Elasticsearch V/s Relational Database

Elasticsearch V/s Relational Database

• 1.
  Elasticsearch V/s RelationalDatabases
• 2.
  Agenda ● Basic DifferenceBetween Elasticsearch And Relational Database ● Use Cases where Relational Db are not suitable ● Basic Terminology Of Elasticsearch ● Elasticsearch – CRUD operations
• 3.
  Basic Difference ● Elasticsearchis a No sql Database. ● It has no relations, no constraints, no joins, no transactional behaviour. ● Easier to scale as compared to a relational Database. Relational DB Elasticsearch DataBase Index Table Type Row/Record Document Column Name Field
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  Usecases where RelationalDatabases are not suitable ● Relevance based searching ● Searching when entered spelling of search term is wrong ● Full text search ● Synonym search ● Phonetic search ● Log analysis
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  Relevance Based searcching ●By default, results are returned sorted by relevance—with the most relevant docs first. ● The relevance score of each document is represented by a positive floating-point number called the _score. The higher the _score, the more relevant the document. ● A query clause generates a _score for each document. How that score is calculated depends on the type of query clause.
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  Relevance Representation inES { "_index": "test", "_type": "product", "_id": "AV0iKK_ZJJfvpLB9dSHl", "_score": 0.51623213, ====> Relevance Score calculated by ES "_source": { "id": 2, "name": "Red Shirt" } }
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  Wrong Spelling searching Query ●{ "query": { "match": { "name": { "query": "shrt", "fuzziness": 2, "prefix_length": 0 } } } } Result { "_index": "test", "_type": "product", "_id": "AV0iKKplJJfvpLB9dSHk", "_score": 0.21576157, "_source": { "id": 1, "name": "Shirt" } }
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  Full Text Search ●Whenever a full-text is given to Elasticsearch, special analyzers are applied in order to simplify it and make it searchable. ● It does not store the text as it is visible. This means that the original text would be modified following special rules before being stored in the Inverted index. ● This process is called the “analysis phase,” and it is applied to all full- text fields.
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  Full Text- AnalysisPhase
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  Full Text- ReverseIndexing
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  Synonym search ● Synonymsare used to broaden the scope of what is considered a matching document. ● Perhaps no documents match a query for “Top Doctor's College,” but documents that contain “Top Medical Institutions” would probably be considered a good match.
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  Phonetic Searching ● Elasticsearchcan search for words that sound similar, even if their spelling differs. ● The Phonetic Analysis plugin provides token filters which convert tokens to their phonetic representation using Soundex, Metaphone, and a variety of other algorithms. ● Generally used while searching for names that sound similar. Consider 'Smith', 'Smythe'. Elasticsearch analyser will produce same tokens for both.
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  Log Analysis UsingElasticsearch ● Elasticsearch is vastly used as a centralized location for storing logs. ● For the purpose of indexing and searching logs, there is a bundled solution offered at the Elasticsearch page - ELK stack, which stands for elasticsearch, logstash and kibana. ●
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  Elasticsearch Terminology ● Elasticsearch:It is a horizontally distributed,data storage, search server, aggregation engine, based on lucene library. It is written in java. Elasticsearch 5.5 is the latest one. ● Cluster: A cluster consists of one or more nodes which share the same cluster name. Each cluster has a single master node which can be replaced if the current master node fails. ● Node: A node is a running instance of elasticsearch which belongs to a cluster. Multiple nodes can be started on a single server. At startup, a node will use unicast to discover an existing cluster with the same cluster name and will try to join that cluster. ● Primary Shard: Each document is stored in a single primary shard. When you index a document, it is indexed first on the primary shard, then on all replicas of the primary shard. By default, an index has 5 primary shards.
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  Elasticsearch Terminology Ctd. ●Replica Shard: Each primary shard can have zero or more replicas. A replica is a copy of the primary shard. By Default there are 1 replica for each primary shards. ● Document: A document is a JSON document which is stored in elasticsearch. It is like a row in a table in a relational database. Each document is stored in an index and has a type and an id. A document is a JSON object which contains zero or more fields, or key-value pairs. ● ID: The ID of a document identifies a document. The index/type/id of a document must be unique. If no ID is provided, then it will be auto-generated. ● Mapping: A mapping is like a schema definition in a relational database. Each index has a mapping, which defines each type within the index, plus a number of index-wide settings.
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  Create Index/Document ● IndexCreation: PUT employee ● Document Creation POST employee/employee/1 { "name" : "John" }
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  Delete Document ● DeleteBy Id DELETE employee/employee/1 ● Delete By query POST employee/employee/_delete_by_query { "query": { "match": { "name": "John" } } }
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  Update Document ● UpdateBy Id: POST employee/employee/1/_update { "doc": { "name": "Johny" } } ● Update By Query: POST employee/_update_by_query { "script": { "inline": "ctx._source.age++", "lang": "painless" }, "query": { "match": { "name": "john" } } }
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  Read/Query Document ● ReadBy Id GET employee/employee/1 ● Read By query GET employee/_search { "query": { "match": { "name": "John" } } }
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  Thank You :)