S3

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.

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

SELECT COUNT(*) FROM cool_dataset;

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

params

Parameter Name	Description
file_format	Specifies the data format. Required if it cannot be inferred from the object URI. Options: parquet, csv, json. Refer to Object Store File Formats for details.
s3_endpoint	S3 endpoint URL (e.g., for MinIO). Default is the region endpoint. E.g. s3_endpoint: https://my.minio.server
s3_region	S3 bucket region. Default: us-east-1.
client_timeout	Timeout for S3 operations. Default: 30s.
hive_partitioning_enabled	Enable partitioning using hive-style partitioning from the folder structure. Defaults to false
s3_auth	Authentication type. Options: public, key and iam_role. Defaults to public if s3_key and s3_secret are not provided, otherwise defaults to key.
s3_key	Access key (e.g. AWS_ACCESS_KEY_ID for AWS)
s3_secret	Secret key (e.g. AWS_SECRET_ACCESS_KEY for AWS)
allow_http	Allow insecure HTTP connections to s3_endpoint. Defaults to false

For CSV-specific parameters, see CSV Parameters.

Authentication

No authentication is required for public endpoints. For private buckets, set s3_auth to key or iam_role. For environments with assigned IAM roles, set s3_auth to iam_role. If using iam_role, the AWS IAM role of the running instance is used.

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/*"
    }
  ]
}

Types

Refer to Object Store Data Types 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

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

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

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

Limitations

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 and sqlite 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.