Delivering Amazon Data Firehose streams to Apache Iceberg Tables in Amazon S3 is in preview and is subject to change.
Convert input data format in Amazon Data Firehose
Amazon Data Firehose can convert the format of your input data from JSON to Apache Parquet
Topics
Prerequisites
Amazon Data Firehose requires the following three elements to convert the format of your record data:
-
A deserializer to read the JSON of your input data – You can choose one of two types of deserializers: Apache Hive JSON SerDe
or OpenX JSON SerDe . Note
When combining multiple JSON documents into the same record, make sure that your input is still presented in the supported JSON format. An array of JSON documents is not a valid input.
For example, this is the correct input:
{"a":1}{"a":2}
And this is the incorrect input:
[{"a":1}, {"a":2}]
-
A schema to determine how to interpret that data – Use AWS Glue to create a schema in the AWS Glue Data Catalog. Amazon Data Firehose then references that schema and uses it to interpret your input data. You can use the same schema to configure both Amazon Data Firehose and your analytics software. For more information, see Populating the AWS Glue Data Catalog in the AWS Glue Developer Guide.
Note
The schema created in AWS Glue Data Catalog should match the input data structure. Otherwise, the converted data will not contain attributes that are not specified in the schema. If you use nested JSON, use a STRUCT type in the schema that mirrors the structure of your JSON data. See this example for how to handle nested JSON with a STRUCT type.
-
A serializer to convert the data to the target columnar storage format (Parquet or ORC) – You can choose one of two types of serializers: ORC SerDe
or Parquet SerDe .
Important
If you enable record format conversion, you can't set your Amazon Data Firehose destination to be Amazon OpenSearch Service, Amazon Redshift, or Splunk. With format conversion enabled, Amazon S3 is the only destination that you can use for your Firehose stream.
You can convert the format of your data even if you aggregate your records before sending them to Amazon Data Firehose.
Choose the JSON deserializer
Choose the OpenX JSON
SerDe
-
yyyy-MM-dd'T'HH:mm:ss[.S]'Z', where the fraction can have up to 9 digits – For example,
2017-02-07T15:13:01.39256Z
. -
yyyy-[M]M-[d]d HH:mm:ss[.S], where the fraction can have up to 9 digits – For example,
2017-02-07 15:13:01.14
. -
Epoch seconds – For example,
1518033528
. -
Epoch milliseconds – For example,
1518033528123
. -
Floating point epoch seconds – For example,
1518033528.123
.
The OpenX JSON SerDe can convert periods (.
) to underscores
(_
). It can also convert JSON keys to lowercase before deserializing
them. For more information about the options that are available with this deserializer
through Amazon Data Firehose, see OpenXJsonSerDe.
If you're not sure which deserializer to choose, use the OpenX JSON SerDe, unless you have time stamps that it doesn't support.
If you have time stamps in formats other than those listed previously, use the Apache Hive JSON SerDeDateTimeFormat
format strings. For more information, see Class DateTimeFormat
You can also use the special value millis
to parse time stamps in epoch
milliseconds. If you don't specify a format, Amazon Data Firehose uses
java.sql.Timestamp::valueOf
by default.
The Hive JSON SerDe doesn't allow the following:
-
Periods (
.
) in column names. -
Fields whose type is
uniontype
. -
Fields that have numerical types in the schema, but that are strings in the JSON. For example, if the schema is (an int), and the JSON is
{"a":"123"}
, the Hive SerDe gives an error.
The Hive SerDe doesn't convert nested JSON into strings. For example, if you have
{"a":{"inner":1}}
, it doesn't treat {"inner":1}
as a
string.
Choose the serializer
The serializer that you choose depends on your business needs. To learn more about the
two serializer options, see ORC
SerDe
Enable record format conversion from console
You can enable data format conversion on the console when you create or update a
Firehose stream. With data format conversion enabled, Amazon S3 is the only destination that
you can configure for the Firehose stream. Also, Amazon S3 compression gets disabled when you
enable format conversion. However, Snappy compression happens automatically as part of
the conversion process. The framing format for Snappy that Amazon Data Firehose uses in this case is
compatible with Hadoop. This means that you can use the results of the Snappy
compression and run queries on this data in Athena. For the Snappy framing format that
Hadoop relies on, see BlockCompressorStream.java
To enable data format conversion for a data Firehose stream
-
Sign in to the AWS Management Console, and open the Amazon Data Firehose console at https://console.aws.amazon.com/firehose/
. -
Choose a Firehose stream to update, or create a new Firehose stream by following the steps in Tutorial: Create a Firehose stream from console.
-
Under Convert record format, set Record format conversion to Enabled.
-
Choose the output format that you want. For more information about the two options, see Apache Parquet
and Apache ORC . -
Choose an AWS Glue table to specify a schema for your source records. Set the Region, database, table, and table version.
Manage record format conversion from Firehose API
If you want Amazon Data Firehose to convert the format of your input data from JSON to Parquet or ORC, specify the optional DataFormatConversionConfiguration element in ExtendedS3DestinationConfiguration or in ExtendedS3DestinationUpdate. If you specify DataFormatConversionConfiguration, the following restrictions apply.
-
In BufferingHints, you can't set
SizeInMBs
to a value less than 64 if you enable record format conversion. Also, when format conversion isn't enabled, the default value is 5. The value becomes 128 when you enable it. -
You must set
CompressionFormat
in ExtendedS3DestinationConfiguration or in ExtendedS3DestinationUpdate toUNCOMPRESSED
. The default value forCompressionFormat
isUNCOMPRESSED
. Therefore, you can also leave it unspecified in ExtendedS3DestinationConfiguration. The data still gets compressed as part of the serialization process, using Snappy compression by default. The framing format for Snappy that Amazon Data Firehose uses in this case is compatible with Hadoop. This means that you can use the results of the Snappy compression and run queries on this data in Athena. For the Snappy framing format that Hadoop relies on, see BlockCompressorStream.java. When you configure the serializer, you can choose other types of compression.
Error handling
When Amazon Data Firehose can't parse or deserialize a record (for example, when the data doesn't match the schema), it writes it to Amazon S3 with an error prefix. If this write fails, Amazon Data Firehose retries it forever, blocking further delivery. For each failed record, Amazon Data Firehose writes a JSON document with the following schema:
{ "attemptsMade": long, "arrivalTimestamp": long, "lastErrorCode": string, "lastErrorMessage": string, "attemptEndingTimestamp": long, "rawData": string, "sequenceNumber": string, "subSequenceNumber": long, "dataCatalogTable": { "catalogId": string, "databaseName": string, "tableName": string, "region": string, "versionId": string, "catalogArn": string } }
Example
For an example of how to set up record format conversion with AWS CloudFormation, see AWS::DataFirehose::DeliveryStream.