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This commit adds two pieces. The first is a small set of documentation providing instructions on how to get setup to run context examples. This will require a download similar to how Kibana works for some of the examples. The second is an ingest processor example using the downloaded data. More examples will follow as ideally one per PR. This also adds a set of tests to individually test each script as a unit test.
81 lines
2.6 KiB
Plaintext
81 lines
2.6 KiB
Plaintext
[[painless-context-examples]]
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=== Context examples
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To run the examples, index the sample seat data into Elasticsearch. The examples
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must be run sequentially to work correctly.
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. Download the
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https://download.elastic.co/demos/painless/contexts/seats.json[seat data]. This
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data set contains booking information for a collection of plays. Each document
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represents a single seat for a play at a particular theater on a specific date
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and time.
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+
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Each document contains the following fields:
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+
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`theatre` ({ref}/keyword.html[`keyword`])::
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The name of the theater the play is in.
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`play` ({ref}/text.html[`text`])::
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The name of the play.
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`actors` ({ref}/text.html[`text`])::
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A list of actors in the play.
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`row` ({ref}/number.html[`integer`])::
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The row of the seat.
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`number` ({ref}/number.html[`integer`])::
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The number of the seat within a row.
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`cost` ({ref}/number.html[`double`])::
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The cost of the ticket for the seat.
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`sold` ({ref}/boolean.html[`boolean`])::
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Whether or not the seat is sold.
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`datetime` ({ref}/date.html[`date`])::
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The date and time of the play as a date object.
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`date` ({ref}/keyword.html[`keyword`])::
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The date of the play as a keyword.
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`time` ({ref}/keyword.html[`keyword`])::
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The time of the play as a keyword.
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. {defguide}/running-elasticsearch.html[Start] Elasticsearch. Note these
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examples assume Elasticsearch and Kibana are running locally. To use the Console
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editor with a remote Kibana instance, click the settings icon and enter the
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Console URL. To submit a cURL request to a remote Elasticsearch instance, edit
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the request URL.
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. Create {ref}/mapping.html[mappings] for the sample data:
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+
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[source,js]
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----
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PUT /seats
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{
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"mappings": {
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"seat": {
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"properties": {
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"theatre": { "type": "keyword" },
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"play": { "type": "text" },
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"actors": { "type": "text" },
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"row": { "type": "integer" },
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"number": { "type": "integer" },
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"cost": { "type": "double" },
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"sold": { "type": "boolean" },
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"datetime": { "type": "date" },
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"date": { "type": "keyword" },
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"time": { "type": "keyword" }
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}
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}
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}
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}
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----
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+
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// CONSOLE
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. Run the <<painless-ingest-processor-context, ingest processor context>>
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example. This sets up a script ingest processor used on each document as the
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seat data is indexed.
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. Index the seat data:
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+
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[source,js]
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----
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curl -XPOST localhost:9200/seats/seat/_bulk?pipeline=seats -H "Content-Type: application/x-ndjson" --data-binary "@/<local-file-path>/seats.json"
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----
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// NOTCONSOLE
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