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Elastic{ON} 2018 - Helping Grubhub Diners Find ...

Elastic Co
March 01, 2018

Elastic{ON} 2018 - Helping Grubhub Diners Find the Perfect Meal

Elastic Co

March 01, 2018
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  1. 2

  2. Where we were before Elasticsearch • Used Solr • Deployed

    servers under an ELB • Multi datacenter hot-hot • Re-build indices and ship them into production with 0 downtime • We were growing and needed to change …. 4
  3. Elasticsearch Deployment 9 AWS Region AWS Availability Zone Elasticsearch •

    AWS Cloud Plugin • Netflix Eureka • Metrics in Datadog • Tags in Eureka • Eureka aware Client • Index & Node Discovery • Shard allocation aware • Snapshots in S3 Master Node Master Node Master Node Master Node Data Nodes Data Nodes Data Nodes Data Nodes Eureka Eureka Eureka Eureka App Master Node App S3
  4. Elasticsearch Snapshots • Snapshots for everyone • Emulating a Production

    dataset: ◦ Snapshot of the market ◦ Test out a sort • Performance testing: ◦ Index in production ◦ Replay production load 10
  5. Data Collection and Feedback 12 Search Cassandra S3 Elasticsearch A

    B TEST Search Logs Clickstream Events Impressions Data Science
  6. Improving Search Features • Create consumable data • What are

    your KPIs? • What user attributes can you use to influence KPIs? • Decouple moving parts to allow for independent testing • Measure, make changes and improve 13
  7. Metrics that matter 15 • Index store size and memory

    • Active searcher threads • Balance Read and Write • Query distribution / Profiler
  8. Location Based Sharding 17 • Balance data in each shard.

    • Tuned for index size. • Tuned for query throughput. • Indices behind an alias to allow for greater flexibility.
  9. Beyond Elasticsearch 18 • Impressions and click tracking • Relevance

    tuning • Data analytics • Machine learning • A/B testing
  10. 19