Use the Amazon S3 Connector to send data from Decodable to an Amazon S3 bucket. This updated version of the Amazon S3 Connector allows you to configure a file rollover policy and has enhanced security over the original Amazon S3 Connector.

Overview

Create a connection to Amazon S3 using the Amazon S3 Connector

Prerequisites

Before you can create the Amazon S3 connection, you must have an Identity and Access Management (IAM) role with the following policies. See the Setting up an IAM User section for more information.

• A Trust Policy that allows access from Decodable’s AWS account. The ExternalId must match your Decodable account name.
• A Permissions Policy with read and write permissions for the destination bucket.

Setting up an IAM User

The following is an example of what the Trust Policy should look like. Replace the <MY_DECODABLE_ACCOUNT_NAME> with your own. In the example, 671293015970 is Decodable’s AWS account ID and cannot be changed.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "AWS": "arn:aws:iam::671293015970:root"
      },
      "Action": "sts:AssumeRole",
      "Condition": {
        "StringEquals": {
          "sts:ExternalId": "<MY_DECODABLE_ACCOUNT_NAME>"
        }
      }
    }
  ]
}

Note: To allow several Decodable Accounts (say, in different AWS Regions) to write to the same bucket, use an array of Account names for the ExternalId value:

{ "sts:ExternalId": ["my-acct-1", "my-acct-2"] }

See AWS Identity and Access Management • The confused deputy problem for more information about why the ExternalId value is required.

You must also have read and write permissions on the destination S3 bucket. See the following list of permissions and replace <YOUR_BUCKET> and /some/dir appropriately. If you want to send data directly at the root level of the bucket, then leave the path blank with the trailing /* included.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["s3:PutObject", "s3:GetObject", "s3:DeleteObject"],
      "Resource": "arn:aws:s3:::<YOUR_BUCKET>/some/dir/*"
    },
    {
      "Effect": "Allow",
      "Action": ["s3:ListBucket"],
      "Resource": "arn:aws:s3:::<YOUR_BUCKET>"
    }
  ]
}

Steps

1. If you have an existing connection using the older version of the Amazon S3 Connector and would like to switch to the newest version, then do the following steps. If you are not upgrading an existing connection, skip these steps.
   1. Stop the existing Amazon S3 connection.
   2. Stop any pipelines that are using it.
2. From the Connections page, select the Amazon S3 Connector and complete the following fields.

3. Select which stream contains the records that you’d like to send to Amazon S3. Then, select Next.
4. Give the newly created connection a Name and Description and select Save.
5. If you are replacing an existing Amazon S3 connection, then restart any pipelines that were processing data for the previous connection.

Properties

The following properties are supported by the S3 connector.

JSON Format Properties

The following properties are only applicable when format=json.

Parquet Format Properties

The following properties are only applicable when format=parquet.

Other parquet options are available. See ParquetOutputFormat for more information.

S3 Object Key Partitioning

You can partition the S3 Object key paths by value using the partition-cols connection property. When you specify a field to partition with, that field will be used as a prefix to organize the data in the S3 bucket.

For example, a common pattern is to partition based on a date (e.g. 2023-01-01), and a subsequent partition for an hour of the day. Given a schema with datetime=DATE and hour=INTEGER fields, by setting the partition-cols property to be datetime,hour the resulting bucket entries will look like:

s3://my-path/datetime=2023-01-01/hour=0/
s3://my-path/datetime=2023-01-01/hour=1/
...
s3://my-path/datetime=2023-01-01/hour=23/
s3://my-path/datetime=2023-01-02/hour=0/

Note: The fields that are used as partition columns will be removed from the resulting payload in the file in Amazon S3. If you are using any query systems downstream that are relying on those fields, you will need to configure them to read the value from the file path instead.

S3 Object Key Formation

The following is an example of what your object keys look like in Amazon S3. Let’s assume that your Amazon S3 Connection has the following configuration:

• S3 bucket or path: my-awesome-bucket
• Format: JSON
• Partition template or partition-cols: datetime
• Sink.rolling-policy.file-size: 5 minutes
• Compression is not set.

When you start the connection, the Amazon S3 Connector opens a file with a name like part-123e4567-e89b-12d3-a456-426614174000-0.json in the datetime=01-25-2023 subfolder in the my-awesome-bucket S3 bucket and starts streaming data to that file. Once 5 minutes have elapsed, then the part-123e4567-e89b-12d3-a456-426614174000-0.json file is closed and a new file named part-123e4567-e89b-12d3-a456-426614174000-1.json is opened. The Amazon S3 Connector then starts sending data to this newly opened file instead.

In summary, the S3 object parts are joined as: <path>/<partition-col>=<value>/part-<unique-id>-<N>