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Define Mappings in Elasticsearch
Dynamic templates in Elasticsearch make it very easy to index data without having to create explicit mappings for every field. However, sometimes you might prefer to create explicit mappings, or even disable dynamic mapping altogether, in order to have a tighter control over your index structure and datatype requirements. In this learning activity, you are given the opportunity to create explicit field mappings for an index containing log data. Specifically, you will exercise how to: * Create analyzed string fields with a specific analyzer * Create non-analyzed string fields with character limits * Create `geo_point` field mappings * Create numerical field mappings * Create date field mappings * Create IP field mappings * Create nest field mappings (objects) * Reindex data from one index into another with different mappings
Lab Info
Table of Contents
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Challenge
Create the index with explicit field mappings.
Use the Kibana console tool to execute the following:
PUT logs_new { "mappings": { "properties": { "@message": { "type": "text" }, "@tags": { "type": "keyword", "ignore_above": 128 }, "@timestamp": { "type": "date" }, "@version": { "type": "keyword", "ignore_above": 256 }, "agent": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "bytes": { "type": "long" }, "clientip": { "type": "ip" }, "extension": { "type": "keyword", "ignore_above": 256 }, "geo": { "properties": { "coordinates": { "type": "geo_point" }, "dest": { "type": "keyword", "ignore_above": 128 }, "src": { "type": "keyword", "ignore_above": 128 }, "srcdest": { "type": "keyword", "ignore_above": 128 } } }, "headings": { "type": "keyword", "ignore_above": 256 }, "host": { "type": "keyword", "ignore_above": 256 }, "ip": { "type": "ip" }, "links": { "type": "keyword", "ignore_above": 256 }, "machine": { "properties": { "os": { "type": "keyword", "ignore_above": 256 }, "ram": { "type": "long" } } }, "memory": { "type": "long" }, "phpmemory": { "type": "long" }, "referrer": { "type": "keyword", "ignore_above": 256 }, "relatedContent": { "properties": { "article:modified_time": { "type": "date" }, "article:published_time": { "type": "date" }, "article:section": { "type": "keyword", "ignore_above": 128 }, "article:tag": { "type": "keyword", "ignore_above": 128 }, "og:description": { "type": "text" }, "og:image": { "type": "keyword", "ignore_above": 256 }, "og:image:height": { "type": "long" }, "og:image:width": { "type": "long" }, "og:site_name": { "type": "keyword", "ignore_above": 256 }, "og:title": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "og:type": { "type": "keyword", "ignore_above": 128 }, "og:url": { "type": "text", "analyzer": "simple", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "twitter:card": { "type": "keyword", "ignore_above": 128 }, "twitter:description": { "type": "text" }, "twitter:image": { "type": "keyword", "ignore_above": 256 }, "twitter:site": { "type": "keyword", "ignore_above": 128 }, "twitter:title": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 128 } } }, "url": { "type": "text", "analyzer": "simple", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } } } }, "request": { "type": "text", "analyzer": "simple", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "response": { "type": "keyword", "ignore_above": 128 }, "spaces": { "type": "text", "analyzer": "whitespace" }, "url": { "type": "text", "analyzer": "simple", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "utc_time": { "type": "date" }, "xss": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 512 } } } } }, "settings": { "number_of_shards": 4, "number_of_replicas": 3 } } -
Challenge
Reindex the documents from the logs index into the logs_new index.
Use the Kibana console tool to execute the following:
POST _reindex { "source": { "index": "logs" }, "dest": { "index": "logs_new" } }
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