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# syntax=docker/dockerfile:1
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# =============================================================================
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# eBdesk SiCABE — Custom Airflow Image (strategi S1: SATU environment)
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# =============================================================================
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# Base: apache/airflow:3.3.1-python3.11
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# - Python 3.11 → masuk matriks uji openmetadata-ingestion 1.13.x
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# - Airflow 3.3.1 (terbaru)
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#
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# Yang di-bake:
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# - apache-airflow 3.3.1 + providers (trino, amazon, http)
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# - dbt-core 1.11.x + dbt-trino, great_expectations
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# - openmetadata-ingestion==1.13.1.0 (match server OM 1.13.1; TANPA extra [airflow])
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# - Sling CLI (binary Go — BUKAN paket pip `sling`)
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#
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# Tiga aturan penting:
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# 1. ❌ JANGAN pakai extra `openmetadata-ingestion[airflow]` — extra itu mem-pin
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# `apache-airflow==3.2.1` dan langsung merusak resolver.
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# 2. ⚠️ Beberapa dependensi OM hanya tersedia sebagai **sdist** (tanpa wheel):
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# `antlr4-python3-runtime==4.9.2`, `dsnparse==0.1.15`, dst. Dengan build
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# isolation, pip MENGGANTUNG sangat lama; karena itu dipakai
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# `--no-build-isolation` (build hanya beberapa detik) + backend build disiapkan.
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# 3. ✅ `dbt-core<1.12` (lihat requirements.in): 1.12.x menarik
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# `dbt-core-experimental-parser` yang sdist-only.
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# Instalasi memakai `--constraint requirements.lock` (hasil `uv pip compile`)
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# agar tidak ada backtracking.
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#
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# Catatan `pip check` (NON-FATAL, sengaja): setelah langkah (3) tersisa 2 konflik
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# dengan komponen yang TIDAK dipakai DAG SiCABE:
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# - aiobotocore -> botocore>=1.43.3 vs botocore 1.41.6 (OM: boto3~=1.41.5)
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# - providers-mysql -> pymysql<1.2 vs pymysql 1.2.0
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# Bila kelak DAG memakai salah satunya, pindahkan toolchain-nya ke environment
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# terpisah (venv) — lihat strategi S2 di reviews/sicabe-10.
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#
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# Regenerasi lock bila dependensi berubah:
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# docker run --rm -v "$PWD/deploy/docker/airflow:/w" apache/airflow:3.3.1-python3.11 \
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# bash -lc 'pip -q install uv && uv pip compile /w/requirements.in --output-file /w/requirements.lock'
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#
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# Build (konteks = folder ini, karena COPY requirements.*):
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# docker build -t sicabe/airflow:3.3.1 -f deploy/docker/airflow/Dockerfile deploy/docker/airflow
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# =============================================================================
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FROM apache/airflow:3.3.1-python3.11
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ARG AIRFLOW_VERSION=3.3.1
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ARG SLING_VERSION=1.5.23
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USER airflow
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COPY requirements.in /tmp/requirements.in
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COPY requirements.lock /tmp/requirements.lock
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# ─── (1) Backend build untuk paket sdist ────────────────────────────────────
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# Dipakai oleh langkah (2) karena build isolation dimatikan.
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RUN --mount=type=cache,target=/home/airflow/.cache/pip,uid=50000 \
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pip install --user --no-compile \
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"setuptools" "wheel" "hatchling" "hatch-vcs" "poetry-core" "flit-core" "setuptools-scm"
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# ─── (2) Seluruh dependensi dari lock ───────────────────────────────────────
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# --no-build-isolation: kunci utama. Sebagian paket (antlr4-python3-runtime,
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# dsnparse, …) hanya sdist; dengan isolasi pip menggantung sangat lama.
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# --constraint: pin dari lock → tidak ada backtracking.
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# --no-compile: tidak menulis .pyc (menghemat puluhan MB; Python tetap jalan,
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# hanya sedikit lebih lambat saat import pertama).
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# Cache mount pip → rebuild tidak mengunduh ulang seluruh wheel.
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RUN --mount=type=cache,target=/home/airflow/.cache/pip,uid=50000 \
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pip install --user --no-build-isolation --no-compile \
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--constraint /tmp/requirements.lock \
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-r /tmp/requirements.in
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# ─── (3) Buang providers-google + dependensi yatim-piatunya ─────────────────
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# SiCABE hanya memakai provider trino/amazon/http. `apache-airflow-providers-google`
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# (bawaan base image) menarik paket besar: ray (~186 MB), pyarrow, litellm,
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# scikit-learn, google-api-python-client, google-cloud-*, pandas-gbq, dsb.
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# `pip-autoremove` menghapus paket yang tidak lagi dibutuhkan paket lain.
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# Hasil uji: ~/.local 2.5G -> 1.8G (−0.7 GB); airflow/dbt/GE/OM ingestion tetap OK.
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#
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# ⚠️ Bila kelak butuh Google (mis. Workspace/Gmail/Drive), pasang provider-nya
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# kembali — provider Airflow tidak dipecah per produk, jadi akan menarik
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# seluruh dependensi google lagi (≈ +0.7 GB).
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RUN --mount=type=cache,target=/home/airflow/.cache/pip,uid=50000 \
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pip install --no-compile pip-autoremove \
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&& pip-autoremove apache-airflow-providers-google -y \
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&& pip uninstall -y pip-autoremove
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# ─── Verifikasi inti (fatal bila gagal) ─────────────────────────────────────
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# `python -B` → jangan tulis .pyc saat verifikasi (menghemat ~50 MB layer).
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RUN python -B -c "import airflow; print('airflow', airflow.__version__)" \
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&& python -B -c "import dbt, great_expectations; print('dbt + great_expectations OK')"
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# ─── Verifikasi OM (non-fatal: hanya melaporkan) ────────────────────────────
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RUN ( python -B -c "import metadata; print('openmetadata-ingestion OK')" \
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|| echo '[WARN] modul `metadata` gagal di-import — cek nama paket OM' ) \
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&& ( command -v metadata >/dev/null && echo 'CLI metadata OK' \
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|| echo '[WARN] CLI `metadata` tidak ditemukan' ) \
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&& ( pip check || echo '[WARN] pip check melaporkan ketidaksesuaian (lihat log)' )
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# ─── Sling CLI (binary Go) ──────────────────────────────────────────────────
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# Tarball ±99 MB. Jaringan lambat → timeout tinggi + retry + resume (-C -),
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# dan hasil unduhan disimpan di cache mount agar percobaan ulang tidak mulai
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# dari nol. Nama file memuat versi supaya cache tidak salah pakai.
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RUN --mount=type=cache,target=/tmp/sling-dl,uid=50000 \
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( tar -tzf "/tmp/sling-dl/sling-${SLING_VERSION}.tar.gz" >/dev/null 2>&1 \
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|| curl -fL --retry 5 --retry-delay 5 --retry-all-errors \
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--max-time 2400 --connect-timeout 15 --continue-at - \
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"https://github.com/slingdata-io/sling-cli/releases/download/v${SLING_VERSION}/sling_linux_amd64.tar.gz" \
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-o "/tmp/sling-dl/sling-${SLING_VERSION}.tar.gz" ) \
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&& mkdir -p "${HOME}/.local/bin" \
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&& tar -xzf "/tmp/sling-dl/sling-${SLING_VERSION}.tar.gz" -C /tmp/ \
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&& ( command -v strip >/dev/null && strip /tmp/sling 2>/dev/null || true ) \
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&& chmod +x /tmp/sling \
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&& mv /tmp/sling "${HOME}/.local/bin/sling" \
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&& "${HOME}/.local/bin/sling" --version
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USER airflow
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