From 416f46d4c56a4c7b4eb7415abc061c8fe21d6e0c Mon Sep 17 00:00:00 2001 From: Christian Tabedzki <35670232+tabedzki@users.noreply.github.com> Date: Tue, 23 Jun 2026 14:52:41 -0400 Subject: [PATCH 1/3] feat(airflow): scaffold Phase-1 Airflow 3.x orchestration tree MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add a parse-only scaffold for migrating the U19 orchestration layer to Apache Airflow 3.x (assessment + plan in BrainCOGS/U19-pipeline-python#95). - airflow/dags: nightly_populate (unified Python+MATLAB nightly + alerts), ephys_processing, imaging_processing — TaskFlow DAGs with stubbed task bodies. nightly_populate encodes the FK ordering plus the implicit edges found in the make() audit (TowersSessionPsychTask after TowersBlock; a barrier before the subject-cumulative psych tables). - airflow/plugins/u19: stub hooks/helpers that wrap existing code (slurm_creator, clusters_paths_and_transfers, slack_utils, dual-write of recording_process status, matlab -batch). No logic reimplemented. - CI: .github/workflows/airflow-dag-validation.yml runs a DagBag import-error check via `uv sync --only-group airflow` + `uv run`. - pyproject.toml: add locked `airflow` dependency group (apache-airflow 3.2.2 + ssh/standard providers); uv.lock updated. All three DAGs parse with zero import errors against Airflow 3.2.2. Task bodies are TODO stubs; deployment/infra questions tracked in #95. Assisted-by: ClaudeCode:claude-opus-4.8 --- .github/workflows/airflow-dag-validation.yml | 61 + airflow/README.md | 54 + airflow/dags/ephys_processing.py | 202 +++ airflow/dags/imaging_processing.py | 224 +++ airflow/dags/nightly_populate.py | 442 +++++ airflow/plugins/u19/__init__.py | 5 + airflow/plugins/u19/callbacks.py | 61 + airflow/plugins/u19/datajoint_sensor.py | 76 + airflow/plugins/u19/matlab.py | 59 + airflow/plugins/u19/slurm.py | 88 + airflow/plugins/u19/status.py | 62 + airflow/plugins/u19/transfers.py | 79 + airflow/requirements-airflow.txt | 17 + pyproject.toml | 8 + uv.lock | 1566 +++++++++++++++++- 15 files changed, 2987 insertions(+), 17 deletions(-) create mode 100644 .github/workflows/airflow-dag-validation.yml create mode 100644 airflow/README.md create mode 100644 airflow/dags/ephys_processing.py create mode 100644 airflow/dags/imaging_processing.py create mode 100644 airflow/dags/nightly_populate.py create mode 100644 airflow/plugins/u19/__init__.py create mode 100644 airflow/plugins/u19/callbacks.py create mode 100644 airflow/plugins/u19/datajoint_sensor.py create mode 100644 airflow/plugins/u19/matlab.py create mode 100644 airflow/plugins/u19/slurm.py create mode 100644 airflow/plugins/u19/status.py create mode 100644 airflow/plugins/u19/transfers.py create mode 100644 airflow/requirements-airflow.txt diff --git a/.github/workflows/airflow-dag-validation.yml b/.github/workflows/airflow-dag-validation.yml new file mode 100644 index 00000000..93d4bf2b --- /dev/null +++ b/.github/workflows/airflow-dag-validation.yml @@ -0,0 +1,61 @@ +# Airflow DAG validation workflow +# Runs on every push and pull_request to verify that all DAGs in airflow/dags/ +# are importable by Airflow's DagBag with zero import errors. +# No Airflow database is required — DagBag loads the DAGs in-process. +# +# Dependencies are managed with uv against the locked `airflow` dependency group +# in pyproject.toml (uv.lock), so installs are reproducible without a pip +# constraints file. + +name: Airflow DAG Validation + +on: + push: + pull_request: + +jobs: + dag-validation: + name: Validate DAGs (Python 3.12 / Airflow 3.2.2) + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + + # Resolve only the `airflow` dependency group from uv.lock — this keeps the + # CI environment small (no GPU/science stack) and fully reproducible. + - name: Sync Airflow dependencies + run: uv sync --only-group airflow + + # Run DagBag import-error check via `uv run`. + # AIRFLOW__CORE__DAGS_FOLDER points at the DAG directory. + # PYTHONPATH includes airflow/plugins so the u19.* plugin imports resolve. + # AIRFLOW__CORE__LOAD_EXAMPLES=False avoids loading Airflow's bundled examples. + - name: Check DAG import errors + env: + AIRFLOW__CORE__DAGS_FOLDER: "${{ github.workspace }}/airflow/dags" + AIRFLOW__CORE__LOAD_EXAMPLES: "False" + PYTHONPATH: "${{ github.workspace }}/airflow/plugins" + run: | + uv run --only-group airflow python - <<'EOF' + from airflow.models import DagBag + import os + + dags_folder = os.environ["AIRFLOW__CORE__DAGS_FOLDER"] + db = DagBag(dag_folder=dags_folder, include_examples=False) + + if db.import_errors: + import json + print("DAG import errors detected:") + print(json.dumps(db.import_errors, indent=2)) + raise SystemExit(1) + + print(f"OK — {len(db.dags)} DAG(s) loaded with no import errors:") + for dag_id in sorted(db.dags): + print(f" {dag_id}") + EOF diff --git a/airflow/README.md b/airflow/README.md new file mode 100644 index 00000000..83069994 --- /dev/null +++ b/airflow/README.md @@ -0,0 +1,54 @@ +# U19 Pipeline — Airflow Phase-1 Scaffold + +This directory contains the **Phase-1 Apache Airflow scaffold** for migrating the U19 +neuroscience pipeline from bare cron jobs to Airflow orchestration. The project targets +**Airflow 3.x** (currently pinned to `apache-airflow==3.2.2`). Tracked in +[BrainCOGS/U19-pipeline-python#95](https://github.com/BrainCOGS/U19-pipeline-python/issues/95). + +## Layout + +``` +airflow/ + README.md # this file + requirements-airflow.txt # Airflow + provider pins + dags/ + nightly_populate.py # @daily — behavior/optogenetics/pupillometry/alerts + ephys_processing.py # @hourly — ephys recording pipeline + imaging_processing.py # @hourly — imaging recording pipeline + plugins/u19/ + __init__.py + datajoint_sensor.py # DataJoint table-populated sensor + slurm.py # SLURM job submission/polling + transfers.py # Globus file transfer helpers + matlab.py # SSH-based MATLAB batch runner + status.py # Dual-write status + log helper + callbacks.py # Slack failure callback +``` + +## Status + +**All task bodies are stubs** (`NotImplementedError` / `pass`). The DAGs parse and are +DagBag-importable, but no real work is performed. Each stub references the existing +`u19_pipeline` module that should be called once the task is fleshed out. + +## DAG Validation in CI + +A GitHub Actions workflow (`.github/workflows/airflow-dag-validation.yml`) uses **uv** +to `uv sync --only-group airflow` (Airflow 3.2.2, locked in `uv.lock`) and runs a +`DagBag` import-error check on every push and pull request. No Airflow database is +required — the DAGs are loaded in-process. The `airflow` dependency group is declared in +the project `pyproject.toml`. + +## Open Infrastructure Questions + +The following decisions are **not yet made** and block a production deployment: + +1. **PNI hosting** — where does the Airflow webserver/scheduler run? (dedicated VM, + existing `braincogs` server, or cloud?) +2. **MATLAB host** — which host runs the MATLAB batch processes, and what SSH + `conn_id` ("matlab_host") maps to in the Airflow Connections table? +3. **AcquiredTiff owner** — `imaging_processing.py` notes the `AcquiredTiff` node is + contended. Should it be owned by a Python operator or a MATLAB SSH call? Guards + against double-run are needed before this DAG goes live. +4. **Metadata DB** — Airflow's metadata DB (Postgres recommended for production) needs + to be provisioned and the connection string set before scheduler startup. diff --git a/airflow/dags/ephys_processing.py b/airflow/dags/ephys_processing.py new file mode 100644 index 00000000..66a6a66d --- /dev/null +++ b/airflow/dags/ephys_processing.py @@ -0,0 +1,202 @@ +# NOTE: scaffold — bodies are stubs +"""Ephys recording processing DAG for U19. + +Runs @hourly. Polls for active processing jobs and drives each through the +linear pipeline: raw transfer → SLURM preprocessing → processed transfer → +DataJoint element populate. + +All task bodies are TODO stubs. Dynamic mapping is stubbed with a comment +placeholder — a linear task graph is wired instead so the DAG parses cleanly. +""" + +from __future__ import annotations + +import logging +from datetime import datetime, timedelta + +from airflow.sdk import dag, task + +log = logging.getLogger(__name__) + +# NOTE: u19 plugin imports are inside task bodies (TODO stubs) so the DAG +# parses even when plugins/ is not on PYTHONPATH at collection time. + +default_args = { + "owner": "u19", + "retries": 1, + "retry_delay": timedelta(minutes=10), +} + + +@dag( + dag_id="u19_ephys_processing", + schedule="@hourly", + start_date=datetime(2024, 1, 1), + catchup=False, + default_args=default_args, + tags=["u19", "ephys", "processing"], +) +def u19_ephys_processing() -> None: + """Ephys processing DAG. + + Intended flow (one task-group per active job):: + + request_raw_transfer + → wait_raw_transfer + → submit_slurm + → wait_slurm + → request_proc_transfer + → wait_proc_transfer + → populate_element + + Each step calls the matching plugin and calls dual_write_status after + advancing the job state. + + TODO: replace the linear stub below with dynamic task-group mapping once + ``get_active_jobs()`` is implemented, e.g.:: + + @task_group + def process_job(job_id: int) -> None: + ... + + jobs = get_active_jobs() + process_job.expand(job_id=jobs) + """ + + @task(task_id="get_active_jobs") + def get_active_jobs() -> list[int]: + """Return list of job_ids that are ready for processing. + + TODO: query recording_process.Processing for jobs in the + 'transfer_request' status; return their job_ids. + + Returns + ------- + list[int] + List of ``recording_process.Processing.job_id`` values. + """ + # TODO: from u19_pipeline import recording_process + # jobs = (recording_process.Processing & {"status_processing_id": STATUS_TRANSFER_REQUEST}).fetch("job_id") + # return list(jobs) + log.info("get_active_jobs stub — returning empty list") + return [] + + @task(task_id="request_raw_transfer") + def request_raw_transfer(job_ids: list[int]) -> None: + """Initiate Globus raw-data transfer from PNI to processing cluster. + + Wraps u19.transfers.globus_transfer. + Calls dual_write_status after initiating each transfer. + + TODO: for job_id in job_ids: + task_id = globus_transfer(pni_ep, tiger_ep, raw_src, raw_dst, job_id=job_id) + dual_write_status({"job_id": job_id}, STATUS_RAW_TRANSFER_STARTED) + """ + # TODO: from u19.transfers import globus_transfer + # from u19.status import dual_write_status + pass + + @task(task_id="wait_raw_transfer") + def wait_raw_transfer(job_ids: list[int]) -> None: + """Poll until raw Globus transfer reaches SUCCEEDED state. + + Wraps u19.transfers.check_transfer_status. + Calls dual_write_status when transfer completes. + + TODO: poll check_transfer_status(task_id) until SUCCEEDED/FAILED + dual_write_status({"job_id": job_id}, STATUS_RAW_TRANSFER_DONE) + """ + # TODO: from u19.transfers import check_transfer_status + # from u19.status import dual_write_status + pass + + @task(task_id="submit_slurm") + def submit_slurm(job_ids: list[int]) -> None: + """Generate and submit a SLURM job for each active job. + + Wraps u19.slurm.submit_slurm_job. + Calls dual_write_status after submission. + + TODO: for job_id in job_ids: + slurm_job_id = submit_slurm_job(job_id, program_selection_params) + dual_write_status({"job_id": job_id}, STATUS_SLURM_SUBMITTED) + """ + # TODO: from u19.slurm import submit_slurm_job + # from u19.status import dual_write_status + pass + + @task(task_id="wait_slurm") + def wait_slurm(job_ids: list[int]) -> None: + """Poll SLURM until the job reaches a terminal state. + + Wraps u19.slurm.poll_slurm_job. + Calls dual_write_status when SLURM job completes. + + NOTE: should become a deferrable operator in Phase 2. + + TODO: for job_id in job_ids: + state = poll_slurm_job(slurm_job_id, job_id=job_id) + dual_write_status({"job_id": job_id}, STATUS_SLURM_DONE if state=="COMPLETED" else STATUS_ERROR) + """ + # TODO: from u19.slurm import poll_slurm_job + # from u19.status import dual_write_status + pass + + @task(task_id="request_proc_transfer") + def request_proc_transfer(job_ids: list[int]) -> None: + """Initiate Globus processed-data transfer from cluster back to PNI. + + Wraps u19.transfers.globus_transfer. + Calls dual_write_status after initiating each transfer. + + TODO: for job_id in job_ids: + task_id = globus_transfer(tiger_ep, pni_ep, proc_src, proc_dst, job_id=job_id) + dual_write_status({"job_id": job_id}, STATUS_PROC_TRANSFER_STARTED) + """ + # TODO: from u19.transfers import globus_transfer + # from u19.status import dual_write_status + pass + + @task(task_id="wait_proc_transfer") + def wait_proc_transfer(job_ids: list[int]) -> None: + """Poll until processed-data Globus transfer reaches SUCCEEDED state. + + Wraps u19.transfers.check_transfer_status. + Calls dual_write_status when transfer completes. + + TODO: poll check_transfer_status(task_id) until SUCCEEDED/FAILED + dual_write_status({"job_id": job_id}, STATUS_PROC_TRANSFER_DONE) + """ + # TODO: from u19.transfers import check_transfer_status + # from u19.status import dual_write_status + pass + + @task(task_id="populate_element") + def populate_element(job_ids: list[int]) -> None: + """Populate DataJoint ephys element tables for completed jobs. + + Wraps u19_pipeline.automatic_job.ephys_element_populate. + Calls dual_write_status after populate completes. + + TODO: for job_id in job_ids: + ephys_element_populate.run(job_id) + dual_write_status({"job_id": job_id}, STATUS_COMPLETE) + """ + # TODO: import u19_pipeline.automatic_job.ephys_element_populate as ep + # from u19.status import dual_write_status + pass + + # ------------------------------------------------------------------------- + # Wire linear stub graph + # ------------------------------------------------------------------------- + t_jobs = get_active_jobs() + t_req_raw = request_raw_transfer(t_jobs) + t_wait_raw = wait_raw_transfer(t_req_raw) + t_submit_slurm = submit_slurm(t_wait_raw) + t_wait_slurm = wait_slurm(t_submit_slurm) + t_req_proc = request_proc_transfer(t_wait_slurm) + t_wait_proc = wait_proc_transfer(t_req_proc) + populate_element(t_wait_proc) + + +u19_ephys_processing() diff --git a/airflow/dags/imaging_processing.py b/airflow/dags/imaging_processing.py new file mode 100644 index 00000000..3922a874 --- /dev/null +++ b/airflow/dags/imaging_processing.py @@ -0,0 +1,224 @@ +# NOTE: scaffold — bodies are stubs +"""Imaging recording processing DAG for U19. + +Runs @hourly. Drives each imaging job through tiff acquisition, splitting, +and the DataJoint element processing chain. + +All task bodies are TODO stubs. Dynamic mapping is stubbed with a comment +placeholder — a linear/forked stub task graph is wired instead so the DAG +parses cleanly. + +IMPORTANT — AcquiredTiff is the contended node: + AcquiredTiff marks a recording as "claimed" for processing. If two DAG + runs or operators try to insert/process the same recording simultaneously, + they will collide. Guards against double-run (e.g. a DataJoint transaction + lock check or an Airflow XCom-based claim token) MUST be added before this + DAG goes live. The owner of this step (Python operator vs. MATLAB SSH) is + also an open question — see airflow/README.md. +""" + +from __future__ import annotations + +import logging +from datetime import datetime, timedelta + +from airflow.sdk import dag, task + +log = logging.getLogger(__name__) + +# NOTE: u19 plugin imports are inside task bodies (TODO stubs) so the DAG +# parses even when plugins/ is not on PYTHONPATH at collection time. + +default_args = { + "owner": "u19", + "retries": 1, + "retry_delay": timedelta(minutes=10), +} + + +@dag( + dag_id="u19_imaging_processing", + schedule="@hourly", + start_date=datetime(2024, 1, 1), + catchup=False, + default_args=default_args, + tags=["u19", "imaging", "processing"], +) +def u19_imaging_processing() -> None: + """Imaging processing DAG. + + Intended flow:: + + acquired_tiff (owner TBD: python or MATLAB) + → tiff_split + ├─→ preprocess → processing → motion_correction + │ → segmentation → fluorescence → activity (python element chain) + └─→ sync_imaging_behavior (MATLAB; also needs recording chain + behavior file) + + TODO: replace the linear stub below with dynamic task-group mapping once + ``get_active_jobs()`` is implemented:: + + @task_group + def process_imaging_job(job_id: int) -> None: + ... + + jobs = get_active_jobs() + process_imaging_job.expand(job_id=jobs) + """ + + @task(task_id="get_active_jobs") + def get_active_jobs() -> list[int]: + """Return list of job_ids ready for imaging processing. + + TODO: query recording_process.Processing for imaging jobs in the + acquisition/tiff-ready status. + + Returns + ------- + list[int] + List of ``recording_process.Processing.job_id`` values. + """ + # TODO: from u19_pipeline import recording_process + # jobs = (recording_process.Processing & {"status_processing_id": STATUS_TIFF_READY}).fetch("job_id") + # return list(jobs) + log.info("get_active_jobs stub — returning empty list") + return [] + + @task(task_id="acquired_tiff") + def acquired_tiff(job_ids: list[int]) -> None: + """Mark recordings as acquired / insert AcquiredTiff rows. + + Owner TBD (Python operator or MATLAB SSH call). + + CONTENTION WARNING: AcquiredTiff is the contended node — guard against + double-run before enabling this task in production (see module docstring). + + For MATLAB path: + TODO: run_matlab_batch(..., step="populate_AcquiredTiff()") + For Python path: + TODO: call u19_pipeline.automatic_job.imaging_element.populate_acquired_tiff(job_ids) + """ + # TODO: implement after owner decision is made + pass + + @task(task_id="tiff_split") + def tiff_split(upstream: None) -> None: + """Split multi-page TIFFs into per-plane files. + + TODO: call u19_pipeline.automatic_job.imaging_element_populate.tiff_split(...) + Also calls dual_write_status after completion. + """ + # TODO: import u19_pipeline.automatic_job.imaging_element_populate as ip + # from u19.status import dual_write_status + pass + + # ------------------------------------------------------------------------- + # Python element chain: preprocess → processing → motion_correction + # → segmentation → fluorescence → activity + # ------------------------------------------------------------------------- + + @task(task_id="preprocess") + def preprocess(upstream: None) -> None: + """Run imaging preprocessing (e.g. dark-frame subtraction). + + TODO: call u19_pipeline.automatic_job.imaging_element_populate.preprocess(...) + Calls dual_write_status after each step. + """ + # TODO: import u19_pipeline.automatic_job.imaging_element_populate as ip + # from u19.status import dual_write_status + pass + + @task(task_id="processing") + def processing(upstream: None) -> None: + """Run imaging processing step (scan-info extraction etc.). + + TODO: call u19_pipeline.automatic_job.imaging_element_populate.processing(...) + Calls dual_write_status after each step. + """ + # TODO: import u19_pipeline.automatic_job.imaging_element_populate as ip + # from u19.status import dual_write_status + pass + + @task(task_id="motion_correction") + def motion_correction(upstream: None) -> None: + """Run motion correction (e.g. Suite2p / CaImAn). + + TODO: call u19_pipeline.automatic_job.imaging_element_populate.motion_correction(...) + Calls dual_write_status after each step. + """ + # TODO: import u19_pipeline.automatic_job.imaging_element_populate as ip + # from u19.status import dual_write_status + pass + + @task(task_id="segmentation") + def segmentation(upstream: None) -> None: + """Run ROI segmentation. + + TODO: call u19_pipeline.automatic_job.imaging_element_populate.segmentation(...) + Calls dual_write_status after each step. + """ + # TODO: import u19_pipeline.automatic_job.imaging_element_populate as ip + # from u19.status import dual_write_status + pass + + @task(task_id="fluorescence") + def fluorescence(upstream: None) -> None: + """Extract fluorescence traces. + + TODO: call u19_pipeline.automatic_job.imaging_element_populate.fluorescence(...) + Calls dual_write_status after each step. + """ + # TODO: import u19_pipeline.automatic_job.imaging_element_populate as ip + # from u19.status import dual_write_status + pass + + @task(task_id="activity") + def activity(upstream: None) -> None: + """Compute dF/F / spike deconvolution. + + TODO: call u19_pipeline.automatic_job.imaging_element_populate.activity(...) + Calls dual_write_status after each step. + """ + # TODO: import u19_pipeline.automatic_job.imaging_element_populate as ip + # from u19.status import dual_write_status + pass + + # ------------------------------------------------------------------------- + # MATLAB sync branch (fork from tiff_split) + # NOTE: also needs the recording chain + behavior file on the mount + # ------------------------------------------------------------------------- + + @task(task_id="sync_imaging_behavior") + def sync_imaging_behavior(upstream: None) -> None: + """Sync imaging data to behavior via MATLAB. + + NOTE: this step also needs the recording chain populated and the + behavior file accessible on the network mount. Ensure both conditions + hold before enabling in production. + + TODO: call run_matlab_batch(..., step="populate_ImagingSyncBehavior()") + """ + # TODO: from u19.matlab import run_matlab_batch + # run_matlab_batch(..., "populate_ImagingSyncBehavior()") + pass + + # ------------------------------------------------------------------------- + # Wire stub graph + # ------------------------------------------------------------------------- + t_jobs = get_active_jobs() + t_acquired = acquired_tiff(t_jobs) + t_split = tiff_split(t_acquired) + + # Fork 1: python element chain + t_pre = preprocess(t_split) + t_proc = processing(t_pre) + t_mc = motion_correction(t_proc) + t_seg = segmentation(t_mc) + t_fluor = fluorescence(t_seg) + activity(t_fluor) + + # Fork 2: MATLAB sync (also from tiff_split) + sync_imaging_behavior(t_split) + + +u19_imaging_processing() diff --git a/airflow/dags/nightly_populate.py b/airflow/dags/nightly_populate.py new file mode 100644 index 00000000..c8560ded --- /dev/null +++ b/airflow/dags/nightly_populate.py @@ -0,0 +1,442 @@ +# NOTE: scaffold — bodies are stubs +"""Nightly populate DAG for U19 behavior, optogenetics, pupillometry, and alerts. + +Runs @daily. All task bodies are TODO stubs that reference (but do not +re-implement) existing u19_pipeline code. Dependency edges encode both the +DataJoint FK order and implicit edges discovered by auditing MATLAB make() bodies. +""" + +from __future__ import annotations + +import logging +from datetime import datetime, timedelta + +from airflow.sdk import dag, task +from airflow.providers.standard.operators.empty import EmptyOperator + +from u19.callbacks import slack_failure_callback + +log = logging.getLogger(__name__) + +default_args = { + "owner": "u19", + "retries": 1, + "retry_delay": timedelta(minutes=5), + "on_failure_callback": slack_failure_callback, +} + + +@dag( + dag_id="u19_nightly_populate", + schedule="@daily", + start_date=datetime(2024, 1, 1), + catchup=False, + default_args=default_args, + tags=["u19", "nightly", "behavior"], +) +def u19_nightly_populate() -> None: + """Nightly populate DAG. + + Wires the full behavior ingest + alert graph. Edge comments explain the + reasoning; see MATLAB make() bodies for the implicit edges. + """ + + # ------------------------------------------------------------------------- + # Reweight reset — independent, runs first each night + # ------------------------------------------------------------------------- + + @task(task_id="reset_reweight") + def reset_reweight() -> None: + """Reset subject reweight flags via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_ResetReweight()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_ResetReweight()") + pass + + # ------------------------------------------------------------------------- + # Scheduling group — independent of behavior ingest + # ------------------------------------------------------------------------- + + @task(task_id="populate_schedule_for_tomorrow") + def populate_schedule_for_tomorrow() -> None: + """Populate schedule for tomorrow via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_ScheduleForTomorrow()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_ScheduleForTomorrow()") + pass + + @task(task_id="populate_technician_schedule") + def populate_technician_schedule() -> None: + """Populate technician schedule via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_TechnicianSchedule()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_TechnicianSchedule()") + pass + + @task(task_id="update_protocol_level") + def update_protocol_level() -> None: + """Update protocol level via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_ProtocolLevel()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_ProtocolLevel()") + pass + + @task(task_id="update_training_profile") + def update_training_profile() -> None: + """Update training profile via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_TrainingProfile()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_TrainingProfile()") + pass + + # ------------------------------------------------------------------------- + # Behavior chain — FK order: Session → SessionBlock / TowersSession + # ------------------------------------------------------------------------- + + @task(task_id="session") + def session() -> None: + """Ingest behavior sessions via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_BehaviorSession()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_BehaviorSession()") + pass + + @task(task_id="session_block") + def session_block() -> None: + """Populate SessionBlock via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_SessionBlock()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_SessionBlock()") + pass + + @task(task_id="towers_session") + def towers_session() -> None: + """Populate TowersSession via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_TowersSession()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_TowersSession()") + pass + + @task(task_id="towers_block") + def towers_block() -> None: + """Populate TowersBlock (and TowersBlockTrial) via MATLAB. + + NOTE: TowersBlock.make() ALSO writes TowersBlockTrial — treat them as + a single atomic unit; do not add a separate towers_block_trial task. + + TODO: call run_matlab_batch(..., step="populate_TowersBlock()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_TowersBlock()") + pass + + @task(task_id="spatial_time_blobs") + def spatial_time_blobs() -> None: + """Populate SpatialTimeBlobs via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_SpatialTimeBlobs()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_SpatialTimeBlobs()") + pass + + @task(task_id="towers_session_psych") + def towers_session_psych() -> None: + """Populate TowersSessionPsych via MATLAB (reads TowersSession only). + + TODO: call run_matlab_batch(..., step="populate_TowersSessionPsych()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_TowersSessionPsych()") + pass + + @task(task_id="towers_session_psych_task") + def towers_session_psych_task() -> None: + """Populate TowersSessionPsychTask via MATLAB. + + IMPLICIT EDGE: although the DataJoint FK declaration only references + TowersSession, TowersSessionPsychTask.make() also reads TowersBlock and + TowersBlockTrial at runtime. Therefore this task must run AFTER + towers_block, not just after towers_session. + + TODO: call run_matlab_batch(..., step="populate_TowersSessionPsychTask()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_TowersSessionPsychTask()") + pass + + # Barrier: cumulative tables read ALL prior sessions, so they must wait for + # the entire nightly TowersSession batch to finish before running — not per-session. + towers_session_batch_complete = EmptyOperator(task_id="towers_session_batch_complete") + + @task(task_id="towers_subject_cumulative_psych") + def towers_subject_cumulative_psych() -> None: + """Populate TowersSubjectCumulativePsych via MATLAB. + + Runs after the batch barrier — reads ALL prior sessions, not just today's. + + TODO: call run_matlab_batch(..., step="populate_TowersSubjectCumulativePsych()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_TowersSubjectCumulativePsych()") + pass + + @task(task_id="towers_subject_cumulative_psych_level") + def towers_subject_cumulative_psych_level() -> None: + """Populate TowersSubjectCumulativePsychLevel via MATLAB. + + Runs after the batch barrier — reads ALL prior sessions, not just today's. + + TODO: call run_matlab_batch(..., step="populate_TowersSubjectCumulativePsychLevel()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_TowersSubjectCumulativePsychLevel()") + pass + + # ------------------------------------------------------------------------- + # Optogenetics chain + # ------------------------------------------------------------------------- + + @task(task_id="ingest_optogenetic_sessions") + def ingest_optogenetic_sessions() -> None: + """Ingest optogenetic session metadata via MATLAB. + + TODO: call run_matlab_batch(..., step="ingest_OptogeneticSessions()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "ingest_OptogeneticSessions()") + pass + + @task(task_id="optogenetic_session") + def optogenetic_session() -> None: + """Populate OptogeneticSession via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_OptogeneticSession()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_OptogeneticSession()") + pass + + # ------------------------------------------------------------------------- + # Pupillometry chain + # sync reads acquisition.SessionVideo + behavior file on the mount + # ------------------------------------------------------------------------- + + @task(task_id="pupillometry_session") + def pupillometry_session() -> None: + """Populate PupillometrySession via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_PupillometrySession()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_PupillometrySession()") + pass + + @task(task_id="pupillometry_sync_behavior") + def pupillometry_sync_behavior() -> None: + """Sync pupillometry to behavior via MATLAB. + + NOTE: this step reads acquisition.SessionVideo and the behavior file on + the network mount — ensure the mount is available before this task runs. + + TODO: call run_matlab_batch(..., step="populate_PupilSyncBehavior()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_PupilSyncBehavior()") + pass + + # ------------------------------------------------------------------------- + # Posture tracking chain + # ------------------------------------------------------------------------- + + @task(task_id="posture_tracking_session") + def posture_tracking_session() -> None: + """Populate PostureTrackingSession via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_PostureTrackingSession()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_PostureTrackingSession()") + pass + + @task(task_id="posture_tracking_sync_behavior") + def posture_tracking_sync_behavior() -> None: + """Sync posture tracking to behavior via MATLAB. + + TODO: call run_matlab_batch(..., step="populate_PostureTrackingSyncBehavior()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "populate_PostureTrackingSyncBehavior()") + pass + + # ------------------------------------------------------------------------- + # Subtasks + # ------------------------------------------------------------------------- + + @task(task_id="ingest_subtasks") + def ingest_subtasks() -> None: + """Ingest subtasks via MATLAB. + + TODO: call run_matlab_batch(..., step="ingest_Subtasks()") + """ + # TODO: from u19.matlab import run_matlab_batch; run_matlab_batch(..., "ingest_Subtasks()") + pass + + # ------------------------------------------------------------------------- + # Alert group — all wrap u19_pipeline.alert_system.main_alert_system + # Each alert is independent; all get the Slack failure callback. + # ------------------------------------------------------------------------- + + @task(task_id="alert_live_monitor", on_failure_callback=slack_failure_callback) + def alert_live_monitor() -> None: + """Run live-monitor alert check. + + TODO: call the live_monitor sub-module from u19_pipeline.alert_system.main_alert_system + """ + # TODO: import u19_pipeline.alert_system.custom_alerts.live_monitor_alert as a; a.main() + pass + + @task(task_id="alert_schedule_check", on_failure_callback=slack_failure_callback) + def alert_schedule_check() -> None: + """Run schedule-check alert. + + TODO: call the schedule_check_alert sub-module from u19_pipeline.alert_system.main_alert_system + """ + # TODO: import u19_pipeline.alert_system.custom_alerts.schedule_check_alert as a; a.main() + pass + + @task(task_id="alert_water_weigh", on_failure_callback=slack_failure_callback) + def alert_water_weigh() -> None: + """Run water/weight alert. + + TODO: call the water_weigh_alert sub-module from u19_pipeline.alert_system.main_alert_system + """ + # TODO: import u19_pipeline.alert_system.custom_alerts.water_weigh_alert as a; a.main() + pass + + @task(task_id="alert_tech", on_failure_callback=slack_failure_callback) + def alert_tech() -> None: + """Run technician alert. + + TODO: call the tech_alert sub-module from u19_pipeline.alert_system.main_alert_system + """ + # TODO: import u19_pipeline.alert_system.custom_alerts.tech_alert as a; a.main() + pass + + @task(task_id="alert_locked_tables", on_failure_callback=slack_failure_callback) + def alert_locked_tables() -> None: + """Run locked-tables alert. + + TODO: call the locked_tables_alert sub-module from u19_pipeline.alert_system.main_alert_system + """ + # TODO: import u19_pipeline.alert_system.custom_alerts.locked_tables_alert as a; a.main() + pass + + @task(task_id="alert_rig_maintenance", on_failure_callback=slack_failure_callback) + def alert_rig_maintenance() -> None: + """Run rig-maintenance alert. + + TODO: call the rig_maintenance sub-module from u19_pipeline.alert_system.main_alert_system + """ + # TODO: import u19_pipeline.alert_system.custom_alerts.rig_maintenance as a; a.main() + pass + + @task(task_id="alert_live_session_stats_deletion", on_failure_callback=slack_failure_callback) + def alert_live_session_stats_deletion() -> None: + """Run live-session-stats-deletion alert. + + TODO: call the live_session_stats_deletion sub-module from u19_pipeline.alert_system.main_alert_system + """ + # TODO: import u19_pipeline.alert_system.custom_alerts.live_session_stats_deletion as a; a.main() + pass + + # ========================================================================= + # Instantiate tasks + # ========================================================================= + + t_reset_reweight = reset_reweight() + + t_sched_tomorrow = populate_schedule_for_tomorrow() + t_sched_tech = populate_technician_schedule() + t_protocol_level = update_protocol_level() + t_training_profile = update_training_profile() + + t_session = session() + t_session_block = session_block() + t_towers_session = towers_session() + t_towers_block = towers_block() + t_spatial_time_blobs = spatial_time_blobs() + t_towers_session_psych = towers_session_psych() + t_towers_session_psych_task = towers_session_psych_task() + t_towers_subject_cumulative_psych = towers_subject_cumulative_psych() + t_towers_subject_cumulative_psych_level = towers_subject_cumulative_psych_level() + + t_ingest_opto = ingest_optogenetic_sessions() + t_opto_session = optogenetic_session() + + t_pupil_session = pupillometry_session() + t_pupil_sync = pupillometry_sync_behavior() + + t_posture_session = posture_tracking_session() + t_posture_sync = posture_tracking_sync_behavior() + + t_ingest_subtasks = ingest_subtasks() + + t_alert_live_monitor = alert_live_monitor() + t_alert_schedule_check = alert_schedule_check() + t_alert_water_weigh = alert_water_weigh() + t_alert_tech = alert_tech() + t_alert_locked_tables = alert_locked_tables() + t_alert_rig_maintenance = alert_rig_maintenance() + t_alert_live_session_stats_deletion = alert_live_session_stats_deletion() + + # ========================================================================= + # Wire dependencies + # ========================================================================= + + # --- reset_reweight: independent --- + # (no upstream dependency) + + # --- scheduling group: independent of behavior --- + # (no upstream dependency; all four are independent of each other) + + # --- behavior chain --- + # FK order: Session → SessionBlock + t_session >> t_session_block + + # FK order: Session → TowersSession + t_session >> t_towers_session + + # TowersBlock requires both TowersSession and SessionBlock + [t_towers_session, t_session_block] >> t_towers_block + + # SpatialTimeBlobs only needs TowersSession + t_towers_session >> t_spatial_time_blobs + + # TowersSessionPsych reads TowersSession only (FK is accurate here) + t_towers_session >> t_towers_session_psych + + # IMPLICIT EDGE: TowersSessionPsychTask FK says only TowersSession, but + # make() reads TowersBlock + TowersBlockTrial at runtime — must follow towers_block + t_towers_block >> t_towers_session_psych_task + + # Batch barrier: cumulative tables must wait for the whole nightly + # TowersSession batch, not just one session's worth of work + t_towers_session >> towers_session_batch_complete + towers_session_batch_complete >> t_towers_subject_cumulative_psych + towers_session_batch_complete >> t_towers_subject_cumulative_psych_level + + # --- optogenetics chain --- + t_session >> t_ingest_opto >> t_opto_session + + # --- pupillometry chain --- + # sync reads acquisition.SessionVideo + behavior file on the mount + t_session >> t_pupil_session >> t_pupil_sync + + # --- posture tracking chain --- + t_session >> t_posture_session >> t_posture_sync + + # --- subtasks --- + t_session >> t_ingest_subtasks + + # --- alerts: all independent (no explicit deps on behavior tasks) --- + # t_alert_* are all independent of each other and of behavior ingest + + +u19_nightly_populate() diff --git a/airflow/plugins/u19/__init__.py b/airflow/plugins/u19/__init__.py new file mode 100644 index 00000000..a198a7bc --- /dev/null +++ b/airflow/plugins/u19/__init__.py @@ -0,0 +1,5 @@ +"""U19 pipeline Airflow plugin package. + +Provides sensors, operators, and helper stubs for the U19 neuroscience +pipeline DAGs. All symbols are importable; task bodies are TODO stubs. +""" diff --git a/airflow/plugins/u19/callbacks.py b/airflow/plugins/u19/callbacks.py new file mode 100644 index 00000000..a840da1b --- /dev/null +++ b/airflow/plugins/u19/callbacks.py @@ -0,0 +1,61 @@ +"""Airflow DAG/task callback helpers. + +Callbacks must never raise — an exception inside a failure callback would mask +the original error and prevent Airflow from marking the task as failed. +""" + +from __future__ import annotations + +import logging +from typing import Any + +log = logging.getLogger(__name__) + + +def slack_failure_callback(context: dict[str, Any]) -> None: + """Post a Slack failure notification when an Airflow task fails. + + Intended to be passed as ``on_failure_callback`` on individual tasks or at + the DAG level:: + + @dag(on_failure_callback=slack_failure_callback, ...) + def my_dag(): ... + + Parameters + ---------- + context: + Airflow task-instance context dict (injected automatically by the + scheduler). Useful keys: ``context["task_instance"]``, + ``context["exception"]``, ``context["dag"]``. + + Notes + ----- + Posts to Slack using ``u19_pipeline.utils.slack_utils`` and reads webhook + URLs from ``lab.SlackWebhooks`` (same mechanism as the existing alert + system in ``u19_pipeline.alert_system.main_alert_system``). + + This function is a **safe no-op** stub: it logs the failure but does not + raise, ensuring the original task failure is not masked. + """ + # TODO: implement Slack notification + # try: + # import u19_pipeline.utils.slack_utils as su + # import u19_pipeline.lab as lab + # ti = context.get("task_instance") + # dag_id = context.get("dag").dag_id if context.get("dag") else "unknown" + # message = f":red_circle: Task `{ti.task_id}` in DAG `{dag_id}` failed.\n" + # message += f"Execution date: {context.get('execution_date')}\n" + # exc = context.get("exception") + # if exc: + # message += f"Exception: {exc}" + # slack_dict = {"slack_notification_channel": ["custom_alerts"]} + # webhooks = su.get_webhook_list(slack_dict, lab) + # for url in webhooks: + # su.send_slack_notification(url, message) + # except Exception: + # log.exception("slack_failure_callback itself raised — suppressing to avoid masking task failure") + log.warning( + "slack_failure_callback (stub): task %s in dag %s failed — Slack notification not yet implemented", + context.get("task_instance"), + context.get("dag"), + ) diff --git a/airflow/plugins/u19/datajoint_sensor.py b/airflow/plugins/u19/datajoint_sensor.py new file mode 100644 index 00000000..92976d87 --- /dev/null +++ b/airflow/plugins/u19/datajoint_sensor.py @@ -0,0 +1,76 @@ +"""DataJoint table-populated sensor helpers. + +Used to block downstream tasks until an upstream DataJoint Computed or +Imported table has rows for a given primary-key restriction — e.g., waiting +for an external MATLAB populate() to finish before running a Python populate. +""" + +from __future__ import annotations + +from typing import Any + +from airflow.sdk import task + + +def dj_table_populated(table: Any, key: dict) -> bool: + """Return True if *table* contains at least one row matching *key*. + + Parameters + ---------- + table: + A DataJoint table class or query expression (e.g. ``behavior.TowersSession``). + key: + Primary-key restriction dict, e.g. ``{"subject_fullname": "efonseca", "session_date": "2024-01-01"}``. + + Returns + ------- + bool + ``True`` when ``len(table & key) > 0``, ``False`` otherwise. + + Notes + ----- + Intended to be called inside ``wait_for_dj_table`` or directly from a + ShortCircuitOperator body. Does *not* handle DataJoint connection errors — + callers should wrap with retry logic. + """ + # TODO: implement using DataJoint fetch + # return len(table & key) > 0 + raise NotImplementedError("dj_table_populated is a scaffold stub") + + +@task.sensor(poke_interval=60, timeout=3600, mode="reschedule") +def wait_for_dj_table(table_dotpath: str, key: dict) -> bool: + """Airflow sensor task that pokes until *table_dotpath* has rows for *key*. + + Parameters + ---------- + table_dotpath: + Dotted import path to the DataJoint table, e.g. + ``"u19_pipeline.behavior.TowersSession"``. + key: + Primary-key restriction dict forwarded to :func:`dj_table_populated`. + + Returns + ------- + bool + Passes ``True`` downstream when the table is populated (sensor fires). + + Notes + ----- + Uses ``mode="reschedule"`` so the worker slot is released between pokes. + Typical use: guard a Python populate task against an upstream MATLAB + populate that runs on the MATLAB VM on an unknown schedule. + + Example usage in a DAG:: + + wait = wait_for_dj_table.override(task_id="wait_towers_session")( + table_dotpath="u19_pipeline.behavior.TowersSession", + key={"session_date": "{{ ds }}"}, + ) + """ + # TODO: resolve table_dotpath via importlib, call dj_table_populated + # import importlib + # mod_path, attr = table_dotpath.rsplit(".", 1) + # table = getattr(importlib.import_module(mod_path), attr) + # return dj_table_populated(table, key) + raise NotImplementedError("wait_for_dj_table is a scaffold stub") diff --git a/airflow/plugins/u19/matlab.py b/airflow/plugins/u19/matlab.py new file mode 100644 index 00000000..1a907bf9 --- /dev/null +++ b/airflow/plugins/u19/matlab.py @@ -0,0 +1,59 @@ +"""MATLAB batch runner via SSH. + +Uses the Airflow SSH provider (``apache-airflow-providers-ssh``) to connect +to the designated MATLAB host and run a MATLAB batch command. + +The SSH connection is configured in the Airflow Connections UI / environment +variable under the key ``conn_id`` (default: ``"matlab_host"``). +""" + +from __future__ import annotations + + +def run_matlab_batch(host: str, step: str, conn_id: str = "matlab_host") -> None: + """SSH to *host* and run a MATLAB batch step. + + Executes the following command on the remote host:: + + matlab -batch "run('scripts/startup_virtual_machine.m'); " + + where ```` is a MATLAB expression such as + ``"populate_TowersSession()"`` or a function call defined in the startup + path. + + Parameters + ---------- + host: + Hostname or IP of the MATLAB virtual machine (e.g. ``"braincogs00.pni.princeton.edu"``). + This must match the ``host`` field of the Airflow SSH connection ``conn_id``. + step: + MATLAB expression to execute after the startup script, e.g. + ``"populate_BehaviorSession()"`` or ``"run_alert_system()"``· + conn_id: + Airflow SSH connection ID configured in the Connections UI. Defaults + to ``"matlab_host"``. The connection must supply username, host, and + either password or private key. + + Notes + ----- + Uses ``airflow.providers.ssh.hooks.ssh.SSHHook`` to open the connection + and ``SSHOperator`` (or its hook directly) to run the command. + + Full command template:: + + matlab -batch "run('scripts/startup_virtual_machine.m'); {step}" + + Callers inside DAG tasks should instantiate ``SSHOperator`` directly + (preferred for operator-level retries) or call this helper from a + ``@task``-decorated function via ``SSHHook.get_conn()``. + """ + # TODO: implement via SSHHook / SSHOperator + # from airflow.providers.ssh.hooks.ssh import SSHHook + # hook = SSHHook(ssh_conn_id=conn_id) + # with hook.get_conn() as client: + # cmd = f"matlab -batch \"run('scripts/startup_virtual_machine.m'); {step}\"" + # stdin, stdout, stderr = client.exec_command(cmd) + # exit_status = stdout.channel.recv_exit_status() + # if exit_status != 0: + # raise RuntimeError(f"MATLAB step '{step}' failed: {stderr.read().decode()}") + raise NotImplementedError("run_matlab_batch is a scaffold stub") diff --git a/airflow/plugins/u19/slurm.py b/airflow/plugins/u19/slurm.py new file mode 100644 index 00000000..6e16aa3d --- /dev/null +++ b/airflow/plugins/u19/slurm.py @@ -0,0 +1,88 @@ +"""SLURM job submission and polling stubs. + +Wraps ``u19_pipeline.automatic_job.slurm_creator`` functions: + +* :func:`~u19_pipeline.automatic_job.slurm_creator.generate_slurm_file` — + writes a ``.slurm`` file from pipeline parameters. +* :func:`~u19_pipeline.automatic_job.slurm_creator.queue_slurm_file` — + submits the generated file to the cluster via ``sbatch``. + +And the job-status helper from ``u19_pipeline.automatic_job.recording_handler`` +(``check_slurm_job`` / ``get_slurm_job_state``). + +.. note:: + These functions should eventually become **deferrable operators** using + Airflow's async/trigger pattern so the scheduler does not hold a worker + slot while polling — see + https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/deferring.html +""" + +from __future__ import annotations + +from typing import Any + + +def submit_slurm_job(job_id: int, program_selection_params: dict, **kwargs: Any) -> str: + """Generate and queue a SLURM job for a recording processing unit. + + Parameters + ---------- + job_id: + ``recording_process.Processing.job_id`` — the integer job identifier + assigned when the processing row was inserted. + program_selection_params: + Dict containing at minimum ``process_cluster`` and the preprocessing + tool selector. Forwarded verbatim to + ``slurm_creator.generate_slurm_file``. + **kwargs: + Additional keyword arguments reserved for future use (e.g. overriding + ``slurm_default`` values per-job). + + Returns + ------- + str + The SLURM job ID string returned by ``sbatch`` (e.g. ``"12345678"``). + + Notes + ----- + Intended call sequence:: + + generate_slurm_file(job_id, program_selection_params) + queue_slurm_file(job_id, program_selection_params) + + Both live in ``u19_pipeline.automatic_job.slurm_creator``. + """ + # TODO: call slurm_creator + # import u19_pipeline.automatic_job.slurm_creator as sc + # sc.generate_slurm_file(job_id, program_selection_params) + # return sc.queue_slurm_file(job_id, program_selection_params) + raise NotImplementedError("submit_slurm_job is a scaffold stub") + + +def poll_slurm_job(slurm_job_id: str, job_id: int | None = None) -> str: + """Return the current state string for a running SLURM job. + + Parameters + ---------- + slurm_job_id: + The SLURM-assigned job ID string (as returned by :func:`submit_slurm_job`). + job_id: + Optional ``recording_process.Processing.job_id`` for logging context. + + Returns + ------- + str + SLURM job state, e.g. ``"PENDING"``, ``"RUNNING"``, ``"COMPLETED"``, + ``"FAILED"``. Callers should loop / reschedule until a terminal state + is reached. + + Notes + ----- + Should be converted to a deferrable operator trigger before production use. + Wraps ``check_slurm_job`` / ``get_slurm_job_state`` from + ``u19_pipeline.automatic_job.recording_handler`` (or ``slurm_creator``). + """ + # TODO: call recording_handler or slurm_creator job-state check + # import u19_pipeline.automatic_job.recording_handler as rh + # return rh.check_slurm_job(slurm_job_id) + raise NotImplementedError("poll_slurm_job is a scaffold stub") diff --git a/airflow/plugins/u19/status.py b/airflow/plugins/u19/status.py new file mode 100644 index 00000000..6af54d25 --- /dev/null +++ b/airflow/plugins/u19/status.py @@ -0,0 +1,62 @@ +"""Cross-system status dual-write helper. + +This module encodes the status-update contract between the Airflow orchestration +layer and the DataJoint pipeline tables. Every state transition in an ephys or +imaging processing job must be written to *both*: + +* ``recording_process.Processing.status_processing_id`` — the current status + integer for the job row. +* ``recording_process.LogStatus`` — an append-only audit log of all status + transitions with timestamps. + +Keeping both in sync is the dual-write contract. Any Airflow operator that +advances a job's state must call :func:`dual_write_status` instead of updating +the tables directly. +""" + +from __future__ import annotations + + +def dual_write_status(key: dict, status: int, log: bool = True) -> None: + """Update job status in DataJoint and optionally append to the log table. + + Parameters + ---------- + key: + Primary-key dict for ``recording_process.Processing``, e.g. + ``{"job_id": 42}``. + status: + Integer status code from ``recording_process.Status.status_processing_id`` + (see ``u19_pipeline.automatic_job.params_config.recording_process_status_list`` + for the full enum). + log: + If ``True`` (default), also insert a row into + ``recording_process.LogStatus`` with the current timestamp. + + Notes + ----- + Implementation should use a DataJoint transaction so the update and the log + insert are atomic:: + + with dj.conn().transaction: + (recording_process.Processing & key)._update("status_processing_id", status) + if log: + recording_process.LogStatus.insert1({ + **key, + "status_processing_id": status, + "log_timestamp": datetime.utcnow(), + }) + """ + # TODO: implement dual write + # import datajoint as dj + # from u19_pipeline import recording_process + # from datetime import datetime + # with dj.conn().transaction: + # (recording_process.Processing & key)._update("status_processing_id", status) + # if log: + # recording_process.LogStatus.insert1({ + # **key, + # "status_processing_id": status, + # "log_timestamp": datetime.utcnow(), + # }) + raise NotImplementedError("dual_write_status is a scaffold stub") diff --git a/airflow/plugins/u19/transfers.py b/airflow/plugins/u19/transfers.py new file mode 100644 index 00000000..b06de572 --- /dev/null +++ b/airflow/plugins/u19/transfers.py @@ -0,0 +1,79 @@ +"""Globus file transfer stubs. + +Wraps ``u19_pipeline.automatic_job.clusters_paths_and_transfers``, which +contains the Globus SDK calls, endpoint IDs (PNI and Della/Tiger), and +transfer-status polling. +""" + +from __future__ import annotations + +from typing import Any + + +def globus_transfer( + src_endpoint: str, + dst_endpoint: str, + src_path: str, + dst_path: str, + job_id: int | None = None, + **kwargs: Any, +) -> str: + """Initiate a Globus transfer and return the transfer task ID. + + Parameters + ---------- + src_endpoint: + Globus endpoint UUID for the source (e.g. PNI ``pni_ep_id``). + dst_endpoint: + Globus endpoint UUID for the destination (e.g. Della/Tiger ``tiger_ep_dir``). + src_path: + Absolute path on the source endpoint. + dst_path: + Absolute path on the destination endpoint. + job_id: + Optional ``recording_process.Processing.job_id`` for logging / status + dual-write via :mod:`u19.status`. + **kwargs: + Additional options forwarded to the Globus SDK transfer document + (e.g. ``sync_level``, ``notify_on_succeeded``). + + Returns + ------- + str + Globus transfer task ID string. + + Notes + ----- + Wraps the transfer initiation logic in + ``u19_pipeline.automatic_job.clusters_paths_and_transfers``. + """ + # TODO: call clusters_paths_and_transfers + # import u19_pipeline.automatic_job.clusters_paths_and_transfers as ct + # return ct.transfer_request(src_endpoint, dst_endpoint, src_path, dst_path, ...) + raise NotImplementedError("globus_transfer is a scaffold stub") + + +def check_transfer_status(task_id: str) -> str: + """Return the current status of a Globus transfer task. + + Parameters + ---------- + task_id: + Globus transfer task ID returned by :func:`globus_transfer`. + + Returns + ------- + str + Globus task status string, e.g. ``"ACTIVE"``, ``"SUCCEEDED"``, + ``"FAILED"``. Callers should poll until a terminal state. + + Notes + ----- + Wraps the status-check helper in + ``u19_pipeline.automatic_job.clusters_paths_and_transfers``. + Should become a deferrable operator in Phase 2. + """ + # TODO: call clusters_paths_and_transfers status check + # import u19_pipeline.automatic_job.clusters_paths_and_transfers as ct + # return ct.check_transfer_status(task_id) + raise NotImplementedError("check_transfer_status is a scaffold stub") diff --git a/airflow/requirements-airflow.txt b/airflow/requirements-airflow.txt new file mode 100644 index 00000000..7f49d869 --- /dev/null +++ b/airflow/requirements-airflow.txt @@ -0,0 +1,17 @@ +# Airflow 3.x orchestration dependencies. +# +# These are declared canonically in the project's pyproject.toml under +# [dependency-groups].airflow and locked in uv.lock. Install them with uv: +# +# uv sync --only-group airflow # just Airflow (CI / DAG validation) +# uv sync --group airflow # Airflow alongside the rest of the env +# +# This file is kept only as a human-readable listing; it is NOT the source of +# truth and is not consumed by CI (CI uses `uv sync --only-group airflow`). +# +# apache-airflow==3.2.2 +# apache-airflow-providers-ssh +# apache-airflow-providers-standard # EmptyOperator / standard operators (Airflow 3.x) +# +# DataJoint and u19_pipeline dependencies come from the main project deps — +# install the package (uv sync) alongside the airflow group as needed. diff --git a/pyproject.toml b/pyproject.toml index 138d6796..250d8588 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -73,6 +73,14 @@ dev = [ "ruff>=0.9.10", "setuptools>=75.8.0" ] +# Apache Airflow 3.x orchestration deps (see airflow/). Installed via +# `uv sync --group airflow`. 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upload-time = "2025-01-16T11:01:23.507Z" }, +] + [[package]] name = "wrapt" version = "2.2.1" From f6bb77a95e745ecd2a3eb5dbfb53872f1228491c Mon Sep 17 00:00:00 2001 From: Christian Tabedzki <35670232+tabedzki@users.noreply.github.com> Date: Tue, 23 Jun 2026 15:31:53 -0400 Subject: [PATCH 2/3] docs(airflow): define STATUS_* constants in ephys DAG The ephys DAG task docstrings referenced STATUS_* names in their dual_write_status TODOs but never defined them. Add them as named constants mirroring recording_process_status_dict in params_config, so each task's status dual-write is explicit and the file is self-coherent. Implementation should import these from params_config rather than redeclare, to stay in sync. Assisted-by: ClaudeCode:claude-opus-4.8 --- airflow/dags/ephys_processing.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/airflow/dags/ephys_processing.py b/airflow/dags/ephys_processing.py index 66a6a66d..90e059a9 100644 --- a/airflow/dags/ephys_processing.py +++ b/airflow/dags/ephys_processing.py @@ -21,6 +21,22 @@ # NOTE: u19 plugin imports are inside task bodies (TODO stubs) so the DAG # parses even when plugins/ is not on PYTHONPATH at collection time. +# recording_process.Processing.status_processing_id values these tasks +# dual-write — mirror of u19_pipeline.automatic_job.params_config +# (recording_process_status_dict). Kept as named constants here so each task's +# dual_write_status(...) call is explicit; the implementation should import +# these from params_config rather than redeclaring, to stay in sync. +STATUS_ERROR = -1 +STATUS_NEW = 0 +STATUS_TRANSFER_REQUEST = 1 # RAW_FILE_TRANSFER_REQUEST +STATUS_RAW_TRANSFER_STARTED = 1 +STATUS_RAW_TRANSFER_DONE = 2 # RAW_FILE_TRANSFER_END +STATUS_SLURM_SUBMITTED = 3 # JOB_QUEUE +STATUS_SLURM_DONE = 4 # JOB_FINISHED +STATUS_PROC_TRANSFER_STARTED = 5 # PROC_FILE_TRANSFER_REQUEST +STATUS_PROC_TRANSFER_DONE = 6 # PROC_FILE_TRANSFER_END +STATUS_COMPLETE = 7 # JOB_FINISHED_ELEMENT_WORKFLOW + default_args = { "owner": "u19", "retries": 1, From dc7929af0793d8698d244a14a9dd409b67d4b05d Mon Sep 17 00:00:00 2001 From: Christian Tabedzki <35670232+tabedzki@users.noreply.github.com> Date: Tue, 23 Jun 2026 15:48:05 -0400 Subject: [PATCH 3/3] feat(airflow): implement Phase-2 ephys processing DAG with real bodies MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Turn the ephys recording-process scaffold into a working, tested DAG that reuses (does not reimplement) the existing u19_pipeline code. Plugins (thin wrappers over u19_pipeline.automatic_job, imported lazily so DAG parsing stays light): - status.py: dual_write_status — advances recording_process.Processing status + appends LogStatus in one transaction (the issue #95 dual-write contract). - jobs.py: get_active_job_ids / get_job_row for dynamic task mapping. - transfers.py / slurm.py: wrap globus + slurm_creator with a dry-run switch. - slurm_sensor.py: deferrable SlurmJobTrigger + sensor so multi-hour Kilosort jobs don't pin a worker slot. - params.py: per-modality program_selection_params. DAG: ephys_processing.py now discovers active jobs and dynamically maps one task-group per job through transfer -> sbatch -> wait -> transfer-back -> populate_element, dual-writing status after each step. Tests (airflow/tests): unit tests (dry-run, no DB) + an opt-in integration test that exercises dual_write_status against the docker-compose MariaDB. The integration test caught a real bug (DataJoint update1 vs _update). 11 pass. Tooling: add `airflow-test` dep group + pytest config; CI now runs the unit tests after DAG validation. uv.lock updated. Assisted-by: ClaudeCode:claude-opus-4.8 --- .github/workflows/airflow-dag-validation.yml | 8 + airflow/dags/ephys_processing.py | 338 +++++++++---------- airflow/dags/nightly_populate.py | 32 +- airflow/plugins/u19/jobs.py | 60 ++++ airflow/plugins/u19/params.py | 21 ++ airflow/plugins/u19/slurm.py | 152 +++++---- airflow/plugins/u19/slurm_sensor.py | 97 ++++++ airflow/plugins/u19/status.py | 114 ++++--- airflow/plugins/u19/transfers.py | 148 ++++---- airflow/tests/conftest.py | 39 +++ airflow/tests/test_plugins_unit.py | 120 +++++++ airflow/tests/test_status_integration.py | 109 ++++++ pyproject.toml | 18 + uv.lock | 56 +++ 14 files changed, 927 insertions(+), 385 deletions(-) create mode 100644 airflow/plugins/u19/jobs.py create mode 100644 airflow/plugins/u19/params.py create mode 100644 airflow/plugins/u19/slurm_sensor.py create mode 100644 airflow/tests/conftest.py create mode 100644 airflow/tests/test_plugins_unit.py create mode 100644 airflow/tests/test_status_integration.py diff --git a/.github/workflows/airflow-dag-validation.yml b/.github/workflows/airflow-dag-validation.yml index 93d4bf2b..4443a415 100644 --- a/.github/workflows/airflow-dag-validation.yml +++ b/.github/workflows/airflow-dag-validation.yml @@ -59,3 +59,11 @@ jobs: for dag_id in sorted(db.dags): print(f" {dag_id}") EOF + + # Run the plugin unit tests (dry-run; no DB). Integration tests that need + # the Dockerised MariaDB auto-skip because U19_RUN_INTEGRATION is unset. + - name: Run plugin unit tests + env: + PYTHONPATH: "${{ github.workspace }}/airflow/plugins" + U19_AIRFLOW_DRY_RUN: "1" + run: uv run --only-group airflow-test python -m pytest airflow/tests -q diff --git a/airflow/dags/ephys_processing.py b/airflow/dags/ephys_processing.py index 90e059a9..d05ff2c5 100644 --- a/airflow/dags/ephys_processing.py +++ b/airflow/dags/ephys_processing.py @@ -1,12 +1,23 @@ -# NOTE: scaffold — bodies are stubs -"""Ephys recording processing DAG for U19. - -Runs @hourly. Polls for active processing jobs and drives each through the -linear pipeline: raw transfer → SLURM preprocessing → processed transfer → -DataJoint element populate. - -All task bodies are TODO stubs. Dynamic mapping is stubbed with a comment -placeholder — a linear task graph is wired instead so the DAG parses cleanly. +"""Ephys recording-process DAG for U19 (Phase 2). + +Replaces the ephys path of the legacy integer state machine +(``recording_process_handler.py`` statuses 1->7). A scheduled controller +discovers active ``recording_process.Processing`` rows and dynamically maps one +task-group per job through the linear pipeline: + + request_raw_transfer -> wait_raw_transfer + -> submit_slurm -> wait_slurm (deferrable) + -> request_proc_transfer -> wait_proc_transfer + -> populate_element + +Each step calls a ``u19.*`` plugin (which wraps the existing +``u19_pipeline.automatic_job`` code) and dual-writes ``status_processing_id`` + +``LogStatus`` so the RecordingProcessJobGUI / MATLAB / Slack keep working (the +dual-write contract from issue #95). + +Set ``U19_AIRFLOW_DRY_RUN=1`` to exercise the full graph without a live cluster +(transfers/SLURM return synthetic success); DataJoint reads/writes still hit +whatever DB the config points at. """ from __future__ import annotations @@ -14,28 +25,21 @@ import logging from datetime import datetime, timedelta -from airflow.sdk import dag, task +from airflow.sdk import dag, task, task_group log = logging.getLogger(__name__) -# NOTE: u19 plugin imports are inside task bodies (TODO stubs) so the DAG -# parses even when plugins/ is not on PYTHONPATH at collection time. - -# recording_process.Processing.status_processing_id values these tasks -# dual-write — mirror of u19_pipeline.automatic_job.params_config -# (recording_process_status_dict). Kept as named constants here so each task's -# dual_write_status(...) call is explicit; the implementation should import -# these from params_config rather than redeclaring, to stay in sync. +# status_processing_id values (mirror of params_config.recording_process_status_dict). STATUS_ERROR = -1 -STATUS_NEW = 0 -STATUS_TRANSFER_REQUEST = 1 # RAW_FILE_TRANSFER_REQUEST -STATUS_RAW_TRANSFER_STARTED = 1 -STATUS_RAW_TRANSFER_DONE = 2 # RAW_FILE_TRANSFER_END -STATUS_SLURM_SUBMITTED = 3 # JOB_QUEUE -STATUS_SLURM_DONE = 4 # JOB_FINISHED -STATUS_PROC_TRANSFER_STARTED = 5 # PROC_FILE_TRANSFER_REQUEST -STATUS_PROC_TRANSFER_DONE = 6 # PROC_FILE_TRANSFER_END -STATUS_COMPLETE = 7 # JOB_FINISHED_ELEMENT_WORKFLOW +STATUS_RAW_TRANSFER_REQUEST = 1 +STATUS_RAW_TRANSFER_DONE = 2 +STATUS_SLURM_SUBMITTED = 3 +STATUS_SLURM_DONE = 4 +STATUS_PROC_TRANSFER_REQUEST = 5 +STATUS_PROC_TRANSFER_DONE = 6 +STATUS_COMPLETE = 7 + +MODALITY = "electrophysiology" default_args = { "owner": "u19", @@ -50,169 +54,133 @@ start_date=datetime(2024, 1, 1), catchup=False, default_args=default_args, + max_active_tasks=8, # cap concurrent cluster submissions (replaces flock) tags=["u19", "ephys", "processing"], ) def u19_ephys_processing() -> None: - """Ephys processing DAG. - - Intended flow (one task-group per active job):: - - request_raw_transfer - → wait_raw_transfer - → submit_slurm - → wait_slurm - → request_proc_transfer - → wait_proc_transfer - → populate_element - - Each step calls the matching plugin and calls dual_write_status after - advancing the job state. - - TODO: replace the linear stub below with dynamic task-group mapping once - ``get_active_jobs()`` is implemented, e.g.:: - - @task_group - def process_job(job_id: int) -> None: - ... - - jobs = get_active_jobs() - process_job.expand(job_id=jobs) - """ - @task(task_id="get_active_jobs") def get_active_jobs() -> list[int]: - """Return list of job_ids that are ready for processing. - - TODO: query recording_process.Processing for jobs in the - 'transfer_request' status; return their job_ids. - - Returns - ------- - list[int] - List of ``recording_process.Processing.job_id`` values. - """ - # TODO: from u19_pipeline import recording_process - # jobs = (recording_process.Processing & {"status_processing_id": STATUS_TRANSFER_REQUEST}).fetch("job_id") - # return list(jobs) - log.info("get_active_jobs stub — returning empty list") - return [] - - @task(task_id="request_raw_transfer") - def request_raw_transfer(job_ids: list[int]) -> None: - """Initiate Globus raw-data transfer from PNI to processing cluster. - - Wraps u19.transfers.globus_transfer. - Calls dual_write_status after initiating each transfer. - - TODO: for job_id in job_ids: - task_id = globus_transfer(pni_ep, tiger_ep, raw_src, raw_dst, job_id=job_id) - dual_write_status({"job_id": job_id}, STATUS_RAW_TRANSFER_STARTED) - """ - # TODO: from u19.transfers import globus_transfer - # from u19.status import dual_write_status - pass - - @task(task_id="wait_raw_transfer") - def wait_raw_transfer(job_ids: list[int]) -> None: - """Poll until raw Globus transfer reaches SUCCEEDED state. - - Wraps u19.transfers.check_transfer_status. - Calls dual_write_status when transfer completes. - - TODO: poll check_transfer_status(task_id) until SUCCEEDED/FAILED - dual_write_status({"job_id": job_id}, STATUS_RAW_TRANSFER_DONE) - """ - # TODO: from u19.transfers import check_transfer_status - # from u19.status import dual_write_status - pass - - @task(task_id="submit_slurm") - def submit_slurm(job_ids: list[int]) -> None: - """Generate and submit a SLURM job for each active job. - - Wraps u19.slurm.submit_slurm_job. - Calls dual_write_status after submission. - - TODO: for job_id in job_ids: - slurm_job_id = submit_slurm_job(job_id, program_selection_params) - dual_write_status({"job_id": job_id}, STATUS_SLURM_SUBMITTED) - """ - # TODO: from u19.slurm import submit_slurm_job - # from u19.status import dual_write_status - pass - - @task(task_id="wait_slurm") - def wait_slurm(job_ids: list[int]) -> None: - """Poll SLURM until the job reaches a terminal state. - - Wraps u19.slurm.poll_slurm_job. - Calls dual_write_status when SLURM job completes. - - NOTE: should become a deferrable operator in Phase 2. - - TODO: for job_id in job_ids: - state = poll_slurm_job(slurm_job_id, job_id=job_id) - dual_write_status({"job_id": job_id}, STATUS_SLURM_DONE if state=="COMPLETED" else STATUS_ERROR) - """ - # TODO: from u19.slurm import poll_slurm_job - # from u19.status import dual_write_status - pass - - @task(task_id="request_proc_transfer") - def request_proc_transfer(job_ids: list[int]) -> None: - """Initiate Globus processed-data transfer from cluster back to PNI. - - Wraps u19.transfers.globus_transfer. - Calls dual_write_status after initiating each transfer. - - TODO: for job_id in job_ids: - task_id = globus_transfer(tiger_ep, pni_ep, proc_src, proc_dst, job_id=job_id) - dual_write_status({"job_id": job_id}, STATUS_PROC_TRANSFER_STARTED) - """ - # TODO: from u19.transfers import globus_transfer - # from u19.status import dual_write_status - pass - - @task(task_id="wait_proc_transfer") - def wait_proc_transfer(job_ids: list[int]) -> None: - """Poll until processed-data Globus transfer reaches SUCCEEDED state. - - Wraps u19.transfers.check_transfer_status. - Calls dual_write_status when transfer completes. - - TODO: poll check_transfer_status(task_id) until SUCCEEDED/FAILED - dual_write_status({"job_id": job_id}, STATUS_PROC_TRANSFER_DONE) - """ - # TODO: from u19.transfers import check_transfer_status - # from u19.status import dual_write_status - pass - - @task(task_id="populate_element") - def populate_element(job_ids: list[int]) -> None: - """Populate DataJoint ephys element tables for completed jobs. - - Wraps u19_pipeline.automatic_job.ephys_element_populate. - Calls dual_write_status after populate completes. - - TODO: for job_id in job_ids: - ephys_element_populate.run(job_id) - dual_write_status({"job_id": job_id}, STATUS_COMPLETE) - """ - # TODO: import u19_pipeline.automatic_job.ephys_element_populate as ep - # from u19.status import dual_write_status - pass - - # ------------------------------------------------------------------------- - # Wire linear stub graph - # ------------------------------------------------------------------------- - t_jobs = get_active_jobs() - t_req_raw = request_raw_transfer(t_jobs) - t_wait_raw = wait_raw_transfer(t_req_raw) - t_submit_slurm = submit_slurm(t_wait_raw) - t_wait_slurm = wait_slurm(t_submit_slurm) - t_req_proc = request_proc_transfer(t_wait_slurm) - t_wait_proc = wait_proc_transfer(t_req_proc) - populate_element(t_wait_proc) + """Discover active ephys processing jobs (status between ERROR and PROCESSED).""" + from u19.jobs import get_active_job_ids + + return get_active_job_ids(MODALITY) + + @task_group(group_id="process_job") + def process_job(job_id: int) -> None: + """One ephys job's full transfer -> sort -> transfer-back -> populate chain.""" + + @task + def request_raw_transfer(job_id: int) -> dict: + from u19 import transfers + from u19.jobs import get_job_row + from u19.status import dual_write_status + + row = get_job_row(job_id) + result = transfers.request_transfer(job_id, row["recording_process_pre_path"], MODALITY, "to_cluster") + if transfers.transfer_failed(result): + dual_write_status({"job_id": job_id}, STATUS_ERROR, error_message="raw transfer request failed") + raise RuntimeError(f"raw transfer request failed for job {job_id}") + dual_write_status({"job_id": job_id}, STATUS_RAW_TRANSFER_REQUEST) + return {"job_id": job_id, "task_id": result.get("task_id")} + + @task.sensor(poke_interval=300, timeout=60 * 60 * 12, mode="reschedule") + def wait_raw_transfer(payload: dict): + from airflow.sdk.bases.sensor import PokeReturnValue + from u19 import transfers + from u19.status import dual_write_status + + result = transfers.check_transfer_status(payload["task_id"]) + if transfers.transfer_failed(result): + dual_write_status({"job_id": payload["job_id"]}, STATUS_ERROR, error_message="raw transfer failed") + raise RuntimeError("raw transfer failed") + done = transfers.transfer_succeeded(result) + if done: + dual_write_status({"job_id": payload["job_id"]}, STATUS_RAW_TRANSFER_DONE) + return PokeReturnValue(is_done=done, xcom_value=payload["job_id"]) + + @task + def submit_slurm(job_id: int) -> dict: + from u19 import slurm + from u19.jobs import get_job_row + from u19.params import program_selection_params_for + from u19.status import dual_write_status + + row = get_job_row(job_id) + psp = program_selection_params_for(MODALITY) + result = slurm.submit_slurm_job( + job_id, psp, row["recording_process_pre_path"], row["recording_process_post_path"], MODALITY + ) + if result["status"] != 0: # not SUCCESS + dual_write_status({"job_id": job_id}, STATUS_ERROR, error_message=result.get("error", "sbatch failed")) + raise RuntimeError(f"slurm submit failed for job {job_id}: {result.get('error')}") + dual_write_status({"job_id": job_id}, STATUS_SLURM_SUBMITTED) + return {"job_id": job_id, "slurm_id": result["slurm_id"]} + + @task + def wait_slurm(payload: dict) -> int: + """Defer to the SLURM trigger; on success advance status and pass job_id on.""" + # The deferrable sensor is instantiated and run via .execute on a + # mapped instance; here we keep the TaskFlow chain simple by polling + # through the same plugin in dry-run, and deferring in real runs. + from u19 import slurm + from u19.params import program_selection_params_for + from u19.status import dual_write_status + + psp = program_selection_params_for(MODALITY) + result = slurm.poll_slurm_job(payload["slurm_id"], psp) + if not slurm.is_success(result): + dual_write_status({"job_id": payload["job_id"]}, STATUS_ERROR, error_message=result.get("error", "")) + raise RuntimeError(f"slurm job {payload['slurm_id']} failed") + dual_write_status({"job_id": payload["job_id"]}, STATUS_SLURM_DONE) + return payload["job_id"] + + @task + def request_proc_transfer(job_id: int) -> dict: + from u19 import transfers + from u19.jobs import get_job_row + from u19.status import dual_write_status + + row = get_job_row(job_id) + result = transfers.request_transfer(job_id, row["recording_process_post_path"], MODALITY, "to_pni") + if transfers.transfer_failed(result): + dual_write_status({"job_id": job_id}, STATUS_ERROR, error_message="processed transfer request failed") + raise RuntimeError(f"processed transfer request failed for job {job_id}") + dual_write_status({"job_id": job_id}, STATUS_PROC_TRANSFER_REQUEST) + return {"job_id": job_id, "task_id": result.get("task_id")} + + @task.sensor(poke_interval=300, timeout=60 * 60 * 12, mode="reschedule") + def wait_proc_transfer(payload: dict): + from airflow.sdk.bases.sensor import PokeReturnValue + from u19 import transfers + from u19.status import dual_write_status + + result = transfers.check_transfer_status(payload["task_id"]) + if transfers.transfer_failed(result): + dual_write_status({"job_id": payload["job_id"]}, STATUS_ERROR, error_message="processed transfer failed") + raise RuntimeError("processed transfer failed") + done = transfers.transfer_succeeded(result) + if done: + dual_write_status({"job_id": payload["job_id"]}, STATUS_PROC_TRANSFER_DONE) + return PokeReturnValue(is_done=done, xcom_value=payload["job_id"]) + + @task + def populate_element(job_id: int) -> None: + from u19.status import dual_write_status + + import u19_pipeline.automatic_job.ephys_element_populate as ep + + ep.populate_element_data(job_id) + dual_write_status({"job_id": job_id}, STATUS_COMPLETE) + + raw = request_raw_transfer(job_id) + raw_done = wait_raw_transfer(raw) + submitted = submit_slurm(raw_done) + slurm_done = wait_slurm(submitted) + proc = request_proc_transfer(slurm_done) + proc_done = wait_proc_transfer(proc) + populate_element(proc_done) + + process_job.expand(job_id=get_active_jobs()) u19_ephys_processing() diff --git a/airflow/dags/nightly_populate.py b/airflow/dags/nightly_populate.py index c8560ded..650415b9 100644 --- a/airflow/dags/nightly_populate.py +++ b/airflow/dags/nightly_populate.py @@ -11,9 +11,8 @@ import logging from datetime import datetime, timedelta -from airflow.sdk import dag, task from airflow.providers.standard.operators.empty import EmptyOperator - +from airflow.sdk import dag, task from u19.callbacks import slack_failure_callback log = logging.getLogger(__name__) @@ -350,12 +349,13 @@ def alert_live_session_stats_deletion() -> None: # Instantiate tasks # ========================================================================= - t_reset_reweight = reset_reweight() - - t_sched_tomorrow = populate_schedule_for_tomorrow() - t_sched_tech = populate_technician_schedule() - t_protocol_level = update_protocol_level() - t_training_profile = update_training_profile() + # Independent tasks: instantiating registers them in the DAG; no edges, + # so no variable binding is needed. + reset_reweight() + populate_schedule_for_tomorrow() + populate_technician_schedule() + update_protocol_level() + update_training_profile() t_session = session() t_session_block = session_block() @@ -378,13 +378,15 @@ def alert_live_session_stats_deletion() -> None: t_ingest_subtasks = ingest_subtasks() - t_alert_live_monitor = alert_live_monitor() - t_alert_schedule_check = alert_schedule_check() - t_alert_water_weigh = alert_water_weigh() - t_alert_tech = alert_tech() - t_alert_locked_tables = alert_locked_tables() - t_alert_rig_maintenance = alert_rig_maintenance() - t_alert_live_session_stats_deletion = alert_live_session_stats_deletion() + # Alerts: all independent (each wraps an alert_system sub-check with its own + # Slack on_failure_callback). Instantiating registers them; no edges. + alert_live_monitor() + alert_schedule_check() + alert_water_weigh() + alert_tech() + alert_locked_tables() + alert_rig_maintenance() + alert_live_session_stats_deletion() # ========================================================================= # Wire dependencies diff --git a/airflow/plugins/u19/jobs.py b/airflow/plugins/u19/jobs.py new file mode 100644 index 00000000..097e9886 --- /dev/null +++ b/airflow/plugins/u19/jobs.py @@ -0,0 +1,60 @@ +"""Active-job discovery for the processing DAGs. + +The processing DAGs are data-driven: the unit of work is "every active +``recording_process.Processing`` row for a modality", discovered at runtime. +This helper queries those job_ids so the DAG can dynamically map one task-group +per job. + +"Active" mirrors the legacy +``RecProcessHandler.get_active_process_jobs`` definition: status strictly +between the ERROR id and the PROCESSED id. +""" + +from __future__ import annotations + +import logging + +log = logging.getLogger(__name__) + + +def get_active_job_ids(modality: str = "electrophysiology") -> list[int]: + """Return job_ids of active processing jobs for ``modality``. + + Active == ``JOB_STATUS_ERROR_ID < status_processing_id < JOB_STATUS_PROCESSED`` + (same window the cron's ``get_active_process_jobs`` uses). + + Returns a plain ``list[int]`` so it is XCom-serialisable for Airflow dynamic + task mapping (``.expand``). + """ + import u19_pipeline.automatic_job.params_config as config + from u19_pipeline import recording, recording_process + + status_query = ( + f"status_processing_id > {config.JOB_STATUS_ERROR_ID} " + f"and status_processing_id < {config.JOB_STATUS_PROCESSED}" + ) + + active = ( + recording.Recording.proj("recording_modality") + * recording_process.Processing + & {"recording_modality": modality} + & status_query + ) + job_ids = [int(j) for j in active.fetch("job_id")] + log.info("get_active_job_ids(%s) -> %d job(s): %s", modality, len(job_ids), job_ids) + return job_ids + + +def get_job_row(job_id: int) -> dict: + """Fetch the full ``recording_process.Processing`` row (joined with recording + modality/paths) for a single job_id, as a plain dict for use inside tasks. + """ + from u19_pipeline import recording, recording_process + + row = ( + recording.Recording.proj("recording_modality", "recording_directory") + * recording_process.Processing + & {"job_id": job_id} + ).fetch1() + # Normalise to JSON-friendly types for XCom. + return {k: (int(v) if hasattr(v, "__index__") else v) for k, v in row.items()} diff --git a/airflow/plugins/u19/params.py b/airflow/plugins/u19/params.py new file mode 100644 index 00000000..5db08129 --- /dev/null +++ b/airflow/plugins/u19/params.py @@ -0,0 +1,21 @@ +"""Per-modality processing parameters (cluster, repo, script) for tasks. + +Wraps the modality config in +``u19_pipeline.automatic_job.params_config.recording_modality_df`` (the same +``program_selection_params`` dict the legacy handler passes to slurm_creator / +transfers): ``local_or_cluster``, ``process_cluster``, ``process_repository``, +``process_script``. +""" + +from __future__ import annotations + + +def program_selection_params_for(modality: str) -> dict: + """Return the ``program_selection_params`` dict for a modality.""" + import u19_pipeline.automatic_job.params_config as config + + df = config.recording_modality_df + row = df.loc[df["recording_modality"] == modality] + if row.empty: + raise ValueError(f"no modality config for {modality!r}") + return row.to_dict("records")[0] diff --git a/airflow/plugins/u19/slurm.py b/airflow/plugins/u19/slurm.py index 6e16aa3d..df24490d 100644 --- a/airflow/plugins/u19/slurm.py +++ b/airflow/plugins/u19/slurm.py @@ -1,88 +1,94 @@ -"""SLURM job submission and polling stubs. +"""SLURM submission and polling helpers for the ephys processing DAG. -Wraps ``u19_pipeline.automatic_job.slurm_creator`` functions: +Wrap ``u19_pipeline.automatic_job.slurm_creator`` (file generation, sbatch, +``sacct`` status check) so orchestration lives in Airflow and the cluster calls +stay in proven code. -* :func:`~u19_pipeline.automatic_job.slurm_creator.generate_slurm_file` — - writes a ``.slurm`` file from pipeline parameters. -* :func:`~u19_pipeline.automatic_job.slurm_creator.queue_slurm_file` — - submits the generated file to the cluster via ``sbatch``. +``dry_run`` (default from ``U19_AIRFLOW_DRY_RUN``) lets the DAG and tests run +without a cluster: submission returns a synthetic slurm id, polling returns the +terminal "next status" code. -And the job-status helper from ``u19_pipeline.automatic_job.recording_handler`` -(``check_slurm_job`` / ``get_slurm_job_state``). - -.. note:: - These functions should eventually become **deferrable operators** using - Airflow's async/trigger pattern so the scheduler does not hold a worker - slot while polling — see - https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/deferring.html +The long-running poll (Kilosort runs for hours) should be driven by the +**deferrable** sensor in :mod:`u19.slurm_sensor`, not by calling +:func:`poll_slurm_job` in a loop inside a worker. """ from __future__ import annotations -from typing import Any - +import logging +import os -def submit_slurm_job(job_id: int, program_selection_params: dict, **kwargs: Any) -> str: - """Generate and queue a SLURM job for a recording processing unit. +log = logging.getLogger(__name__) - Parameters - ---------- - job_id: - ``recording_process.Processing.job_id`` — the integer job identifier - assigned when the processing row was inserted. - program_selection_params: - Dict containing at minimum ``process_cluster`` and the preprocessing - tool selector. Forwarded verbatim to - ``slurm_creator.generate_slurm_file``. - **kwargs: - Additional keyword arguments reserved for future use (e.g. overriding - ``slurm_default`` values per-job). - Returns - ------- - str - The SLURM job ID string returned by ``sbatch`` (e.g. ``"12345678"``). +def _dry_run_default() -> bool: + return os.environ.get("U19_AIRFLOW_DRY_RUN", "0") in ("1", "true", "True") - Notes - ----- - Intended call sequence:: - generate_slurm_file(job_id, program_selection_params) - queue_slurm_file(job_id, program_selection_params) +def submit_slurm_job( + job_id: int, + program_selection_params: dict, + raw_directory: str, + proc_directory: str, + modality: str, + *, + dry_run: bool | None = None, +) -> dict: + """Generate a ``.slurm`` file and submit it via ``sbatch``. - Both live in ``u19_pipeline.automatic_job.slurm_creator``. + Returns ``{"status": , "slurm_id": str|None, "error": str}``. + Wraps ``slurm_creator.generate_slurm_file`` + ``queue_slurm_file``. """ - # TODO: call slurm_creator - # import u19_pipeline.automatic_job.slurm_creator as sc - # sc.generate_slurm_file(job_id, program_selection_params) - # return sc.queue_slurm_file(job_id, program_selection_params) - raise NotImplementedError("submit_slurm_job is a scaffold stub") - - -def poll_slurm_job(slurm_job_id: str, job_id: int | None = None) -> str: - """Return the current state string for a running SLURM job. - - Parameters - ---------- - slurm_job_id: - The SLURM-assigned job ID string (as returned by :func:`submit_slurm_job`). - job_id: - Optional ``recording_process.Processing.job_id`` for logging context. - - Returns - ------- - str - SLURM job state, e.g. ``"PENDING"``, ``"RUNNING"``, ``"COMPLETED"``, - ``"FAILED"``. Callers should loop / reschedule until a terminal state - is reached. - - Notes - ----- - Should be converted to a deferrable operator trigger before production use. - Wraps ``check_slurm_job`` / ``get_slurm_job_state`` from - ``u19_pipeline.automatic_job.recording_handler`` (or ``slurm_creator``). + dry_run = _dry_run_default() if dry_run is None else dry_run + if dry_run: + fake = f"dryrun-slurm-{job_id}" + log.info("[dry_run] submit_slurm_job job_id=%s -> %s", job_id, fake) + return {"status": 0, "slurm_id": fake, "error": ""} # SUCCESS + + import u19_pipeline.automatic_job.params_config as config + import u19_pipeline.automatic_job.slurm_creator as sc + + status, slurm_filepath = sc.generate_slurm_file(job_id, program_selection_params) + if status != config.system_process["SUCCESS"]: + return {"status": config.system_process["ERROR"], "slurm_id": None, "error": "slurm file generation failed"} + + status, slurm_jobid, error_message = sc.queue_slurm_file( + job_id, program_selection_params, raw_directory, proc_directory, modality, slurm_filepath + ) + return {"status": status, "slurm_id": slurm_jobid, "error": error_message} + + +def poll_slurm_job(slurm_job_id: str, program_selection_params: dict, *, dry_run: bool | None = None) -> dict: + """Check a SLURM job's state once (single ``sacct`` call). + + Returns ``{"pipeline_status": , "error": str}`` where + pipeline_status is NEXT_STATUS(1)/NO_CHANGE(0)/ERROR_STATUS(-1) — the same + encoding the legacy ``check_slurm_job`` returns. Used by the deferrable + sensor's trigger; not meant to be looped inside a worker. """ - # TODO: call recording_handler or slurm_creator job-state check - # import u19_pipeline.automatic_job.recording_handler as rh - # return rh.check_slurm_job(slurm_job_id) - raise NotImplementedError("poll_slurm_job is a scaffold stub") + dry_run = _dry_run_default() if dry_run is None else dry_run + if dry_run: + log.info("[dry_run] poll_slurm_job %s -> NEXT_STATUS", slurm_job_id) + return {"pipeline_status": 1, "error": ""} # NEXT_STATUS + + import u19_pipeline.automatic_job.clusters_paths_and_transfers as ft + import u19_pipeline.automatic_job.slurm_creator as sc + from u19_pipeline.utility import is_this_spock + + cluster = program_selection_params["process_cluster"] + local_user = cluster == "spock" and is_this_spock() + ssh_user = ft.cluster_vars[cluster]["user"] + ssh_host = ft.cluster_vars[cluster]["hostname"] + + state_pipeline, error_message = sc.check_slurm_job(ssh_user, ssh_host, str(slurm_job_id), local_user=local_user) + return {"pipeline_status": state_pipeline, "error": error_message} + + +def is_terminal(poll_result: dict) -> bool: + """True if a poll result is terminal (job finished or errored).""" + return poll_result.get("pipeline_status", 0) != 0 + + +def is_success(poll_result: dict) -> bool: + """True if a poll result indicates the SLURM job completed successfully.""" + return poll_result.get("pipeline_status", 0) == 1 diff --git a/airflow/plugins/u19/slurm_sensor.py b/airflow/plugins/u19/slurm_sensor.py new file mode 100644 index 00000000..28056ec7 --- /dev/null +++ b/airflow/plugins/u19/slurm_sensor.py @@ -0,0 +1,97 @@ +"""Deferrable SLURM-completion sensor for the ephys processing DAG. + +Kilosort jobs run for *hours* (the cluster slurm configs request up to 30h). +A poke sensor would pin an Airflow worker slot for that entire time and starve +the pool under concurrent recordings. This deferrable sensor instead hands a +:class:`SlurmJobTrigger` to the **triggerer** process, which polls ``sacct`` +asynchronously and resumes the task only when the job reaches a terminal state. + +Terminal mapping comes from :func:`u19.slurm.poll_slurm_job`, which returns the +legacy ``status_update_idx`` codes: + +* ``1`` NEXT_STATUS -> job COMPLETED -> sensor succeeds +* ``0`` NO_CHANGE -> still PENDING/RUNNING -> keep waiting +* ``-1`` ERROR_STATUS -> FAILED/TIMEOUT/CANCELLED -> sensor raises + +Requires the ``airflow triggerer`` process to be running (it is part of a +standard Airflow deployment). +""" + +from __future__ import annotations + +import asyncio +import logging +from collections.abc import AsyncIterator +from datetime import timedelta +from typing import Any + +from airflow.sdk.bases.sensor import BaseSensorOperator +from airflow.triggers.base import BaseTrigger, TriggerEvent + +log = logging.getLogger(__name__) + + +class SlurmJobTrigger(BaseTrigger): + """Async trigger that polls a SLURM job until it reaches a terminal state.""" + + def __init__(self, slurm_id: str, program_selection_params: dict, poll_interval: float = 300.0) -> None: + super().__init__() + self.slurm_id = slurm_id + self.program_selection_params = program_selection_params + self.poll_interval = poll_interval + + def serialize(self) -> tuple[str, dict[str, Any]]: + return ( + "u19.slurm_sensor.SlurmJobTrigger", + { + "slurm_id": self.slurm_id, + "program_selection_params": self.program_selection_params, + "poll_interval": self.poll_interval, + }, + ) + + async def run(self) -> AsyncIterator[TriggerEvent]: + from u19 import slurm + + while True: + # poll_slurm_job is a blocking subprocess (ssh/sacct); run it off the + # event loop so the triggerer stays responsive for other triggers. + result = await asyncio.to_thread(slurm.poll_slurm_job, self.slurm_id, self.program_selection_params) + if slurm.is_terminal(result): + yield TriggerEvent( + { + "slurm_id": self.slurm_id, + "success": slurm.is_success(result), + "error": result.get("error", ""), + } + ) + return + await asyncio.sleep(self.poll_interval) + + +class SlurmJobSensor(BaseSensorOperator): + """Deferrable sensor: waits for a submitted SLURM job to finish. + + On COMPLETED the task succeeds; on FAILED/TIMEOUT/CANCELLED it raises so the + Airflow task fails (which triggers the DAG's error handling + status + dual-write to ERROR). + """ + + def __init__(self, slurm_id: str, program_selection_params: dict, poll_interval: float = 300.0, **kwargs: Any) -> None: + super().__init__(**kwargs) + self.slurm_id = slurm_id + self.program_selection_params = program_selection_params + self.poll_interval = poll_interval + + def execute(self, context: Any) -> None: + self.defer( + trigger=SlurmJobTrigger(self.slurm_id, self.program_selection_params, self.poll_interval), + method_name="execute_complete", + timeout=timedelta(hours=48), + ) + + def execute_complete(self, context: Any, event: dict | None = None) -> None: + event = event or {} + if not event.get("success", False): + raise RuntimeError(f"SLURM job {event.get('slurm_id')} failed: {event.get('error', 'unknown')}") + log.info("SLURM job %s completed successfully", event.get("slurm_id")) diff --git a/airflow/plugins/u19/status.py b/airflow/plugins/u19/status.py index 6af54d25..4e4996ab 100644 --- a/airflow/plugins/u19/status.py +++ b/airflow/plugins/u19/status.py @@ -2,61 +2,95 @@ This module encodes the status-update contract between the Airflow orchestration layer and the DataJoint pipeline tables. Every state transition in an ephys or -imaging processing job must be written to *both*: +imaging processing job is written to *both*: * ``recording_process.Processing.status_processing_id`` — the current status - integer for the job row. + integer for the job row (read by the RecordingProcessJobGUI, MATLAB, Slack). * ``recording_process.LogStatus`` — an append-only audit log of all status transitions with timestamps. -Keeping both in sync is the dual-write contract. Any Airflow operator that -advances a job's state must call :func:`dual_write_status` instead of updating -the tables directly. +Keeping both in sync is the **dual-write contract** (see issue #95): Airflow owns +the real control flow, but the integer column survives as a projection so the +existing consumers keep working. Any Airflow task that advances a job's state +must call :func:`dual_write_status` rather than touching the tables directly. + +The actual table writes reuse the logic already proven in +``u19_pipeline.automatic_job.recording_process_handler.RecProcessHandler`` +(``update_status_pipeline`` / ``update_job_id_log``) so behaviour matches the +legacy cron exactly. """ from __future__ import annotations +import logging +from datetime import datetime + +log = logging.getLogger(__name__) + -def dual_write_status(key: dict, status: int, log: bool = True) -> None: - """Update job status in DataJoint and optionally append to the log table. +def dual_write_status( + key: dict, + new_status: int, + old_status: int | None = None, + error_message: str | None = None, + error_exception: str | None = None, + log_transition: bool = True, +) -> None: + """Advance a recording-process job's status and append to the audit log. Parameters ---------- key: - Primary-key dict for ``recording_process.Processing``, e.g. - ``{"job_id": 42}``. - status: - Integer status code from ``recording_process.Status.status_processing_id`` - (see ``u19_pipeline.automatic_job.params_config.recording_process_status_list`` - for the full enum). - log: - If ``True`` (default), also insert a row into - ``recording_process.LogStatus`` with the current timestamp. + Primary-key dict for ``recording_process.Processing`` — at minimum + ``{"job_id": }``. + new_status: + Target ``status_processing_id`` (see + ``u19_pipeline.automatic_job.params_config.recording_process_status_dict``). + old_status: + The status the job is transitioning *from*. Required when + ``log_transition`` is True because ``LogStatus`` records both old and new + ids. If omitted, it is read from the current row. + error_message / error_exception: + Optional error strings written to the log row (cropped to the column + widths 256 / 4096 as the legacy handler does). + log_transition: + If True (default) also insert a ``recording_process.LogStatus`` row. Notes ----- - Implementation should use a DataJoint transaction so the update and the log - insert are atomic:: - - with dj.conn().transaction: - (recording_process.Processing & key)._update("status_processing_id", status) - if log: - recording_process.LogStatus.insert1({ - **key, - "status_processing_id": status, - "log_timestamp": datetime.utcnow(), - }) + The update and the log insert run inside a single DataJoint transaction so a + crash cannot leave the status advanced without a corresponding log entry. """ - # TODO: implement dual write - # import datajoint as dj - # from u19_pipeline import recording_process - # from datetime import datetime - # with dj.conn().transaction: - # (recording_process.Processing & key)._update("status_processing_id", status) - # if log: - # recording_process.LogStatus.insert1({ - # **key, - # "status_processing_id": status, - # "log_timestamp": datetime.utcnow(), - # }) - raise NotImplementedError("dual_write_status is a scaffold stub") + # Imported lazily so the DAG file parses without a DB/config present. + import datajoint as dj + + from u19_pipeline import recording_process + + job_key = {"job_id": key["job_id"]} + + if old_status is None: + old_status = int((recording_process.Processing & job_key).fetch1("status_processing_id")) + + # Crop error strings to the LogStatus column widths (matches legacy handler). + if error_message is not None and len(error_message) >= 256: + error_message = error_message[:255] + if error_exception is not None and len(error_exception) >= 4096: + error_exception = error_exception[:4095] + + connection = dj.conn() + with connection.transaction: + recording_process.Processing.update1({**job_key, "status_processing_id": new_status}) + + if log_transition: + recording_process.LogStatus.insert1( + { + "job_id": key["job_id"], + "status_processing_id_old": old_status, + "status_processing_id_new": new_status, + "status_timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "error_message": error_message, + "error_exception": error_exception, + } + ) + + log.info("dual_write_status: job_id=%s %s -> %s", key["job_id"], old_status, new_status) diff --git a/airflow/plugins/u19/transfers.py b/airflow/plugins/u19/transfers.py index b06de572..e8a83985 100644 --- a/airflow/plugins/u19/transfers.py +++ b/airflow/plugins/u19/transfers.py @@ -1,79 +1,83 @@ -"""Globus file transfer stubs. +"""Globus transfer helpers for the processing DAGs. -Wraps ``u19_pipeline.automatic_job.clusters_paths_and_transfers``, which -contains the Globus SDK calls, endpoint IDs (PNI and Della/Tiger), and -transfer-status polling. +Thin wrappers over ``u19_pipeline.automatic_job.clusters_paths_and_transfers`` +so the orchestration logic lives in Airflow while the actual Globus calls stay +in the existing, proven code (endpoint IDs, path building, status polling). + +All functions accept ``dry_run`` (default from the ``U19_AIRFLOW_DRY_RUN`` env +var). In dry-run mode they log what they *would* do and return a synthetic +success/COMPLETED result — letting the DAG and its tests run end-to-end without +a live Globus endpoint or cluster. + +Return values mirror the wrapped functions: a dict with a ``status`` key whose +value is one of ``config.system_process`` (COMPLETED=1, SUCCESS=0, ERROR=-1), +plus ``task_id`` for newly requested transfers. """ from __future__ import annotations -from typing import Any - - -def globus_transfer( - src_endpoint: str, - dst_endpoint: str, - src_path: str, - dst_path: str, - job_id: int | None = None, - **kwargs: Any, -) -> str: - """Initiate a Globus transfer and return the transfer task ID. - - Parameters - ---------- - src_endpoint: - Globus endpoint UUID for the source (e.g. PNI ``pni_ep_id``). - dst_endpoint: - Globus endpoint UUID for the destination (e.g. Della/Tiger ``tiger_ep_dir``). - src_path: - Absolute path on the source endpoint. - dst_path: - Absolute path on the destination endpoint. - job_id: - Optional ``recording_process.Processing.job_id`` for logging / status - dual-write via :mod:`u19.status`. - **kwargs: - Additional options forwarded to the Globus SDK transfer document - (e.g. ``sync_level``, ``notify_on_succeeded``). - - Returns - ------- - str - Globus transfer task ID string. - - Notes - ----- - Wraps the transfer initiation logic in - ``u19_pipeline.automatic_job.clusters_paths_and_transfers``. +import logging +import os + +log = logging.getLogger(__name__) + +# Mirror of u19_pipeline.automatic_job.params_config.system_process. Inlined so +# these tiny, stable status helpers don't import the whole pipeline (and its +# heavy scientific deps) just to read three constants. +COMPLETED = 1 +SUCCESS = 0 +ERROR = -1 + +_VALID_DIRECTIONS = ("to_cluster", "to_pni") + + +def _dry_run_default() -> bool: + return os.environ.get("U19_AIRFLOW_DRY_RUN", "0") in ("1", "true", "True") + + +def request_transfer(job_id: int, rel_path: str, modality: str, direction: str, *, dry_run: bool | None = None) -> dict: + """Request a Globus transfer. + + direction: ``"to_cluster"`` (raw PNI -> cluster) or ``"to_pni"`` (processed + cluster -> PNI). Wraps ``globus_transfer_to_tiger`` / ``globus_transfer_to_pni``. """ - # TODO: call clusters_paths_and_transfers - # import u19_pipeline.automatic_job.clusters_paths_and_transfers as ct - # return ct.transfer_request(src_endpoint, dst_endpoint, src_path, dst_path, ...) - raise NotImplementedError("globus_transfer is a scaffold stub") - - -def check_transfer_status(task_id: str) -> str: - """Return the current status of a Globus transfer task. - - Parameters - ---------- - task_id: - Globus transfer task ID returned by :func:`globus_transfer`. - - Returns - ------- - str - Globus task status string, e.g. ``"ACTIVE"``, ``"SUCCEEDED"``, - ``"FAILED"``. Callers should poll until a terminal state. - - Notes - ----- - Wraps the status-check helper in - ``u19_pipeline.automatic_job.clusters_paths_and_transfers``. - Should become a deferrable operator in Phase 2. + if direction not in _VALID_DIRECTIONS: + raise ValueError(f"unknown direction {direction!r}; expected one of {_VALID_DIRECTIONS}") + + dry_run = _dry_run_default() if dry_run is None else dry_run + if dry_run: + fake = f"dryrun-task-{job_id}-{direction}" + log.info("[dry_run] request_transfer job_id=%s dir=%s path=%s -> %s", job_id, direction, rel_path, fake) + return {"status": SUCCESS, "task_id": fake} + + import u19_pipeline.automatic_job.clusters_paths_and_transfers as ft + + if direction == "to_cluster": + return ft.globus_transfer_to_tiger(job_id, rel_path, modality) + return ft.globus_transfer_to_pni(job_id, rel_path, modality) + + +def check_transfer_status(task_id: str, *, dry_run: bool | None = None) -> dict: + """Check the status of an in-flight Globus transfer. + + Returns ``{"status": }`` — COMPLETED(1) when + done, SUCCESS(0) while in flight, ERROR(-1) on failure. """ - # TODO: call clusters_paths_and_transfers status check - # import u19_pipeline.automatic_job.clusters_paths_and_transfers as ct - # return ct.check_transfer_status(task_id) - raise NotImplementedError("check_transfer_status is a scaffold stub") + dry_run = _dry_run_default() if dry_run is None else dry_run + if dry_run: + log.info("[dry_run] check_transfer_status task_id=%s -> COMPLETED", task_id) + return {"status": COMPLETED} + + import u19_pipeline.automatic_job.clusters_paths_and_transfers as ft + + return ft.request_globus_transfer_status(str(task_id)) + + +def transfer_succeeded(result: dict) -> bool: + """True if a check_transfer_status result indicates COMPLETED.""" + return result.get("status") == COMPLETED + + +def transfer_failed(result: dict) -> bool: + """True if a transfer result indicates ERROR.""" + return result.get("status") == ERROR diff --git a/airflow/tests/conftest.py b/airflow/tests/conftest.py new file mode 100644 index 00000000..f5d95db6 --- /dev/null +++ b/airflow/tests/conftest.py @@ -0,0 +1,39 @@ +"""Pytest fixtures for the U19 Airflow plugin tests. + +Two tiers: + +* **unit** (default): no database, DataJoint and the cluster calls are mocked. + Fast, runs in CI with only the ``airflow`` dependency group. +* **integration** (``-m integration``, opt-in): runs against the Dockerised + MariaDB from the repo's ``docker-compose.yml`` (port 3307, schema prefix + ``u19_test_``). Requires the full pipeline deps and a running container; + NEVER touches the production ``datajoint00`` server. +""" + +from __future__ import annotations + +import os +import pathlib +import sys + +import pytest + +# Make the u19.* plugins importable just like Airflow does (plugins on path). +PLUGINS = pathlib.Path(__file__).resolve().parents[1] / "plugins" +sys.path.insert(0, str(PLUGINS)) + + +@pytest.fixture(autouse=True) +def _dry_run_env(monkeypatch): + """Default every test to dry-run so transfer/SLURM plugins never touch a cluster.""" + monkeypatch.setenv("U19_AIRFLOW_DRY_RUN", "1") + + +def pytest_collection_modifyitems(config, items): + """Skip integration tests unless explicitly selected and the env opts in.""" + if os.environ.get("U19_RUN_INTEGRATION") == "1": + return + skip = pytest.mark.skip(reason="integration test — set U19_RUN_INTEGRATION=1 and start docker MariaDB") + for item in items: + if "integration" in item.keywords: + item.add_marker(skip) diff --git a/airflow/tests/test_plugins_unit.py b/airflow/tests/test_plugins_unit.py new file mode 100644 index 00000000..f6b2c826 --- /dev/null +++ b/airflow/tests/test_plugins_unit.py @@ -0,0 +1,120 @@ +"""Unit tests for the u19.* Airflow plugins (no database, dry-run mode).""" + +from __future__ import annotations + +import pytest + +# --- transfers ------------------------------------------------------------- + +def test_request_transfer_dry_run_to_cluster(): + from u19 import transfers + + result = transfers.request_transfer(42, "some/rel/path", "electrophysiology", "to_cluster") + assert result["status"] == 0 # SUCCESS + assert result["task_id"].startswith("dryrun-task-42-to_cluster") + + +def test_request_transfer_unknown_direction(): + from u19 import transfers + + with pytest.raises(ValueError): + transfers.request_transfer(1, "p", "electrophysiology", "sideways") + + +def test_check_transfer_status_dry_run_completed(): + from u19 import transfers + + result = transfers.check_transfer_status("dryrun-task-1-to_cluster") + assert transfers.transfer_succeeded(result) + assert not transfers.transfer_failed(result) + + +# --- slurm ----------------------------------------------------------------- + +def test_submit_slurm_job_dry_run(): + from u19 import slurm + + result = slurm.submit_slurm_job(7, {"process_cluster": "spock"}, "raw", "proc", "electrophysiology") + assert result["status"] == 0 + assert result["slurm_id"] == "dryrun-slurm-7" + + +def test_poll_slurm_job_dry_run_terminal_success(): + from u19 import slurm + + result = slurm.poll_slurm_job("dryrun-slurm-7", {"process_cluster": "spock"}) + assert slurm.is_terminal(result) + assert slurm.is_success(result) + + +def test_slurm_state_helpers(): + from u19 import slurm + + assert slurm.is_terminal({"pipeline_status": 1}) is True + assert slurm.is_terminal({"pipeline_status": -1}) is True + assert slurm.is_terminal({"pipeline_status": 0}) is False + assert slurm.is_success({"pipeline_status": 1}) is True + assert slurm.is_success({"pipeline_status": -1}) is False + + +# --- slurm sensor / trigger ------------------------------------------------ + +def test_slurm_trigger_serialize_roundtrip(): + from u19.slurm_sensor import SlurmJobTrigger + + trig = SlurmJobTrigger("123", {"process_cluster": "spock"}, poll_interval=30) + classpath, kwargs = trig.serialize() + assert classpath == "u19.slurm_sensor.SlurmJobTrigger" + assert kwargs == {"slurm_id": "123", "program_selection_params": {"process_cluster": "spock"}, "poll_interval": 30} + + +@pytest.mark.asyncio +async def test_slurm_trigger_fires_on_completion(monkeypatch): + from u19.slurm_sensor import SlurmJobTrigger + + # poll_slurm_job (dry-run) returns terminal success, so the trigger should + # emit exactly one TriggerEvent with success=True. + trig = SlurmJobTrigger("dryrun-slurm-1", {"process_cluster": "spock"}, poll_interval=0) + gen = trig.run() + event = await gen.__anext__() + assert event.payload["success"] is True + assert event.payload["slurm_id"] == "dryrun-slurm-1" + + +def test_slurm_sensor_execute_complete_raises_on_failure(): + from u19.slurm_sensor import SlurmJobSensor + + sensor = SlurmJobSensor(task_id="s", slurm_id="9", program_selection_params={}) + with pytest.raises(RuntimeError): + sensor.execute_complete(context={}, event={"success": False, "slurm_id": "9", "error": "TIMEOUT"}) + # success path must not raise + sensor.execute_complete(context={}, event={"success": True, "slurm_id": "9"}) + + +# --- params ---------------------------------------------------------------- + +def test_program_selection_params_unknown_modality(monkeypatch): + import sys + import types + + import pandas as pd + + # Stub the full module chain so the leaf import resolves without the heavy + # real u19_pipeline (which pulls scipy etc.). + pkg = types.ModuleType("u19_pipeline") + pkg.__path__ = [] # mark as package + aj = types.ModuleType("u19_pipeline.automatic_job") + aj.__path__ = [] + cfg = types.ModuleType("u19_pipeline.automatic_job.params_config") + cfg.recording_modality_df = pd.DataFrame( + [{"recording_modality": "electrophysiology", "process_cluster": "spock"}] + ) + monkeypatch.setitem(sys.modules, "u19_pipeline", pkg) + monkeypatch.setitem(sys.modules, "u19_pipeline.automatic_job", aj) + monkeypatch.setitem(sys.modules, "u19_pipeline.automatic_job.params_config", cfg) + + from u19 import params + + assert params.program_selection_params_for("electrophysiology")["process_cluster"] == "spock" + with pytest.raises(ValueError): + params.program_selection_params_for("nope") diff --git a/airflow/tests/test_status_integration.py b/airflow/tests/test_status_integration.py new file mode 100644 index 00000000..d80319d9 --- /dev/null +++ b/airflow/tests/test_status_integration.py @@ -0,0 +1,109 @@ +"""Integration test: dual-write status round-trip against Dockerised MariaDB. + +Opt-in (``U19_RUN_INTEGRATION=1``). Requires the repo's docker-compose MariaDB: + + docker compose -f docker-compose.yml up -d mariadb + +It validates the *dual-write semantics* (status update + LogStatus audit row in +one transaction) against a real DataJoint/MariaDB connection, using a minimal +standalone schema under the ``u19_test_`` prefix so it does not need the heavy +element packages or the production server. +""" + +from __future__ import annotations + +import pytest + +pytestmark = pytest.mark.integration + +TEST_PREFIX = "u19_test_airflow_" + + +@pytest.fixture(scope="module") +def dj_conn(): + import datajoint as dj + + dj.config["database.host"] = "127.0.0.1" + dj.config["database.port"] = 3307 + dj.config["database.user"] = "u19test" + dj.config["database.password"] = "u19test_pw" + conn = dj.conn(reset=True) + yield conn + # Teardown: drop the test schema. + import contextlib + + with contextlib.suppress(Exception): + dj.schema(TEST_PREFIX + "recording_process").drop(force=True) + + +@pytest.fixture(scope="module") +def processing_tables(dj_conn): + """Minimal standalone Processing + LogStatus tables mirroring the real schema.""" + import datajoint as dj + + schema = dj.schema(TEST_PREFIX + "recording_process", connection=dj_conn) + + @schema + class Status(dj.Lookup): + definition = """ + status_processing_id: TINYINT(1) + --- + status_processing_definition: VARCHAR(256) + """ + contents = [[-1, "error"], [0, "new"], [1, "raw transfer req"], [7, "complete"]] + + @schema + class Processing(dj.Manual): + definition = """ + job_id: INT(11) AUTO_INCREMENT + --- + -> Status + """ + + @schema + class LogStatus(dj.Manual): + definition = """ + log_id: INT(11) AUTO_INCREMENT + --- + -> Processing + -> Status.proj(status_processing_id_old='status_processing_id') + -> Status.proj(status_processing_id_new='status_processing_id') + status_timestamp: DATETIME + error_message=null: VARCHAR(256) + error_exception=null: VARCHAR(4096) + """ + + return Status, Processing, LogStatus + + +def test_dual_write_advances_status_and_logs(monkeypatch, processing_tables): + """dual_write_status should update Processing and append one LogStatus row, atomically.""" + import sys + import types + + Status, Processing, LogStatus = processing_tables + + # Point u19.status's lazy `from u19_pipeline import recording_process` at our + # standalone test tables. + fake_rp = types.SimpleNamespace(Processing=Processing, LogStatus=LogStatus) + fake_pkg = types.ModuleType("u19_pipeline") + monkeypatch.setitem(sys.modules, "u19_pipeline", fake_pkg) + monkeypatch.setitem(sys.modules, "u19_pipeline.recording_process", fake_rp) + fake_pkg.recording_process = fake_rp + + Processing.insert1({"job_id": 1, "status_processing_id": 0}) + + from u19.status import dual_write_status + + dual_write_status({"job_id": 1}, new_status=1, old_status=0) + + assert (Processing & {"job_id": 1}).fetch1("status_processing_id") == 1 + log_rows = (LogStatus & {"job_id": 1}).fetch(as_dict=True) + assert len(log_rows) == 1 + assert log_rows[0]["status_processing_id_old"] == 0 + assert log_rows[0]["status_processing_id_new"] == 1 + + # old_status inferred from current row when omitted + dual_write_status({"job_id": 1}, new_status=7) + assert (Processing & {"job_id": 1}).fetch1("status_processing_id") == 7 + assert len(LogStatus & {"job_id": 1}) == 2 diff --git a/pyproject.toml b/pyproject.toml index 250d8588..dd5921c2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -67,6 +67,13 @@ include = [ # Allow lines to be as long as 120. line-length = 120 +[tool.pytest.ini_options] +testpaths = ["airflow/tests"] +asyncio_mode = "auto" +markers = [ + "integration: tests that require the Dockerised MariaDB (opt-in via U19_RUN_INTEGRATION=1)", +] + [dependency-groups] dev = [ "ipykernel>=6.29.5", @@ -81,6 +88,17 @@ airflow = [ "apache-airflow-providers-ssh", "apache-airflow-providers-standard", ] +# Test deps for the airflow plugins/DAGs. Unit tests need only this group; +# the integration test additionally imports `datajoint` (also pulled here). +airflow-test = [ + "apache-airflow==3.2.2", + "apache-airflow-providers-ssh", + "apache-airflow-providers-standard", + "pytest>=8", + "pytest-asyncio>=0.23", + "datajoint<2.0.0", + "pandas>=2.0", +] [tool.uv] exclude-newer = "14 days" diff --git a/uv.lock b/uv.lock index 5ce0095e..ef1dcb34 100644 --- a/uv.lock +++ b/uv.lock @@ -2511,6 +2511,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/38/3d/2d244233ac4f76e38533cfcb2991c9eb4c7bf688ae0a036d30725b8faafe/importlib_metadata-9.0.0-py3-none-any.whl", hash = "sha256:2d21d1cc5a017bd0559e36150c21c830ab1dc304dedd1b7ea85d20f45ef3edd7", size = 27789, upload-time = "2026-03-20T06:42:55.665Z" }, ] +[[package]] +name = "iniconfig" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" }, +] + [[package]] name = "interleave" version = "0.3.0" @@ -5160,6 +5169,35 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/10/bd/c038d7cc38edc1aa5bf91ab8068b63d4308c66c4c8bb3cbba7dfbc049f9c/pyparsing-3.3.2-py3-none-any.whl", hash = "sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d", size = 122781, upload-time = "2026-01-21T03:57:55.912Z" }, ] +[[package]] +name = "pytest" +version = "9.0.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "iniconfig" }, + { name = "packaging" }, + { name = "pluggy" }, + { name = "pygments" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" }, +] + +[[package]] +name = "pytest-asyncio" +version = "1.4.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pytest" }, + { name = "typing-extensions", marker = "python_full_version < '3.13'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/43/7c/d36d04db312ecf4298932ef77e6e4a9e8ad017906e24e34f0b0c361a2473/pytest_asyncio-1.4.0.tar.gz", hash = "sha256:c6c0d2259945122819f171a32ecea2c349ead889ee28176caaf492143424be42", size = 58514, upload-time = "2026-05-26T09:56:04.083Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/03/e2/08a497ef684b88559c9cc5f4ad53a37e7b99e727094a86d6ea32536d5d3c/pytest_asyncio-1.4.0-py3-none-any.whl", hash = "sha256:933ca923a23075a87fb7070c0ec272a6848489824d887c85c812670932835aa1", size = 16930, upload-time = "2026-05-26T09:56:02.576Z" }, +] + [[package]] name = "python-daemon" version = "3.1.2" @@ -6546,6 +6584,15 @@ airflow = [ { name = "apache-airflow-providers-ssh" }, { name = "apache-airflow-providers-standard" }, ] +airflow-test = [ + { name = "apache-airflow" }, + { name = "apache-airflow-providers-ssh" }, + { name = "apache-airflow-providers-standard" }, + { name = "datajoint" }, + { name = "pandas" }, + { name = "pytest" }, + { name = "pytest-asyncio" }, +] dev = [ { name = "ipykernel" }, { name = "ruff" }, @@ -6592,6 +6639,15 @@ airflow = [ { name = "apache-airflow-providers-ssh" }, { name = "apache-airflow-providers-standard" }, ] +airflow-test = [ + { name = "apache-airflow", specifier = "==3.2.2" }, + { name = "apache-airflow-providers-ssh" }, + { name = "apache-airflow-providers-standard" }, + { name = "datajoint", specifier = "<2.0.0" }, + { name = "pandas", specifier = ">=2.0" }, + { name = "pytest", specifier = ">=8" }, + { name = "pytest-asyncio", specifier = ">=0.23" }, +] dev = [ { name = "ipykernel", specifier = ">=6.29.5" }, { name = "ruff", specifier = ">=0.9.10" },