eBay has one of the largest Hadoop clusters in the industry with many petabytes of data. This talk will give an overview of how Hadoop and HBase have been used within eBay, the lessons we have learned from supporting large-scale production clusters, as well as how we plan to use and improve Hadoop and HBase moving forward. Specific use cases, production issues and platform improvement work will be discussed.

1. Hadoop @ eBay Marketplaces
Ming Ma
June 27th, 2013

2. Overview
• Hadoop growth @ eBay Marketplaces
• Availability study
• Opportunities ahead

3. Big Data @ eBay Marketplaces
120+ Million Active users
300+ Million search queries every single day
350+ Million items available
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4. Data Sets
•Inventory Data
– Product Listings, Catalogue, Quantity etc.
•Transactional Data
– Buying, Returning etc.
•User Behavioral Data
– Click stream, comments, suggestions, user activities etc.
•Customer profiles
– Buyer, Seller, Partner information etc.
•Machine data
– Logs, application data etc.
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5. Hadoop Evolution @ eBay Marketplaces
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
2013
• Shared
clusters
• 4k+ node
• 40,000+ core
• 50s PB
• HDP
2009
Search
• 10s-
nodes
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6. Shared vs. Dedicated Clusters
Shared clusters
– 10s of PB and 10s of thousands of slots per cluster
– Run HDP 1.2
– Used 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 clusters
– Very specific use cases like Index Building
– Tight SLAs for jobs (in order of minutes)
– Immediate revenue impact
– Usually smaller than our shared clusters, but still big (100s of nodes…)
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7. Job Distribution by Type
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8. Use Case Examples
•Cassini, full re-write of eBay’s search engine:
– Use MR to build full and incremental near-real-time indexes
– Data for indexing is stored in HBase for efficient updates and random read
– Strong SLAs
– 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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9. eBay Hadoop Data Platform
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Data Ingest
Extract
Load Validate
Transform
Clients
Java
Scala
Pig
Hive Cascading
Mobius
Hadoop Behavioral Transactional Inventory
Metadata Metastore Type System ServiceAPI
Data Access
Java POJO
Pig UDF
Hive UDF
Tools
ETL Monitor
Metadata Mgmt
Data Catalog
User Mgmt

10. Platform Innovation
•Many reliability improvements
•New Security features
– Multi-realm support
– Encryption
– https in hadoop 1
•Hadoop 2.0
– MR 1 and YARN binary compatibility
•Automation for operations
– Machine decommission and re-commission process
•Data and user management
– Metadata management
– User account provisioning
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11. Overview
• Hadoop growth @ eBay
• Availability study
• Next steps

12. Case study – defective applications
•HBase: A test app created heavy write load
– Test app used all region server RPC threads
– All RPCs are blocked by region flush
– RPC requests from production HBase MR job timed out
•HDFS: An app created lots of small files inside map tasks
– NN RPC Queue length spiked
– DN heartbeat RPC can’t be processed
– HDFS replication storm
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13. Case study – platform bugs
•Hadoop:
– DFSClient.LeaseChecker thread leak in job tracker -> bi-weekly JT restart
– dfs.datanode.balance.bandwidthPerSec set to 200MB -> big performance impact
•JVM:
– leap second bug -> All clusters were down the same time
– GC setting -> NN full GC happened regularly
•OS:
– “Divide by zero” in CentOS and RH 6.1 -> machine reboot
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14. Case study – cluster maintenance
•Code rollout:
– NN SPOF
– RPC compatibility between old and new versions
•Hadoop configuration change:
– Likely required Hadoop JVM restart
– Rolling restart has impact on job latency
– Datanode rolling restart caused HBase region servers to exit
•Machines re-commission:
– Hadoop version drift
– OS configuration bug reappeared
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15. Metrics
•Definition:
– Availability = MTBF ( mean time between failure ) / MTBF + MDT ( mean down time )
– Down time includes planned maintenance
•Measurement:
– Synthetic transaction approach
– Run regular canary work count MR job
– Canary job times out in X minutes
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16. More about metrics
•Availability != MTTR ( mean time to recover )
– MTTR is more important for applications like Cassini index build
•What is considered “available”?
– Performance degradation
– % of live slave nodes
– Other entry points such as Web UI
– Core data set availability
– Multi-tenancy scenario
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17. Ways to improve availability
•Automation
– Use puppet and daemontools
– Monitor system health
•Redundancy
– Namenode HA
– Hot standby region server
•Isolation
– HDFS federation
– Region server grouping
•Congestion control
– RPC congestion control, Hadoop-9640
– Apply to both HDFS and HBase
•Features to enable “no downtime maintenance”
– Dynamic configuration update
– RPC compatibility
– Better ways to do rolling restart
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18. Overview
• Hadoop growth @ eBay
• Availability study
• Next steps

19. Opportunities ahead
•More automation
•Availability and scalability
– Hadoop 2.0
– HBase fast recovery time
•Multi-tenancy
– Run production jobs with strong SLAs in big shared clusters
– QoS in HDFS and HBase
•New scenarios
– Interactive Analysis with SQL language
– Direct Hadoop Access from dev machines
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