More Related Content
What's hot
What's hot (20)
Similar to ElasticSearch Basics
Similar to ElasticSearch Basics (20)
Recently uploaded
Recently uploaded (20)
ElasticSearch Basics
- 2. Why we need SEARCH ENGINE!!! Work Life without Google ? Amazon,Flipkart,Facebook,Twitter,GitHub, StackOverflow,Zomato don’t have a search capability ?
- 3. You know, for search … Lot of DATA around us but less INFORMATION. Make our life easier. Find relevant stuff. Find it faster. For research,to shop,for entertainment etc
- 4. Elasticsearch !!! Real-time(near real time) distributed search & analytics engine Runs on top of Apache Lucene, written in Java,supports REST API Search Engine Software Private search engine service (like a Bing or a Google) but with say, private, sensitive, or confidential data/documents that you don’t want on the public web Developed by Shay Banon
- 5. ElasticSearch Clients Git Hub StackOverflow Wikipedia The Guardian SoundCloud McGraw-Hill
- 6. Relational db vs Elasticsearch
- 7. ElasticSearch Concepts Document : JSON document stored in ES. Like row in table in Relational DB Id : Uniquely identifies a document Field : key-value pairs. Like column in Relational DB - Simple value like string ,integer, date - Array or an object Type : Like a table in realational DB.Has list of fields.
- 8. ElasticSearch Concepts Near RealTime(nrt) : Slight time lag between index a document and being searchable. Shard : Low level worker unit,Single lucene instance - Primary Shard (Physically stored document) - Replica Shard (Copy of primary shard) Index : Like Database in relational db,Logical namespace which maps to primary and replica shard Node : Running instance of elasticsearch
- 9. ElasticSearch Concepts Cluster : Collection of one or more nodes - Facilitates indexing - Search capabilities across nodes
- 10. Inside a Node
- 11. Inside a cluster
- 12. ElasticSearch Getting Started … Recent version of Java elasticsearch.org/download Latest version of any browser Marvel & Sense Marvel : monitoring and management tool Sense : interactive console
- 13. Talking to ElasticSearch RESTful api json over http A request to Elasticsearch consists of the same parts as any HTTP request: curl -X<VERB> '<PROTOCOL>://<HOST>/<PATH>' -d '<BODY>‘ curl -X<VERB> '<PROTOCOL>://<HOST>/<PATH>?<QUERY_STRING>'
- 14. Document Oriented Stores entire objects or documents. It not only stores them, but also indexes the contents of each document in order to make them searchable Elasticsearch uses JavaScript Object Notation, or JSON, { "email": "john@smith.com", "first_name": "John", "last_name": "Smith", "info": { "bio": "Eco-warrior and defender of the weak", "age": 25, "interests": [ "dolphins", "whales" ] }, "join_date": "2014/05/01" }
- 15. Create Index,Insert Data … Cluster : myelasticsearch Node : The Dark Knight Index : Megacorp Type : Employee A request to Elasticsearch consists of the same parts as any HTTP request(Using Sense): <VERB> '<PROTOCOL>://<HOST>/<INDEX>/<TYPE>/ID ' '<BODY>‘ <VERB> '<PROTOCOL>://<HOST>/<PATH><INDEX>/<TYPE>/ID /?<QUERY_STRING>' Create Index Example : PUT localhost:9201/megacorp/employee/1 { "first_name" : "John", "last_name" : "Smith", "age" : 25, "about" : "I love to go rock climbing", "interests": [ "sports", "music" ] }
- 16. What is Inverted Index? It allows very fast full text search. Doc_1 : The quick brown fox jumped over the lazy dog Doc_2 : Quick brown foxes leap over lazy dogs in summer.
- 17. Inverted Index cont Search “quick brown” Both docs match here. Doc_1 is more relevant.
- 18. Analysis and Analyzers Character filters A character filter could be used to strip out HTML, or to convert &characters to the word and. Tokenizer Next, the string is tokenized into individual terms by a tokenizer. Token filters Last, each term is passed through any token filters in turn, which can change terms (for example, lowercasing Quick), remove terms (for example, stopwords such as a, and, the) or add terms (for example, synonyms like jump and leap). Examples: Standard Analyzer,Simple Analyzer,Whitespace analyzer,Language Analyzers etc
- 19. Inverted Index After Analysis Doc_1 : The quick brown fox jumped over the lazy dog Doc_2 : Quick brown foxes leap over lazy dogs in summer. Quick can be lowercased to become quick. foxes can be stemmed--reduced to its root form—to Become fox. Similarly, dogs could be stemmed to dog. jumped and leap are synonyms and can be indexed as just the single term jump.
- 20. Retrieve Using Query String (SearchLite)… Retrieve Example : GET localhost:9201/megacorp/employee/1 Query String Search Example : GET /megacorp/employee/_search?q=last_name:Smith
- 21. Retrieve Using DSL … Query DSL Search Build complicated and robust queries. The domain-specific language (DSL) is specified using a JSON request body. Example : GET /megacorp/employee/_search { "query": { "match": { "last_name": "Smith" } } }
- 22. Querying First part is the range Filter. Second Part is the Match query
- 23. Full Text Search Elasticsearch can search within full-text fields and return the most relevant results first. This concept of relevance is important to Elasticsearch, and is a concept that is completely foreign to traditional relational databases, in which a record either matches or it doesn’t.
- 24. Full Text Search Example
- 25. Phrase Search Matching Exact sequence of words or phrases
- 26. Highlight Searches Highlight snippets of text from each search result.
- 27. QUESTIONS ???
- 28. THANK YOU !!!