{"id":3008,"date":"2019-11-03T13:13:06","date_gmt":"2019-11-03T13:13:06","guid":{"rendered":"https:\/\/prwatech.in\/blog\/?p=3008"},"modified":"2023-07-19T12:03:39","modified_gmt":"2023-07-19T12:03:39","slug":"document-apis","status":"publish","type":"post","link":"https:\/\/prwatech.in\/blog\/elastic-search\/elasticsearch-modules\/document-apis\/","title":{"rendered":"DOCUMENT API\u2019s"},"content":{"rendered":"
    \n
  1. Add Document<\/u><\/strong><\/li>\n<\/ol>\n

    Documents in Elasticsearch are stored as a JSON object. Also, documents are added to indices, and documents have a type. One Index can have many types & you can store any number of documents in an Index.<\/p>\n

    In this example, ” information”, “person” and\u00a0 “1” are index, type and id respectively, Elasticsearch will automatically create the index if it does not exist.<\/p>\n

    Example:- <\/em>
    \nPOST \/information\/person\/1
    \n{
    \n\u201cname\u201d : \u201cPaul\u201d,
    \n\u201clastname\u201d : \u201cWheeler\u201d,
    \n\u201cjob_desc\u201d : \u201cManager\u201d
    \n}<\/em><\/p>\n

      \n
    1. Get Document<\/u><\/strong><\/li>\n<\/ol>\n

      Now that the document is stored, to retrieve the document we use the below API.<\/p>\n

      Example:- \u00a0\u00a0\u00a0\u00a0\u00a0 GET\u00a0 \/information\/person\/1<\/em><\/p>\n

        \n
      1. Update Document<\/u><\/strong><\/li>\n<\/ol>\n

        To update a document we use below API.<\/p>\n

        Example:-<\/em>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 POST\u00a0 \/information\/person\/1\/_update<\/em><\/p>\n

        {<\/em><\/p>\n

        \u201cdoc\u201d:{
        \n\u201cjob_description\u201d : \u201cData analyst\u201d
        \n}<\/em><\/p>\n

        }<\/em><\/p>\n

          \n
        1. Delete Document<\/u><\/strong><\/li>\n<\/ol>\n

          To delete a document use the below API.<\/p>\n

          Example:-\u00a0 <\/em><\/p>\n

          \u00a0 DELETE \u00a0\/information\/person\/1<\/em><\/p>\n

            \n
          1. Search Document<\/u><\/strong><\/li>\n<\/ol>\n

            We can search the stored document using either \u201c\/_search?q=something\u201d.<\/p>\n

            Example:-<\/p>\n

            GET localhost:9200\/_search?q=Paul<\/em><\/p>\n

            \u25baAnalysis Phase In ElasticSearch <\/u><\/strong>
            \nElasticsearch\u00a0uses a special data structure called “Inverted index<\/u>” for very fast searches. An inverted index is a list of all the unique words that a document contains, and for each word, a list of the documents in which it appears. An inverted index is created from a document indexed in elasticsearch. the process of creating an Inverted index from a document <\/u>called analysis (tokenization and Filterization).<\/p>\n

            Analysis happens during Indexing a document as well as Searching a document<\/u>. We will see how elasticsearch creates an Inverted index and how it is stored in shards which later used for searching documents.<\/p>\n

            \"\"\u25baThe analysis process is the key phase in creating an inverted index in shards. whenever you index a document in Elasticsearch, it goes through the analysis phase where documents are tokenized, filtered processed (stemming, synonyms detected and remove stop words).<\/p>\n

            \u25baFor every document, this inverted index will be created and stored in a temporary buffer until it becomes full. Once the buffer is full, it is flushed into segments.<\/p>\n

            \u25baA segment is the smallest logical unit of a Shard basically small blocks where you can store a list of the inverted index. Shard is like a collection of segments. Segments are filled with a flushed inverted index.<\/p>\n

            \u25baOnce a segment is filled completely with an inverted index, shards become eligible for searching. Segments created are an immutable collection of the immutable inverted index.<\/p>\n

            EXAMPLE: <\/u><\/strong>Consider two documents text below for analysis.<\/p>\n

            Here, we\u2019re indexing these documents to ELS.<\/p>\n

            POST \/user\/tweets\/<\/p>\n

            {\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u201c name\u201d: \u201cRohit\u201d<\/p>\n

            \u201c comment\u201d: \u201cThe thin lifeguard was swimming in the lake.\u201d<\/p>\n

            \u201c date\u201d: \u201c2018-10-27\u201d\u00a0\u00a0\u00a0\u00a0 }<\/p>\n

            {\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u201c name\u201d: \u201cAmol\u201d<\/p>\n

            \u201c comment\u201d: \u201cSwimmers race with the skinny lifeguard in the lake\u201d<\/p>\n

            \u201c date\u201d: \u201c2018-10-28\u201d\u00a0\u00a0\u00a0\u00a0 }<\/p>\n

            Let’s assume we are interested in the comment fields of a document. We have two texts to consider for analysis.
            \n1. The thin lifeguard was swimming in the lake
            \n2. Swimmers race with the skinny lifeguard in lake
            \n\u25baTokenization<\/u><\/strong>:<\/u> To create an inverted index, we simply split the comment of each document into separate words (which we call terms or tokens), creates a sorted list of all the unique terms, along with the list in which document each term\/token appears.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
            Token<\/strong><\/td>\nPresent in Document<\/strong><\/td>\n<\/tr>\n
            \u00a0<\/strong><\/td>\n\u00a0<\/strong><\/td>\n<\/tr>\n
            Swimmers<\/td>\n2<\/td>\n<\/tr>\n
            The<\/td>\n1<\/td>\n<\/tr>\n
            in<\/td>\n1,2<\/td>\n<\/tr>\n
            lifeguard<\/td>\n1,2<\/td>\n<\/tr>\n
            lake<\/td>\n1,2<\/td>\n<\/tr>\n
            race<\/td>\n2<\/td>\n<\/tr>\n
            skinny<\/td>\n2<\/td>\n<\/tr>\n
            swimming<\/td>\n2<\/td>\n<\/tr>\n
            the<\/td>\n1,2<\/td>\n<\/tr>\n
            thin<\/td>\n1<\/td>\n<\/tr>\n
            was<\/td>\n1<\/td>\n<\/tr>\n
            with<\/td>\n1<\/td>\n<\/tr>\n
            <\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            \u25baFiltering<\/strong>: After the tokenization filtering process is applied to these. Filters are such as:<\/p>\n

            \u25baRemoving stop words (a, an, the, in, etc. of the English word)<\/p>\n

            \u25baLowercasing (To make search case insensitive)<\/p>\n

            \u25bastemming (swimming to swim)<\/p>\n

            \u25basynonymous ( thin == skinny )<\/p>\n

            After these operations, the output which is an inverted index is pushed into buffer.<\/p>\n

            \u25baANALYSERS In ElasticSearch<\/u><\/strong>
            \nElasticsearch provides pre-builtin analyzers which can be used in any index without further configuration. Here is a list of elastic search built-in analyzers.<\/p>\n

              \n
            1. Standard Analyzer (Default)<\/li>\n
            2. Simple Analyzer<\/li>\n
            3. Whitespace Analyzer<\/li>\n
            4. Stop Analyzer<\/li>\n
            5. Keyword Analyzer<\/li>\n
            6. Pattern Analyzer<\/li>\n
            7. Language Analyzers (English, Hindi, French, Spanish & many more)<\/li>\n
            8. Custom Analyser (we can define our own custom analyzer as well)<\/li>\n<\/ol>\n

              \u2666<\/strong><\/u>Bulk Load in ElasticSearch<\/u><\/strong><\/h3>\n

              Bulk load is nothing but indexing\/inserting more than one documents at a time.<\/p>\n

              We have to use the _bulk keyword to upload bulk data.<\/p>\n

              Example.<\/u><\/p>\n

              (this command will index these 3 documents into vehicles index inside cars type.)<\/p>\n

              POST \/vehicles\/cars\/_bulk<\/p>\n

              { “index”: {}}\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \/\/index for doc 1<\/p>\n

              { “price” : 10000, “colour” : “white”, “make” : “Honda”, “sold” : “2016-10-28”, “condition”: “okay”}<\/p>\n

              { “index”: {}}\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \/\/index for doc 2<\/p>\n

              { “price” : 20000, “colour” : “white”, “make” : “Honda”, “sold” : “2016-11-05”, “condition”: “new”}<\/p>\n

              { “index”: {}}\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \/\/index for doc 3<\/p>\n

              { “price” : 30000, “colour” : “green”, “make” : “ford”, “sold” : “2016-05-18”, “condition”: “new”}<\/p>\n","protected":false},"excerpt":{"rendered":"

              Add Document Documents in Elasticsearch are stored as a JSON object. Also, documents are added to indices, and documents have a type. One Index can have many types & you can store any number of documents in an Index. In this example, ” information”, “person” and\u00a0 “1” are index, type and id respectively, Elasticsearch will […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[57,1702],"tags":[],"class_list":["post-3008","post","type-post","status-publish","format-standard","hentry","category-elastic-search","category-elasticsearch-modules"],"acf":[],"yoast_head":"\nDOCUMENT API\u2019s - Prwatech<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DOCUMENT API\u2019s - Prwatech\" \/>\n<meta property=\"og:description\" content=\"Add Document Documents in Elasticsearch are stored as a JSON object. 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