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Nested columns

Apache Druid supports directly storing nested data structures in COMPLEX<json> columns. COMPLEX<json> columns store a copy of the structured data in JSON format and specialized internal columns and indexes for nested literal values—STRING, LONG, and DOUBLE types, as well as ARRAY of STRING, LONG, and DOUBLE values. An optimized virtual column allows Druid to read and filter these values at speeds consistent with standard Druid LONG, DOUBLE, and STRING columns.

Druid SQL JSON functions allow you to extract, transform, and create COMPLEX<json> values in SQL queries, using the specialized virtual columns where appropriate. You can use the JSON nested columns functions in native queries using expression virtual columns, and in native ingestion with a transformSpec.

You can use the JSON functions in INSERT and REPLACE statements in SQL-based ingestion, or in a transformSpec in native ingestion as an alternative to using a flattenSpec object to "flatten" nested data for ingestion.

Columns ingested as COMPLEX<json> are automatically optimized to store the most appropriate physical column based on the data processed. For example, if only LONG values are processed, Druid stores a LONG column, ARRAY columns if the data consists of arrays, or COMPLEX<json> in the general case if the data is actually nested. This is the same functionality that powers 'type aware' schema discovery.

Druid supports directly ingesting nested data with the following formats: JSON, Parquet, Avro, ORC, Protobuf.

Example nested data​

The examples in this topic use the JSON data in nested_example_data.json. The file contains a simple facsimile of an order tracking and shipping table.

When pretty-printed, a sample row in nested_example_data looks like this:

{
    "time":"2022-6-14T10:32:08Z",
    "product":"Keyboard",
    "department":"Computers",
    "shipTo":{
        "firstName": "Sandra",
        "lastName": "Beatty",
        "address": {
            "street": "293 Grant Well",
            "city": "Loischester",
            "state": "FL",
            "country": "TV",
            "postalCode": "88845-0066"
        },
        "phoneNumbers": [
            {"type":"primary","number":"1-788-771-7028 x8627" },
            {"type":"secondary","number":"1-460-496-4884 x887"}
        ]
    },
    "details"{"color":"plum","price":"40.00"}
}

Native batch ingestion​

For native batch ingestion, you can use the SQL JSON functions to extract nested data as an alternative to using the flattenSpec input format.

To configure a dimension as a nested data type, specify the json type for the dimension in the dimensions list in the dimensionsSpec property of your ingestion spec.

For example, the following ingestion spec instructs Druid to ingest shipTo and details as JSON-type nested dimensions:

{
  "type": "index_parallel",
  "spec": {
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "http",
        "uris": [
          "https://static.imply.io/data/nested_example_data.json"
        ]
      },
      "inputFormat": {
        "type": "json"
      }
    },
    "dataSchema": {
      "granularitySpec": {
        "segmentGranularity": "day",
        "queryGranularity": "none",
        "rollup": false
      },
      "dataSource": "nested_data_example",
      "timestampSpec": {
        "column": "time",
        "format": "auto"
      },
      "dimensionsSpec": {
        "dimensions": [
          "product",
          "department",
          {
            "type": "json",
            "name": "shipTo"
          },
          {
            "type": "json",
            "name": "details"
          }
        ]
      },
      "transformSpec": {}
    },
    "tuningConfig": {
      "type": "index_parallel",
      "partitionsSpec": {
        "type": "dynamic"
      }
    }
  }
}

Transform data during batch ingestion​

You can use the SQL JSON functions to transform nested data and reference the transformed data in your ingestion spec.

To do this, define the output name and expression in the transforms list in the transformSpec object of your ingestion spec.

For example, the following ingestion spec extracts firstName, lastName and address from shipTo and creates a composite JSON object containing product, details and department.

{
  "type": "index_parallel",
  "spec": {
    "ioConfig": {
      "type": "index_parallel",
      "inputSource": {
        "type": "http",
        "uris": [
          "https://static.imply.io/data/nested_example_data.json"
        ]
      },
      "inputFormat": {
        "type": "json"
      }
    },
    "dataSchema": {
      "granularitySpec": {
        "segmentGranularity": "day",
        "queryGranularity": "none",
        "rollup": false
      },
      "dataSource": "nested_data_transform_example",
      "timestampSpec": {
        "column": "time",
        "format": "auto"
      },
      "dimensionsSpec": {
        "dimensions": [
          "firstName",
          "lastName",
          {
            "type": "json",
            "name": "address"
          },
          {
            "type": "json",
            "name": "productDetails"
          }
        ]
      },
      "transformSpec": {
        "transforms":[
            { "type":"expression", "name":"firstName", "expression":"json_value(shipTo, '$.firstName')"},
            { "type":"expression", "name":"lastName", "expression":"json_value(shipTo, '$.lastName')"},
            { "type":"expression", "name":"address", "expression":"json_query(shipTo, '$.address')"},
            { "type":"expression", "name":"productDetails", "expression":"json_object('product', product, 'details', details, 'department', department)"}
        ]
      }
    },
    "tuningConfig": {
      "type": "index_parallel",
      "partitionsSpec": {
        "type": "dynamic"
      }
    }
  }
}

SQL-based ingestion​

To ingest nested data using SQL-based ingestion, specify COMPLEX<json> as the value for type when you define the row signature—shipTo and details in the following example ingestion spec:

SQL-based ingestion

REPLACE INTO msq_nested_data_example OVERWRITE ALL
SELECT
  TIME_PARSE("time") as __time,
  product,
  department,
  shipTo,
  details
FROM (
  SELECT * FROM
  TABLE(
    EXTERN(
      '{"type":"http","uris":["https://static.imply.io/data/nested_example_data.json"]}',
      '{"type":"json"}',
      '[{"name":"time","type":"string"},{"name":"product","type":"string"},{"name":"department","type":"string"},{"name":"shipTo","type":"COMPLEX<json>"},{"name":"details","type":"COMPLEX<json>"}]'
    )
  )
)
PARTITIONED BY ALL

Streaming ingestion​

You can ingest nested data into Druid using the streaming method—for example, from a Kafka topic.

When you define your supervisor spec, include a dimension with type json for each nested column. For example, the following supervisor spec from the Kafka ingestion tutorial contains dimensions for the nested columns event, agent, and geo_ip in datasource kttm-kafka.

{
   "type": "kafka",
   "spec": {
      "ioConfig": {
         "type": "kafka",
         "consumerProperties": {
           "bootstrap.servers": "localhost:9092"
      },
      "topic": "kttm",
      "inputFormat": {
         "type": "json"
      },
      "useEarliestOffset": true
   },
   "tuningConfig": {
     "type": "kafka"
   },
   "dataSchema": {
      "dataSource": "kttm-kafka",
      "timestampSpec": {
         "column": "timestamp",
         "format": "iso"
      },
      "dimensionsSpec": {
         "dimensions": [
            "session",
             "number",
             "client_ip",
             "language",
             "adblock_list",
             "app_version",
             "path",
             "loaded_image",
             "referrer",
             "referrer_host",
             "server_ip",
             "screen",
             "window",
             {
               "type": "long",
               "name": "session_length"
             },
             "timezone",
             "timezone_offset",
             {
               "type": "json",
               "name": "event"
             },
             {
               "type": "json",
               "name": "agent"
             },
             {
               "type": "json",
               "name": "geo_ip"
             }
           ]
         },
      "granularitySpec": {
         "queryGranularity": "none",
         "rollup": false,
         "segmentGranularity": "day"
      }
    }
  }
}

The Kafka tutorial guides you through the steps to load sample nested data into a Kafka topic, then ingest the data into Druid.

Transform data during SQL-based ingestion​

You can use the SQL JSON functions to transform nested data in your ingestion query.

For example, the following ingestion query is the SQL-based version of the previous batch example—it extracts firstName, lastName, and address from shipTo and creates a composite JSON object containing product, details, and department.

SQL-based ingestion

REPLACE INTO msq_nested_data_transform_example OVERWRITE ALL
SELECT
  TIME_PARSE("time") as __time,
  JSON_VALUE(shipTo, '$.firstName') as firstName,
  JSON_VALUE(shipTo, '$.lastName') as lastName,
  JSON_QUERY(shipTo, '$.address') as address,
  JSON_OBJECT('product':product,'details':details, 'department':department) as productDetails
FROM (
  SELECT * FROM
  TABLE(
    EXTERN(
      '{"type":"http","uris":["https://static.imply.io/data/nested_example_data.json"]}',
      '{"type":"json"}',
      '[{"name":"time","type":"string"},{"name":"product","type":"string"},{"name":"department","type":"string"},{"name":"shipTo","type":"COMPLEX<json>"},{"name":"details","type":"COMPLEX<json>"}]'
    )
  )
)
PARTITIONED BY ALL

Ingest a JSON string as COMPLEX<json>​

If your source data contains serialized JSON strings, you can ingest the data as COMPLEX<JSON> as follows:

  • During native batch ingestion, call the parse_json function in a transform object in the transformSpec.
  • During SQL-based ingestion, use the PARSE_JSON keyword within your SELECT statement to transform the string values to JSON.
  • If you are concerned that your data may not contain valid JSON, you can use try_parse_json for native batch or TRY_PARSE_JSON for SQL-based ingestion. For cases where the column does not contain valid JSON, Druid inserts a null value.

If you are using a text input format like tsv, you need to use this method to ingest data into a COMPLEX<json> column.

For example, consider the following deserialized row of the sample data set:

{"time": "2022-06-13T10:10:35Z", "product": "Bike", "department":"Sports", "shipTo":"{\"firstName\": \"Henry\",\"lastName\": \"Wuckert\",\"address\": {\"street\": \"5643 Jan Walk\",\"city\": \"Lake Bridget\",\"state\": \"HI\",\"country\":\"ME\",\"postalCode\": \"70204-2939\"},\"phoneNumbers\": [{\"type\":\"primary\",\"number\":\"593.475.0449 x86733\" },{\"type\":\"secondary\",\"number\":\"638-372-1210\"}]}", "details":"{\"color\":\"ivory\", \"price\":955.00}"}

The following examples demonstrate how to ingest the shipTo and details columns both as string type and as COMPLEX<json> in the shipTo_parsed and details_parsed columns.

REPLACE INTO deserialized_example OVERWRITE ALL
WITH source AS (SELECT * FROM TABLE(
  EXTERN(
    '{"type":"inline","data":"{\"time\": \"2022-06-13T10:10:35Z\", \"product\": \"Bike\", \"department\":\"Sports\", \"shipTo\":\"{\\\"firstName\\\": \\\"Henry\\\",\\\"lastName\\\": \\\"Wuckert\\\",\\\"address\\\": {\\\"street\\\": \\\"5643 Jan Walk\\\",\\\"city\\\": \\\"Lake Bridget\\\",\\\"state\\\": \\\"HI\\\",\\\"country\\\":\\\"ME\\\",\\\"postalCode\\\": \\\"70204-2939\\\"},\\\"phoneNumbers\\\": [{\\\"type\\\":\\\"primary\\\",\\\"number\\\":\\\"593.475.0449 x86733\\\" },{\\\"type\\\":\\\"secondary\\\",\\\"number\\\":\\\"638-372-1210\\\"}]}\", \"details\":\"{\\\"color\\\":\\\"ivory\\\", \\\"price\\\":955.00}\"}\n"}',
    '{"type":"json"}',
    '[{"name":"time","type":"string"},{"name":"product","type":"string"},{"name":"department","type":"string"},{"name":"shipTo","type":"string"},{"name":"details","type":"string"}]'
  )
))
SELECT
  TIME_PARSE("time") AS __time,
  "product",
  "department",
  "shipTo",
  "details",
  PARSE_JSON("shipTo") as "shipTo_parsed",
  PARSE_JSON("details") as "details_parsed"
FROM source
PARTITIONED BY DAY

Querying nested columns​

Once ingested, Druid stores the JSON-typed columns as native JSON objects and presents them as COMPLEX<json>.

See the Nested columns functions reference for information on the functions in the examples below.

Druid supports a small, simplified subset of the JSONPath syntax operators, primarily limited to extracting individual values from nested data structures. See the SQL JSON functions page for details.

Displaying data types​

The following example illustrates how you can display the data types for your columns. Note that details and shipTo display as COMPLEX<json>.

Example query: Display data types​

Display data types

SELECT TABLE_NAME, COLUMN_NAME, DATA_TYPE
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = 'nested_data_example'

Example query results:

[["TABLE_NAME","COLUMN_NAME","DATA_TYPE"],["STRING","STRING","STRING"],["VARCHAR","VARCHAR","VARCHAR"],["nested_data_example","__time","TIMESTAMP"],["nested_data_example","department","VARCHAR"],["nested_data_example","details","COMPLEX<json>"],["nested_data_example","product","VARCHAR"],["nested_data_example","shipTo","COMPLEX<json>"]]

Retrieving JSON data​

You can retrieve JSON data directly from a table. Druid returns the results as a JSON object, so you can't use grouping, aggregation, or filtering operators.

Example query: Retrieve JSON data​

The following example query extracts all data from nested_data_example:

Retrieve JSON data

SELECT * FROM nested_data_example

Example query results:

[["__time","department","details","product","shipTo"],["LONG","STRING","COMPLEX<json>","STRING","COMPLEX<json>"],["TIMESTAMP","VARCHAR","OTHER","VARCHAR","OTHER"],["2022-06-13T07:52:29.000Z","Sports","{\"color\":\"sky blue\",\"price\":542.0}","Bike","{\"firstName\":\"Russ\",\"lastName\":\"Cole\",\"address\":{\"street\":\"77173 Rusty Station\",\"city\":\"South Yeseniabury\",\"state\":\"WA\",\"country\":\"BL\",\"postalCode\":\"01893\"},\"phoneNumbers\":[{\"type\":\"primary\",\"number\":\"891-374-6188 x74568\"},{\"type\":\"secondary\",\"number\":\"1-248-998-4426 x33037\"}]}"],["2022-06-13T10:10:35.000Z","Sports","{\"color\":\"ivory\",\"price\":955.0}","Bike","{\"firstName\":\"Henry\",\"lastName\":\"Wuckert\",\"address\":{\"street\":\"5643 Jan Walk\",\"city\":\"Lake Bridget\",\"state\":\"HI\",\"country\":\"ME\",\"postalCode\":\"70204-2939\"},\"phoneNumbers\":[{\"type\":\"primary\",\"number\":\"593.475.0449 x86733\"},{\"type\":\"secondary\",\"number\":\"638-372-1210\"}]}"],["2022-06-13T13:57:38.000Z","Grocery","{\"price\":8.0}","Sausages","{\"firstName\":\"Forrest\",\"lastName\":\"Brekke\",\"address\":{\"street\":\"41548 Collier Divide\",\"city\":\"Wintheiserborough\",\"state\":\"WA\",\"country\":\"AD\",\"postalCode\":\"27577-6784\"},\"phoneNumbers\":[{\"type\":\"primary\",\"number\":\"(904) 890-0696 x581\"},{\"type\":\"secondary\",\"number\":\"676.895.6759\"}]}"],["2022-06-13T21:37:06.000Z","Computers","{\"color\":\"olive\",\"price\":90.0}","Mouse","{\"firstName\":\"Rickey\",\"lastName\":\"Rempel\",\"address\":{\"street\":\"6232 Green Glens\",\"city\":\"New Fermin\",\"state\":\"HI\",\"country\":\"CW\",\"postalCode\":\"98912-1195\"},\"phoneNumbers\":[{\"type\":\"primary\",\"number\":\"(689) 766-4272 x60778\"},{\"type\":\"secondary\",\"number\":\"375.662.4737 x24707\"}]}"],["2022-06-14T10:32:08.000Z","Computers","{\"color\":\"plum\",\"price\":40.0}","Keyboard","{\"firstName\":\"Sandra\",\"lastName\":\"Beatty\",\"address\":{\"street\":\"293 Grant Well\",\"city\":\"Loischester\",\"state\":\"FL\",\"country\":\"TV\",\"postalCode\":\"88845-0066\"},\"phoneNumbers\":[{\"type\":\"primary\",\"number\":\"1-788-771-7028 x8627\"},{\"type\":\"secondary\",\"number\":\"1-460-496-4884 x887\"}]}"]]

Extracting nested data elements​

The JSON_VALUE function is specially optimized to provide native Druid level performance when processing nested literal values, as if they were flattened, traditional, Druid column types. It does this by reading from the specialized nested columns and indexes that are built and stored in JSON objects when Druid creates segments.

Some operations using JSON_VALUE run faster than those using native Druid columns. For example, filtering numeric types uses the indexes built for nested numeric columns, which are not available for Druid DOUBLE, FLOAT, or LONG columns.

JSON_VALUE only returns literal types. Any paths that reference JSON objects or array types return null.

info

To achieve the best possible performance, use the JSON_VALUE function whenever you query JSON objects.

Example query: Extract nested data elements​

The following example query illustrates how to use JSON_VALUE to extract specified elements from a COMPLEX<json> object. Note that the returned values default to type VARCHAR.

Extract nested data elements

SELECT
  product,
  department,
  JSON_VALUE(shipTo, '$.address.country') as country,
  JSON_VALUE(shipTo, '$.phoneNumbers[0].number') as primaryPhone,
  JSON_VALUE(details, '$.price') as price
FROM nested_data_example

Example query results:

[["product","department","country","primaryPhone","price"],["STRING","STRING","STRING","STRING","STRING"],["VARCHAR","VARCHAR","VARCHAR","VARCHAR","VARCHAR"],["Bike","Sports","BL","891-374-6188 x74568","542.0"],["Bike","Sports","ME","593.475.0449 x86733","955.0"],["Sausages","Grocery","AD","(904) 890-0696 x581","8.0"],["Mouse","Computers","CW","(689) 766-4272 x60778","90.0"],["Keyboard","Computers","TV","1-788-771-7028 x8627","40.0"]]

Extracting nested data elements as a suggested type​

You can use the RETURNING keyword to provide type hints to the JSON_VALUE function. This way the SQL planner produces the correct native Druid query, leading to expected results. This keyword allows you to specify a SQL type for the path value.

Example query: Extract nested data elements as suggested types​

The following example query illustrates how to use JSON_VALUE and the RETURNING keyword to extract an element of nested data and return it as specified types.

Extract nested data elements as a suggested type

SELECT
  product,
  department,
  JSON_VALUE(shipTo, '$.address.country') as country,
  JSON_VALUE(details, '$.price' RETURNING BIGINT) as price_int,
  JSON_VALUE(details, '$.price' RETURNING DECIMAL) as price_decimal,
  JSON_VALUE(details, '$.price' RETURNING VARCHAR) as price_varchar
FROM nested_data_example

Query results:

[["product","department","country","price_int","price_decimal","price_varchar"],["STRING","STRING","STRING","LONG","DOUBLE","STRING"],["VARCHAR","VARCHAR","VARCHAR","BIGINT","DECIMAL","VARCHAR"],["Bike","Sports","BL",542,542.0,"542.0"],["Bike","Sports","ME",955,955.0,"955.0"],["Sausages","Grocery","AD",8,8.0,"8.0"],["Mouse","Computers","CW",90,90.0,"90.0"],["Keyboard","Computers","TV",40,40.0,"40.0"]]

Grouping, aggregating, and filtering​

You can use JSON_VALUE expressions in any context where you can use traditional Druid columns, such as grouping, aggregation, and filtering.

Example query: Grouping and filtering​

The following example query illustrates how to use SUM, WHERE, GROUP BY, and ORDER BY operators with JSON_VALUE.

Group, aggregate, filter

SELECT
  product,
  JSON_VALUE(shipTo, '$.address.country'),
  SUM(JSON_VALUE(details, '$.price' RETURNING BIGINT))
FROM nested_data_example
WHERE JSON_VALUE(shipTo, '$.address.country') in ('BL', 'CW')
GROUP BY 1,2
ORDER BY 3 DESC

Example query results:

[["product","EXPR$1","EXPR$2"],["STRING","STRING","LONG"],["VARCHAR","VARCHAR","BIGINT"],["Bike","BL",542],["Mouse","CW",90]]

Transforming JSON object data​

In addition to JSON_VALUE, Druid offers a number of operators that focus on transforming JSON object data:

  • JSON_QUERY
  • JSON_OBJECT
  • PARSE_JSON
  • TO_JSON_STRING

These functions are primarily intended for use with SQL-based ingestion to transform data during insert operations, but they also work in traditional Druid SQL queries. Because most of these functions output JSON objects, they have the same limitations when used in traditional Druid queries as interacting with the JSON objects directly.

Example query: Return results in a JSON object​

You can use the JSON_QUERY function to extract a partial structure from any JSON input and return results in a JSON object. Unlike JSON_VALUE it can extract objects and arrays.

The following example query illustrates the differences in output between JSON_VALUE and JSON_QUERY. The two output columns for JSON_VALUE contain null values only because JSON_VALUE only returns literal types.

Return results in a JSON object

SELECT
  JSON_VALUE(shipTo, '$.address'),
  JSON_QUERY(shipTo, '$.address'),
  JSON_VALUE(shipTo, '$.phoneNumbers'),
  JSON_QUERY(shipTo, '$.phoneNumbers')
FROM nested_data_example

Example query results:

[["EXPR$0","EXPR$1","EXPR$2","EXPR$3"],["STRING","COMPLEX<json>","STRING","COMPLEX<json>"],["VARCHAR","OTHER","VARCHAR","OTHER"],["","{\"street\":\"77173 Rusty Station\",\"city\":\"South Yeseniabury\",\"state\":\"WA\",\"country\":\"BL\",\"postalCode\":\"01893\"}","","[{\"type\":\"primary\",\"number\":\"891-374-6188 x74568\"},{\"type\":\"secondary\",\"number\":\"1-248-998-4426 x33037\"}]"],["","{\"street\":\"5643 Jan Walk\",\"city\":\"Lake Bridget\",\"state\":\"HI\",\"country\":\"ME\",\"postalCode\":\"70204-2939\"}","","[{\"type\":\"primary\",\"number\":\"593.475.0449 x86733\"},{\"type\":\"secondary\",\"number\":\"638-372-1210\"}]"],["","{\"street\":\"41548 Collier Divide\",\"city\":\"Wintheiserborough\",\"state\":\"WA\",\"country\":\"AD\",\"postalCode\":\"27577-6784\"}","","[{\"type\":\"primary\",\"number\":\"(904) 890-0696 x581\"},{\"type\":\"secondary\",\"number\":\"676.895.6759\"}]"],["","{\"street\":\"6232 Green Glens\",\"city\":\"New Fermin\",\"state\":\"HI\",\"country\":\"CW\",\"postalCode\":\"98912-1195\"}","","[{\"type\":\"primary\",\"number\":\"(689) 766-4272 x60778\"},{\"type\":\"secondary\",\"number\":\"375.662.4737 x24707\"}]"],["","{\"street\":\"293 Grant Well\",\"city\":\"Loischester\",\"state\":\"FL\",\"country\":\"TV\",\"postalCode\":\"88845-0066\"}","","[{\"type\":\"primary\",\"number\":\"1-788-771-7028 x8627\"},{\"type\":\"secondary\",\"number\":\"1-460-496-4884 x887\"}]"]]

Example query: Combine multiple JSON inputs into a single JSON object value​

The following query illustrates how to use JSON_OBJECT to combine nested data elements into a new object.

Combine JSON inputs

SELECT
  JSON_OBJECT(KEY 'shipTo' VALUE JSON_QUERY(shipTo, '$'), KEY 'details' VALUE JSON_QUERY(details, '$')) as combinedJson
FROM nested_data_example

Example query results:

[["combinedJson"],["COMPLEX<json>"],["OTHER"],["{\"details\":{\"color\":\"sky blue\",\"price\":542.0},\"shipTo\":{\"firstName\":\"Russ\",\"lastName\":\"Cole\",\"address\":{\"street\":\"77173 Rusty Station\",\"city\":\"South Yeseniabury\",\"state\":\"WA\",\"country\":\"BL\",\"postalCode\":\"01893\"},\"phoneNumbers\":[{\"type\":\"primary\",\"number\":\"891-374-6188 x74568\"},{\"type\":\"secondary\",\"number\":\"1-248-998-4426 x33037\"}]}}"],["{\"details\":{\"color\":\"ivory\",\"price\":955.0},\"shipTo\":{\"firstName\":\"Henry\",\"lastName\":\"Wuckert\",\"address\":{\"street\":\"5643 Jan Walk\",\"city\":\"Lake Bridget\",\"state\":\"HI\",\"country\":\"ME\",\"postalCode\":\"70204-2939\"},\"phoneNumbers\":[{\"type\":\"primary\",\"number\":\"593.475.0449 x86733\"},{\"type\":\"secondary\",\"number\":\"638-372-1210\"}]}}"],["{\"details\":{\"price\":8.0},\"shipTo\":{\"firstName\":\"Forrest\",\"lastName\":\"Brekke\",\"address\":{\"street\":\"41548 Collier Divide\",\"city\":\"Wintheiserborough\",\"state\":\"WA\",\"country\":\"AD\",\"postalCode\":\"27577-6784\"},\"phoneNumbers\":[{\"type\":\"primary\",\"number\":\"(904) 890-0696 x581\"},{\"type\":\"secondary\",\"number\":\"676.895.6759\"}]}}"],["{\"details\":{\"color\":\"olive\",\"price\":90.0},\"shipTo\":{\"firstName\":\"Rickey\",\"lastName\":\"Rempel\",\"address\":{\"street\":\"6232 Green Glens\",\"city\":\"New Fermin\",\"state\":\"HI\",\"country\":\"CW\",\"postalCode\":\"98912-1195\"},\"phoneNumbers\":[{\"type\":\"primary\",\"number\":\"(689) 766-4272 x60778\"},{\"type\":\"secondary\",\"number\":\"375.662.4737 x24707\"}]}}"],["{\"details\":{\"color\":\"plum\",\"price\":40.0},\"shipTo\":{\"firstName\":\"Sandra\",\"lastName\":\"Beatty\",\"address\":{\"street\":\"293 Grant Well\",\"city\":\"Loischester\",\"state\":\"FL\",\"country\":\"TV\",\"postalCode\":\"88845-0066\"},\"phoneNumbers\":[{\"type\":\"primary\",\"number\":\"1-788-771-7028 x8627\"},{\"type\":\"secondary\",\"number\":\"1-460-496-4884 x887\"}]}}"]]

Using other transform functions​

Druid provides the following additional transform functions:

  • PARSE_JSON: Deserializes a string value into a JSON object.
  • TO_JSON_STRING: Performs the operation of TO_JSON and then serializes the value into a string.

Example query: Parse and deserialize data​

The following query illustrates how to use the transform functions to parse and deserialize data.

Parse and deserialize data

SELECT
  PARSE_JSON('{"x":"y"}'),
  TO_JSON_STRING('{"x":"y"}'),
  TO_JSON_STRING(PARSE_JSON('{"x":"y"}'))

Example query results:

[["EXPR$0","EXPR$2","EXPR$3"],["COMPLEX<json>","STRING","STRING"],["OTHER","VARCHAR","VARCHAR"],["{\"x\":\"y\"}","\"{\\\"x\\\":\\\"y\\\"}\"","{\"x\":\"y\"}"]]

Using helper operators​

The JSON_KEYS and JSON_PATHS functions are helper operators that you can use to examine JSON object schema. Use them to plan your queries, for example to work out which paths to use in JSON_VALUE.

Example query: Examine JSON object schema​

The following query illustrates how to use the helper operators to examine a nested data object.

Examine JSON object schema

SELECT
  ARRAY_CONCAT_AGG(DISTINCT JSON_KEYS(shipTo, '$.')),
  ARRAY_CONCAT_AGG(DISTINCT JSON_KEYS(shipTo, '$.address')),
  ARRAY_CONCAT_AGG(DISTINCT JSON_PATHS(shipTo))
FROM nested_data_example

Example query results:

[["EXPR$0","EXPR$1","EXPR$2","EXPR$3"],["COMPLEX<json>","COMPLEX<json>","STRING","STRING"],["OTHER","OTHER","VARCHAR","VARCHAR"],["{\"x\":\"y\"}","\"{\\\"x\\\":\\\"y\\\"}\"","\"{\\\"x\\\":\\\"y\\\"}\"","{\"x\":\"y\"}"]]

Known issues​

Before you start using the nested columns feature, consider the following known issues:

  • Directly using COMPLEX<json> columns and expressions is not well integrated into the Druid query engine. It can result in errors or undefined behavior when grouping and filtering, and when you use COMPLEX<json> objects as inputs to aggregators. As a workaround, consider using TO_JSON_STRING to coerce the values to strings before you perform these operations.
  • Directly using array-typed outputs from JSON_KEYS and JSON_PATHS is moderately supported by the Druid query engine. You can group on these outputs, and there are a number of array expressions that can operate on these values, such as ARRAY_CONCAT_AGG. However, some operations are not well defined for use outside array-specific functions, such as filtering using = or IS NULL.
  • Input validation for JSON SQL operators is currently incomplete, which sometimes results in undefined behavior or unhelpful error messages.
  • Ingesting data with a very complex nested structure is potentially an expensive operation and may require you to tune ingestion tasks and/or cluster parameters to account for increased memory usage or overall task run time. When you tune your ingestion configuration, treat each nested literal field inside an object as a flattened top-level Druid column.

Further reading​

For more information, see the following pages: