eBay Marketplaces Ming Ma June 27 th, 2013

Overview Hadoop eBay Marketplaces Availability study Opportunities ahead

Big eBay Marketplaces 120+ Million Active users 300+ Million search queries every single day 350+ Million items available eBay Marketplaces 3

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. eBay Marketplaces 4

Hadoop eBay Marketplaces 2007 Single digit nodes 2010 Shared cluster 100s nodes 1000s + core PB CDH 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 eBay Marketplaces 5

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…) eBay Marketplaces 6

Job Distribution by Type eBay Marketplaces 7

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 eBay Marketplaces 8

eBay Hadoop Data Platform eBay Marketplaces 9 Data Ingest Extract Load Validate Transform Clients Java Scala Pig Hive Cascading Mobius Hadoop Behavioral Transactional Inventory Metadata Metastore Type System Service API Data Access Java POJO Pig UDF Hive UDF Tools ETL Monitor Metadata Mgmt Data Catalog User Mgmt

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 eBay Marketplaces 10

Overview Hadoop eBay Availability study Next steps

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 eBay Marketplaces 12

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 eBay Marketplaces 13

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 eBay Marketplaces 14

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 eBay Marketplaces 15

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 eBay Marketplaces 16

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 eBay Marketplaces 17

Overview Hadoop eBay Availability study Next steps

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 eBay Marketplaces 19