s3

The s3 sink saves batches of events to Amazon Simple Storage Service (Amazon S3) objects.

Usage

The following example creates a pipeline configured with an s3 sink. It contains additional options for customizing the event and size thresholds for which the pipeline sends record events and sets the codec type ndjson:

pipeline:
  ...
  sink:
    - s3:
        aws:
          region: us-east-1
          sts_role_arn: arn:aws:iam::123456789012:role/Data-Prepper
          sts_header_overrides:
        max_retries: 5
        bucket:
          name: bucket_name
          object_key:
            path_prefix: my-elb/%{yyyy}/%{MM}/%{dd}/
        threshold:
          event_count: 2000
          maximum_size: 50mb
          event_collect_timeout: 15s
        codec:
          ndjson:
        buffer_type: in_memory

Configuration

Use the following options when customizing the s3 sink.

aws

Threshold configuration

Use the following options to set ingestion thresholds for the s3 sink.

Buffer type

buffer_type is an optional configuration that records stored events temporarily before flushing them into an S3 bucket. The default value is in_memory. Use one of the following options:

• in_memory: Stores the record in memory.
• local_file: Flushes the record into a file on your machine.
• multipart: Writes using the S3 multipart upload. Every 10 MB is written as a part.

Object key configuration

codec

The codec determines how the s3 source formats data written to each S3 object.

avro codec

The avro codec writes an event as an Apache Avro document.

Because Avro requires a schema, you may either define the schema yourself, or Data Prepper will automatically generate a schema. In general, you should define your own schema because it will most accurately reflect your needs.

We recommend that you make your Avro fields use a null union. Without the null union, each field must be present or the data will fail to write to the sink. If you can be certain that each each event has a given field, you can make it non-nullable.

When you provide your own Avro schema, that schema defines the final structure of your data. Therefore, any extra values inside any incoming events that are not mapped in the Arvo schema will not be included in the final destination. To avoid confusion between a custom Arvo schema and the include_keys or exclude_keys sink configurations, Data Prepper does not allow the use of the include_keys or exclude_keys with a custom schema.

In cases where your data is uniform, you may be able to automatically generate a schema. Automatically generated schemas are based on the first event received by the codec. The schema will only contain keys from this event. Therefore, you must have all keys present in all events in order for the automatically generated schema to produce a working schema. Automatically generated schemas make all fields nullable. Use the sink’s include_keys and exclude_keys configurations to control what data is included in the auto-generated schema.

ndjson codec

The ndjson codec writes each line as a JSON object.

The ndjson codec does not take any configurations.

json codec

The json codec writes events in a single large JSON file. Each event is written into an object within a JSON array.

parquet codec

The parquet codec writes events into a Parquet file. When using the Parquet codec, set the buffer_type to in_memory.

The Parquet codec writes data using the Avro schema. Because Parquet requires an Avro schema, you may either define the schema yourself, or Data Prepper will automatically generate a schema. However, we generally recommend that you define your own schema so that it can best meet your needs.

For details on the Avro schema and recommendations, see the Avro codec documentation.