Complex Type (Array, Map) Handling

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Complex-type handling in Apache Pinot.
It's common for ingested data to have a complex structure. For example, Avro schemas have records and arrays and JSON supports objects and arrays.
Apache Pinot's data model supports primitive data types (including int, long, float, double, BigDecimal string, bytes), as well as limited multi-value types such as an array of primitive types (multi-valued BigDecimal type is not supported). Such simple data types allow Pinot to build fast indexing structures for good query performance, but it requires some handling of the complex structures.
Support for BIG_DECIMAL type is added after release 0.10.0.
There are in general two options for such handling:
Convert the complex-type data into JSON string and then build a JSON index
Use the inbuilt complex-type handling rules in the ingestion config.
On this page, we'll show how to handle this complex-type structure with these two approaches, to process the example data in the following figure, which is a field group from the Meetup events Quickstart example.
This object has two child fields and the child group is a nested array with elements of object type.
Example JSON data
JSON indexing
Apache Pinot provides a powerful JSON index to accelerate the value lookup and filtering for the column. To convert an object group with complex type to JSON, you can add the following config to table config.
json_meetupRsvp_realtime_table_config.json
{
    "ingestionConfig":{
      "transformConfigs": [
        {
          "columnName": "group_json",
          "transformFunction": "jsonFormat(\"group\")"
        }
      ],
    },
    ...
    "tableIndexConfig": {
    "loadMode": "MMAP",
    "noDictionaryColumns": [
      "group_json"
    ],
    "jsonIndexColumns": [
      "group_json"
    ]
  },
​
}
The config transformConfigs transforms the object group to a JSON string group_json, which then creates the JSON indexing with config jsonIndexColumns. To read the full spec, see json_meetupRsvp_realtime_table_config.json.
Also, note that group is a reserved keyword in SQL and therefore needs to be quoted in transformFunction.
The columnName can't use the same name as any of the fields in the source JSON data e.g. if our source data contains the field group and we want to transform the data in that field before persisting it, the destination column name would need to be something different, like group_json.
Additionally, you need to overwrite the maxLength of the field group_json on the schema, because by default, a string column has a limited length. For example,
json_meetupRsvp_realtime_table_schema.json
{
  {
      "name": "group_json",
      "dataType": "JSON",
      "maxLength": 2147483647
    }
    ...
}
For the full spec, see json_meetupRsvp_schema.json.
With this, you can start to query the nested fields under group. For the details about the supported JSON function, see guide).
Ingestion configurations
Though JSON indexing is a handy way to process the complex types, there are some limitations:
It’s not performant to group by or order by a JSON field, because JSON_EXTRACT_SCALAR is needed to extract the values in the GROUP BY and ORDER BY clauses, which invokes the function evaluation.
For cases that you want to use Pinot's multi-column functions such as DISTINCTCOUNTMV
Alternatively, from Pinot 0.8, you can use the complex-type handling in ingestion configurations to flatten and unnest the complex structure and convert them into primitive types. Then you can reduce the complex-type data into a flattened Pinot table, and query it via SQL. With the inbuilt processing rules, you do not need to write ETL jobs in another compute framework such as Flink or Spark.
To process this complex type, you can add the configuration complexTypeConfig to the ingestionConfig. For example:
complexTypeHandling_meetupRsvp_realtime_table_config.json
{
  "ingestionConfig": {    
    "complexTypeConfig": {
      "delimiter": '.',
      "fieldsToUnnest": ["group.group_topics"],
      "collectionNotUnnestedToJson": "NON_PRIMITIVE"
    }
  }
}
With the complexTypeConfig , all the map objects will be flattened to direct fields automatically. And with unnestFields , a record with the nested collection will unnest into multiple records. For instance, the example at the beginning will transform into two rows with this configuration example.
Flattened/unnested data
Note that
The nested field group_id under group is flattened to group.group_id. The default value of the delimiter is . You can choose another delimiter by specifying the configuration delimiter under complexTypeConfig. This flattening rule also applies to maps in the collections to be unnested.
The nested array group_topics under group is unnested into the top-level, and converts the output to a collection of two rows. Note the handling of the nested field within group_topics, and the eventual top-level field of group.group_topics.urlkey. All the collections to unnest shall be included in the configuration fieldsToUnnest.
Collections not specified in fieldsToUnnestwill be serialized into JSON string, except for the array of primitive values, which will be ingested as a multi-value column by default. The behavior is defined by the collectionNotUnnestedToJson config, which takes the following values:
NON_PRIMITIVE- Converts the array to a multi-value column. (default)
ALL- Converts the array of primitive values to JSON string.
NONE- Does not do any conversion.
You can find the full spec of the table config here and the table schema here.
You can then query the table with primitive values using the following SQL query:
SELECT "group.group_topics.urlkey", 
       "group.group_topics.topic_name", 
       "group.group_id" 
FROM meetupRsvp
LIMIT 10
. is a reserved character in SQL, so you need to quote the flattened columns in the query.
Infer the Pinot schema from the Avro schema and JSON data
When there are complex structures, it can be challenging and tedious to figure out the Pinot schema manually. To help with schema inference, Pinot provides utility tools to take the Avro schema or JSON data as input and output the inferred Pinot schema.
To infer the Pinot schema from Avro schema, you can use the command like the following:
bin/pinot-admin.sh AvroSchemaToPinotSchema \
  -timeColumnName fields.hoursSinceEpoch \
  -avroSchemaFile /tmp/test.avsc \
  -pinotSchemaName myTable \
  -outputDir /tmp/test \
  -fieldsToUnnest entries
Note you can input configurations like fieldsToUnnest similar to the ones in complexTypeConfig. And this will simulate the complex-type handling rules on the Avro schema and output the Pinot schema in the file specified in outputDir.
Similarly, you can use the command like the following to infer the Pinot schema from a file of JSON objects.
bin/pinot-admin.sh JsonToPinotSchema \
  -timeColumnName hoursSinceEpoch \
  -jsonFile /tmp/test.json \
  -pinotSchemaName myTable \
  -outputDir /tmp/test \
  -fieldsToUnnest payload.commits
You can check out an example of this run in this PR.
Input formats
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Indexing
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Outline
JSON indexing
Ingestion configurations
Infer the Pinot schema from the Avro schema and JSON data