This document discusses Deezer's use of Elasticsearch for search, recommendations, and analysis of music metadata. It provides an overview of Deezer's Elasticsearch architecture, which includes indexing over 50 million tracks from Hadoop and replicating indexes across clusters. It also discusses how Deezer queries Elasticsearch using custom analyzers, multi search APIs, and function score queries for recommendations. Finally, it describes Deezer's use of the ELK stack to analyze over 2 billion logs and metrics documents through Kibana dashboards.

1. Elastic @Deezer
Aurélien Saint Requier, Search Data Scientist
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2. /01
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Where?
Elasticsearch architecture
Querying Elasticsearch
ELK stack for analysis
Table of contents
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3. Where?
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4. For search features
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5. For chart and new release features
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6. For recommendation features
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7. Elasticsearch Architecture
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8. Elasticsearch architecture
Our needs
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● Search and recommend
○ 3 millions of artists
○ 5 millions of albums
○ 50 millions of tracks
○ 2 millions of playlists
● Search and recommend content based on
○ metadata and other features
○ tag description
● New releases should become available in less than 2 hours
● Queries have to respond in less than 100ms

9. Elasticsearch architecture
Overview
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10. Elasticsearch architecture
Data workflow
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11. Elasticsearch architecture
Data workflow
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12. How we deploy full indexes in production ?
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1. Get json data from Hadoop cluster (using WebHDFS)
2. Index documents on mastersearch (using ES bulk api)
3. Package the new index :
3.1. compress the ES index directory
3.2. generate a deployment script
4. Copy the package on the temporary node of each cluster (using
assassin, an homemade rsync deploy script)
5. Run deployment script :
5.1. Start a temporary ES instance and load the new index
5.2. Set the required number of replica
5.3. Wait until data is replicated and then shutting down the
temporary ES instance
5.4. Warm the new index
5.5. Switch alias on the new index and close the old index
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13. Querying Elasticsearch
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14. How we analyze musical data?
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Use custom analyzers
Black Pearl (He's A Pirate) [feat. Sidney Housen] - EP
The Black Eyed Peas
● Lowercase asciifolding and char filters, music field synonyms :
● Edge_ngram tokenizer :

15. How we search in our data?
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● Using a Java internal Elasticsearch plugin :

16. How we search in our data?
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● Using Multi Search API and Query DSL:

17. How we recommend our data?
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● Using function score queries :

18. How we explore our data?
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● Using aggregation:

19. Some feedbacks
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● In numbers:
○ More 25 millions queries a day, around 5000 queries / minute
○ Around 95% queries respond in less 100ms
● In lessons :
○ Be careful with fielddata usage
○ Big jvm ES instance = Long gc time
○ Avoid prefix queries : use edge-ngram tokenizer and do match
queries*
● In future :
○ Use a dedicated client/data/master architecture
○ Stop fuzzy queries (replaced by a “Did you mean“ approach)*
○ Migrate to Elasticsearch v2
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*https://www.elastic.co/blog/elasticsearch-queries-or-term-queries-are-really-fast

20. ELK for analysis
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21. Use of ELK
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● Elasticsearch v1.7.5 :
○ cluster of 3 nodes
○ index logs from Logstash and homemade scripts
○ Around 2 billions of documents
● Logstash 1.5
● Kibana v 4.1.1
○ 26 dashboards / 189 visualisations
● Tools:
○ curator for index retention
○ elasticdump for saving kibana settings
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22. Use cases
Monitoring
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23. Use cases
Analysis what our users search
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24. Thanks for your attention
We are hiring !
jobs.deezer.com
Questions?