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

Raw Format

Format: Serialization Schema Format: Deserialization Schema

• Example
• Format Options
• Data Type Mapping

The Raw format allows to read and write raw (byte based) values as a single column.

Note: this format encodes null values as null of byte[] type. This may have limitation when used in upsert-kafka, because upsert-kafka treats null values as a tombstone message (DELETE on the key). Therefore, we recommend avoiding using upsert-kafka connector and the raw format as a value.format if the field can have a null value.

The Raw connector is built-in into the Blink planner, no additional dependencies are required.

Example

For example, you may have following raw log data in Kafka and want to read and analyse such data using Flink SQL.

47.29.201.179 - - [28/Feb/2019:13:17:10 +0000] "GET /?p=1 HTTP/2.0" 200 5316 "https://domain.com/?p=1" "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.119 Safari/537.36" "2.75"

The following creates a table where it reads from (and can writes to) the underlying Kafka topic as an anonymous string value in UTF-8 encoding by using raw format:

CREATE TABLE nginx_log (
  log STRING
) WITH (
  'connector' = 'kafka',
  'topic' = 'nginx_log',
  'properties.bootstrap.servers' = 'localhost:9092',
  'properties.group.id' = 'testGroup',
  'format' = 'raw'
)

Then you can read out the raw data as a pure string, and split it into multiple fields using an user-defined-function for further analysing, e.g. my_split in the example.

SELECT t.hostname, t.datetime, t.url, t.browser, ...
FROM(
  SELECT my_split(log) as t FROM nginx_log
);

In contrast, you can also write a single column of STRING type into this Kafka topic as an anonymous string value in UTF-8 encoding.

Format Options

Data Type Mapping

The table below details the SQL types the format supports, including details of the serializer and deserializer class for encoding and decoding.