For instructions on downloading and building Calcite, start with the [tutorial]({{ site.baseurl }}/docs/tutorial.html).
Once you've managed to compile the project, you can return here to start querying Elasticsearch with Calcite. First, we need a [model definition]({{ site.baseurl }}/docs/model.html). The model gives Calcite the necessary parameters to create an instance of the Elasticsearch adapter. The models can contain definitions of [materializations]({{ site.baseurl }}/docs/model.html#materialization). The name of the tables defined in the model definition corresponds to indices in Elasticsearch.
A basic example of a model file is given below:
{% highlight json %} { “version”: “1.0”, “defaultSchema”: “elasticsearch”, “schemas”: [ { “type”: “custom”, “name”: “elasticsearch”, “factory”: “org.apache.calcite.adapter.elasticsearch.ElasticsearchSchemaFactory”, “operand”: { “coordinates”: “{‘127.0.0.1’: 9200}” } } ] } {% endhighlight %}
Assuming this file is stored as model.json, you can connect to Elasticsearch via sqlline as follows:
{% highlight bash %} $ ./sqlline sqlline> !connect jdbc:calcite:model=model.json admin admin {% endhighlight %}
You can also specify the index name and path prefix that is represented by the index and pathPrefix parameter in the model definition:
{% highlight json %} ...
"operand": {
"coordinates": "{'127.0.0.1': 9200}",
"index": "usa",
"pathPrefix": "path"
}
... {% endhighlight %}
sqlline will now accept SQL queries which access your Elasticsearch. 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.
We can issue a simple query to fetch the names of all the states stored in the index usa.
{% highlight sql %} sqlline> SELECT * from “usa”; {% endhighlight %}
{% highlight json %} _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} {% endhighlight %}
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:
{% highlight json %} { “query”: { “constant_score”: { “filter”: { “bool”: { “must”: [ { “term”: { “city”: “springfield” } } ] } } } }, “fields”: [ “city”, “state” ], “script_fields”: {}, “sort”: [ { “state”: “asc” } ] } {% endhighlight %}
You can also query elastic search index without prior view definition:
{% highlight sql %} sqlline> SELECT _MAP[‘city’], _MAP[‘state’] from “elasticsearch”.“usa” order by _MAP[‘state’]; {% endhighlight %}
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.
Currently this adapter supports ElasticSearch versions 6.x (or newer). Generally we try to follow official support schedule. Also, types are not supported (this adapter only supports indices).