Date nanoseconds field type
- Date nanoseconds field type
  - Limitations
  - Synthetic _source

Date nanoseconds field type

This data type is an addition to the date data type. However there is an important distinction between the two. The existing date data type stores dates in millisecond resolution. The date_nanos data type stores dates in nanosecond resolution, which limits its range of dates from roughly 1970 to 2262, as dates are still stored as a long representing nanoseconds since the epoch.

Queries on nanoseconds are internally converted to range queries on this long representation, and the result of aggregations and stored fields is converted back to a string depending on the date format that is associated with the field.

Date formats can be customised, but if no format is specified then it uses the default:

    "strict_date_optional_time_nanos||epoch_millis"

For instance:

resp = client.indices.create(
    index="my-index-000001",
    mappings={
        "properties": {
            "date": {
                "type": "date_nanos"
            }
        }
    },
)
print(resp)
resp1 = client.bulk(
    index="my-index-000001",
    refresh=True,
    operations=[
        {
            "index": {
                "_id": "1"
            }
        },
        {
            "date": "2015-01-01"
        },
        {
            "index": {
                "_id": "2"
            }
        },
        {
            "date": "2015-01-01T12:10:30.123456789Z"
        },
        {
            "index": {
                "_id": "3"
            }
        },
        {
            "date": 1420070400000
        }
    ],
)
print(resp1)
resp2 = client.search(
    index="my-index-000001",
    sort={
        "date": "asc"
    },
    runtime_mappings={
        "date_has_nanos": {
            "type": "boolean",
            "script": "emit(doc['date'].value.nano != 0)"
        }
    },
    fields=[
        {
            "field": "date",
            "format": "strict_date_optional_time_nanos"
        },
        {
            "field": "date_has_nanos"
        }
    ],
)
print(resp2)

response = client.indices.create(
  index: 'my-index-000001',
  body: {
    mappings: {
      properties: {
        date: {
          type: 'date_nanos'
        }
      }
    }
  }
)
puts response
response = client.bulk(
  index: 'my-index-000001',
  refresh: true,
  body: [
    {
      index: {
        _id: '1'
      }
    },
    {
      date: '2015-01-01'
    },
    {
      index: {
        _id: '2'
      }
    },
    {
      date: '2015-01-01T12:10:30.123456789Z'
    },
    {
      index: {
        _id: '3'
      }
    },
    {
      date: 1_420_070_400_000
    }
  ]
)
puts response
response = client.search(
  index: 'my-index-000001',
  body: {
    sort: {
      date: 'asc'
    },
    runtime_mappings: {
      date_has_nanos: {
        type: 'boolean',
        script: "emit(doc['date'].value.nano != 0)"
      }
    },
    fields: [
      {
        field: 'date',
        format: 'strict_date_optional_time_nanos'
      },
      {
        field: 'date_has_nanos'
      }
    ]
  }
)
puts response

const response = await client.indices.create({
  index: "my-index-000001",
  mappings: {
    properties: {
      date: {
        type: "date_nanos",
      },
    },
  },
});
console.log(response);
const response1 = await client.bulk({
  index: "my-index-000001",
  refresh: "true",
  operations: [
    {
      index: {
        _id: "1",
      },
    },
    {
      date: "2015-01-01",
    },
    {
      index: {
        _id: "2",
      },
    },
    {
      date: "2015-01-01T12:10:30.123456789Z",
    },
    {
      index: {
        _id: "3",
      },
    },
    {
      date: 1420070400000,
    },
  ],
});
console.log(response1);
const response2 = await client.search({
  index: "my-index-000001",
  sort: {
    date: "asc",
  },
  runtime_mappings: {
    date_has_nanos: {
      type: "boolean",
      script: "emit(doc['date'].value.nano != 0)",
    },
  },
  fields: [
    {
      field: "date",
      format: "strict_date_optional_time_nanos",
    },
    {
      field: "date_has_nanos",
    },
  ],
});
console.log(response2);

PUT my-index-000001
{
  "mappings": {
    "properties": {
      "date": {
        "type": "date_nanos" 
      }
    }
  }
}
PUT my-index-000001/_bulk?refresh
{ "index" : { "_id" : "1" } }
{ "date": "2015-01-01" } 
{ "index" : { "_id" : "2" } }
{ "date": "2015-01-01T12:10:30.123456789Z" } 
{ "index" : { "_id" : "3" } }
{ "date": 1420070400000 } 
GET my-index-000001/_search
{
  "sort": { "date": "asc"}, 
  "runtime_mappings": {
    "date_has_nanos": {
      "type": "boolean",
      "script": "emit(doc['date'].value.nano != 0)" 
    }
  },
  "fields": [
    {
      "field": "date",
      "format": "strict_date_optional_time_nanos" 
    },
    {
      "field": "date_has_nanos"
    }
  ]
}

	The `date` field uses the default `format`.
	This document uses a plain date.
	This document includes a time.
	This document uses milliseconds-since-the-epoch.
	Note that the `sort` values that are returned are all in nanoseconds-since-the-epoch.
	Use `.nano` in scripts to return the nanosecond component of the date.
	You can specify the format when fetching data using the fields parameter. Use strict_date_optional_time_nanos or you’ll get a rounded result.

You can also specify multiple date formats separated by ||. The same mapping parameters than with the date field can be used.

Date nanoseconds will accept numbers with a decimal point like {"date": 1618249875.123456} but there are some cases (#70085) where we’ll lose precision on those dates so they should be avoided.

Limitations

Aggregations are still on millisecond resolution, even when using a date_nanos field. This limitation also affects transforms.

Synthetic `_source`

Synthetic _source is Generally Available only for TSDB indices (indices that have index.mode set to time_series). For other indices synthetic _source is in technical preview. Features in technical preview may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

Synthetic source may sort date_nanos field values. For example:

resp = client.indices.create(
    index="idx",
    settings={
        "index": {
            "mapping": {
                "source": {
                    "mode": "synthetic"
                }
            }
        }
    },
    mappings={
        "properties": {
            "date": {
                "type": "date_nanos"
            }
        }
    },
)
print(resp)
resp1 = client.index(
    index="idx",
    id="1",
    document={
        "date": [
            "2015-01-01T12:10:30.000Z",
            "2014-01-01T12:10:30.000Z"
        ]
    },
)
print(resp1)

const response = await client.indices.create({
  index: "idx",
  settings: {
    index: {
      mapping: {
        source: {
          mode: "synthetic",
        },
      },
    },
  },
  mappings: {
    properties: {
      date: {
        type: "date_nanos",
      },
    },
  },
});
console.log(response);
const response1 = await client.index({
  index: "idx",
  id: 1,
  document: {
    date: ["2015-01-01T12:10:30.000Z", "2014-01-01T12:10:30.000Z"],
  },
});
console.log(response1);

PUT idx
{
  "settings": {
    "index": {
      "mapping": {
        "source": {
          "mode": "synthetic"
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "date": { "type": "date_nanos" }
    }
  }
}
PUT idx/_doc/1
{
  "date": ["2015-01-01T12:10:30.000Z", "2014-01-01T12:10:30.000Z"]
}

Will become:

{
  "date": ["2014-01-01T12:10:30.000Z", "2015-01-01T12:10:30.000Z"]
}

Date nanoseconds

Date nanoseconds field type