Elasticsearch for SQL Users

As given at the All Things Open 2016 conference.

Transcript

1. 1 Shaunak Kashyap Developer at Elastic @shaunak Elasticsearch for SQL users
2. The Elastic Stack 2 Store, Index & Analyze Ingest User Interface Plugins Hosted Service
3. 3 Agenda Search queries Data modeling Architecture 1 2 3
4. 2 4 Agenda Search queries Data modeling Architecture 1 3
5. 5 Agenda Search queries Data modeling 1 2 3 Architecture
6. 6 Search Queries https://www.flickr.com/photos/samhames/4422128094
7. 7 CREATE TABLE IF NOT EXISTS emails ( sender VARCHAR(255) NOT NULL, recipients TEXT, cc TEXT, bcc TEXT, subject VARCHAR(1024), body MEDIUMTEXT, datetime DATETIME ); CREATE INDEX emails_sender ON emails(sender); CREATE FULLTEXT INDEX emails_subject ON emails(subject); CREATE FULLTEXT INDEX emails_body ON emails(body); curl -XPUT 'http://localhost:9200/enron' -d' { "mappings": { "email": { "properties": { "sender": { "type": "keyword" }, "recipients": { "type": "keyword" }, "cc": { "type": "keyword" }, "bcc": { "type": "keyword" }, "subject": { "type": "text", "analyzer": "english" }, "datetime": { "type": "date" } } } } Schemas
8. 8 Loading the data
9. 9 [LIVE DEMO] • Search for text in a single field • Search for text in multiple fields • Search for a phrase https://github.com/ycombinator/es-enron
10. 10 Other Search Features Stemming Synonyms Did you mean? • Jump, jumped, jumping • Queen, monarch • Monetery => Monetary
11. 11 Data Modeling https://www.flickr.com/photos/samhames/4422128094 https://www.flickr.com/photos/ericparker/7854157310
12. 12 To analyze (text) or not to analyze (keyword)? PUT cities/city/1 { "city": "Raleigh", "population": 431746 } PUT cities/city/2 { "city": "New Albany", "population": 8829 } PUT cities/city/3 { "city": "New York", "population": 8406000 } POST cities/_search { "query": { "match": { "city": "New Albany" } } } QUERY + = ?
13. PUT cities/city/1 { "city": "Raleigh", "population": 431746 } 13 To analyze (text) or not to analyze (keyword)? PUT cities/city/2 { "city": "New Albany", "population": 8829 } PUT cities/city/3 { "city": "New York", "population": 8406000 } Term Document IDs albany 2 new 2,3 raleigh 1 york 3
14. 14 To analyze (text) or not to analyze (keyword)? PUT cities { "mappings": { "city": { "properties": { "city": { "type": "keyword" } } } } } MAPPING Term Document IDs New Albany 2 New York 3 Raleigh 1
15. PUT blog/post/1 { "author_id": 1, "title": "...", "body": "..." } PUT blog/post/2 { "author_id": 1, "title": "...", "body": "..." } PUT blog/post/3 { "author_id": 1, "title": "...", "body": "..." } 15 Relationships: Application-side joins PUT blog/author/1 { "name": "John Doe", "bio": "..." } POST blog/author/_search { "query": { "match": { "name": "John" } } } QUERY 1 POST blog/post/_search { "query": { "match": { "author_id": <each id from query 1 result> } } } QUERY 2
16. PUT blog/post/1 { "author_name": "John Doe", "title": "...", "body": "..." } PUT blog/post/2 { "author_name": "John Doe", "title": "...", "body": "..." } 16 Relationships: Data denormalization POST blog/post/_search { "query": { "match": { "author_name": "John" } } } QUERY PUT blog/post/3 { "author_name": "John Doe", "title": "...", "body": "..." }
17. 17 Relationships: Nested objects PUT blog/author/1 { "name": "John Doe", "bio": "...", "blog_posts": [ { "title": "...", "body": "..." }, { "title": "...", "body": "..." }, { "title": "...", "body": "..." } ] } POST blog/author/_search { "query": { "match": { "name": "John" } } } QUERY
18. 18 Relationships: Parent-child documents PUT blog/author/1 { "name": "John Doe", "bio": "..." } POST blog/post/_search { "query": { "has_parent": { "type": "author", "query": { "match": { "name": "John" } } } QUERY PUT blog { "mappings": { "author": {}, "post": { "_parent": { "type": "author" } } } } PUT blog/post/1?parent=1 { "title": "...", "body": "..." } PUT blog/post/2?parent=1 { "title": "...", "body": "..." } PUT blog/post/3?parent=1 { "title": "...", "body": "..." }
19. 19 Architecture https://www.flickr.com/photos/samhames/4422128094 https://www.flickr.com/photos/haribote/4871284379/
20. 20 RDBMS Triggers database by Creative Stall from the Noun Project 1 2
21. 21 Async replication to Elasticsearch 1 2 3 ESSynchronizer flow by Yamini Ahluwalia from the Noun Project
22. 22 Async replication to Elasticsearch with Logstash 1 2 3
23. 23 Forked writes from application 1 2
24. 24 Forked writes from application (more robust) 1 2 queue by Huu Nguyen from the Noun Project ESSynchronizer 3 4
25. 25 Forked writes from application (more robust with Logstash) 1 2 3 4
26. 26 Questions? @shaunak https://www.flickr.com/photos/nicknormal/2245559230/