Script Fields & Debugging

Script Fields & Debugging

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Script Fields in Practice

As mentioned in the chapter Aggregation Data Tables, each individual hit returned from a _search request can contain custom attributes generated on-the-fly through a script.
These custom attributes (script fields) are typically either:
  • modifications of the original values (e.g. price conversions, differently sorted arrays, โ€ฆ)
  • or newly computed values (e.g. sums, boosts, exponentials, distance measurements, etc.)
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A _search request can specify more than one script field at once:
POST myindex/_search
{
  "script_fields": {
    "using_doc_values": {
      "script": "doc['price'].value * 42"
    },
    "using_source": {
      "script": "params['_source']['price'] * 42"
    }
  }
}
Two ways of multiplying a numeric field.
Unlike in script queries where you can only access doc_values, script fields also allow for accessing the original document _source.
โš ๏ธ
Quoting the docs: the doc[...] notation will cause the terms for that field to be loaded to memory (cached), which will result in faster execution. But keep in mind that this notation only allows for accessing simple-valued fields (you canโ€™t get a JSON object from it โ€” only from the _source). You can, however, target array fields too, as I've shown here.
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Despite the fact that accessing _source directly will be slower that accessing the doc-values, scripts in script fields are only executed for the top N documents. As such, they aren't really a big performance consideration โ€” particularly when you imagine that your requestโ€™s size would typically be in the lower double digits. I discuss script speed in more detail in the parent chapter.
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Script fields can be used in Kibana charts as a means of data cleansing but also as part of regular _search requests.

Use Case: Script Field Possibilities for Various Data Types

Given an index containing smartphones:
POST smartphones/_doc
{
  

  "name": "iPhone 12 Pro Max",


  "color": "gold",


  "last_modified_utc": 1609521634371,


  "price": {
    "amount": 1600000,
    "currency": "usd"
  },


  "availability_per_gb": [
    {
      "gigabytes": 128, "units": 58
    },
    {
      "gigabytes": 256, "units": 32
    },
    {
      "gigabytes": 512, "units": 0
    }
  ],


  "warehouse_location": [
    -97.71703, 30.32035
	]
}
A sample smartphone document
PUT smartphones
{
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } },
      "color": {
        "type": "keyword"
      },
      "last_modified_utc": {
        "type": "date", "format": "epoch_millis"
      },
      "price": {
        "properties": {
          "amount": { "type": "long" },
          "currency": { "type": "keyword" }
        }
      },
      "availability_per_gb": {
        "type": "nested",
        "properties": {
          "gigabytes": {
            "type": "integer"
          },
          "units": {
            "type": "integer"
          }
        }
      },
      
   
      "warehouse_location": { 
        "type": "geo_point"
			} } }
}
The index mapping
You want to retrieve:
  1. the color in upper case
  1. the last_modified_utc date in the format yyyy/MM/dd HH:mm:ss & in PST instead of UTC
  1. the geo distance in miles from the warehouse_location to a customer in Phoenix, AZ
  1. the number of all available units inside the field availability_per_gb.
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Approach

After familiarizing yourself with the available Painless methods on doc_values, you'll discover that points 1, 2, and 3 can all be achieved via doc-values, and point 4 requires that you access the document _source.
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