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PyIceberg currently has native support for REST, SQL, Hive, Glue and DynamoDB.
There are three ways to pass in configuration:
~/.pyiceberg.yaml configuration fileThe configuration file is recommended since that's the easiest way to manage the credentials.
Another option is through environment variables:
export PYICEBERG_CATALOG__DEFAULT__URI=thrift://localhost:9083 export PYICEBERG_CATALOG__DEFAULT__S3__ACCESS_KEY_ID=username export PYICEBERG_CATALOG__DEFAULT__S3__SECRET_ACCESS_KEY=password
The environment variable picked up by Iceberg starts with PYICEBERG_ and then follows the yaml structure below, where a double underscore __ represents a nested field, and the underscore _ is converted into a dash -.
For example, PYICEBERG_CATALOG__DEFAULT__S3__ACCESS_KEY_ID, sets s3.access-key-id on the default catalog.
Iceberg tables support table properties to configure table behavior.
| Key | Options | Default | Description |
|---|---|---|---|
write.parquet.compression-codec | {uncompressed,zstd,gzip,snappy} | zstd | Sets the Parquet compression coddec. |
write.parquet.compression-level | Integer | null | Parquet compression level for the codec. If not set, it is up to PyIceberg |
write.parquet.page-size-bytes | Size in bytes | 1MB | Set a target threshold for the approximate encoded size of data pages within a column chunk |
write.parquet.page-row-limit | Number of rows | 20000 | Set a target threshold for the approximate encoded size of data pages within a column chunk |
write.parquet.dict-size-bytes | Size in bytes | 2MB | Set the dictionary page size limit per row group |
write.parquet.row-group-limit | Number of rows | 122880 | The Parquet row group limit |
Iceberg works with the concept of a FileIO which is a pluggable module for reading, writing, and deleting files. By default, PyIceberg will try to initialize the FileIO that‘s suitable for the scheme (s3://, gs://, etc.) and will use the first one that’s installed.
PyArrowFileIO, FsspecFileIOPyArrowFileIOPyArrowFileIOPyArrowFileIOFsspecFileIOYou can also set the FileIO explicitly:
| Key | Example | Description |
|---|---|---|
| py-io-impl | pyiceberg.io.fsspec.FsspecFileIO | Sets the FileIO explicitly to an implementation, and will fail explicitly if it can't be loaded |
For the FileIO there are several configuration options available:
| Key | Example | Description |
|---|---|---|
| s3.endpoint | https://10.0.19.25/ | Configure an alternative endpoint of the S3 service for the FileIO to access. This could be used to use S3FileIO with any s3-compatible object storage service that has a different endpoint, or access a private S3 endpoint in a virtual private cloud. |
| s3.access-key-id | admin | Configure the static secret access key used to access the FileIO. |
| s3.secret-access-key | password | Configure the static session token used to access the FileIO. |
| s3.signer | bearer | Configure the signature version of the FileIO. |
| s3.region | us-west-2 | Sets the region of the bucket |
| s3.proxy-uri | http://my.proxy.com:8080 | Configure the proxy server to be used by the FileIO. |
| s3.connect-timeout | 60.0 | Configure socket connection timeout, in seconds. |
| Key | Example | Description |
|---|---|---|
| hdfs.host | https://10.0.19.25/ | Configure the HDFS host to connect to |
| hdfs.port | 9000 | Configure the HDFS port to connect to. |
| hdfs.user | user | Configure the HDFS username used for connection. |
| hdfs.kerberos_ticket | kerberos_ticket | Configure the path to the Kerberos ticket cache. |
| Key | Example | Description |
|---|---|---|
| adlfs.connection-string | AccountName=devstoreaccount1;AccountKey=Eby8vdM02xNOcqF...;BlobEndpoint=http://localhost/ | A connection string. This could be used to use FileIO with any adlfs-compatible object storage service that has a different endpoint (like azurite). |
| adlfs.account-name | devstoreaccount1 | The account that you want to connect to |
| adlfs.account-key | Eby8vdM02xNOcqF... | The key to authentication against the account. |
| adlfs.sas-token | NuHOuuzdQN7VRM%2FOpOeqBlawRCA845IY05h9eu1Yte4%3D | The shared access signature |
| adlfs.tenant-id | ad667be4-b811-11ed-afa1-0242ac120002 | The tenant-id |
| adlfs.client-id | ad667be4-b811-11ed-afa1-0242ac120002 | The client-id |
| adlfs.client-secret | oCA3R6P*ka#oa1Sms2J74z... | The client-secret |
| Key | Example | Description |
|---|---|---|
| gcs.project-id | my-gcp-project | Configure Google Cloud Project for GCS FileIO. |
| gcs.oauth.token | ya29.dr.AfM... | Configure method authentication to GCS for FileIO. Can be the following, ‘google_default’, ‘cache’, ‘anon’, ‘browser’, ‘cloud’. If not specified your credentials will be resolved in the following order: gcloud CLI default, gcsfs cached token, google compute metadata service, anonymous. |
| gcs.oauth.token-expires-at | 1690971805918 | Configure expiration for credential generated with an access token. Milliseconds since epoch |
| gcs.access | read_only | Configure client to have specific access. Must be one of ‘read_only’, ‘read_write’, or ‘full_control’ |
| gcs.consistency | md5 | Configure the check method when writing files. Must be one of ‘none’, ‘size’, or ‘md5’ |
| gcs.cache-timeout | 60 | Configure the cache expiration time in seconds for object metadata cache |
| gcs.requester-pays | False | Configure whether to use requester-pays requests |
| gcs.session-kwargs | {} | Configure a dict of parameters to pass on to aiohttp.ClientSession; can contain, for example, proxy settings. |
| gcs.endpoint | http://0.0.0.0:4443 | Configure an alternative endpoint for the GCS FileIO to access (format protocol://host:port) If not given, defaults to the value of environment variable “STORAGE_EMULATOR_HOST”; if that is not set either, will use the standard Google endpoint. |
| gcs.default-location | US | Configure the default location where buckets are created, like ‘US’ or ‘EUROPE-WEST3’. |
| gcs.version-aware | False | Configure whether to support object versioning on the GCS bucket. |
catalog: default: uri: http://rest-catalog/ws/ credential: t-1234:secret default-mtls-secured-catalog: uri: https://rest-catalog/ws/ ssl: client: cert: /absolute/path/to/client.crt key: /absolute/path/to/client.key cabundle: /absolute/path/to/cabundle.pem
| Key | Example | Description |
|---|---|---|
| uri | https://rest-catalog/ws | URI identifying the REST Server |
| ugi | t-1234:secret | Hadoop UGI for Hive client. |
| credential | t-1234:secret | Credential to use for OAuth2 credential flow when initializing the catalog |
| token | FEW23.DFSDF.FSDF | Bearer token value to use for Authorization header |
| scope | openid offline corpds:ds:profile | Desired scope of the requested security token (default : catalog) |
| resource | rest_catalog.iceberg.com | URI for the target resource or service |
| audience | rest_catalog | Logical name of target resource or service |
| rest.sigv4-enabled | true | Sign requests to the REST Server using AWS SigV4 protocol |
| rest.signing-region | us-east-1 | The region to use when SigV4 signing a request |
| rest.signing-name | execute-api | The service signing name to use when SigV4 signing a request |
| rest.authorization-url | https://auth-service/cc | Authentication URL to use for client credentials authentication (default: uri + ‘v1/oauth/tokens’) |
To configure custom headers in RESTCatalog, include them in the catalog properties with the prefix header.. This ensures that all HTTP requests to the REST service include the specified headers.
catalog: default: uri: http://rest-catalog/ws/ credential: t-1234:secret header.content-type: application/vnd.api+json
The SQL catalog requires a database for its backend. PyIceberg supports PostgreSQL and SQLite through psycopg2. The database connection has to be configured using the uri property. See SQLAlchemy's documentation for URL format:
For PostgreSQL:
catalog: default: type: sql uri: postgresql+psycopg2://username:password@localhost/mydatabase
In the case of SQLite:
!!! warning inline end “Development only” SQLite is not built for concurrency, you should use this catalog for exploratory or development purposes.
catalog: default: type: sql uri: sqlite:////tmp/pyiceberg.db
catalog: default: uri: thrift://localhost:9083 s3.endpoint: http://localhost:9000 s3.access-key-id: admin s3.secret-access-key: password
When using Hive 2.x, make sure to set the compatibility flag:
catalog: default: ... hive.hive2-compatible: true
Your AWS credentials can be passed directly through the Python API. Otherwise, please refer to How to configure AWS credentials to set your AWS account credentials locally. If you did not set up a default AWS profile, you can configure the profile_name.
catalog: default: type: glue aws_access_key_id: <ACCESS_KEY_ID> aws_secret_access_key: <SECRET_ACCESS_KEY> aws_session_token: <SESSION_TOKEN> region_name: <REGION_NAME>
catalog: default: type: glue profile_name: <PROFILE_NAME> region_name: <REGION_NAME>
If you want to use AWS DynamoDB as the catalog, you can use the last two ways to configure the pyiceberg and refer How to configure AWS credentials to set your AWS account credentials locally.
catalog: default: type: dynamodb table-name: iceberg
If you prefer to pass the credentials explicitly to the client instead of relying on environment variables,
catalog: default: type: dynamodb table-name: iceberg aws_access_key_id: <ACCESS_KEY_ID> aws_secret_access_key: <SECRET_ACCESS_KEY> aws_session_token: <SESSION_TOKEN> region_name: <REGION_NAME>
PyIceberg uses multiple threads to parallelize operations. The number of workers can be configured by supplying a max-workers entry in the configuration file, or by setting the PYICEBERG_MAX_WORKERS environment variable. The default value depends on the system hardware and Python version. See the Python documentation for more details.
Previous versions of Java (<1.4.0) implementations incorrectly assume the optional attribute current-snapshot-id to be a required attribute in TableMetadata. This means that if current-snapshot-id is missing in the metadata file (e.g. on table creation), the application will throw an exception without being able to load the table. This assumption has been corrected in more recent Iceberg versions. However, it is possible to force PyIceberg to create a table with a metadata file that will be compatible with previous versions. This can be configured by setting the legacy-current-snapshot-id entry as “True” in the configuration file, or by setting the LEGACY_CURRENT_SNAPSHOT_ID environment variable. Refer to the PR discussion for more details on the issue