From 085069317e67f2a26737dae4368a6736e68d6112 Mon Sep 17 00:00:00 2001 From: Evan Molinelli Date: Thu, 3 Apr 2025 14:21:48 -0400 Subject: [PATCH 1/3] initial docs for monitoring formatting monitoring with some plots clean up example notebook --- migrations/README.md | 43 + migrations/docs/diagrams/migration_sfn.md | 29 + migrations/notebooks/monitoring_example.ipynb | 57292 ++++++++++++++++ migrations/pyproject.toml | 10 + migrations/src/config.py | 53 + migrations/src/jobs/__init__.py | 0 migrations/src/jobs/details.py | 64 + migrations/src/jobs/jobs.py | 55 + migrations/src/logs/__init__.py | 0 migrations/src/logs/logs.py | 45 + migrations/src/logs/messages.py | 19 + migrations/src/reporting/__init__.py | 0 migrations/src/reporting/datasets.py | 211 + migrations/src/reporting/jobs.py | 39 + migrations/src/reporting/logs.py | 67 + migrations/src/reporting/validation.py | 23 + migrations/src/utils.py | 8 + migrations/uv.lock | 1308 + 18 files changed, 59266 insertions(+) create mode 100644 migrations/README.md create mode 100644 migrations/docs/diagrams/migration_sfn.md create mode 100644 migrations/notebooks/monitoring_example.ipynb create mode 100644 migrations/pyproject.toml create mode 100644 migrations/src/config.py create mode 100644 migrations/src/jobs/__init__.py create mode 100644 migrations/src/jobs/details.py create mode 100644 migrations/src/jobs/jobs.py create mode 100644 migrations/src/logs/__init__.py create mode 100644 migrations/src/logs/logs.py create mode 100644 migrations/src/logs/messages.py create mode 100644 migrations/src/reporting/__init__.py create mode 100644 migrations/src/reporting/datasets.py create mode 100644 migrations/src/reporting/jobs.py create mode 100644 migrations/src/reporting/logs.py create mode 100644 migrations/src/reporting/validation.py create mode 100644 migrations/src/utils.py create mode 100644 migrations/uv.lock diff --git a/migrations/README.md b/migrations/README.md new file mode 100644 index 0000000000000..7954ee68f8fe7 --- /dev/null +++ b/migrations/README.md @@ -0,0 +1,43 @@ +# Cxg Schema Migrations + +A Cell-by-Gene migration is a procedure that occurs when a new cellxgene schema is available. The schemas are defined [here](https://github.com/chanzuckerberg/single-cell-curation/tree/main/schema). In brief, a schema expresses requirements and restrictions on the structure, format, value types and interpretation of all single-cell datasets the Cell-by-Gene corpus. The migration procedure creates a new dataset, compliant with the latest schema release, for every dataset in our corpus. + +This project is aimed at CZI Data Service engineers who are tasked with performing and monitoring the migration process. It assumes you have access to and have read the internal document outlining the migration procedure. This project is meant to assist those engineers with additional context and tools to monitor and diagnose in real-time, issues arising from migrations. + +> 💡 **Note:** The migration process does issue a report with failures, but this is only available at the end of migration. Migration will take days. + +## Pre-Requisites + +- You have access to two AWS profiles, one for development environment (i.e. `dev`) and one for production environment (i.e. `prod`). +- You have install the AWS CLI + +## Migration Environments + +We perform a dry-run migration in the `dev` environment. The `dev` environment is a mirror of all the datasets available in `production` plus any additional datasets have been manually ingested into dev. For the remainder of this document, we assume `dev` is a 1:1 mirror of the data in `production`. The purpose of the `dev` migration is to catch, diagnose and squash all errors that arise during migration. Errors may arise from infrastructure issues or unanticipated migration logic issues. + +Ultimately, the entire migration process must be applied to the `production` environment. + +## Migration Procedure + +The migration procedure consists of two stages (a) migration, and (b) ingestion. + +The **migration stage** simply converts every dataset from the previous schema-version (i.e. 5.2.0) to the newest (i.e. 5.3.0) via a migration script. The migration script is tailor made by our team of curators and is available from the `cellxgene_schema` [Python package](https://pypi.org/project/cellxgene-schema/); pinned to the schema version to which it applies. The result is a dataset in our ecosystem, somewhere in storage. + +The **ingestion stage** is responsible for bringing that migrated dataset into our registration ecosystem, persisted by a relational datase. It is the relational database that tracks and manages every dataset and their memership to both public and private collections. The same ingestion stage procedure handles both migrated datasets (ones that were previously in our corpus) and new datasets registered by our curators. + +Both of these stages are encoded as state machines in AWS stepfunction service. These services and their codebase locations are listed in the table. + +| stage | step-function | python file | entry point | +| --------- | ------------------------------------ | --------------------------------------------- | --------------------- | +| migration | dp-dev-devstack-schema-migration-sfn | backend/layers/processing/schema_migration.py | SchemaMigrate.migrate | +| ingestion | dp-dev-devstack-sfn | backend/layers/processing/process.py | ProcessMain.process | + +A simplified overview of these procedures are diagramed below: + + + +## Monitoring an ongoing migration + +We kick off the migration by executing the migration stag step function. Each step in these step functions are executed as jobs, which are added to the same job-queue `schema_migration-STAGE_NAME` (where `STAGE_NAME` is one of `dev` or `prod`). The monitoring boils down to tracking all of the jobs enqueued by either the main migration stage step-function, or the downstream ingestion stage step-functions. + +> 💡 **Note:** We currently don't tag a migration execution. Therefore we will use start times and end times to cull the relelvant jobs diff --git a/migrations/docs/diagrams/migration_sfn.md b/migrations/docs/diagrams/migration_sfn.md new file mode 100644 index 0000000000000..6dedbb78340dd --- /dev/null +++ b/migrations/docs/diagrams/migration_sfn.md @@ -0,0 +1,29 @@ +```mermaid +--- +title: Migration Procedure +--- +flowchart LR + subgraph migration + direction TB + stp-span-col("Span Collections") --> stp-migrate-col@{shape: procs, label: "CollectionMigration"} + stp-migrate-col --> stp-span-ds("Span Datasets") + stp-span-ds --> stp-migrate-ds@{shape: procs, label: "DatasetMigration"} + stp-migrate-ds --> stp-ingest-ds("IngestDataset") + end + + subgraph ingestion + direction TB + stp-enter("Enter") --> parallel_validation + subgraph parallel_validation + direction LR + stp-validate-ad("ValidateAnndata") + stp-validate-atac("ValidateAtac") + stp-validate-ad ~~~ stp-validate-atac + end + parallel_validation --> stp-addlabels("AddLabels") + stp-addlabels --> stp-cxg("CXG") + stp-cxg --> stp-register("Register") + end + stp-ingest-ds --> ingestion + +``` diff --git a/migrations/notebooks/monitoring_example.ipynb b/migrations/notebooks/monitoring_example.ipynb new file mode 100644 index 0000000000000..21ab4688623a3 --- /dev/null +++ b/migrations/notebooks/monitoring_example.ipynb @@ -0,0 +1,57292 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 156, + "id": "68ad9435-9f92-4639-b4fa-81cf5629aba0", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from datetime import datetime, timezone\n", + "import importlib\n", + "sys.path.append(\"../src/\")\n", + "\n", + "from config import set_stage\n", + "\n", + "\n", + "import reporting\n", + "import jobs\n", + "import logs\n", + "from reporting.jobs import plot_job_status_counts, plot_job_durations\n", + "from reporting.datasets import report_datasets\n", + "# reload - reporting\n", + "importlib.reload(reporting.jobs)\n", + "from reporting.jobs import plot_job_status_counts, plot_job_durations" + ] + }, + { + "cell_type": "markdown", + "id": "56ba19d1-7fc1-4bcc-9f9e-e876ff422616", + "metadata": {}, + "source": [ + "# Set the start time and end time of your migration\n", + "We currently do not have a mechanism to tag the execution of a step function, or any jobs kicked off by that step function. As a result, we use timestamps to group jobs. 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Warning

\n", + "
\n", + " Due to an oversight in which data is passed to each step, we cannot track the migration on a single dataset end-to-end. We can't use the dataset version_id because the differ between the migration phase and ingestion phase. As a result, there may be some double-counting. That is a dataset could succeed the migration stage and count as a success, while fail during the ingestin phase and count as a failure.\n", + "
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statusdurationlast_step_namelast_step_durationlast_step_reason
34FAILED10489.0dataset_migrate10489.0CannotPullContainerError: failed to tag image ...
66FAILED3760586.0dataset_migrate3760586.0Essential container in task exited
85FAILED68558.0validate_anndata68475.0The Task state in AWS Step Functions execution...
228FAILED4878.0dataset_migrate4878.0CannotPullContainerError: failed to tag image ...
283FAILED7247.0dataset_migrate7247.0CannotPullContainerError: failed to tag image ...
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" + ], + "text/plain": [ + " status duration last_step_name last_step_duration \\\n", + "34 FAILED 10489.0 dataset_migrate 10489.0 \n", + "66 FAILED 3760586.0 dataset_migrate 3760586.0 \n", + "85 FAILED 68558.0 validate_anndata 68475.0 \n", + "228 FAILED 4878.0 dataset_migrate 4878.0 \n", + "283 FAILED 7247.0 dataset_migrate 7247.0 \n", + "\n", + " last_step_reason \n", + "34 CannotPullContainerError: failed to tag image ... \n", + "66 Essential container in task exited \n", + "85 The Task state in AWS Step Functions execution... \n", + "228 CannotPullContainerError: failed to tag image ... \n", + "283 CannotPullContainerError: failed to tag image ... " + ] + }, + "execution_count": 262, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "failing = dfds[dfds[\"status\"]==\"FAILED\"]\n", + "# sanitzed columns - avoid putting private information on github\n", + "failing[[\"status\",\"duration\",\"last_step_name\",\"last_step_duration\",\"last_step_reason\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "id": "d1eaec36-5e38-4af8-a457-72b4094334ec", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "bingroup": "x", + "hovertemplate": "last_step_name=dataset_migrate
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\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "px.histogram(failing, x=failing[\"last_step_duration\"].map(ms_to_hours), color='last_step_name', title=\"Failed datasets by failing step\")" + ] + }, + { + "cell_type": "markdown", + "id": "0171ff41-9f1b-49c9-9302-fd88c0d1f8f4", + "metadata": {}, + "source": [ + "# Use the Logs to get more information\n", + "The summary data in the job descriptions is helpful but not always sufficient to really know what's going on. Therefore, we'd like to look at the logs.\n", + "\n", + "

Warning: relatively slow

\n", + "
\n", + " Logs are available per-job, so we have to loop through each job and fetch its logs separately. Boto3 is synchronous, which means we have to wait a while for this to complete.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "id": "79e3f3bc-4a99-4731-8ffe-c6b12b04e942", + "metadata": {}, + "outputs": [], + "source": [ + "from reporting.logs import merge_job_logs" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "id": "62e09f3d-bb85-436f-8b86-32a633c97e04", + "metadata": {}, + "outputs": [], + "source": [ + "failed = df_jobs[df_jobs['status']=='FAILED']\n", + "failed = merge_job_logs(failed)" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "id": "c638ba05-36aa-4e2c-80c0-c2a1f401af59", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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jobNamejobIdstatusdurationlogResultslogFetched
0dataset_migration1931e060-eaaf-4f2d-ab6b-d83665a30dd5FAILED3760586[{'levelname': 'INFO', 'asctime': '2025-04-01T...True
1dataset_migration6747da12-b3de-4181-bd91-0254abf9c973FAILED<NA>[]False
2cxgc2130015-828e-4f9b-b34e-3ac2f06ea2aeFAILED51787537[{'levelname': 'INFO', 'asctime': '2025-03-31T...True
3validate_anndata32690702-fce1-41b4-b970-9ae62bf34682FAILED164424[{'levelname': 'INFO', 'asctime': '2025-04-01T...True
4dataset_migrationbde4aa87-c4fa-43f4-9094-045dd818f8ebFAILED<NA>[]False
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" + ], + "text/plain": [ + " jobName jobId status duration \\\n", + "0 dataset_migration 1931e060-eaaf-4f2d-ab6b-d83665a30dd5 FAILED 3760586 \n", + "1 dataset_migration 6747da12-b3de-4181-bd91-0254abf9c973 FAILED \n", + "2 cxg c2130015-828e-4f9b-b34e-3ac2f06ea2ae FAILED 51787537 \n", + "3 validate_anndata 32690702-fce1-41b4-b970-9ae62bf34682 FAILED 164424 \n", + "4 dataset_migration bde4aa87-c4fa-43f4-9094-045dd818f8eb FAILED \n", + "\n", + " logResults logFetched \n", + "0 [{'levelname': 'INFO', 'asctime': '2025-04-01T... True \n", + "1 [] False \n", + "2 [{'levelname': 'INFO', 'asctime': '2025-03-31T... True \n", + "3 [{'levelname': 'INFO', 'asctime': '2025-04-01T... True \n", + "4 [] False " + ] + }, + "execution_count": 261, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sanitized columns - avoid putting private info on github\n", + "failed[[\"jobName\",\"jobId\",\"status\",\"duration\",\"logResults\",\"logFetched\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3691f137-ff02-43af-959a-2c81b73eaf63", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/migrations/pyproject.toml b/migrations/pyproject.toml new file mode 100644 index 0000000000000..21edc95f52a0c --- /dev/null +++ b/migrations/pyproject.toml @@ -0,0 +1,10 @@ +[project] +name = "migrations" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "boto3>=1.37.26", + "notebook>=7.3.3", +] diff --git a/migrations/src/config.py b/migrations/src/config.py new file mode 100644 index 0000000000000..cee25fc894107 --- /dev/null +++ b/migrations/src/config.py @@ -0,0 +1,53 @@ +from typing import Union +import os +from enum import Enum +from boto3 import Session + +class STAGE(Enum): + prod = 'prod' + dev = 'dev' + + +_BATCH_CLIENT = None, +_LOGS_CLIENT = None +_JOB_QUEUE = None + +def _get_profile(stage:STAGE) -> str: + return f"single-cell-{stage.value}" + +# get the aws session object, using the appropriate profile +def _get_session(stage:STAGE) -> Session: + # will error out if you don't have this profile set in your .aws/config + return Session(profile_name=_get_profile(stage), region_name="us-west-2") + + +# set the environment stage against which to monitor jobs and logs +def set_stage(stage: Union[STAGE, str]=STAGE.dev) -> None: + if isinstance(stage, str): + try: + stage = STAGE(stage) + except: + raise ValueError(f"The provided stage '{stage}' is not a valid stage.") + + # set the environment variable + os.environ['MIGRATION_MONITOR_STAGE'] = stage.value + + # get aws session + _session = _get_session(stage) + + # set global constants based on stage + global _BATCH_CLIENT, _LOGS_CLIENT, _JOB_QUEUE + _BATCH_CLIENT = _session.client('batch') + _LOGS_CLIENT = _session.client("logs") + + # set other constants + _JOB_QUEUE = f"schema_migration-{stage.value}" + +def get_batch_client(): + return _BATCH_CLIENT + +def get_logs_client(): + return _LOGS_CLIENT + +def get_job_queue(): + return _JOB_QUEUE \ No newline at end of file diff --git a/migrations/src/jobs/__init__.py b/migrations/src/jobs/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/migrations/src/jobs/details.py b/migrations/src/jobs/details.py new file mode 100644 index 0000000000000..4be5b134e8478 --- /dev/null +++ b/migrations/src/jobs/details.py @@ -0,0 +1,64 @@ +from datetime import datetime, timezone + +import pandas as pd +from config import get_batch_client +from utils import dt_to_utc, utc_to_dt + +# pluck job-description details as a flat dictionary +def _pluck_details(jobdescription): + # top level keys + keys = ['jobName','jobId','jobQueue','status','statusReason','createdAt','stoppedAt','startedAt','jobDefinition'] + d = {k:jobdescription.get(k,None) for k in keys} + + # attempt.container level keys (only pluck last attempt) + attempt_container_keys = ["reason"] + if len(jobdescription["attempts"]): + attempt_container = jobdescription["attempts"][-1]["container"] + d = d | {k:attempt_container.get(k,None) for k in attempt_container_keys} + else: + d = d | {k:pd.NA for k in attempt_container_keys} + + # container level keys + container = jobdescription['container'] + container_keys = ['logStreamName'] + d = d | {k:container.get(k,None) for k in container_keys} + + # container.environment level keys + env = container['environment'] + env_keys = ['DATASET_VERSION_ID','ARTIFACT_BUCKET','DATASET_ID','COLLECTION_VERSION_ID','COLLECTION_ID','DATASETS_BUCKET','STEP_NAME'] + d = d | {e['name']:e['value'] for e in env if e['name'] in env_keys} + + # container.logConfiguration.option keys + logopts = container['logConfiguration']['options'] + logopts_keys = ['awslogs-group'] + d = d | {k:logopts.get(k,None) for k in logopts_keys} + + # compute durations + if d['startedAt'] is not None and d['stoppedAt'] is not None: + d['duration'] = d['stoppedAt'] - d['startedAt'] + elif d['startedAt'] is not None and d['stoppedAt'] is None: + now = dt_to_utc(datetime.now(timezone.utc)) + d['duration'] = now - d['startedAt'] + else: + d['duration'] = pd.NA + + return d + + +# get all job descriptions +def get_job_details(jobs): + results = [] + print(f'Job Count: {len(jobs)}') + client = get_batch_client() + for i in range(0, len(jobs), 100): # describe_jobs takes max 100 + chunk = [j["jobId"] for j in jobs[i:i+100]] + try: + response = client.describe_jobs(jobs=chunk) + except Exception as e: + print(e) + results.extend(response["jobs"]) + return results + + +def get_structured_job_details(jobs): + return [_pluck_details(d) for d in get_job_details(jobs)] \ No newline at end of file diff --git a/migrations/src/jobs/jobs.py b/migrations/src/jobs/jobs.py new file mode 100644 index 0000000000000..80ceabf9b032e --- /dev/null +++ b/migrations/src/jobs/jobs.py @@ -0,0 +1,55 @@ +''' +Querying Batch Jobs +''' +from typing import List, Any +from enum import Enum +from datetime import datetime, timezone +from config import get_batch_client, get_job_queue +from utils import dt_to_utc, utc_to_dt + +class STATUS_ENUM(Enum): + succeeded = "SUCCEEDED" + failed = "FAILED" + running = "RUNNING" + pending = "PENDING" + submitted = "SUBMITTED" + starting = "STARTING" + + + +_BATCH_JOB_STATUS_LIST = ["SUCCEEDED", "FAILED", "RUNNING", "PENDING", "SUBMITTED", "STARTING"] + +# get all jobs that beging inside an optional time range +def get_all_jobs_of_status(queue:str, status:str,start=None, end=None): + all_jobs = [] + has_next = True + r = {} + client = get_batch_client() + while has_next: + if r.get("nextToken",None): + r = client.list_jobs(jobQueue=queue,jobStatus=status,nextToken=r.get("nextToken")) + else: + r = client.list_jobs(jobQueue=queue,jobStatus=status) + all_jobs.extend(r["jobSummaryList"]) + has_next = r.get("nextToken",False) + + # bookends (utc ms) + start = start if start else datetime(2025,1,1,0,0,0) + end = end if end else datetime(2025,12,31,0,0,0) + + # format times as milliseconds UTC + if isinstance(start, datetime): + start = dt_to_utc(start) + if isinstance(end, datetime): + end = dt_to_utc(end) + + # Use 'createdAt' since startedAt won't catch jobs that don't start + return [j for j in all_jobs if j["createdAt"]> start and j["createdAt"] < end] + +# get all jobs of all status +def get_all_jobs(queue:str, start=None, end=None) -> List[Any]: + all_jobs = [] + for status in _BATCH_JOB_STATUS_LIST: + all_jobs.extend(get_all_jobs_of_status(queue, status, start, end)) + return all_jobs + diff --git a/migrations/src/logs/__init__.py b/migrations/src/logs/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/migrations/src/logs/logs.py b/migrations/src/logs/logs.py new file mode 100644 index 0000000000000..65a35a6590d88 --- /dev/null +++ b/migrations/src/logs/logs.py @@ -0,0 +1,45 @@ +from typing import List +import json +from config import get_logs_client + +def _parse_log_events(events:List[str]): + # we only care about logger messages + messages = [] + for e in events: + try: + m = json.loads(e["message"]) + except: + continue + messages.append(m) + return messages + +# fetch logs for a single log-stream +def get_stream_logs(log_group: str, log_stream: str): + client = get_logs_client() + success = True + _ptoken = '__' + _ntoken = '' + events = [] + count = 0 + max_count = 5 + while _ntoken != _ptoken and count < max_count: + count+=1 + # keyword arguments + kwargs = {"logGroupName":log_group, "logStreamName":log_stream, "startFromHead":True} + kwargs = kwargs if _ntoken =='' else kwargs | {"nextToken":_ntoken} + try: + r = client.get_log_events(**kwargs) + events.extend(r["events"]) + _ptoken = _ntoken + _ntoken = r["nextForwardToken"] + except Exception as e: + success = False + + + return (log_group, log_stream, _parse_log_events(events), success) + + +# fetch logs for multiple log-streams +def get_streams_logs(log_group: str, log_streams: List[str]): + #TODO:[EM] this should be made async enabled with aiobotocore + return [get_stream_logs(log_group, stream) for stream in log_streams] \ No newline at end of file diff --git a/migrations/src/logs/messages.py b/migrations/src/logs/messages.py new file mode 100644 index 0000000000000..38d0480bb9a00 --- /dev/null +++ b/migrations/src/logs/messages.py @@ -0,0 +1,19 @@ +''' +Utility functions for parsing logger information stored with each job +''' +from typing import List +import json + +# get all log messages at ERROR log level +def filter_errors(events): + return [e for e in events if e["levelname"]=="ERROR"] + +# get all log messages at INFO log level +def filter_info(events): + return [e for e in events if e["levelname"]=="INFO"] + +# parse error message info from a specific message +def extract_error(event): + keys = ["message","lineno","pathname","exc_info"] + return {k:event.get(k,None) for k in keys} + diff --git a/migrations/src/reporting/__init__.py b/migrations/src/reporting/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/migrations/src/reporting/datasets.py b/migrations/src/reporting/datasets.py new file mode 100644 index 0000000000000..5ec8e5cbf0e8e --- /dev/null +++ b/migrations/src/reporting/datasets.py @@ -0,0 +1,211 @@ +''' +Utilities to monitor the progress and errors for cxg datasets. +This operates on the level of datasets and not jobs. +Jobs can be completed (e.g. dataset_migrate) but the dataset is still being processed (e.g. validate_anndata). + +NOTE: Not all jobs are specific to datasets/collections. For example, 'gather_collections' spans the entire database for collections. These jobs are ignored in this workflow +''' +from typing import Union, List +from datetime import datetime, timezone, timedelta + +import pandas as pd +from numpy import nan +from plotly import express as px +from utils import dt_to_utc +from jobs.jobs import STATUS_ENUM + +_JOBNAME_COLUMN = "jobName" +_STATUS_COL = "status" +_CREATED_COL = "createdAt" +_START_COL = "startedAt" +_STOP_COL = "stoppedAt" +_STEP_NAME_COL = "STEP_NAME" + +_DATASET_DURATION_COL = "duration" +_ACTIVE_STEP_COL = "active_step_name" +_ACTIVE_STEP_DURATION_COL = "active_step_duration" +_LAST_STEP_COL = "last_step_name" +_LAST_STEP_DURATION_COL = "last_step_duration" +_LAST_STEP_REASON_COL = "last_step_reason" +_DATASET_JOB_NAMES = ['dataset_migration','validate_anndata','validate_atac','add_labels','cxg'] # ordered + +# get a start time based on createAt or startedAt +def _get_start_time(df: pd.DataFrame) -> Union[int,None]: + """ + This is to help with edge case reports from AWS where a job may be stopped wihout ever starting. + Then, we'd want to use the createdAt time. + """ + _createdAt = df[_CREATED_COL].min() + _startedAt = df[_START_COL].min() + + if pd.isna(_startedAt) and pd.isna(_createdAt): + # no usable start time + return None + elif pd.isna(_startedAt) and not pd.isna(_createdAt): + # use craeted at - sometimes AWS will stop a job that never started + return _createdAt + else: + return _startedAt + +# select the jobs that are specific to singular datasets +def _filter_dataset_jobs(df:pd.DataFrame) -> pd.DataFrame: + return df[df[_JOBNAME_COLUMN].isin(_DATASET_JOB_NAMES)] + +# aggregate operator: collapse the relevant status +def _aggregate_status(df:pd.DataFrame) -> str: + # get the unique status results + group_status = set(df[_STATUS_COL].unique()) + + # return the aggregate status + if STATUS_ENUM.failed.value in group_status: + # if one job failed, the dataset failed + return STATUS_ENUM.failed.value + elif STATUS_ENUM.running.value in group_status: + # if one job is running (and not failed), the dataset is running + return STATUS_ENUM.running.value + elif len(group_status.difference({STATUS_ENUM.succeeded.value}))==0: + # if all jobs succeed, the datata succeeded + return STATUS_ENUM.succeeded.value + else: + # otherwise it's running + return STATUS_ENUM.running.value + +# aggregate operator: earliest start time +def _aggregate_start_time(df:pd.DataFrame) -> Union[int,None]: + # get the earliest start time + return _get_start_time(df) + +# aggregate operator: latest stop time +def _aggregate_stop_time(df:pd.DataFrame) -> Union[int, None]: + """ + if any job is unfinished, the dataset is unfinished + """ + # get status + status = _aggregate_status(df) + + if status in {STATUS_ENUM.succeeded.value, STATUS_ENUM.failed.value}: + # dataset is finished, use most recently stopped job + return df['stoppedAt'].max() + else: + return None + +# aggregate operator: active step +def _aggregate_active_step(df:pd.DataFrame) -> List[str]: + # get running steps + running = df[df[_STATUS_COL] == STATUS_ENUM.running.value] + return list(running[_STEP_NAME_COL].unique()) + +# aggregate operator: active step duration +def _aggregate_active_step_duration(df:pd.DataFrame) -> int: + # get running steps + dfr = df[df[_STATUS_COL] == STATUS_ENUM.running.value] + + # get the earliest start time + start_time = dfr[_START_COL].min() + + # get the latest stop time + if pd.isna(start_time): + return 0 + else: + now = dt_to_utc(datetime.now(timezone.utc)) + return int(now-start_time) + +# aggregate operator: dataset duration +def _aggregate_duration(df:pd.DataFrame) -> int: + # get the earliest start time + start_time = _aggregate_start_time(df) + stop_time = _aggregate_stop_time(df) + + if not start_time: + # never started - return 0 + return 0 + elif not stop_time: + # started, but hasn't stopped - return time to now + return datetime.now(timezone.utc)-start_time + else: + # return the time difference + return stop_time-start_time + +# aggrgate operator: last step +def _aggregate_last_step(df:pd.DataFrame) -> str: + # get status + status = _aggregate_status(df) + + # determine last step (depending on status) + if status in {STATUS_ENUM.failed.value}: + # if failed, give the most recently started failed job + return df[df[_STATUS_COL]==STATUS_ENUM.failed.value].sort_values(by=_START_COL, ascending=False).iloc[0][_STEP_NAME_COL] + else: + # give the most recently started successful job + return df[df[_STATUS_COL]==STATUS_ENUM.succeeded.value].sort_values(by=_START_COL, ascending=False).iloc[0][_STEP_NAME_COL] + +# aggregate operator: last step duration +def _aggregate_last_step_duration(df:pd.DataFrame) -> int: + # the step name + step = _aggregate_last_step(df) + + # the step row (as a df) + step = df[df[_STEP_NAME_COL]==step] + + start_time = _get_start_time(step) + stop_time = step[_STOP_COL].max() + + return stop_time - start_time + +# aggregate operator: last step reason +def _aggregate_last_step_reason(df:pd.DataFrame) -> Union[str,None]: + ''' + This is only useful for failed jobs + ''' + # get status + status = _aggregate_status(df) + + if status == STATUS_ENUM.failed.value: + # get the most recently started failed job + failed = df[df[_STATUS_COL]==STATUS_ENUM.failed.value].sort_values(by=_START_COL, ascending=False).iloc[0] + + # return the reason + return failed['reason'] or failed["statusReason"] + else: + return None + + +# get only dataset-level jobs +def get_dataset_jobs(jobs:pd.DataFrame) -> pd.DataFrame: + return _filter_dataset_jobs(jobs) + +# create a dataframe to report dataset status +def report_datasets(df:pd.DataFrame) -> pd.DataFrame: + # filter dataset jobs + dfds = _filter_dataset_jobs(df) + + # aggrregate information at the dataset level + records = [] + for DATASET_VERSION_ID, _dfdv in dfds.groupby('DATASET_VERSION_ID'): + # let's make sure the dataset-id exists + _ids = list(set([x for x in _dfdv['DATASET_ID'] if not pd.isna(x)])) + + # aggregate status + records.append({ + 'DATASET_ID': _ids[0] if len(_ids) else nan, + 'DATASET_VERSION_ID': DATASET_VERSION_ID, + _STATUS_COL: _aggregate_status(_dfdv), + _START_COL: _aggregate_start_time(_dfdv), + _STOP_COL: _aggregate_stop_time(_dfdv), + _DATASET_DURATION_COL: _aggregate_duration(_dfdv), + _ACTIVE_STEP_COL: _aggregate_active_step(_dfdv), + _ACTIVE_STEP_DURATION_COL: _aggregate_active_step_duration(_dfdv), + _LAST_STEP_COL: _aggregate_last_step(_dfdv), + _LAST_STEP_DURATION_COL: _aggregate_last_step_duration(_dfdv), + _LAST_STEP_REASON_COL: _aggregate_last_step_reason(_dfdv) + + }) + + return pd.DataFrame.from_records(records) + +# report dataset success breakdown +def plot_dataset_status_counts(df: pd.DataFrame, show:bool = False): + f = px.histogram(df, x="status",histfunc="count", log_y=True, title="dataset migration status") + if show: + f.show() + return f \ No newline at end of file diff --git a/migrations/src/reporting/jobs.py b/migrations/src/reporting/jobs.py new file mode 100644 index 0000000000000..2e79b431e78f8 --- /dev/null +++ b/migrations/src/reporting/jobs.py @@ -0,0 +1,39 @@ +from datetime import datetime +from typing import Dict + +import pandas as pd +from plotly import express as px + +from config import get_job_queue +from jobs.jobs import get_all_jobs, STATUS_ENUM +from jobs.details import get_structured_job_details + + +def ms_to_minutes(time): + return time/(1000*60) + +def ms_to_hours(time): + return ms_to_minutes(time)/60 + +# return job data as a dataframe +def describe_migration_jobs(start_time:datetime, end_time: datetime) -> pd.DataFrame: + # get details + _QUEUE = get_job_queue() + details = get_structured_job_details(get_all_jobs(_QUEUE, start_time, end_time)) + return pd.DataFrame.from_records(details) + + +# create data visualization of job counts by job name and status +def plot_job_status_counts(df:pd.DataFrame, show:bool=False): + f = px.histogram(df, x="jobName",histfunc='count',color="status", title="status by job", log_y=True, barmode="group") + if show: + f.show() + return f + + +# create data visualization of job counts by job name and status +def plot_job_durations(df:pd.DataFrame, show:bool=False): + f = px.histogram(df,x="jobName",histfunc="avg",y=df["duration"].map(ms_to_hours), color="status",barmode='group', log_y=True, title="duration (hours) by job") + if show: + f.show() + return f diff --git a/migrations/src/reporting/logs.py b/migrations/src/reporting/logs.py new file mode 100644 index 0000000000000..eb14c64f4c40e --- /dev/null +++ b/migrations/src/reporting/logs.py @@ -0,0 +1,67 @@ +from typing import List +import json + +import pandas as pd +from logs.logs import get_streams_logs + +_LOG_RESULT_COLNAME = "logResults" +_LOG_FETCHED_COLNAME = "logFetched" + +def filter_errors(events): + return [e for e in events if e["levelname"]=="ERROR"] + +def filter_info(events): + return [e for e in events if e["levelname"]=="INFO"] + +def extract_error(event): + keys = ["message","lineno","pathname","exc_info"] + return {k:event.get(k,None) for k in keys} + +def _parse_log_events(events:List[str]): + # we only care about logger messages + messages = [] + for e in events: + try: + m = json.loads(e["message"]) + except: + continue + messages.append(m) + return messages + + + +# get the logs for all jobs in the dataframe +def get_job_logs(df_jobs:pd.DataFrame): + # initialize dataframe to hold log results + _df_logs = pd.DataFrame() + + # group fetch queries by log-group + for log_group, dfg in df_jobs.groupby("awslogs-group"): + # pull streams + log_streams = dfg['logStreamName'].values + + # get logs from AWS + log_results = get_streams_logs(log_group, log_streams) + + # build the dataframe from dictionary + _df = pd.DataFrame.from_dict({ + "logStreamName":[x[1] for x in log_results], + _LOG_RESULT_COLNAME: [x[2] for x in log_results], + _LOG_FETCHED_COLNAME: [x[3] for x in log_results] + }) + + # concatenate + _df_logs = pd.concat([_df_logs, _df],axis=0) + return _df_logs + +# get the logs for all jobs in the dataframe +def merge_job_logs(df_jobs:pd.DataFrame) -> pd.DataFrame: + # create a dataframe with new columns with data from logs + if _LOG_RESULT_COLNAME not in df_jobs.columns: + df_logs = get_job_logs(df_jobs) + + # return new dataframe with log-columns + return df_jobs.merge(df_logs, on="logStreamName", how="left") + else: + return df_jobs + diff --git a/migrations/src/reporting/validation.py b/migrations/src/reporting/validation.py new file mode 100644 index 0000000000000..d32a4df5bceee --- /dev/null +++ b/migrations/src/reporting/validation.py @@ -0,0 +1,23 @@ +''' +Read validation errors from failing jobs. +Validation errors result from running the cellxgene_schema.validate() method. +Validation errors are logged in the job-stream logs. +''' +from typing import List +import pandas as pd + +_VALIDATION_COL_NAME = "validationError" + +# filter log message for validation error messages +def _filter_logs_with_validation_message(log_results:List[str]): + # only care about the messages with a valdiation message + results = [lr for lr in log_results if lr.get("validation_message",None) is not None] + + # concatenate the error messages + messages = [r["validation_message"] for r in results] + return messages + +# add a column to the dataframe to keep all validation errors +def merge_validation_errors(df:pd.DataFrame): + if _VALIDATION_COL_NAME not in df.columns: + df[_VALIDATION_COL_NAME] = df["logResults"].map(lambda lr: _filter_logs_with_validation_message(lr)) \ No newline at end of file diff --git a/migrations/src/utils.py b/migrations/src/utils.py new file mode 100644 index 0000000000000..07b25ff020144 --- /dev/null +++ b/migrations/src/utils.py @@ -0,0 +1,8 @@ +from datetime import datetime,timezone + +def dt_to_utc(dt:datetime) -> int: + return int(dt.timestamp()*1_000) + +def utc_to_dt(t:int) -> datetime: + return datetime.fromtimestamp(int(t/1_000), timezone.utc) + 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"https://files.pythonhosted.org/packages/5a/84/44687a29792a70e111c5c477230a72c4b957d88d16141199bf9acb7537a3/websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526", size = 58826 }, +] From 5cd9738123932a4bc36eb57fc8b06bf0b0fac071 Mon Sep 17 00:00:00 2001 From: Evan Molinelli Date: Fri, 11 Apr 2025 17:22:50 -0400 Subject: [PATCH 2/3] cxg monitoring - tooling --- migrations/README.md | 229 +++++++++++++++++- migrations/docs/diagrams/migration_sfn.md | 6 +- .../docs/diagrams/migration_sfn.md-1.svg | 1 + migrations/docs/images/cxg_time_deltas.png | Bin 0 -> 69271 bytes migrations/docs/images/datasets_by_status.png | Bin 0 -> 30075 bytes migrations/docs/images/dfj_describe_jobs.png | Bin 0 -> 236566 bytes migrations/docs/images/duration_by_job.png | Bin 0 -> 52906 bytes .../docs/images/failed_datasets_by_step.png | Bin 0 -> 37392 bytes migrations/docs/images/status_by_job.png | Bin 0 -> 53736 bytes .../images/validate_anndata_time_deltas.png | Bin 0 -> 69153 bytes migrations/src/config.py | 24 +- migrations/src/reporting/jobs.py | 8 +- migrations/src/reporting/sfn.py | 225 +++++++++++++++++ migrations/src/sfn/__init__.py | 13 + migrations/src/sfn/executions.py | 54 +++++ migrations/src/utils.py | 5 + 16 files changed, 543 insertions(+), 22 deletions(-) create mode 100644 migrations/docs/diagrams/migration_sfn.md-1.svg create mode 100644 migrations/docs/images/cxg_time_deltas.png create mode 100644 migrations/docs/images/datasets_by_status.png create mode 100644 migrations/docs/images/dfj_describe_jobs.png create mode 100644 migrations/docs/images/duration_by_job.png create mode 100644 migrations/docs/images/failed_datasets_by_step.png create mode 100644 migrations/docs/images/status_by_job.png create mode 100644 migrations/docs/images/validate_anndata_time_deltas.png create mode 100644 migrations/src/reporting/sfn.py create mode 100644 migrations/src/sfn/__init__.py create mode 100644 migrations/src/sfn/executions.py diff --git a/migrations/README.md b/migrations/README.md index 7954ee68f8fe7..446624f70ba7c 100644 --- a/migrations/README.md +++ b/migrations/README.md @@ -2,14 +2,22 @@ A Cell-by-Gene migration is a procedure that occurs when a new cellxgene schema is available. The schemas are defined [here](https://github.com/chanzuckerberg/single-cell-curation/tree/main/schema). In brief, a schema expresses requirements and restrictions on the structure, format, value types and interpretation of all single-cell datasets the Cell-by-Gene corpus. The migration procedure creates a new dataset, compliant with the latest schema release, for every dataset in our corpus. -This project is aimed at CZI Data Service engineers who are tasked with performing and monitoring the migration process. It assumes you have access to and have read the internal document outlining the migration procedure. This project is meant to assist those engineers with additional context and tools to monitor and diagnose in real-time, issues arising from migrations. +This project is aimed at CZI Data Service engineers who are tasked with performing and monitoring the migration process. It assumes you have access to and have read the internal document outlining the migration procedure. This project is meant to assist those engineers with additional context and tools to monitor and diagnose, in real-time, issues arising from migrations. -> 💡 **Note:** The migration process does issue a report with failures, but this is only available at the end of migration. Migration will take days. +> :memo: **Note:** The migration process does issue a report with failures, but this is only available at the end of migration. Migration will take days. ## Pre-Requisites -- You have access to two AWS profiles, one for development environment (i.e. `dev`) and one for production environment (i.e. `prod`). -- You have install the AWS CLI +- AWS profiles + - `single-cell-dev` for development, rdev and staging environments + - `single-cell-prod` for the production environment +- AWS CLI + +> :memo: **Check:** Run the command below to confirm your CLI is configured + +```bash +aws sts get-caller-identity --profile single-cell-dev +``` ## Migration Environments @@ -34,10 +42,217 @@ Both of these stages are encoded as state machines in AWS stepfunction service. A simplified overview of these procedures are diagramed below: - +![ alt text ](./docs/diagrams/migration_sfn.md-1.svg) ## Monitoring an ongoing migration -We kick off the migration by executing the migration stag step function. Each step in these step functions are executed as jobs, which are added to the same job-queue `schema_migration-STAGE_NAME` (where `STAGE_NAME` is one of `dev` or `prod`). The monitoring boils down to tracking all of the jobs enqueued by either the main migration stage step-function, or the downstream ingestion stage step-functions. +Assume you have initiated a migration in the `dev` environment (i.e. `STAGE_NAME=dev`) that began on 3/21/2025 and is currently ongoing (let's say today is 3/23/2025). Let's walk through a few worfklows. + +> **Note:** We currently don't tag a migration execution. Therefore we will use start times and end times to cull the relelvant jobs + +> **Note:** The monitoring capabilities are dependent on both log and excution retention policy in the AWS account. When the information is deleted from AWS, the retroactive analysis is not possible. + +### Setup your monitoring environment + +Before actually performing any monitoring, we need to define the target of our monitoring. This includes the (i) environment, (ii) start date-time and (iii) end date-time. + +```python +from datetime import datetime, timezone +from config import set_stage + +# declare that we are looking in the development environment/stage +set_stage("dev") # use 'prod' if looking in production environment/stage + +# declare start and end time +migration_start = datetime(2025,3, 21, 0, 0, 0, tzinfo=timezone.utc) +migration_end = datetime(2025, 3, 25, 11, 59, 59, tzinfo=timezone.utc) # or use `datetime.now()`, the migration is ongoing, remember ;) +``` + +### What is the status of the migration? + +The migration step-function issues jobs to a job-queue as it executes each step. We can monitor progress by looking at the job data. + +```python +from reporting.jobs import describe_migration_jobs + +# convert jobs to a convenient dataframe for viewing +dfj = describe_migration_jobs(migration_start, migration_end) +``` + +![Example Jobs Descriptions](./docs/images/dfj_describe_jobs.png) + +You can view the job status-values graphically. + +```python +from reporting.jobs import plot_job_status_counts +plot_job_status_counts(dfj) +``` + +![Example Job Status Plot](./docs/images/status_by_job.png) + +### How long are these jobs taking? + +Not all jobs take the same amount of time. We can use the data from the job descriptions dataframe `dfj` and plot the average duration broken down by job (and status). + +```python +from reporting.jobs import plot_job_durations +plot_job_durations(dfj) +``` + +![Example Job Durations Plot](./docs/images/duration_by_job.png) + +From this example, we can tell that the `cxg` jobs are taking a long time to complete. We can also see that successful jobs complete in around an hour, while failed jobs take over 20 hours on average. + +### What kinds of validation errors are we seeing? + +Now that we know some of the jobs are failing, it would be helpful if we can use the monitoring tools to inspect why the jobs are failing. Broadly, there are two families of issues; infrastructural issues, and runtime issues. The monitoring tools can help diagnose runtime issues, while we leave infrastructural issues for another time. + +To get at the runtime issues, we would like to augment the job descriptions with log data. The log data will have all output from the logging system. This includes `INFO`, `WARNING`, and `ERROR` log-level messages. + +> **NOTE:** This is a relatively expensive procedure, given the synchronous nature of the current implementation using boto3. + +```python +from reporting.logs import merge_job_logs + +# filter for 'FAILED' jobs +dff = dfj[dfj["status"]=="FAILED"] + +# augment with logs +dff = merge_job_logs(dff) + +# view logs results (for a single job) +dff.iloc[0]["logResults"] +``` + +> :construction: **TODO:** Filter for `ERROR` log-level messaages. + +### Which datasets have completed migration? + +So far, we have only looked at the status of individual jobs. However, the migration of a dataset involves multiple jobs. We may want to aggregate the job data up to the dataset level. + +```python +from reporting.datasets import report_datasets + +# aggregate job information by DATASET +dfds = report_datasets(dfj) +``` + +> :warning: **TODO:** this aggregation is imperfect as it relies on _DATASET_VERSION_ID_, which changes its value during the course of the ingestion stage. It can be improved by updating job/step definitions to include `DATASET_ID` for every job/step. + +You can view the report as a simple bar chart. + +```python +from reporting.datasets import plot_dataset_status_counts + +# plot dataset counts by status +plot_dataset_status_counts(dfds) +``` + +![Example Plot Dataset Count By Status](./docs/images/datasets_by_status.png) + +### Compare execution durations between environments + +In the process of running a migration, we noticed that some jobs that completed in `dev` failed in `prod`. We wanted to examine this phenomenon globally across two distinct migration runs. In this example, we'll use the `sfn` (step-functions) client. + +> **NOTE:** step-function execution results are returned longer that job descriptions or job-logs. Additionally, the step-function execution for the `ingestion` phase tracks the progress of a single ingestion end-to-end. + +In the following example, we'll imagine a situation where a single migration in `dev` occurred between 4/1/2025 and 4/5/2025, while a `prod` migration occurred between 4/6/2025 and 4/10/2025. We will also imagine that we care about two particular steps in the ingestion phase; `cxg` and `validate_anndata` + +> :warning: **WARNING:** The step function execution query is relatively slow, as it is fetched one-per-execution, of which there are thousands. It can take upwards of 5 or 10 minutes. + +```python +from config import get_sfn_name +from reporting.sfn import describe_sfn_executions, plot_job_duration_differences + +# --------- DEV RESULTS ------------------------------------------- +# dev migration parameters +set_stage("dev") +dev_start = datetime(2025, 4, 1, 0, 0, 0, tzinfo=timezone.utc) +dev_end = datetime(2025, 4, 5, 23, 59, 59, tzinfo=timezone.utc) + +# get results (using step function client) +df_dev = describe_sfn_executions(get_sfn_name(), dev_start, dev_end) + +# --------- PROD RESULTS ------------------------------------------- +set_stage("prod") +prod_start = datetime(2025, 4, 6, 0, 0, 0, tzinfo=timezone.utc) +prod_end = datetime(2025, 4, 10, 23, 59, 59, tzinfo=timezone.utc) + +# get results (using step function client) +df_prod = describe_sfn_executions(get_sfn_name(), prod_start, prod_end) + +# --------- COMPARE ------------------------------------------------- +# plot top 50 differentially long-running 'cxg' jobs +plot_job_duration_differences(df_dev, df_prod, "dev","prod", "cxg",count=50) + +# plot top 50 differentially long-running 'validate_anndata' jobs +plot_job_duration_differences(df_dev, df_prod, "dev","prod", "validate_anndata",count=50) +``` + +![Example CXG duration differences](./docs/images/cxg_time_deltas.png) +![Example Validate Anndata differences](./docs/images/validate_anndata_time_deltas.png) + +> **NOTE:** DATASET_VERSION_ID is obscured for security reasons. + +# Future Work + +This monitoring project is in a very early stage of development. Currently, it helps save a bunch of clicks and it is easier to get a birds-eye view of ongoing (or recently completed) migrations. Some opportunities for improvement are: + +## Passing _COLLECTION_ID_ and _DATASET_ID_ To All Jobs/Steps + +Currently, the monitoring data only directly access information available to that execution's environment variables. In most cases, the environment variables contain values for `COLLECTION_VERSION_ID` and `DATASET_VERSION_ID`. However, these are insufficient. The `DATASET_VERSION_ID` refers to different things depending on the step/job that is running; sometimes it is the ID before the migration runs and sometimes it is the ID of the ongoing revision. This makes it **impossible** to collate the job data by the dataset on which it is operating. There is no cost to including the same collection/dataset identifiers throughout every step of the ingestion stage. + +## AsyncIO Enbled Boto3 Queries + +At this stage in development, only synchronous queries are supported i.e. loop through jobs --> fetch logs -> wait -> continue. We should wrap all io-bound operations with an asynchronous handler to issues io requests in parallel. + +## Standardize Logger Messages for Easy Parsing + +The cloudwatch jobs contain both (i) application logs, and (ii) runtime logs, since both are sent to `stdout` and `stderr`. Both sources produce differently structured messages, that are difficult to parse. Monitoring should filter for "application logs" (i.e. those issued by the `logging` module). Furthermore, we could process the `info` log-level events to track progress (especially for long-running tasks like `cxg` conversion) and the `error` log-level events to track and categorize errors. + +## Handle Infrastructure Related Errors + +Sometimes jobs/steps fail for reasons not having to do with runtime. For example, a job can run out of memory or a container can fail to be installed from a container registry. These, and others, are infrastructural errors. Ideally, we could differentiate them from execution errors and categorize based on common or re-occurring issues. + +## Reconcile/Harmonize the monitoring via step-function and jobs + +The `job` is the more grular unit of work, while `step-functions` organize work at a more semantically meaningful level. The monitoring tooling supports both, but they do not inter-operate very well. + +## Add migration-run tag to step function execution metadata + +Migration step-functions are not run in parallel, so we can group jobs that run in a particular migration attempt by knowing the start and end times. This is, however, rather fragile. A more robust solution is to tag each attempt with an environment variable that gets past to every job triggered in the step-fucntion. For example, we could label attempt-1 `{"MIGRATION_ATTEMPT": "ATTEMPT_1"}` and attempt-2 `{"MIGRATION_ATTEMPT": "ATTEMPT-2"}`. We could then support better culling of jobs/steps by the larger migration attempt to which it belongs. + +```python +from reporting.jobs import describe_migration_jobs + +# convert jobs to a convenient dataframe for viewing +# dfj = describe_migration_jobs(migration_start, migration_end) <-- instead of this... +dfj = describe_migration_jobs("ATTEMPT_1") # <-- ... use this +``` + +### Add COLLECTION and DATASET semantics to monitoring + +Currently, the monitoring operates at the job/step granularity level. However, we are more often interested in the semantically meaningful 'collection' or 'dataset' level. This would enable additional semantics that are meaningful to project managers and curators e.g. "public/private" or "published/unpublished". Imagine the following: + +```python +from reporting.collections import get_collection_status + +# get status for all collections +df_collections = get_collections_status(visibility=None, curator=None) + +# get status for a particular collection +df_collection_status = get_collection_status(collection_id=COLLECTION_ID) +``` + +### Make installable with a CLI + +Currently, these tools are not packaged separately. They are part of the `single-cell-data-portal` repo. It would be nice the package was installable as a CLI from pip. + +```bash +# install into a virtual environment +pip install cxgm -> 💡 **Note:** We currently don't tag a migration execution. Therefore we will use start times and end times to cull the relelvant jobs +# use the CLI +cxgm descirbe jobs --env dev --tag ATTEMPT_1 --out df_attempt_1.csv +cxgm plot jobs --y count --x job_name --color status --env dev --tag ATTEMPT_1 --out plot_jobs.png +``` diff --git a/migrations/docs/diagrams/migration_sfn.md b/migrations/docs/diagrams/migration_sfn.md index 6dedbb78340dd..35c673cb00962 100644 --- a/migrations/docs/diagrams/migration_sfn.md +++ b/migrations/docs/diagrams/migration_sfn.md @@ -3,7 +3,7 @@ title: Migration Procedure --- flowchart LR - subgraph migration + subgraph Migration direction TB stp-span-col("Span Collections") --> stp-migrate-col@{shape: procs, label: "CollectionMigration"} stp-migrate-col --> stp-span-ds("Span Datasets") @@ -11,7 +11,7 @@ flowchart LR stp-migrate-ds --> stp-ingest-ds("IngestDataset") end - subgraph ingestion + subgraph IngestDataset direction TB stp-enter("Enter") --> parallel_validation subgraph parallel_validation @@ -24,6 +24,6 @@ flowchart LR stp-addlabels --> stp-cxg("CXG") stp-cxg --> stp-register("Register") end - stp-ingest-ds --> ingestion + Migration --IngestDataset--> IngestDataset ``` diff --git a/migrations/docs/diagrams/migration_sfn.md-1.svg b/migrations/docs/diagrams/migration_sfn.md-1.svg new file mode 100644 index 0000000000000..f41188a48069f --- /dev/null +++ b/migrations/docs/diagrams/migration_sfn.md-1.svg @@ -0,0 +1 @@ +

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DatasetMigration

IngestDataset

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-> str: @@ -29,25 +31,37 @@ def set_stage(stage: Union[STAGE, str]=STAGE.dev) -> None: except: raise ValueError(f"The provided stage '{stage}' is not a valid stage.") - # set the environment variable + # set the environment variable (and global variable) os.environ['MIGRATION_MONITOR_STAGE'] = stage.value + global _STAGE + _STAGE = stage # get aws session _session = _get_session(stage) # set global constants based on stage - global _BATCH_CLIENT, _LOGS_CLIENT, _JOB_QUEUE + global _BATCH_CLIENT, _LOGS_CLIENT, _JOB_QUEUE, _SFN_CLIENT _BATCH_CLIENT = _session.client('batch') _LOGS_CLIENT = _session.client("logs") + _SFN_CLIENT = _session.client("stepfunctions") # set other constants _JOB_QUEUE = f"schema_migration-{stage.value}" def get_batch_client(): + global _BATCH_CLIENT return _BATCH_CLIENT def get_logs_client(): + global _LOGS_CLIENT return _LOGS_CLIENT def get_job_queue(): - return _JOB_QUEUE \ No newline at end of file + global _JOB_QUEUE + return _JOB_QUEUE + +def get_sfn_client(): + global _SFN_CLIENT + return _SFN_CLIENT + + diff --git a/migrations/src/reporting/jobs.py b/migrations/src/reporting/jobs.py index 2e79b431e78f8..acde499f901e4 100644 --- a/migrations/src/reporting/jobs.py +++ b/migrations/src/reporting/jobs.py @@ -7,13 +7,7 @@ from config import get_job_queue from jobs.jobs import get_all_jobs, STATUS_ENUM from jobs.details import get_structured_job_details - - -def ms_to_minutes(time): - return time/(1000*60) - -def ms_to_hours(time): - return ms_to_minutes(time)/60 +from utils import ms_to_hours # return job data as a dataframe def describe_migration_jobs(start_time:datetime, end_time: datetime) -> pd.DataFrame: diff --git a/migrations/src/reporting/sfn.py b/migrations/src/reporting/sfn.py new file mode 100644 index 0000000000000..3b331bcac662d --- /dev/null +++ b/migrations/src/reporting/sfn.py @@ -0,0 +1,225 @@ +from typing import Union, List +from dataclasses import dataclass +from datetime import datetime, timezone +import json +import re + +import pandas as pd +from plotly import express as px + +from config import get_sfn_client +from sfn.executions import get_all_sfn_executions_by_name, get_sfn_execution_history +from utils import ms_to_hours + +@dataclass +class JobExecution: + startedAt: datetime + stoppedAt: datetime + DATASET_VERSION_ID: str + CURRENT_DATASET_VERSION_ID: Union[str,None] + STEP_NAME: str + status: str + + +# extract the current_dataset_version_id based on information in the job/step environment variables +def _pluck_current_dataset_version_id(environment_variables) -> str: + ''' + DATASET_VERSION_ID: target id for the new dataset revision + We want the original (to be migrated/validated) dataset version id. We'll call this + CURRENT_DATASET_VERSION_ID. + + We get this from the MANIFEST environment variable, passed to a step. From there the CURRENT_DATASET_VERSION_ID + is the name of the directory in the ARTIFACT_BUCKET e.g. s3://corpora-data-prod/__CURRENT_DATASET_VERSION_ID__. + ''' + manifest = environment_variables.get("MANIFEST",None) + # if not present, ignore + if not manifest: + return None + + # the value is a string; make it a json object + manifest = json.loads(manifest) + + # get the anndata + anndata_location = manifest.get("anndata",None) + if not anndata_location: + return None + + # get the ARTIFACT_BUCKET + artifact_bucket = environment_variables.get("ARTIFACT_BUCKET") # this must exist if we get this far + submission_bucket = "cellxgene-dataset-submissions/super" # alternative location for validation steps + _prefix = "|".join([artifact_bucket, submission_bucket]) + + r = re.search(rf"({_prefix})/(?P[0-9A-z-]+)", anndata_location) + return r.group("cdvid") + + +# create JobExecution object from a successful sfn event +def _from_successful_task(event) -> JobExecution: + data = json.loads(event["taskSucceededEventDetails"]["output"]) + env = {e["Name"]:e["Value"] for e in data["Container"]["Environment"]} + + return JobExecution( + startedAt=data.get("StartedAt", data["CreatedAt"]), + stoppedAt=data["StoppedAt"], + DATASET_VERSION_ID=env["DATASET_VERSION_ID"], + CURRENT_DATASET_VERSION_ID= _pluck_current_dataset_version_id(env), + STEP_NAME = env["STEP_NAME"], + status=data["Status"] + ) + + +# create JobExecution object from a failed sfn event +def _from_failed_task(event) -> JobExecution: + data = json.loads(event["taskFailedEventDetails"]["cause"]) + env = {e["Name"]:e["Value"] for e in data["Container"]["Environment"]} + return JobExecution( + startedAt=data.get("StartedAt", data["CreatedAt"]), + stoppedAt=data["StoppedAt"], + DATASET_VERSION_ID=env["DATASET_VERSION_ID"], + CURRENT_DATASET_VERSION_ID= _pluck_current_dataset_version_id(env), + STEP_NAME = env["STEP_NAME"], + status=data["Status"] + ) + + +# extract JobExecution objects from the step function history (only Successfull and Failed events) +def process_sfn_history(history) -> List[JobExecution]: + records = [] + for event in history: + if event["type"] == "TaskFailed": + records.append(_from_failed_task(event)) + elif event["type"]=="TaskSucceeded": + records.append(_from_successful_task(event)) + return records + + +# extract JobExecution objects from all executions of a step-function in a given time range +def batch_process_sfn(sfn_name:str, start:datetime, end:datetime, status: List[str]=["SUCCEEDED","FAILED"]): + ''' + First get all executions in the time range. + Then process the execution-history of each execution. + Return Job Execution Reports + ''' + print("retrieving executions") + executions = get_all_sfn_executions_by_name(sfn_name, start, end,status) + print("--DONE") + + print("retrieving histories") + N = len(executions) + records = [] + for i,e in enumerate(executions): + if i%50==0: + print(f"index={i} of {N}") + history = get_sfn_execution_history(e) + records.extend(process_sfn_history(history)) + print("--DONE") + return records + + +# backfill CURRENT_DATASET_VERSION_ID +def _backfill_dsvid(df:pd.DataFrame) -> pd.DataFrame: + # map DATASET -> CURRENT_DATASET + map_ds = {r["DATASET_VERSION_ID"]:r["CURRENT_DATASET_VERSION_ID"] for _,r in df.iterrows() if r["CURRENT_DATASET_VERSION_ID"] is not None} + + # create new column of CURRENT_DATASET_VERSION_ID based on the above mapping + CDVID = [map_ds.get(dvid,None) for dvid in df["DATASET_VERSION_ID"].values] + + df["CURRENT_DATASET_VERSION_ID"] = CDVID + return df + + +# backfill durations +def _backfill_duration(df:pd.DataFrame) -> pd.DataFrame: + #startedAt_dev stoppedAt_dev + _start = "startedAt" + _stop = "stoppedAt" + + # row-wise duration (in ms) + def duration(r): + return r[_stop] - r[_start] + + df = df.assign(duration=df.apply(duration,axis=1)) + return df + + +# backfill various columns +def backfill(df:pd.DataFrame) -> pd.DataFrame: + df = _backfill_dsvid(df) + df = _backfill_duration(df) + return df + + +# get dataframe with the summary for every step function execution +def describe_sfn_executions(sfn_name:str, start:datetime, end:datetime, status: List[str]=["SUCCEEDED","FAILED"]) -> pd.DataFrame: + ''' + Get all executions of a step-function in a given time range. + ''' + # get details + records = batch_process_sfn(sfn_name, start, end, status) + df = pd.DataFrame.from_records([r.__dict__ for r in records]) + df = backfill(df) + return df + +#---------------------------- COMPARISONS ---------------------------- +''' +Reporting on differences between two environments (or other groups). +''' + +# row-wise computation of differences in duration (right - left) +def _compute_delta_duration(r, left_label:str, right_label:str) -> int: + # ORDER is important + return r[f"duration{right_label}"]- r[f"duration{left_label}"] + + +# backfill duration differences for every execution in the execution table +def _backfill_delta_duration(df:pd.DataFrame, left_label: str, right_label: str) -> pd.DataFrame: + df = df.assign(delta_duration=df.apply(lambda x: _compute_delta_duration(x, left_label, right_label), axis=1)) + return df + + +# join on CURRENT_DATASET_VERSION_ID +def join_on_cdvid(left:pd.DataFrame, right:pd.DataFrame, left_label:str, right_label:str) -> pd.DataFrame: + join_column = 'CURRENT_DATASET_VERSION_ID' + return pd.merge(left,right, how="inner",on=join_column, suffixes=[f"_{left_label}",f"_{right_label}"]) + + +# backfill joined dataset +def backfill_joined(df:pd.DataFrame, left_label: str, right_label: str) -> pd.DataFrame: + df = _backfill_delta_duration(df, left_label, right_label) + return df + +# create comparison dataset +def create_comparison_dataset(left:pd.DataFrame, right:pd.DataFrame, left_label:str, right_label:str) -> pd.DataFrame: + # join together into one table + df = join_on_cdvid(left, right, left_label, right_label) + + # backfill the joined dataset with useful columns for comparison + df = backfill_joined(df, left_label, right_label) + + +# COMPARISON: plot waterfall of duration differences for a single job (in two different environments) +def plot_job_duration_differences(left: pd.DataFrame, right: pd.DataFrame, left_label: str, right_label:str, job_name:str, count:int=50) -> px.Figure: + ''' + Waterfall plot of duration differences. + df: must be pre-joined on CURRENT_DATASET_VERSION_ID + ''' + # create the comparison dataset + df = create_comparison_dataset(left, right, left_label, right_label) + + # filter by job name + df = df[df["STEP_NAME"]==job_name] + + # sort values in descending order, take top N + df = df.sort_values("delta_duration",key=abs,ascending=False).iloc[0:count] + + # return the plot + return px.bar( + df, + x="CURRENT_DATASET_VERSION_ID", + y=df["delta_duration"].map(ms_to_hours), + color=df["delta_duration"].map(lambda x: right_label if x>0 else left_label), + title=f"Duration differences for {job_name}", + labels={"delta_duration":"Duration difference (ms)"}, + text="delta_duration", + orientation="v", + ) \ No newline at end of file diff --git a/migrations/src/sfn/__init__.py b/migrations/src/sfn/__init__.py new file mode 100644 index 0000000000000..d7daf80596637 --- /dev/null +++ b/migrations/src/sfn/__init__.py @@ -0,0 +1,13 @@ +from typing import List, Any +from config import get_sfn_client + + +# get step-function ARN by name +def get_sfn_arn(sfn_name:str) -> str: + client = get_sfn_client() + matches = [m for m in client.list_state_machines()["stateMachines"] if m["name"]==sfn_name] + if len(matches)==0: + raise Exception(f"No state machine found with name:{sfn_name}") + else: + return matches[0]["stateMachineArn"] + \ No newline at end of file diff --git a/migrations/src/sfn/executions.py b/migrations/src/sfn/executions.py new file mode 100644 index 0000000000000..0906cb777fd19 --- /dev/null +++ b/migrations/src/sfn/executions.py @@ -0,0 +1,54 @@ +from typing import List,Any, Union, Dict +from datetime import datetime, timezone + +from config import get_sfn_client +from utils import utc_to_dt +from sfn import get_sfn_arn + +# get execution results by step-function-ARN +def get_sfn_executions_by_arn(sfn_arn, status:str, start=None, end=None) -> List[Any]: + client = get_sfn_client() + executions = [] + has_next = True + r = {} # mock an empty response + + # get ALL of the executions, as the API is limited to 1,000 per call. + while has_next: + kwargs = {"stateMachineArn":sfn_arn, "statusFilter":status, "maxResults":1_000} + if r.get("nextToken",None): + kwargs = kwargs | {"nextToken":r["nextToken"]} + r = client.list_executions(**kwargs) + executions.extend(r["executions"]) + has_next = r.get("nextToken",False) + + start = start if start else datetime(2025, 1, 1, 0, 0, 0) + end = end if end else datetime(2025, 12, 31, 0, 0, 0) + if isinstance(start,int): + start = utc_to_dt(start) + if isinstance(end, int): + end = utc_to_dt(end) + + # NOTE:executions return datetime objects (in boto3) + # filter executions by datetime range + return [e for e in executions if e["startDate"] >= start and e["stopDate"] <= end] + + +# get the executions by name, single status only +def get_sfn_executions_by_name(sfn_name, status:str, start=None, end=None) -> List[Any]: + arn = get_sfn_arn(sfn_name) + return get_sfn_executions_by_arn(arn, status, start, end) + + +# get the executions by name, multiple status values +def get_all_sfn_executions_by_name(sfn_name, start=None, end=None, status:List[str]=["SUCCEEDED","FAILED"]) -> List[Any]: + executions = [] + arn = get_sfn_arn(sfn_name) + for _status in status: + executions.extend(get_sfn_executions_by_arn(arn, _status, start, end)) + return executions + +# get the history of a specific execution +def get_sfn_execution_history(execution: Union[str,Dict[str,str]]) -> List[Any]: + client = get_sfn_client() + arn = execution if isinstance(execution,str) else execution.get("executionArn",None) + return client.get_execution_history(executionArn=arn,reverseOrder=False)["events"] \ No newline at end of file diff --git a/migrations/src/utils.py b/migrations/src/utils.py index 07b25ff020144..f24e141015b30 100644 --- a/migrations/src/utils.py +++ b/migrations/src/utils.py @@ -6,3 +6,8 @@ def dt_to_utc(dt:datetime) -> int: def utc_to_dt(t:int) -> datetime: return datetime.fromtimestamp(int(t/1_000), timezone.utc) +def ms_to_minutes(time): + return time/(1000*60) + +def ms_to_hours(time): + return ms_to_minutes(time)/60 \ No newline at end of file From b4dd0445ec6ce3c961069f856becedfb0efe3b3d Mon Sep 17 00:00:00 2001 From: Evan Molinelli Date: Mon, 14 Apr 2025 14:48:01 -0400 Subject: [PATCH 3/3] remove bad type hint --- migrations/src/reporting/sfn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/migrations/src/reporting/sfn.py b/migrations/src/reporting/sfn.py index 3b331bcac662d..628eb72d784ad 100644 --- a/migrations/src/reporting/sfn.py +++ b/migrations/src/reporting/sfn.py @@ -198,7 +198,7 @@ def create_comparison_dataset(left:pd.DataFrame, right:pd.DataFrame, left_label: # COMPARISON: plot waterfall of duration differences for a single job (in two different environments) -def plot_job_duration_differences(left: pd.DataFrame, right: pd.DataFrame, left_label: str, right_label:str, job_name:str, count:int=50) -> px.Figure: +def plot_job_duration_differences(left: pd.DataFrame, right: pd.DataFrame, left_label: str, right_label:str, job_name:str, count:int=50): ''' Waterfall plot of duration differences. df: must be pre-joined on CURRENT_DATASET_VERSION_ID