High-Scale Entity Resolution in Hadoop
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eBay maintains hundreds of millions of accounts across its properties that are unstructured and in different formats. Identifying which accounts belong to the same person enables eBay to personalize customer experiences, provide customer service, and fight fraud. MapReduce provides a robust design pattern to simplify high-scale entity resolution through parallelized modular operations, including linking accounts pairwise, identifying connected components through iterative MapReduce jobs, and validating the results.
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We will finish the story of a successful rugby team with insight into the special skills needed from each player to win the match!
DETAILS
This session is part business, part technical. We will talk about infrastructure, security and project management as well as the industrial usage of Hive, HBase, Kafka, and Spark within an industrial Corporate and Investment Bank environment, framed by regulatory constraints.
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Building a Large-Scale, Adaptive Recommendation Engine with Apache Flink and ...
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The Challenge of Driving Business Value from the Analytics of Things (AOT)
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- 1. High-Scale Entity Resolution in Hadoop June 29, 2016 Gurpreet Singh & Tom Schweiger
- 2. PROBLEM STATEMENT: eBay maintains hundreds of millions of accounts* across our properties and partners, that are sometimes unstructured, in different formats, different character sets, and are changing over time. Identifying which accounts belong to the same person enables us to personalize each customer's experience, deliver great customer service, and fight fraud.
- 3. PROBLEM SOLVED!: Identifying which accounts belong to the same person is hard under normal circumstances. Doing so daily at this scale is a feat that defies superlatives. MapReduce gives us a robust design pattern to simplify entity recognition as a series of parallelized unit operations.
- 4. Technology Stack High-Scale Entity Resolution in Hadoop 4
- 5. Modular Solution: High-Scale Entity Resolution in Hadoop 5 Sources Edges Graph Table Account Entity 102832 10921 236896 10921 786273 10921 324324 23987 349709 73652 152631 73652 543273 37726
- 6. SOURCES – Overall Data Flow High-Scale Entity Resolution in Hadoop 6
- 7. EDGES: Linking accounts pairwise •Multiple strategies for blocking and matching accounts. •Each strategy writes to its own ‘bucket’ •Each strategy is a configuration-driven MR with Mappers that can: – Read simultaneously from multiple file types (text, sequence, Avro, ORC) and layouts (fixed, delimited, json, CSV) – Extract, transform, normalize, and combine fields – Burst records to create multiple key-value pairs and Reducers that can: – Embed a rules-based matching engine that is configured on load – Embed, build, and search Lucene indexes High-Scale Entity Resolution in Hadoop 7
- 8. GRAPH: Identify and validate connected components •Iterative MR for identifying connected components •Connected components are validated for integrity and over-grouping, and partitioned (connected components are relatively small) High-Scale Entity Resolution in Hadoop 8
- 9. Operational Experience • Infrastructure Issues – Hadoop Upgrades .. – Shared Environment • Source Data Issues – Owner Changes – ID Issues – Knowledge? • Too many Mappers • Too many Versions • Space Issues • Large Clusters High-Scale Entity Resolution in Hadoop 9
- 10. Performance Numbers High-Scale Entity Resolution in Hadoop 10
- 11. Summary: •Entity resolution at scale •Daily processing of full data set •Accurate results •Reliable, stable process High-Scale Entity Resolution in Hadoop 11