Amazon S3 Parquet Import Integration

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The import data connector for Amazon S3 enables you to import the data from Parquet files stored in S3 buckets.

About Authentication Methods for Amazon S3 Parquet

Prerequisites

• Basic knowledge of Treasure Data.

S3 Bucket Policy Configuration

If you are using an AWS S3 bucket located in the same region as your TD region, the IP address from which TD is accessing to the bucket will be private and dynamically changing. If you would like to restrict access, please specify the ID of VPC instead of static IP Addresses. For example, if in the US region, configure access through vpc-df7066ba. If in the Tokyo region, configure access through vpc-e630c182 and, for the EU01 region, vpc-f54e6a9e.

Look up the region of TD Console by the URL you are logging in to TD, then refer to the data connector of your region in the URL.

See the API Documentation for details.

Static IP Address of Treasure Data Integration

If your security policy requires IP whitelisting, you must add Treasure Data's IP addresses to your allowlist to ensure a successful connection.

Please find the complete list of static IP addresses, organized by region, at the following link:
https://api-docs.treasuredata.com/en/overview/ip-addresses-integrations-result-workers/

Limitation

Treasure Data recommends that you limit the size of individual parquet files to no larger than 100 MB for optimal performance. However, this is not a hard requirement, and you can tune your partition size to meet your needs.

Creating a New Connection on TD Console

You can use TD Console to create your data connector.

1. Open TD Console.
2. Navigate to Integrations Hub > Catalog.
3. Search for S3 parquet and select Amazon S3 parquet.
4. Select Create Authentication.

The New Authentication dialog opens. Depending on the Authentication Method you choose, the dialog may look like one of these screens:

basic

session

assume_role

1. Configure the authentication fields, and then select Continue.

1. Name your new AWS S3 connection, and select Done.

Creating an Authentication with the assume_role authentication method

1. Create a new authentication with the assume_role authentication method.
2. Make a note of the numeric portion of the value in the TD's Instance Profile field.

3. Create your AWS IAM role.

Transfer Your AWS S3 Data to Treasure Data

After creating the authenticated connection, the Authentications screen displays.

1. Search for the connection you created.
2. Select New Source.

Connection

1. Type a name for your Source in the Data Transfer field**.**
2. Click Next.

Source Table

The Source dialog opens.

1. Edit the following parameters.

There are instances where you might need to scan all the files in a directory (such as from the top-level directory "/"). In such instances, you must use the CLI to do the import.

Example

You can configure your EMR to create parquet files that contain your data in files as follow:

[your_bucket] - [YM=202010] - [E231A697YXWD39.2020-10-29-15.a103fd5a.parquet]
[your_bucket] - [YM=202010] - [E231A697YXWD39.2020-10-30-15.b2aede4a.parquet]
[your_bucket] - [YM=202010] - [E231A697YXWD39.2020-10-31-01.594fa8e6.parquet]

In this case, the Source Table settings are as shown:

• Bucket: your_bucket
• Path Prefix: YM=202010a/
• Path Regex: * (Not Required)
• Start after path: YM=202010/E231A697YXWD39.2020-10-29-15.a103fd5a.parquet

Data Settings

1. Select Next.

   The Data Settings page opens.

2. Optionally, edit the data settings or skip this page of the dialog.

Filters

Filters are available in the Create Source or Edit Source import settings for your S3, FTP, or SFTP connectors.

Import Integration Filters enable you to modify your imported data after you have completed Editing Data Settings for your import.

To apply import integration filters:

1. Select Next in Data Settings.The Filters dialog opens.
2. Select the filter option you want to add.
3. Select Add Filter. The parameter dialog for that filter opens.
4. Edit the parameters. For information on each filter type, see one of the following:

• Retaining Columns Filter
• Adding Columns Filter
• Dropping Columns Filter
• Expanding JSON Filter
• Digesting Filter

5. Optionally, to add another filter of the same type, select Add within the specific column filter dialog.
6. Optionally, to add another filter of a different type, select the filter option from the list and repeat the same steps.
7. After you have added the filters you want, select **Next.**The Data Preview dialog opens.

Data Preview

You can see a preview of your data before running the import by selecting Generate Preview. Data preview is optional and you can safely skip to the next page of the dialog if you choose to.

1. Select Next. The Data Preview page opens.
2. If you want to preview your data, select Generate Preview.
3. Verify the data.

Data Placement

For data placement, select the target database and table where you want your data placed and indicate how often the import should run.

1. Select Next. Under Storage, you will create a new or select an existing database and create a new or select an existing table for where you want to place the imported data.

2. Select a Database > Select an existing or Create New Database.

3. Optionally, type a database name.

4. Select a Table> Select an existing or Create New Table.

5. Optionally, type a table name.

6. Choose the method for importing the data.

   • Append (default)-Data import results are appended to the table. If the table does not exist, it will be created.
   • Always Replace-Replaces the entire content of an existing table with the result output of the query. If the table does not exist, a new table is created.
   • Replace on New Data-Only replace the entire content of an existing table with the result output when there is new data.

7. Select the Timestamp-based Partition Key column. If you want to set a different partition key seed than the default key, you can specify the long or timestamp column as the partitioning time. As a default time column, it uses upload_time with the add_time filter.

8. Select the Timezone for your data storage.

9. Under Schedule, you can choose when and how often you want to run this query.

Run once

1. Select Off.
2. Select Scheduling Timezone.
3. Select Create & Run Now.

Repeat Regularly

1. Select On.
2. Select the Schedule. The UI provides these four options: @hourly, @daily and @monthly or custom cron.
3. You can also select Delay Transfer and add a delay of execution time.
4. Select Scheduling Timezone.
5. Select Create & Run Now.

After your transfer has run, you can see the results of your transfer in Data Workbench > Databases.

Data type mapping

The Parquet file format uses minimal data types, known as primitive types, which are designed to optimize disk storage efficiency. On the other hand, logical types are used to extend the types, by specifying how the primitive types should be interpreted. When imported, those types will be converted into data types supported by TD

Importing from AWS S3 Parquet via CLI (Toolbelt)

Optionally, you can use the TD Toolbelt to configure the connection, create the job, and schedule job execution. Ensure that the latest version of the TD Toolbelt is installed before setting up the integration

Create Seed Config File (seed.yml)

Configure the seed.yml file as shown in the following example with your AWS access keys. You must also specify the bucket name and source file name. Optionally you can specify path_prefix to match multiple files. In the example below, path_prefix: path/to/sample_file will match

• path/to/sample_201501.parquet
• path/to/sample_201502.parquet
• path/to/sample_201505.parquet
• etc.

Using path_prefix with leading '/', can lead to unintended results. For example: "path_prefix: /path/to/sample_file" would result in plugin looking for file in s3://sample_bucket//path/to/sample_file which is different on S3 than the intended path of s3://sample_bucket/path/to/sample_file.

in:
  type: s3_parquet
  access_key_id: XXXXXXXXXX
  secret_access_key: YYYYYYYYYY
  bucket: sample_bucket
  # path to the *.parquet file on your s3 bucket
  path_prefix: path/to/sample_file
  path_match_pattern: \.parquet$ # a file will be skipped if its path doesn't match with this pattern

  ## some examples of regexp:
  #path_match_pattern: /archive/ # match files in .../archive/... directory
  #path_match_pattern: /data1/|/data2/ # match files in .../data1/... or .../data2/... directory
out:
  mode: append

if you reuse an existing authentication, set the Authentication ID to the value of td_authentication_id config key.  This is required for the assume-role authentication method. See Reusing the existing Authentication.

in:
  type: s3_qarquet
  td_authentication: xxxx
  bucket: sample_bucket
  path_prefix: path/to/sample_file

out:
  mode: append

Guess Fields (Generate load.yml)

connector:guess automatically reads the source files and assesses the file format and the fields and columns.

td connector:guess seed.yml -o load.yml

If you look at the load.yml file, you can see the "guessed"  file format definitions, including file formats, encodings, column names, and types.

in:
  type: s3_parquet
  access_key_id: XXXXXXXXXX
  secret_access_key: YYYYYYYYYY
  bucket: sample_bucket
  path_prefix: path/to/sample_file

out:
  mode: append

Execute Load Job

Submit the load job. It may take a couple of hours, depending on the size of the data. Specify the Treasure Data database and table where the data should be stored.

Treasure Data recommends that you specify s --time-column option because Treasure Data’s storage is partitioned by time (see data partitioning). If the option is not provided, the data connector chooses the first long or timestamp column as the partitioning time. The type of the column specified by --time-column must be either of type long or timestamp.

If your data doesn’t have a time column you can add a time column by using add_time filter option. For more details see add_time filter plugin.

td connector:issue load.yml --database td_sample_db --table td_sample_table \
--time-column created_at

In the example below, the connector:issue command assumes that you have already created a *database(td_sample_db)*and a table(td_sample_table). If the database or the table do not exist in Treasure Data, this command will fail. Create the database and table manually or use --auto-create-table option with td connector:issue command to auto-create the database and table:

td connector:issue load.yml --database td_sample_db --table td_sample_table --time-column created_at --auto-create-table

The data connector does not sort records on the server-side. To use time-based partitioning effectively, sort records in files beforehand.

If you have a field called time, you don’t have to specify the --time-column option.

$ td connector:issue load.yml --database td_sample_db --table td_sample_table

Setting IAM Permissions

The IAM credentials specified in the YML configuration file, which are used for the connector:guess and connector:issue commands, need to have permissions for the AWS S3 resources that they need to access. If the IAM user does not have these permissions, configure the user with one of the predefined Policy Definitions or create a new Policy Definition in JSON format.

The following example is based on the Policy Definition reference format. It gives the IAM user read only permissions (through GetObject and ListBucket actions) to "your-bucket."

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:ListBucket"
      ],
      "Resource": [
        "arn:aws:s3:::your-bucket",
        "arn:aws:s3:::your-bucket/*"
      ]
    }
  ]
}

Replace "your-bucket" with the actual name of your S3 bucket.

Using AWS Security Token Service (STS) as a Temporary Credentials Provider

In certain cases, IAM basic authentication through access_key_id and secret_access_key might be too risky (even though the secret_access_key is never clearly shown when a job is executed or after a session is created).

The S3 data connector can use AWS Secure Token Service (STS) to provide Temporary Security Credentials. Using AWS STS, any IAM user can use his own access_key_id and secret_access_key to create these temporary keys with specific expiration times :

• new_access_key_id
• new_secret_access_key
• session_token keys

The following are types of Temporary Security Credentials:

• Session Token

  The simplest Security Credentials with a specified expiration time. The temporary credentials have the same access as the IAM user the that generated them. These credentials are valid as long as they are not expired and the permissions of the original IAM user have not changed.

• Federation Token

  This adds an extra layer of permission control over the Session Token above. When generating a Federation Token, the IAM user is required to specify a Permission Policy definition. The scope can be used to restrict which resources the bearer of the Federation Token can have access to (which can be less that the access of IAM user granting the permission). Any Permission Policy definition can be used, but the scope of the permissions is limited to the same, or a subset of, permissions of the IAM who generated the token. As for the Session Token, the Federation Token credentials are valid as long as they are not expired and the permissions associated to the original IAM credentials don’t change.

AWS STS Temporary Security Credentials can be generated using the AWS CLI or the AWS SDK in the language of your choice.

Session Token

$ aws sts get-session-token --duration-seconds 900

Federation Token

In this example,

• temp_creds is the name of the Federated token or the user's temp credentials.
• bucketname is the name of the S3 bucket being granted access. (Refer to the ARN specification for more details)
• s3:GetObject and s3:ListBucket are the basic read operation for a AWS S3 bucket.

$ aws sts get-federation-token --name temp_creds --duration-seconds 900 \
  --policy '{"Statement": [{"Effect": "Allow", "Action": ["s3:GetObject", "s3:ListBucket"], "Resource": "arn:aws:s3:::bucketname"}]}'

When your Temporary Security Credentials are generated, include the SecretAccessKey, AccessKeyId, and SessionToken in your seed.yml file  and execute the Data Connector for S3 as usual..

in:
  type: s3_parquet
  auth_method: session
  access_key_id: XXXXXXXXXX
  secret_access_key: YYYYYYYYYY
  session_token: ZZZZZZZZZZ
  bucket: sample_bucket
  path_prefix: path/to/sample_file

Credential Expiration

Because STS credentials expire after a specified amount of time, the data connector job that uses the credential might eventually start failing. Currently, if the STS credentials are reported expired, the data connector job retries up to the maximum number of times (5) and eventually completes with a statis pf "error."

To confirm the import, see the steps in Validating Your Data Connector Jobs.

Related Topics

• See Scheduling Using TD Toolbelt for periodic execution of this integration
• See Using TD Workflow with Integrations to trigger your created Source from a workflow

What can I do if the data connector for the S3 job is running for a long time?

Check the count of S3 files that your connector job is ingesting. If there are over 10,000 files, the performance degrades. To mitigate this issue, you can:

• Narrow path_prefix option and reduce the count of S3 files.
• Set the  min_task_size option to 268,435,456 (256MB).

Some best practices we should follow

• Provide files with many row_groups, not one row_group per file

• If there are many files in one place, please try to split them into multiple jobs using the parameters Path Prefix and Path Regex. Each job should have 20-40 files.

  For example:

path_prefix: folder/sub_folderpath_match_pattern: folder/sub_folder/regrex