SQL-based ingestion reference

This page describes SQL-based batch ingestion using the druid-multi-stage-query extension, new in Druid 24.0. Refer to the ingestion methods table to determine which ingestion method is right for you.

SQL reference

This topic is a reference guide for the multi-stage query architecture in Apache Druid. For examples of real-world usage, refer to the Examples page.

INSERT and REPLACE load data into a Druid datasource from either an external input source, or from another datasource. When loading from an external datasource, you typically must provide the kind of input source, the data format, and the schema (signature) of the input file. Druid provides table functions to allow you to specify the external file. There are two kinds. EXTERN works with the JSON-serialized specs for the three items, using the same JSON you would use in native ingest. A set of other, input-source-specific functions use SQL syntax to specify the format and the input schema. There is one function for each input source. The input-source-specific functions allow you to use SQL query parameters to specify the set of files (or URIs), making it easy to reuse the same SQL statement for each ingest: just specify the set of files to use each time.

EXTERN Function

Use the EXTERN function to read external data. The function has two variations.

Function variation 1, with the input schema expressed as JSON:

SELECT
 <column>
FROM TABLE(
  EXTERN(
    '<Druid input source>',
    '<Druid input format>',
    '<row signature>'
  )
)

EXTERN consists of the following parts:

1. Any Druid input source as a JSON-encoded string.
2. Any Druid input format as a JSON-encoded string.
3. A row signature, as a JSON-encoded array of column descriptors. Each column descriptor must have a name and a type. The type can be string, long, double, or float. This row signature is used to map the external data into the SQL layer.

Variation 2, with the input schema expressed in SQL using an EXTEND clause. (See the next section for more detail on EXTEND). This format also uses named arguments to make the SQL a bit easier to read:

SELECT
 <column>
FROM TABLE(
  EXTERN(
    inputSource => '<Druid input source>',
    inputFormat => '<Druid input format>'
  )) (<columns>)

The input source and format are as above. The columns are expressed as in a SQL CREATE TABLE. Example: (timestamp VARCHAR, metricType VARCHAR, value BIGINT). The optional EXTEND keyword can precede the column list: EXTEND (timestamp VARCHAR...).

For more information, see Read external data with EXTERN.

INSERT

Use the INSERT statement to insert data.

Unlike standard SQL, INSERT loads data into the target table according to column name, not positionally. If necessary, use AS in your SELECT column list to assign the correct names. Do not rely on their positions within the SELECT clause.

Statement format:

INSERT INTO <table name>
< SELECT query >
PARTITIONED BY <time frame>
[ CLUSTERED BY <column list> ]

INSERT consists of the following parts:

1. Optional context parameters.
2. An INSERT INTO <dataSource> clause at the start of your query, such as INSERT INTO your-table.
3. A clause for the data you want to insert, such as SELECT ... FROM .... You can use EXTERN to reference external tables using FROM TABLE(EXTERN(...)).
4. A PARTITIONED BY clause, such as PARTITIONED BY DAY.
5. An optional CLUSTERED BY clause.

For more information, see Load data with INSERT.

REPLACE

You can use the REPLACE function to replace all or some of the data.

Unlike standard SQL, REPLACE loads data into the target table according to column name, not positionally. If necessary, use AS in your SELECT column list to assign the correct names. Do not rely on their positions within the SELECT clause.

REPLACE all data

Function format to replace all data:

REPLACE INTO <target table>
OVERWRITE ALL
< SELECT query >
PARTITIONED BY <time granularity>
[ CLUSTERED BY <column list> ]

REPLACE specific time ranges

Function format to replace specific time ranges:

REPLACE INTO <target table>
OVERWRITE WHERE __time >= TIMESTAMP '<lower bound>' AND __time < TIMESTAMP '<upper bound>'
< SELECT query >
PARTITIONED BY <time granularity>
[ CLUSTERED BY <column list> ]

REPLACE consists of the following parts:

1. Optional context parameters.
2. A REPLACE INTO <dataSource> clause at the start of your query, such as REPLACE INTO "your-table".
3. An OVERWRITE clause after the datasource, either OVERWRITE ALL or OVERWRITE WHERE:
   • OVERWRITE ALL replaces the entire existing datasource with the results of the query.
   • OVERWRITE WHERE drops the time segments that match the condition you set. Conditions are based on the __time column and use the format __time [< > = <= >=] TIMESTAMP. Use them with AND, OR, and NOT between them, inclusive of the timestamps specified. No other expressions or functions are valid in OVERWRITE.
4. A clause for the actual data you want to use for the replacement.
5. A PARTITIONED BY clause, such as PARTITIONED BY DAY.
6. An optional CLUSTERED BY clause.

For more information, see Overwrite data with REPLACE.

PARTITIONED BY

The PARTITIONED BY <time granularity> clause is required for INSERT and REPLACE. See Partitioning for details.

The following granularity arguments are accepted:

• Time unit keywords: HOUR, DAY, MONTH, or YEAR. Equivalent to FLOOR(__time TO TimeUnit).
• Time units as ISO 8601 period strings: :'PT1H', ‘P1D, etc. (Druid 26.0 and later.)
• TIME_FLOOR(__time, 'granularity_string'), where granularity_string is one of the ISO 8601 periods listed below. The first argument must be __time.
• FLOOR(__time TO TimeUnit), where TimeUnit is any unit supported by the FLOOR function. The first argument must be __time.
• ALL or ALL TIME, which effectively disables time partitioning by placing all data in a single time chunk. To use LIMIT or OFFSET at the outer level of your INSERT or REPLACE query, you must set PARTITIONED BY to ALL or ALL TIME.

Earlier versions required the TIME_FLOOR notation to specify a granularity other than the keywords. In the current version, the string constant provides a simpler equivalent solution.

The following ISO 8601 periods are supported for TIME_FLOOR and the string constant:

• PT1S
• PT1M
• PT5M
• PT10M
• PT15M
• PT30M
• PT1H
• PT6H
• P1D
• P1W
• P1M
• P3M
• P1Y

For more information about partitioning, see Partitioning.

CLUSTERED BY

The CLUSTERED BY <column list> clause is optional for INSERT and REPLACE. It accepts a list of column names or expressions.

For more information about clustering, see Clustering.

Context parameters

In addition to the Druid SQL context parameters, the multi-stage query task engine accepts certain context parameters that are specific to it.

Use context parameters alongside your queries to customize the behavior of the query. If you’re using the API, include the context parameters in the query context when you submit a query:

{
  "query": "SELECT 1 + 1",
  "context": {
    "<key>": "<value>",
    "maxNumTasks": 3
  }
}

If you’re using the web console, you can specify the context parameters through various UI options.

The following table lists the context parameters for the MSQ task engine:

Joins

Joins in multi-stage queries use one of two algorithms, based on the context parameter sqlJoinAlgorithm. This context parameter applies to the entire SQL statement, so it is not possible to mix different join algorithms in the same query.

Broadcast

Set sqlJoinAlgorithm to broadcast.

The default join algorithm for multi-stage queries is a broadcast hash join, which is similar to how joins are executed with native queries. First, any adjacent joins are flattened into a structure with a “base” input (the bottom-leftmost one) and other leaf inputs (the rest). Next, any subqueries that are inputs the join (either base or other leafs) are planned into independent stages. Then, the non-base leaf inputs are all connected as broadcast inputs to the “base” stage.

Together, all of these non-base leaf inputs must not exceed the limit on broadcast table footprint. There is no limit on the size of the base (leftmost) input.

Only LEFT JOIN, INNER JOIN, and CROSS JOIN are supported with with broadcast.

Join conditions, if present, must be equalities. It is not necessary to include a join condition; for example, CROSS JOIN and comma join do not require join conditions.

As an example, the following statement has a single join chain where orders is the base input, and products and customers are non-base leaf inputs. The query will first read products and customers, then broadcast both to the stage that reads orders. That stage loads the broadcast inputs (products and customers) in memory, and walks through orders row by row. The results are then aggregated and written to the table orders_enriched. The broadcast inputs (products and customers) must fall under the limit on broadcast table footprint, but the base orders input can be unlimited in size.

REPLACE INTO orders_enriched
OVERWRITE ALL
SELECT
  orders.__time,
  products.name AS product_name,
  customers.name AS customer_name,
  SUM(orders.amount) AS amount
FROM orders
LEFT JOIN products ON orders.product_id = products.id
LEFT JOIN customers ON orders.customer_id = customers.id
GROUP BY 1, 2
PARTITIONED BY HOUR
CLUSTERED BY product_name

Sort-merge

Set sqlJoinAlgorithm to sortMerge.

Multi-stage queries can use a sort-merge join algorithm. With this algorithm, each pairwise join is planned into its own stage with two inputs. The two inputs are partitioned and sorted using a hash partitioning on the same key. This approach is generally less performant, but more scalable, than broadcast. There are various scenarios where broadcast join would return a BroadcastTablesTooLarge error, but a sort-merge join would succeed.

There is no limit on the overall size of either input, so sort-merge is a good choice for performing a join of two large inputs, or for performing a self-join of a large input with itself.

There is a limit on the amount of data associated with each individual key. If both sides of the join exceed this limit, the query returns a TooManyRowsWithSameKey error. If only one side exceeds the limit, the query does not return this error.

Join conditions, if present, must be equalities. It is not necessary to include a join condition; for example, CROSS JOIN and comma join do not require join conditions.

All join types are supported with sortMerge: LEFT, RIGHT, INNER, FULL, and CROSS.

As an example, the following statement runs using a single sort-merge join stage that receives eventstream (partitioned on user_id) and users (partitioned on id) as inputs. There is no limit on the size of either input.

REPLACE INTO eventstream_enriched
OVERWRITE ALL
SELECT
  eventstream.__time,
  eventstream.user_id,
  eventstream.event_type,
  eventstream.event_details,
  users.signup_date AS user_signup_date
FROM eventstream
LEFT JOIN users ON eventstream.user_id = users.id
PARTITIONED BY HOUR
CLUSTERED BY user

Durable Storage

Using durable storage with your SQL-based ingestions can improve their reliability by writing intermediate files to a storage location temporarily.

To prevent durable storage from getting filled up with temporary files in case the tasks fail to clean them up, a periodic cleaner can be scheduled to clean the directories corresponding to which there isn’t a controller task running. It utilizes the storage connector to work upon the durable storage. The durable storage location should only be utilized to store the output for cluster’s MSQ tasks. If the location contains other files or directories, then they will get cleaned up as well.

Enabling durable storage also enables the use of local disk to store temporary files, such as the intermediate files produced by the super sorter. The limit set by druid.indexer.task.tmpStorageBytesPerTask for maximum number of bytes of local storage to be used per task will be respected by MSQ tasks. If the configured limit is too low, NotEnoughTemporaryStorageFault may be thrown.

Enable durable storage

To enable durable storage, you need to set the following common service properties:

druid.msq.intermediate.storage.enable=true
druid.msq.intermediate.storage.type=s3
druid.msq.intermediate.storage.bucket=YOUR_BUCKET
druid.msq.intermediate.storage.prefix=YOUR_PREFIX
druid.msq.intermediate.storage.tempDir=/path/to/your/temp/dir

For detailed information about the settings related to durable storage, see Durable storage configurations.

Use durable storage for queries

When you run a query, include the context parameter durableShuffleStorage and set it to true.

For queries where you want to use fault tolerance for workers, set faultTolerance to true, which automatically sets durableShuffleStorage to true.

Durable storage configurations

The following common service properties control how durable storage behaves:

In addition to the common service properties, there are certain properties that you configure on the Overlord specifically to clean up intermediate files:

Limits

Knowing the limits for the MSQ task engine can help you troubleshoot any errors that you encounter. Many of the errors occur as a result of reaching a limit.

The following table lists query limits:

Error codes

The following table describes error codes you may encounter in the multiStageQuery.payload.status.errorReport.error.errorCode field: