Complex Type (Array, Map) Handling

Complex-type handling in Apache Pinot.

It's common for the ingested data to have complex structure. For example, Avro schema has records and arrays, and JSON data has objects and arrays. In Apache Pinot, the data model supports primitive data types (including int, long, float, double, string, bytes), as well as limited multi-value types such as an array of primitive types. Such simple data types allow Pinot to build fast indexing structures for good query performance, but it requires some handling on the complex structures. There are in general two options for such handling: convert the complex-type data into JSON string and then build JSON index; or use the inbuilt complex-type handling rules in the ingestion config.

In 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. Note this object has two child fields, and the child group is a nested array with the element of object type.

Example JSON data

Handle the complex type with JSON indexing

Apache Pinot provides 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"
    ]
  },

}

Note 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 this file. 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, please check out this file.

With this, you can start to query the nested fields under group. For the details about the supported JSON function, see guide).

Handle the complex type with 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 in 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 field group.group_id. The default value of the delimiter is ., you can choose other delimiter by changing the configuration delimiter under complexTypeConfig. This flattening rule also apllies on the maps in the collections to be unnested.

• The nested array group_topics under group is unnested into the top-level, and convert 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 configuration fieldsToUnnest.

• For the collections not in specified in fieldsToUnnest, the ingestion by default will serialize them into JSON string, except for the array of primitive values, which will be ingested as multi-value column by default. The behavior is defined in config collectionNotUnnestedToJson with default value to NON_PRIMITIVE. Other behaviors include (1) ALL, which aslo convert the array of primitive values to JSON string; (2) NONE, this does not do conversion, but leave it to the users to use transform functions for handling.

You can find the full spec of the table config here and the table schema here.

With the flattening/unnesting, you can then query the table with primitive values using the SQL query like:

SELECT "group.group_topics.urlkey", 
       "group.group_topics.topic_name", 
       "group.group_id" 
FROM meetupRsvp
LIMIT 10

Note . is a reserved character in SQL, so you need to quote the flattened column.

Infer the Pinot schema from the Avro schema and JSON data

When there are complex structures, it could be challenging and tedious to figure out the Pinot schema manually. To help the 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.