Elasticsearch adapter

For instructions on downloading and building Calcite, start with the tutorial.

Once you’ve managed to compile the project, you can return here to start querying Elasticsearch with Calcite. First, we need a model definition. The model gives Calcite the necessary parameters to create an instance of the Elasticsearch adapter. The models can contain definitions of materializations. The name of the tables defined in the model definition corresponds to types in Elasticsearch. The schema/database is represented by the index parameter in the model definition.

A basic example of a model file is given below:

{
  "version": "1.0",
  "defaultSchema": "elasticsearch",
  "schemas": [
    {
      "type": "custom",
      "name": "elasticsearch",
      "factory": "org.apache.calcite.adapter.elasticsearch.ElasticsearchSchemaFactory",
      "operand": {
        "coordinates": "{'127.0.0.1': 9300}",
        "index": "usa"
      }
    }
  ]
}

Assuming this file is stored as model.json, you can connect to Elasticsearch via sqlline as follows:

$ ./sqlline
sqlline> !connect jdbc:calcite:model=model.json admin admin

sqlline will now accept SQL queries which access your Elasticsearch types. The purpose of this adapter is to compile the query into the most efficient Elasticsearch SEARCH JSON possible by exploiting filtering and sorting directly in Elasticsearch where possible.

For example, in the example dataset there is an Elasticsearch type named zips under index named usa.

We can issue a simple query to fetch the names of all the states stored in the type zips.

sqlline> SELECT * from "zips";

_MAP={pop=13367, loc=[-72.505565, 42.067203], city=EAST LONGMEADOW, id=01028, state=MA}
_MAP={pop=1652, loc=[-72.908793, 42.070234], city=TOLLAND, id=01034, state=MA}
_MAP={pop=3184, loc=[-72.616735, 42.38439], city=HATFIELD, id=01038, state=MA}
_MAP={pop=43704, loc=[-72.626193, 42.202007], city=HOLYOKE, id=01040, state=MA}
_MAP={pop=2084, loc=[-72.873341, 42.265301], city=HUNTINGTON, id=01050, state=MA}
_MAP={pop=1350, loc=[-72.703403, 42.354292], city=LEEDS, id=01053, state=MA}
_MAP={pop=8194, loc=[-72.319634, 42.101017], city=MONSON, id=01057, state=MA}
_MAP={pop=1732, loc=[-72.204592, 42.062734], city=WALES, id=01081, state=MA}
_MAP={pop=9808, loc=[-72.258285, 42.261831], city=WARE, id=01082, state=MA}
_MAP={pop=4441, loc=[-72.203639, 42.20734], city=WEST WARREN, id=01092, state=MA}

While executing this query, the Elasticsearch adapter is able to recognize that city can be filtered by Elasticsearch and state can be sorted by Elasticsearch in ascending order.

The final source json given to Elasticsearch is below:

{
  "query": {
    "constant_score": {
      "filter": {
        "bool": {
          "must": [
            {
              "term": {
                "city": "springfield"
              }
            }
          ]
        }
      }
    }
  },
  "fields": [
    "city",
    "state"
  ],
  "script_fields": {},
  "sort": [
    {
      "state": "asc"
    }
  ]
}

You can also query elastic search index without prior view definition:

sqlline> SELECT _MAP['city'], _MAP['state'] from "elasticsearch"."zips" order by _MAP['state'];

Use of Scrolling API

For queries without aggregate functions (like COUNT, MAX etc.) elastic adapter uses scroll API, by default. This ensures that consistent and full data-set is returned to end user (lazily and in batches). Please note that scroll is automatically cleared (removed) when all query resuts are consumed.

Supported versions

Currently this adapter supports ElasticSearch versions 2.x (or newer). Generally we try to follow official support schedule.