opteryx-catalog 0.3.1

Opteryx Cloud Catalog

pyiceberg-firestore-gcs

A Firestore + Google Cloud Storage (GCS) backed implementation of the PyIceberg catalog interface. This package provides a straightforward, opinionated catalog implementation for storing table metadata documents in Firestore while storing the Iceberg table metadata JSON in GCS.

This project is intended to be used as a catalog component for PyIceberg in GCP-based environments.

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Features ✅

• Firestore-backed catalog and namespace storage
• GCS-based Iceberg table metadata storage (with optional compatibility mode)
• GCS-based table metadata storage; export/import utilities provide Iceberg Avro interoperability
• Table creation, registration, listing, loading, renaming, and deletion
• Commit operations that write updated metadata to GCS and persist references in Firestore
• Simple, opinionated defaults (e.g., default GCS location derived from catalog properties)
• Lightweight schema handling compatible with PyIceberg (supports pyarrow schemas and PyIceberg Schema)

Quick start 💡

1. Ensure you have GCP credentials available to the environment. Typical approaches:

   • Set GOOGLE_APPLICATION_CREDENTIALS to a service account JSON key file, or
   • Use gcloud auth application-default login for local development.

2. Install locally (or publish to your package repo):

python -m pip install -e .

3. Create a FirestoreCatalog and use it in your application:

from pyiceberg_firestore_gcs import create_catalog
from pyiceberg.schema import Schema, NestedField
from pyiceberg.types import IntegerType, StringType

catalog = create_catalog(
	"my_catalog",
	firestore_project="my-gcp-project",
	gcs_bucket="my-default-bucket",
)

# Create a namespace
catalog.create_namespace("example_namespace")

# Create a simple PyIceberg schema
schema = Schema(
	NestedField(field_id=1, name="id", field_type=IntegerType(), required=True),
	NestedField(field_id=2, name="name", field_type=StringType(), required=False),
)

# Create a new table (metadata written to a GCS path derived from the bucket property)
table = catalog.create_table(("example_namespace", "users"), schema)

# Or register a table if you already have a metadata JSON in GCS
catalog.register_table(("example_namespace", "events"), "gs://my-bucket/path/to/events/metadata/00000001.json")

# Load a table
tbl = catalog.load_table(("example_namespace", "users"))
print(tbl.metadata)

Configuration and environment 🔧

• GCP authentication: Use GOOGLE_APPLICATION_CREDENTIALS or Application Default Credentials
• firestore_project and firestore_database can be supplied when creating the catalog
• gcs_bucket is recommended to allow create_table to write metadata automatically; otherwise pass location explicitly to create_table
• The catalog does not write Iceberg Avro/manifest-list artifacts in the hot path; use export_to_iceberg / import_from_iceberg for interoperability

Example environment variables:

export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"
export GOOGLE_CLOUD_PROJECT="my-gcp-project"

Iceberg interoperability

This catalog implementation does not write Iceberg Avro manifest-list/Avro manifest files or Iceberg metadata JSON in the hot path. Instead, table metadata is stored in Firestore and the runtime writes a consolidated Parquet manifest for fast query planning.

If you need full Iceberg-compatible artifacts for other engines or tools, use the export_to_iceberg utility to generate Avro manifests and manifest-lists from the Parquet-first storage layout. To ingest existing Iceberg Avro artifacts into this catalog, use import_from_iceberg which will convert Avro manifests into the Parquet manifest + Firestore snapshot representation used here.

API overview 📚

The package exports a factory helper create_catalog and the FirestoreCatalog class.

Key methods include:

• create_namespace(namespace, properties={}, exists_ok=False)
• drop_namespace(namespace)
• list_namespaces()
• create_table(identifier, schema, location=None, partition_spec=None, sort_order=None, properties={})
• register_table(identifier, metadata_location)
• load_table(identifier)
• list_tables(namespace)
• drop_table(identifier)
• rename_table(from_identifier, to_identifier)
• commit_table(table, requirements, updates)
• create_view(identifier, sql, schema=None, author=None, description=None, properties={})
• load_view(identifier)
• list_views(namespace)
• view_exists(identifier)
• drop_view(identifier)
• update_view_execution_metadata(identifier, row_count=None, execution_time=None)

Views 👁️

Views are SQL queries stored in the catalog that can be referenced like tables. Each view includes:

• SQL statement: The query that defines the view
• Schema: The expected result schema (optional but recommended)
• Metadata: Author, description, creation/update timestamps
• Execution history: Last run time, row count, execution time

Example usage:

from pyiceberg.schema import Schema, NestedField
from pyiceberg.types import IntegerType, StringType

# Create a schema for the view
schema = Schema(
    NestedField(field_id=1, name="user_id", field_type=IntegerType(), required=True),
    NestedField(field_id=2, name="username", field_type=StringType(), required=False),
)

# Create a view
view = catalog.create_view(
    identifier=("my_namespace", "active_users"),
    sql="SELECT user_id, username FROM users WHERE active = true",
    schema=schema,
    author="data_team",
    description="View of all active users in the system"
)

# Load a view
view = catalog.load_view(("my_namespace", "active_users"))
print(f"SQL: {view.sql}")
print(f"Schema: {view.metadata.schema}")

# Update execution metadata after running the view
catalog.update_view_execution_metadata(
    ("my_namespace", "active_users"),
    row_count=1250,
    execution_time=0.45
)

Notes about behavior:

• create_table will try to infer a default GCS location using the provided gcs_bucket property if location is omitted.
• register_table validates that the provided metadata_location points to an existing GCS blob.
• Views are stored as Firestore documents with complete metadata including SQL, schema, authorship, and execution history.
• Table transactions are intentionally unimplemented.

Development & Linting 🧪

This package includes a small Makefile target to run linting and formatting tools (ruff, isort, pycln).

Install dev tools and run linters with:

python -m pip install --upgrade pycln isort ruff
make lint

Running tests (if you add tests):

python -m pytest

Compaction 🔧

This catalog supports small file compaction to improve query performance. See COMPACTION.md for detailed design documentation.

Quick Start

from pyiceberg_firestore_gcs import create_catalog
from pyiceberg_firestore_gcs.compaction import compact_table, get_compaction_stats

catalog = create_catalog(...)

# Check if compaction is needed
table = catalog.load_table(("namespace", "table_name"))
stats = get_compaction_stats(table)
print(f"Small files: {stats['small_file_count']}")

# Run compaction
result = compact_table(catalog, ("namespace", "table_name"))
print(f"Compacted {result.files_rewritten} files")

Configuration

Control compaction behavior via table properties:

table = catalog.create_table(
    identifier=("namespace", "table_name"),
    schema=schema,
    properties={
        "compaction.enabled": "true",
        "compaction.min-file-count": "10",
        "compaction.max-small-file-size-bytes": "33554432",  # 32 MB
        "write.target-file-size-bytes": "134217728"  # 128 MB
    }
)

Limitations & KNOWN ISSUES ⚠️

• No support for table-level transactions. create_table_transaction raises NotImplementedError.
• The catalog stores metadata location references in Firestore; purging metadata files from GCS is not implemented.
• This is an opinionated implementation intended for internal or controlled environments. Review for production constraints before use in multi-tenant environments.

Contributing 🤝

Contributions are welcome. Please follow these steps:

1. Fork the repository and create a feature branch.
2. Run and pass linting and tests locally.
3. Submit a PR with a clear description of the change.

Please add unit tests and docs for new behaviors.

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If you'd like, I can also add usage examples that show inserting rows using PyIceberg readers/writers, or add CI testing steps to the repository. ✅