druid/docs/content/querying/sql.md

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layout: doc_page
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# SQL Support for Druid

## Built-in SQL

<div class="note caution">
Built-in SQL is an <a href="../development/experimental.html">experimental</a> feature. The API described here is
subject to change.
</div>

Druid includes a native SQL layer with an [Apache Calcite](https://calcite.apache.org/)-based parser and planner. All
parsing and planning takes place on the Broker, where SQL is converted to native Druid queries. Those native Druid
queries are then passed down to data nodes. Each Druid datasource appears as a table in the "druid" schema. Datasource
and column names are both case-sensitive and can optionally be quoted using double quotes. Literal strings should be
quoted with single quotes, like `'foo'`. Literal strings with Unicode escapes can be written like `U&'fo\00F6'`, where
character codes in hex are prefixed by a backslash.

Add "EXPLAIN PLAN FOR" to the beginning of any query to see how Druid will plan that query.

### Querying with JDBC

You can make Druid SQL queries using the [Avatica JDBC driver](https://calcite.apache.org/avatica/downloads/). Once
you've downloaded the Avatica client jar, add it to your classpath and use the connect string:

```
jdbc:avatica:remote:url=http://BROKER:8082/druid/v2/sql/avatica/
```

Example code:

```java
Connection connection = DriverManager.getConnection("jdbc:avatica:remote:url=http://localhost:8082/druid/v2/sql/avatica/");
ResultSet resultSet = connection.createStatement().executeQuery("SELECT COUNT(*) AS cnt FROM data_source");
while (resultSet.next()) {
  // Do something
}
```

Table metadata is available over JDBC using `connection.getMetaData()`.

Parameterized queries don't work properly, so avoid those.

### Querying with JSON over HTTP

You can make Druid SQL queries using JSON over HTTP by POSTing to the endpoint `/druid/v2/sql/`. The request format
is:

```json
{
  "query" : "SELECT COUNT(*) FROM data_source WHERE foo = 'bar'"
}
```

You can use _curl_ to send these queries from the command-line:

```bash
curl -XPOST -H'Content-Type: application/json' http://BROKER:8082/druid/v2/sql/ -d '{"query":"SELECT COUNT(*) FROM data_source"}'
```

Metadata is only available over the HTTP API by querying the "INFORMATION_SCHEMA" tables (see below).

### Metadata

Druid brokers cache column type metadata for each dataSource and use it to plan SQL queries. This cache is updated
on broker startup and also periodically in the background through
[SegmentMetadata queries](../querying/segmentmetadataquery.html). Background metadata refreshing is triggered by
segments entering and exiting the cluster, and can also be throttled through configuration.

This cached metadata is queryable through "INFORMATION_SCHEMA" tables. For example, to retrieve metadata for the Druid
datasource "foo", use the query:

```sql
SELECT * FROM INFORMATION_SCHEMA.COLUMNS WHERE SCHEMA_NAME = 'druid' AND TABLE_NAME = 'foo'
```

See the [INFORMATION_SCHEMA tables](#information_schema-tables) section below for details on the available metadata.

You can also access table and column metadata through JDBC using `connection.getMetaData()`.

### Approximate queries

The following SQL queries and features may be executed using approximate algorithms:

- `COUNT(DISTINCT col)` and `APPROX_COUNT_DISTINCT(col)` aggregations use
[HyperLogLog](http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf), a fast approximate distinct counting
algorithm. If you need exact distinct counts, you can instead use
`SELECT COUNT(*) FROM (SELECT DISTINCT col FROM data_source)`, which will use a slower and more resource intensive exact
algorithm.
- TopN-style queries with a single grouping column, like
`SELECT col1, SUM(col2) FROM data_source GROUP BY col1 ORDER BY SUM(col2) DESC LIMIT 100`, by default will be executed
as [TopN queries](topnquery.html), which use an approximate algorithm. To disable this behavior, and use exact
algorithms for topN-style queries, set
[druid.sql.planner.useApproximateTopN](../configuration/broker.html#sql-planner-configuration) to "false".

### Time functions

Druid's SQL language supports a number of time operations, including:

- `FLOOR(__time TO <granularity>)` for grouping or filtering on time buckets, like `SELECT FLOOR(__time TO MONTH), SUM(cnt) FROM data_source GROUP BY FLOOR(__time TO MONTH)`
- `EXTRACT(<granularity> FROM __time)` for grouping or filtering on time parts, like `SELECT EXTRACT(HOUR FROM __time), SUM(cnt) FROM data_source GROUP BY EXTRACT(HOUR FROM __time)`
- Comparisons to `TIMESTAMP '<time string>'` for time filters, like `SELECT COUNT(*) FROM data_source WHERE __time >= TIMESTAMP '2000-01-01 00:00:00' AND __time < TIMESTAMP '2001-01-01 00:00:00'`

### Subqueries

Druid's SQL layer supports many types of subqueries, including the ones listed below.

#### Nested groupBy

Subqueries involving `FROM (SELECT ... GROUP BY ...)` may be executed as
[nested groupBys](groupbyquery.html#nested-groupbys). For example, the following query can be used to perform an
exact distinct count using a nested groupBy.

```sql
SELECT COUNT(*) FROM (SELECT DISTINCT col FROM data_source)
```

Note that groupBys require a separate merge buffer on the broker for each layer beyond the first layer of the groupBy.
This merge buffer is immediately released once they are not used anymore during the query processing. However, deeply
nested groupBys (there are two or more groupBy layers beyond the first one) can potentially lead to deadlocks since the
merge buffers are limited in number and are acquired one-by-one instead of a complete set. At this time, we recommend
that you avoid too many concurrent execution of deeply nested groupBys with the v2 strategy.

#### Semi-joins

Semi-join subqueries involving `WHERE ... IN (SELECT ...)`, like the following, are executed with a special process.

```sql
SELECT x, COUNT(*)
FROM data_source_1
WHERE x IN (SELECT x FROM data_source_2 WHERE y = 'baz')
GROUP BY x
```

For this query, the broker will first translate the inner select on data_source_2 into a groupBy to find distinct
`x` values. Then it'll use those distinct values to build an "in" filter on data_source_1 for the outer query. The
configuration parameter `druid.sql.planner.maxSemiJoinRowsInMemory` controls the maximum number of values that will be
materialized for this kind of plan.

### Configuration

Druid's SQL layer can be configured on the [Broker node](../configuration/broker.html#sql-planner-configuration).

### Extensions

Some Druid extensions also include SQL language extensions.

If the [approximate histogram extension](../development/extensions-core/approximate-histograms.html) is loaded:

- `APPROX_QUANTILE(column, probability)` or `APPROX_QUANTILE(column, probability, resolution)` on numeric or
approximate histogram columns computes approximate quantiles. The "probability" should be between 0 and 1 (exclusive).
The "resolution" is the number of centroids to use for the computation. Higher resolutions will be give more
precise results but also have higher overhead. If not provided, the default resolution is 50.

### Unsupported features

Druid does not support all SQL features. Most of these are due to missing features in Druid's native JSON-based query
language. Some unsupported SQL features include:

- Grouping on functions of multiple columns, like concatenation: `SELECT COUNT(*) FROM data_source GROUP BY dim1 || ' ' || dim2`
- Filtering on non-boolean interactions between columns, like two columns equaling each other: `SELECT COUNT(*) FROM data_source WHERE dim1 = dim2`.
- A number of miscellaneous functions, like `TRIM`.
- Joins, other than semi-joins as described above.

Additionally, some Druid features are not supported by the SQL language. Some unsupported Druid features include:

- [Multi-value dimensions](multi-value-dimensions.html).
- [Query-time lookups](lookups.html).
- [DataSketches](../development/extensions-core/datasketches-aggregators.html).

## Third-party SQL libraries

A number of third parties have also released SQL libraries for Druid. Links to popular options can be found on
our [libraries](/libraries.html) page. These libraries make native Druid JSON queries and do not use Druid's SQL layer.

## INFORMATION_SCHEMA tables

Druid metadata is queryable through "INFORMATION_SCHEMA" tables described below.

### SCHEMATA table

|Column|Notes|
|------|-----|
|CATALOG_NAME|Unused|
|SCHEMA_NAME||
|SCHEMA_OWNER|Unused|
|DEFAULT_CHARACTER_SET_CATALOG|Unused|
|DEFAULT_CHARACTER_SET_SCHEMA|Unused|
|DEFAULT_CHARACTER_SET_NAME|Unused|
|SQL_PATH|Unused|

### TABLES table

|Column|Notes|
|------|-----|
|TABLE_CATALOG|Unused|
|TABLE_SCHEMA||
|TABLE_NAME||
|TABLE_TYPE|"TABLE" or "SYSTEM_TABLE"|

### COLUMNS table

|Column|Notes|
|------|-----|
|TABLE_CATALOG|Unused|
|TABLE_SCHEMA||
|TABLE_NAME||
|COLUMN_NAME||
|ORDINAL_POSITION||
|COLUMN_DEFAULT|Unused|
|IS_NULLABLE||
|DATA_TYPE||
|CHARACTER_MAXIMUM_LENGTH|Unused|
|CHARACTER_OCTET_LENGTH|Unused|
|NUMERIC_PRECISION||
|NUMERIC_PRECISION_RADIX||
|NUMERIC_SCALE||
|DATETIME_PRECISION||
|CHARACTER_SET_NAME||
|COLLATION_NAME||
|JDBC_TYPE|Type code from java.sql.Types (Druid extension)|