mooncompute 0.3.2

Ergonomic GCP data-access helpers: BigQuery <-> polars, GCS, and a SQL-hash parquet cache.

mooncompute

Ergonomic GCP data-access helpers, so you stop re-writing the same BigQuery <-> polars, GCS, and credential boilerplate in every project.

mooncompute.gcp gives you:

• bq2pl(sql) - run SQL, get a polars DataFrame (Decimal columns cast to Float64 by default).
• extract_cached(sql, cache, ...) - the same, wrapped in a local parquet cache keyed by a hash of the SQL. You must pick an invalidation mode: content_only=True (re-query on SQL change only) or max_age=<timedelta> (also re-query past a TTL); a bare call raises.
• pl2bq(df, dataset=, table=) - load a polars DataFrame into BigQuery, with enable_list_inference set so ARRAY columns load correctly.
• gcs.* - read/write parquet, JSON, and bytes over gs:// URIs.

Install

Dependencies are split into extras so you install only what you use. The base package is dependency-free (credential helper + gs:// URI parsing); pull an extra for actual I/O:

uv add "mooncompute[bq]"        # BigQuery <-> polars (polars, pyarrow, bigquery, db-dtypes)
uv add "mooncompute[gcs]"       # GCS JSON/bytes I/O (google-cloud-storage only)
uv add "mooncompute[bq,gcs]"    # both; also what you need for parquet on GCS
uv add "mooncompute[all]"       # alias for [bq,gcs]

GCS parquet helpers (gcs.read_parquet / write_parquet) lazily require polars, so install [bq,gcs] for them; the JSON/bytes path stays polars-free under [gcs] alone, keeping Cloud Function cold-starts light.

Configuration

The common path: set GOOGLE_CLOUD_PROJECT once and omit project= everywhere. The gcloud SDK already sets it, so on a configured machine there is nothing to do; otherwise:

export GOOGLE_CLOUD_PROJECT=my-project

Pass project= on any call to override it (e.g. multi-project work). A call that is given neither an explicit project= nor the env var raises.

Credentials use Application Default Credentials; in a Modal container, set a GOOGLE_APPLICATION_CREDENTIALS_JSON secret and it is materialized to ADC automatically.

Usage

from pathlib import Path

from mooncompute.gcp import bq, gcs

# With GOOGLE_CLOUD_PROJECT set, calls need no project= (pass project= to override):
df = bq.bq2pl("SELECT * FROM `proj.ds.tbl` LIMIT 10")

# SQL-hash-keyed parquet cache. You must pick an invalidation mode; a bare
# call raises. For a deterministic query (e.g. pinned to a snapshot date) pass
# content_only=True to invalidate on SQL change only:
df = bq.extract_cached(
    bq.read_sql("features.sql", snapshot="2024-10-01"),
    Path("data/features.parquet"),
    content_only=True,
)

# For a live/relative query, pass max_age to also re-query past a TTL (or use
# bq2pl for an always-live read). content_only and max_age are mutually exclusive.
from datetime import timedelta

df = bq.extract_cached(
    bq.read_sql("daily_active.sql"),
    Path("data/dau.parquet"),
    max_age=timedelta(hours=12),
)

# polars -> BQ (ARRAY columns handled via enable_list_inference)
bq.pl2bq(df, dataset="scratch", table="out")

# GCS round-trips over gs:// URIs
gcs.write_parquet(df, "gs://my-bucket/out.parquet")
df = gcs.read_parquet("gs://my-bucket/out.parquet")

Deploying on GCP

mooncompute is happiest baked into a container. ADC comes from the workload's service account, so credential materialization no-ops and the BQ/GCS clients just work.

Recommended path (Cloud Run service or job, Vertex custom job, Kubeflow container component): install at image-build time, then push to your registry.

FROM python:3.11-slim
ENV PYTHONUNBUFFERED=1 PYTHONDONTWRITEBYTECODE=1
RUN pip install "mooncompute[bq,gcs]"

Caveats by target:

• Cloud Run (service or job): best fit. No install-auth or cold-start concerns once baked into the image.
• Kubeflow lightweight components (packages_to_install=[...]): the install runs in-pod at runtime; prefer a container component for a public/private install you control.
• Cloud Functions: weakest fit, but install mooncompute[gcs] to keep the JSON/bytes path free of polars + pyarrow and their cold-start cost. Note extract_cached's parquet cache only lives in ephemeral /tmp; prefer gcs.* for durable artifacts there.

Scope

v1 ships mooncompute.gcp only. Modal, LLM/eval, and run-utils tiers may follow; see docs/ROADMAP.md.

Develop

just            # fmt + lint + typecheck + test
just ci         # non-mutating gate (check formatting, lint, typecheck, test)