1. Connectors
2. Table & SQL Connectors
3. Formats
4. Parquet

Parquet Format

Format: Serialization Schema Format: Deserialization Schema

• Dependencies
• How to create a table with Parquet format
• Format Options
• Data Type Mapping

The Apache Parquet format allows to read and write Parquet data.

Dependencies

In order to setup the Parquet format, the following table provides dependency information for both projects using a build automation tool (such as Maven or SBT) and SQL Client with SQL JAR bundles.

How to create a table with Parquet format

Here is an example to create a table using Filesystem connector and Parquet format.

CREATE TABLE user_behavior (
  user_id BIGINT,
  item_id BIGINT,
  category_id BIGINT,
  behavior STRING,
  ts TIMESTAMP(3),
  dt STRING
) PARTITIONED BY (dt) WITH (
 'connector' = 'filesystem',
 'path' = '/tmp/user_behavior',
 'format' = 'parquet'
)

Format Options

Parquet format also supports configuration from ParquetOutputFormat. For example, you can configure parquet.compression=GZIP to enable gzip compression.

Data Type Mapping

Currently, Parquet format type mapping is compatible with Apache Hive, but different with Apache Spark:

• Timestamp: mapping timestamp type to int96 whatever the precision is.
• Decimal: mapping decimal type to fixed length byte array according to the precision.

The following table lists the type mapping from Flink type to Parquet type.

Attention Composite data type: Array, Map and Row are not supported.