title: 'S3 Data Connector' sidebar_label: 'S3 Data Connector' description: 'S3 Data Connector Documentation'

The S3 Data Connector enables federated SQL querying on files stored in S3 or S3-compatible systems (e.g., MinIO, Cloudflare R2).

If a folder path is specified as the dataset source, all files within the folder will be loaded.

File formats are specified using the file_format parameter, as described in Object Store File Formats (opens in a new tab).

datasets:
  - from: s3://spiceai-demo-datasets/taxi_trips/2024/
    name: taxi_trips
    params:
      file_format: parquet

datasets:
  - from: s3://spiceai-demo-datasets/taxi_trips/2024/
    name: taxi_trips
    params:
      file_format: parquet

Configuration

from

S3-compatible URI to a folder or file, in the format s3://<bucket>/<path>

Example: from: s3://my-bucket/path/to/file.parquet

name

The dataset name. This will be used as the table name within Spice.

Example:

datasets:
  - from: s3://s3-bucket-name/taxi_sample.csv
    name: cool_dataset
    params:
      file_format: csv

datasets:
  - from: s3://s3-bucket-name/taxi_sample.csv
    name: cool_dataset
    params:
      file_format: csv

SELECT COUNT(*) FROM cool_dataset;

SELECT COUNT(*) FROM cool_dataset;

+----------+
| count(*) |
+----------+
| 6001215  |
+----------+

+----------+
| count(*) |
+----------+
| 6001215  |
+----------+

The dataset name cannot be a reserved keyword (opens in a new tab).

params

For additional CSV, JSON, and Parquet specific parameters, see File Formats (opens in a new tab).

Authentication

No authentication is required for public endpoints. For private buckets, set s3_auth to key or iam_role.

If s3_auth is set to iam_role, the connector will automatically load credentials from the following sources in order.

1. Environment Variables:

   • AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
   • AWS_SESSION_TOKEN (if using temporary credentials)

2. Shared AWS Config/Credentials Files:

   • Config file: ~/.aws/config (Linux/Mac) or %UserProfile%\.aws\config (Windows)

   • Credentials file: ~/.aws/credentials (Linux/Mac) or %UserProfile%\.aws\credentials (Windows)

   • The AWS_PROFILE environment variable can be used to specify a named profile, otherwise the [default] profile is used.

   • Supports both static credentials and SSO sessions

   • Example credentials file:

     # Static credentials (in .aws/credentials)
     [default]
     aws_access_key_id = YOUR_ACCESS_KEY
     aws_secret_access_key = YOUR_SECRET_KEY
     
     # SSO profile (in .aws/config)
     [profile sso-profile]
     sso_start_url = https://my-sso-portal.awsapps.com/start
     sso_region = us-west-2
     sso_account_id = 123456789012
     sso_role_name = MyRole
     region = us-west-2
     

     # Static credentials (in .aws/credentials)
     [default]
     aws_access_key_id = YOUR_ACCESS_KEY
     aws_secret_access_key = YOUR_SECRET_KEY
     
     # SSO profile (in .aws/config)
     [profile sso-profile]
     sso_start_url = https://my-sso-portal.awsapps.com/start
     sso_region = us-west-2
     sso_account_id = 123456789012
     sso_role_name = MyRole
     region = us-west-2
     

   :::tip To set up SSO authentication:

   1. Run aws configure sso to configure a new SSO profile
   2. Use the profile by setting AWS_PROFILE=sso-profile
   3. Run aws sso login --profile sso-profile to start a new SSO session :::

3. AWS STS Web Identity Token Credentials:

   • Used primarily with OpenID Connect (OIDC) and OAuth
   • Common in Kubernetes environments using IAM roles for service accounts (IRSA (opens in a new tab))
   • Relies on the environment variables AWS_WEB_IDENTITY_TOKEN_FILE and AWS_ROLE_ARN to be present.
     • These environment variables are automatically injected by EKS when the IAM Role is annotated on the pod Service Account.

4. ECS Container Credentials:

   • Used when running in Amazon ECS containers
   • Automatically uses the task's IAM role
   • Retrieved from the ECS credential provider endpoint.
   • Relies on the environment variable AWS_CONTAINER_CREDENTIALS_RELATIVE_URI or AWS_CONTAINER_CREDENTIALS_FULL_URI which are automatically injected by ECS.

5. AWS EC2 Instance Metadata Service (IMDSv2):

   • Used when running on EC2 instances.
   • Automatically uses the instance's IAM role.
   • Retrieved securely using IMDSv2 (opens in a new tab).

The connector will try each source in order until valid credentials are found. If no valid credentials are found, an authentication error will be returned.

:::note[IAM Permissions] Regardless of the credential source, the IAM role or user must have appropriate S3 permissions (e.g., s3:ListBucket, s3:GetObject) to access the files. If the Spicepod connects to multiple different AWS services, the permissions should cover all of them. :::

:::note[kube2iam] kube2iam (opens in a new tab) is a project that provides IAM roles to Kubernetes pods based on annotations. It has been superceded by IAM Roles for service accounts (IRSA) (opens in a new tab), which should be preferred for new deployments.

Spice requires kube2iam >= 0.12 - versions prior to 0.12 (opens in a new tab) only supported IMDSv1. :::

Required IAM Permissions

Minimum IAM policy for S3 access:

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

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

Permission Details

Types

Refer to Object Store Data Types (opens in a new tab) for data type mapping from object store files to arrow data type.

Examples

Public bucket Example

Create a dataset named taxi_trips from a public S3 folder.

- from: s3://spiceai-demo-datasets/taxi_trips/2024/
  name: taxi_trips
  params:
    file_format: parquet

- from: s3://spiceai-demo-datasets/taxi_trips/2024/
  name: taxi_trips
  params:
    file_format: parquet

MinIO Example

Create a dataset named cool_dataset from a Parquet file stored in MinIO.

- from: s3://s3-bucket-name/path/to/parquet/cool_dataset.parquet
  name: cool_dataset
  params:
    s3_endpoint: http://my.minio.server
    s3_region: 'us-east-1' # Best practice for MinIO
    allow_http: true

- from: s3://s3-bucket-name/path/to/parquet/cool_dataset.parquet
  name: cool_dataset
  params:
    s3_endpoint: http://my.minio.server
    s3_region: 'us-east-1' # Best practice for MinIO
    allow_http: true

Hive Partitioning Example

Hive partitioning is a data organization technique that improves query performance by storing data in a hierarchical directory structure based on partition column values. This allows for efficient data retrieval by skipping unnecessary data scans.

For example, a dataset partitioned by year, month, and day might have a directory structure like:

s3://bucket/dataset/year=2024/month=03/day=15/data_file.parquet
s3://bucket/dataset/year=2024/month=03/day=16/data_file.parquet

s3://bucket/dataset/year=2024/month=03/day=15/data_file.parquet
s3://bucket/dataset/year=2024/month=03/day=16/data_file.parquet

Spice can automatically infer these partition columns from the directory structure when hive_partitioning_enabled is set to true.

version: v1
kind: Spicepod
name: hive_data

datasets:
  - from: s3://spiceai-public-datasets/hive_partitioned_data/
    name: hive_data_infer
    params:
      file_format: parquet
      hive_partitioning_enabled: true

version: v1
kind: Spicepod
name: hive_data

datasets:
  - from: s3://spiceai-public-datasets/hive_partitioned_data/
    name: hive_data_infer
    params:
      file_format: parquet
      hive_partitioning_enabled: true

Schema Source Path example

Use schema_source_path to speed up dataset registration by specifying a URL to use to infer the schema.

- from: s3://spiceai-demo-datasets/taxi_trips/
  name: taxi_trips
  params:
    file_format: parquet
    schema_source_path: s3://spiceai-demo-datasets/taxi_trips/2014/1/trips_01.parquet # or s3://spiceai-demo-datasets/taxi_trips/2014/1/

- from: s3://spiceai-demo-datasets/taxi_trips/
  name: taxi_trips
  params:
    file_format: parquet
    schema_source_path: s3://spiceai-demo-datasets/taxi_trips/2014/1/trips_01.parquet # or s3://spiceai-demo-datasets/taxi_trips/2014/1/

Secrets

Spice integrates with multiple secret stores to help manage sensitive data securely. For detailed information on supported secret stores, refer to the secret stores documentation (opens in a new tab). Additionally, learn how to use referenced secrets in component parameters by visiting the using referenced secrets guide (opens in a new tab).

Limitations

:::warning[Performance Considerations]

When using the S3 Data connector without acceleration, data is loaded into memory during query execution. Ensure sufficient memory is available, including overhead for queries and the runtime, especially with concurrent queries.

Memory limitations can be mitigated by storing acceleration data on disk, which is supported by duckdb (opens in a new tab) and sqlite (opens in a new tab) accelerators by specifying mode: file.

Each query retrieves data from the S3 source, which might result in significant network requests and bandwidth consumption. This can affect network performance and incur costs related to data transfer from S3.

:::

Cookbook

• A cookbook recipe to configure S3 as a data connector in Spice. S3 Data Connector (opens in a new tab)