Input formats

This section contains a collection of guides that will show you how to import data from a Pinot supported input format.

Pinot offers support for various popular input formats during ingestion. By changing the input format, you can reduce the time that goes in serialization-deserialization and speed up the ingestion.

The input format can be changed using the recordReaderSpec config in the ingestion job spec.

recordReaderSpec:
dataFormat: 'csv'
className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
configs:
key1 : 'value1'
key2 : 'value2'

The config consists of the following keys -

dataFormat - Name of the data format to consume.

className - name of the class that implements the RecordReader interface. This class is used for parsing the data.

configClassName - name of the class that implements the RecordReaderConfig interface. This class is used the parse the values mentioned in configs

configs - Key value pair for format specific configs. This field can be left out.

Pinot supports the multiple input formats out of the box. You just need to specify the corresponding readers and the associated custom configs to switch between the formats.

CSV

dataFormat: 'csv'
className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
configs:
fileFormat: 'default' #should be one of default, rfc4180, excel, tdf, mysql
header: 'columnName seperated by delimiter'
delimiter: ','
multiValueDelimiter: '-'

CSV Record Reader supports the following configs -

fileFormat - can be one of default, rfc4180, excel, tdf, mysql

header - header of the file. The columnNames should be seperated by the delimiter mentioned in the config

delimiter - The character seperating the columns

multiValueDelimiter - The character seperating multiple values in a single column. This can be used to split a column into a list.

Your CSV file may have raw text fields that cannot be reliably delimited using any character. In this case, explicitly set the multiValueDelimeter field to empty in the ingestion config. multiValueDelimiter: ''

AVRO

dataFormat: 'avro'
className: 'org.apache.pinot.plugin.inputformat.avro.AvroRecordReader'

The Avro record reader converts the data in file to a GenericRecord. A java class or .avro file is not required.

JSON

dataFormat: 'json'
className: 'org.apache.pinot.plugin.inputformat.json.JSONRecordReader'

Thrift

dataFormat: 'thrift'
className: 'org.apache.pinot.plugin.inputformat.thrift.ThriftRecordReader'
configs:
thriftClass: 'ParserClassName'

Note: Thrift requires the generated class using .thrift file to parse the data. The .class file should be available in the Pinot's classpath. You can put the files in the lib/ folder of pinot distribution directory.

Parquet

dataFormat: 'parquet'
className: 'org.apache.pinot.plugin.inputformat.parquet.ParquetRecordReader'

ORC

dataFormat: 'orc'
className: 'org.apache.pinot.plugin.inputformat.orc.ORCRecordReader'

ORC record reader supports the following data types -

ORC Data Type

Java Data Type

BOOLEAN

String

SHORT

Integer

INT

Integer

LONG

Integer

FLOAT

Float

DOUBLE

Double

STRING

String

VARCHAR

String

CHAR

String

LIST

Object[]

MAP

Map<Object, Object>

DATE

Long

TIMESTAMP

Long

BINARY

byte[]

BYTE

Integer

In LIST and MAP types, the object should only belong to one of the data types supported by Pinot.

Protocol Buffers

dataFormat: 'proto'
className: 'org.apache.pinot.plugin.inputformat.protobuf.ProtoBufRecordReader'
configs:
descriptorFile: 'file:///path/to/sample.desc'

The reader requires a descriptor file to deserialize the data present in the files. You can generate the descriptor file (.desc) from the .proto file using the command -

protoc --include_imports --descriptor_set_out=/absolute/path/to/output.desc /absolute/path/to/input.proto