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Amazon Kinesis Data Analytics
Developer Guide

Using the Schema Discovery Feature on Streaming Data

Providing an input schema that describes how records on the streaming input map to an in-application stream can be cumbersome and error prone. You can use the DiscoverInputSchema API (called the discovery API) to infer a schema. Using random samples of records on the streaming source, the API can infer a schema (that is, column names, data types, and position of the data element in the incoming data).

Note

To use the Discovery API to generate a schema from a file stored in Amazon S3, see Using the Schema Discovery Feature on Static Data.

The console uses the Discovery API to generate a schema for a specified streaming source. Using the console, you can also update the schema, including adding or removing columns, changing column names or data types, and so on. However, make changes carefully to ensure that you do not create an invalid schema.

After you finalize a schema for your in-application stream, there are functions you can use to manipulate string and datetime values. You can use these functions in your application code when working with rows in the resulting in-application stream. For more information, see Example: Transforming DateTime Values.

Column Naming During Schema Discovery

During schema discovery, Amazon Kinesis Data Analytics tries to retain as much of the original column name as possible from the streaming input source, except in the following cases:

  • The source stream column name is a reserved SQL keyword, such as TIMESTAMP, USER, VALUES, or YEAR.

  • The source stream column name contains unsupported characters. Only letters, numbers, and the underscore character ( _ ) are supported.

  • The source stream column name begins with a number.

  • The source stream column name is longer than 100 characters.

If a column is renamed, the renamed schema column name begins with COL_. In some cases, none of the original column name can be retained—for example, if the entire name is unsupported characters. In such a case, the column is named COL_#, with # being a number indicating the column's place in the column order.

After discovery completes, you can update the schema using the console to add or remove columns, or change column names, data types, or data size.

Examples of Discovery-Suggested Column Names

Source Stream Column Name Discovery-Suggested Column Name

USER

COL_USER

USER@DOMAIN

COL_USERDOMAIN

@@

COL_0

Schema Discovery Issues

What happens if Kinesis Data Analytics does not infer a schema for a given streaming source?

Kinesis Data Analytics infers your schema for common formats, such as CSV and JSON, which are UTF-8 encoded. Kinesis Data Analytics supports any UTF-8 encoded records (including raw text like application logs and records) with a custom column and row delimiter. If Kinesis Data Analytics doesn't infer a schema, you can define a schema manually using the schema editor on the console (or using the API).

If your data does not follow a pattern (which you can specify using the schema editor), you can define a schema as a single column of type VARCHAR(N), where N is the largest number of characters you expect your record to include. From there, you can use string and date-time manipulation to structure your data after it is in an in-application stream. For examples, see Example: Transforming DateTime Values.