past, Present and Future

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