Cloud storage

Polars can read and write to AWS S3, Azure Blob Storage and Google Cloud Storage. The API is the same for all three storage providers.

To read from cloud storage, additional dependencies may be needed depending on the use case and cloud storage provider:

=== ":fontawesome-brands-python: Python"

```shell
$ pip install fsspec s3fs adlfs gcsfs
```

=== ":fontawesome-brands-rust: Rust"

```shell
$ cargo add aws_sdk_s3 aws_config tokio --features tokio/full
```

Reading from cloud storage

Polars supports reading Parquet, CSV, IPC and NDJSON files from cloud storage:

{{code_block('user-guide/io/cloud-storage','read_parquet',['read_parquet','read_csv','read_ipc'])}}

Scanning from cloud storage with query optimisation

Using pl.scan_* functions to read from cloud storage can benefit from predicate and projection pushdowns, where the query optimizer will apply them before the file is downloaded. This can significantly reduce the amount of data that needs to be downloaded. The query evaluation is triggered by calling collect.

{{code_block('user-guide/io/cloud-storage','scan_parquet_query',[])}}

Cloud authentication

Polars is able to automatically load default credential configurations for some cloud providers. For cases when this does not happen, it is possible to manually configure the credentials for Polars to use for authentication. This can be done in a few ways:

Using storage_options:

• Credentials can be passed as configuration keys in a dict with the storage_options parameter:

{{code_block('user-guide/io/cloud-storage','scan_parquet_storage_options_aws',['scan_parquet'])}}

Using one of the available CredentialProvider* utility classes

• There may be a utility class pl.CredentialProvider* that provides the required authentication functionality. For example, pl.CredentialProviderAWS supports selecting AWS profiles, as well as assuming an IAM role:

{{code_block('user-guide/io/cloud-storage','credential_provider_class',['scan_parquet', 'CredentialProviderAWS'])}}

Using a custom credential_provider function

• Some environments may require custom authentication logic (e.g. AWS IAM role-chaining). For these cases a Python function can be provided for Polars to use to retrieve credentials:

{{code_block('user-guide/io/cloud-storage','credential_provider_custom_func',['scan_parquet'])}}

• Example for Azure:

{{code_block('user-guide/io/cloud-storage','credential_provider_custom_func_azure',['scan_parquet', 'CredentialProviderAzure'])}}

Set a default credential provider to use

• It is possible to globally configure a default credential provider, so that it does not need to be passed to every I/O function call. This can be convenient in the case where there are many cloud I/O operations that use the same credential provider.

{{code_block('user-guide/io/cloud-storage','credential_provider_class_global_default',['scan_parquet', 'CredentialProviderAWS'])}}

Cloud retry configuration

• Retry behavior such as maximum retries and backoff can be configured via storage_options:

{{code_block('user-guide/io/cloud-storage','storage_options_retry_configuration',['scan_parquet'])}}

Scanning with PyArrow

We can also scan from cloud storage using PyArrow. This is particularly useful for partitioned datasets such as Hive partitioning.

We first create a PyArrow dataset and then create a LazyFrame from the dataset.

{{code_block('user-guide/io/cloud-storage','scan_pyarrow_dataset',['scan_pyarrow_dataset'])}}

Writing to cloud storage

DataFrames can also be written to cloud storage by passing a cloud URL:

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Note that DataFrames can also be written to any Python file object that supports writes. This can be helpful for performing operations that are not yet natively supported, e.g. writing a compressed CSV directly to cloud:

{{code_block('user-guide/io/cloud-storage','write_file_object',['write_csv'])}}