From a56b14bb0fc37164fe36c8ae5522bcbc5238e107 Mon Sep 17 00:00:00 2001 From: Abhishek Bhatia Date: Mon, 30 Dec 2024 16:25:21 +0530 Subject: [PATCH 1/7] (wip): add initial draft for gcp dataproc deployment Signed-off-by: Abhishek Bhatia --- docs/source/deployment/gcp_dataproc.md | 456 +++++++++++++++++++++++++ 1 file changed, 456 insertions(+) create mode 100644 docs/source/deployment/gcp_dataproc.md diff --git a/docs/source/deployment/gcp_dataproc.md b/docs/source/deployment/gcp_dataproc.md new file mode 100644 index 0000000000..3da9e2a88a --- /dev/null +++ b/docs/source/deployment/gcp_dataproc.md @@ -0,0 +1,456 @@ +# GCP Dataproc + +The guide details kedro pipeline deployment steps for Dataproc compute engine and Dataproc serverless. + + +## Prerequisite Setup + +#### Create service accounts + +NOTE: + +> 1. The service account creation method below assigns all permissions needed for this walkthrough in one service account. +> 2. Different teired environments may have their own GCP Projects. +> 3. This does not indicate best practice, and you should create multiple service accounts with fine grained permissions to services and resources in different tiered environments. + +Create service account: + +```bash +gcloud iam service-accounts create sa-dataproc-runner --display-name "Dataproc spark pipeline runner service account" +``` + +Assign roles: + +```bash + +#!/bin/bash + +PROJECT_ID="xxxx" + +# Define the service account email, change the xxxxx +SERVICE_ACCOUNT_EMAIL="sa-dataproc-runner@${PROJECT_ID}" + +# Define the roles that you want in an array +roles=( + "roles/dataproc.admin" + "roles/artifactregistry.admin" + "roles/artifactregistry.repoAdmin" + "roles/dataproc.admin" + "roles/storage.admin" +) + +# Loop through each role and assign it to the service account +for role in "${roles[@]}"; do + gcloud iam service-accounts add-iam-policy-binding $SERVICE_ACCOUNT_EMAIL \ + --role="$role" \ + --member="serviceAccount:$SERVICE_ACCOUNT_EMAIL" +done + +``` + +Create and download service account JSON key: + +```bash +gcloud iam service-accounts keys create ${KEY_FILE} \ + --iam-account=sa-dataproc-runner@${PROJECT_ID}.iam.gserviceaccount.com +``` + +> NOTE: The service account JSON keys can be stored as secrets, and can be made accessible to CI in your custom workflows. + +### Create GCS Buckets + +The following GCS Buckets are created for development (`env=dev`) and production (`env=prod`) workflows. + +#### Dataproc temporary staging bucket + + +```bash +gcloud storage buckets create gs://dataproc-staging-${REGION}-${UID}-${ENV} --location ${REGION} +``` + + +#### Dataproc run submit artifacts bucket + +```bash +gcloud storage buckets create gs://dataproc-run-submit-${REGION}-${UID}-${ENV} --location ${REGION} +``` + +```bash +dataproc-run-submit-{REGION}-{UID}-dev/ +└── {branch}/ + └── {run_id}/ +``` + +```bash +dataproc-run-submit-{REGION}-{UID}-prod/ +└── release/ + └── {version}/ # for e.g. v0.1.2 +``` + +#### Dataproc run output bucket + +```bash +gcloud storage buckets create gs://dataproc-run-output-${REGION}-${UID}-${ENV} --location {REGION} +``` + +```bash +dataproc-run-output-{REGION}-{UID}-dev/ +└── {branch}/ + └── {run_id}/ +``` + +```bash +dataproc-run-output-{REGION}-{UID}-prod/ +└── release/ + └── {version}/ # for e.g. v0.1.2 + └── {run_id} +``` + + +#### Authorize with service account + + +```bash +gcloud auth activate-service-account --key-file=${KEY_FILE} +``` + +#### Define entrypoint script + +`deployment/dataproc/entrypoint.py` + +```python +import os +import argparse +import sys +import re + +def dequote(s: str): + return re.sub(r"^['\"]|['\"]$", '', s) + + +parser = argparse.ArgumentParser() +parser.add_argument("--kedro-run-args", type=str, default='', help="Arguments for kedro run") + +args = parser.parse_args() + +KEDRO_RUN_ARGS = dequote(args.KEDRO_RUN_ARGS) + +folders = os.listdir() + +# If code folder is not present, then we must be passing it as an archive on job submission +if 'code' not in folders: + subprocess.run([ + "unzip", "code.zip", "-d", "code" + ]) + +# Change to code directory +os.chdir("code") + +kedro_cmd = "kedro run" +if KEDRO_RUN_ARGS: + kedro_cmd += " " + KEDRO_RUN_ARGS + +subprocess.run(kedro_cmd, check=True, shell=True) +``` + +#### Define run submit artifact preparation script + +`deployment/dataproc/prepare_run_submit_artifacts.py` + +```python +import argparse +import subprocess +import sys + +# NOTE: Suggested pattern => Define your own project_utils module to store common functions +from project_utils import load_project_config + +import re + + +def run_subprocess_commands(commands): + for command in commands: + print(f"Running command: {command}") + try: + subprocess.run(command, check=True) + except subprocess.CalledProcessError as e: + print(f"An error occurred: {e}") + sys.exit(1) + + +parser = argparse.ArgumentParser(description='Submit Kedro job to Dataproc') +parser.add_argument('--env', choices=['dev', 'prod']) +parser.add_argument('--release', type=str, help='Release version', required=False) +parser.add_argument('--run-id', type=str, help='run id', required=False) +parser.add_argument('--kedro-run-args', type=str, help='Arguments for kedro run', required=False) + +args = parser.parse_args() + +if args.env == 'dev' and args.release: + parser.error("--release should not be provided when env is 'dev'") +if args.env == 'dev' and not args.run_id: + parser.error("--run-id must be provided when env is 'dev'") +if args.env == 'prod' and not args.release: + parser.error("--release must be provided when env is 'prod'") +if args.env == 'prod' and args.run_id: + parser.error("--run-id should not be provided when env is 'prod'") + +ENV = args.env +RELEASE = args.release +RUN_ID = args.run_id +KEDRO_RUN_ARGS = args.kedro_run_args + +proc = subprocess.Popen(["git", "rev-parse", "--abbrev-ref", "HEAD"], stdout=subprocess.PIPE) +BRANCH_NAME, err = proc.communicate() +BRANCH_NAME = BRANCH_NAME.decode().strip() +BRANCH_NAME_CLEAN = re.sub(r'[^a-zA-Z0-9]', '-', BRANCH_NAME) + +PROJECT_CONFIG = load_project_config()[ENV] + +PROJECT_ID = PROJECT_CONFIG["PROJECT_ID"] +USECASE = PROJECT_CONFIG["USECASE"] +REGION = PROJECT_CONFIG["REGION"] +RUN_SUBMIT_BUCKET = PROJECT_CONFIG["DATAPROC"]["RUN_SUBMIT_BUCKET"] + +if ENV == 'dev': + GCS_RUN_SUBMIT_DIR = f'gs://{RUN_SUBMIT_BUCKET}/{BRANCH_NAME_CLEAN}/{RUN_ID}' +elif ENV == 'prod': + GCS_RUN_SUBMIT_DIR = f'gs://{RUN_SUBMIT_BUCKET}/{RELEASE}' + + +MAIN_ENTRYPOINT_PY = f"{GCS_RUN_SUBMIT_DIR}/entrypoint.py" +GCS_CODE_ARCHIVE_URI = f'{GCS_RUN_SUBMIT_DIR}/code.zip' + +LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR = "dataproc_runs" + +if ENV == 'dev': + CONTAINER_IMAGE_URI = f"{REGION}-docker.pkg.dev/{PROJECT_ID}/{USECASE}/dataproc-serverless:{BRANCH_NAME_CLEAN}" +elif ENV == 'prod': + CONTAINER_IMAGE_URI = f"{REGION}-docker.pkg.dev/{PROJECT_ID}/{USECASE}/dataproc-serverless:{RELEASE}" + + +commands = [ + # create staging directory + ['mkdir', '-p', f'{LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR}'] +] + + +# For dev workflows, we create a new branch for each run with all changes auto-committed +if ENV == 'dev': + + commands += [ + # Stash uncommited changes, checkout new branch, apply stash, commit changes + ["git", "stash"], + ["git", "checkout", '-B', f"pipeline-run/{RUN_ID}"], + ["git", "stash", "apply"], + ["git", "add", "."], + ["git", "commit", '-m', f"pipeline run: {RUN_ID}"] +] + +commands += [ + # Archive code + ['git', 'archive', '-o', f'{LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR}/code.zip', 'HEAD'], + + # Copy run submit artifacts to GCS + ['gsutil', 'cp', '-r', 'scripts/dataproc/main.py', MAIN_ENTRYPOINT_PY], + ['gsutil', 'cp', '-r', f'{LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR}/code.zip', GCS_CODE_ARCHIVE_URI], + + # Return back to original git changes + ["git", "checkout", "-"], + ["git", "stash", "pop"] +] + +run_subprocess_commands(commands) +``` + +## Dataproc Serverless + +Dataproc Serverless allows you to run Spark jobs without needing to manage the underlying infrastructure. It automatically scales resources up and down based on the job requirements. +Also, refer to the github repo on [kedro pyspark dataproc demo](https://github.com/getindata/kedro-pyspark-dataproc-demo) by getindata. + +### Repo structure + + +```bash +. +├── conf +├── deployment/ +│ └── dataproc/ +│ └── serverless/ +│ ├── build_push_docker.sh +│ ├── Dockerfile +│ └── submit_batches.py +└── src +``` + +### Build Docker Image + +`deployment/dataproc/serverless/Dockerfile` + +```docker +ARG BASE_IMAGE=python:3.9-buster + +FROM ${BASE_IMAGE} + +# overwrite default Dataproc PYSPARK_PYTHON path +ENV PYSPARK_PYTHON /usr/local/bin/python +ENV PYSPARK_DRIVER_PYTHON /usr/local/bin/python + +# Add extra jars. +ENV SPARK_EXTRA_JARS_DIR="/opt/spark/jars/" +ENV SPARK_EXTRA_CLASSPATH="/opt/spark/jars/*" +RUN mkdir -p "${SPARK_EXTRA_JARS_DIR}" + + +# (Required) Install utilities required by Spark scripts. +RUN apt update && apt install -y procps tini openjdk-11-jre-headless + + +# add kedro user +ARG KEDRO_UID=999 +ARG KEDRO_GID=0 + + +RUN groupadd -f -g ${KEDRO_GID} kedro_group && \ +useradd -d /home/kedro -s /bin/bash -g ${KEDRO_GID} -u ${KEDRO_UID} kedro + +# (Required) Create the 'spark' group/user. +# The GID and UID must be 1099. Home directory is required. +RUN groupadd -g 1099 spark +RUN useradd -u 1099 -g 1099 -d /home/spark -m spark + +# copy the whole project except what is in .dockerignore +WORKDIR /home/kedro + +# Install and setup uv +RUN pip install --upgrade pip uv build +ENV UV_INDEX_STRATEGY=unsafe-any-match + +# Install dependencies +COPY src/requirements.txt /tmp/requirements.txt +RUN uv pip install --system \ + -r /tmp/requirements.txt \ + && rm /tmp/requirements.txt + + +RUN chown -R kedro:${KEDRO_GID} /home/kedro + +USER kedro + +# development image +FROM base as dev + +# production image +FROM base as prod + +# copy source code +COPY . /code + +RUN chmod -R a+w /home/kedro + +``` + + +### Configure Artifact registry + +```bash +gcloud artifacts repositories create my-kedro-project \ + --repository-format=docker \ + --location=${REGION} \ + --description="Kedro project docker repository" +``` + +#### Gcloud configure docker auth + +```bash +gcloud auth configure-docker {region}-docker.pkg.dev +``` + +#### Push Image to Artifact Registry + + +`deployment/dataproc/serverless/build_push_docker.sh` + +```bash +#!/usr/bin/env bash + +set -ex + +BRANCH_NAME=$(git rev-parse --abbrev-ref HEAD) +BRANCH_NAME_CLEAN=$(echo ${BRANCH_NAME} | sed 's/[^a-zA-Z0-9]/-/g') + +# Parse arguments +while [[ "$#" -gt 0 ]]; do + case $1 in + --env) ENV="$2"; shift ;; + --release) RELEASE="$2"; shift ;; + *) echo "Unknown parameter passed: $1"; exit 1 ;; + esac + shift +done + +if [ "$ENV" == "dev" ]; then + CONTAINER_IMAGE_TAG=${BRANCH_NAME_CLEAN} +elif [ "$ENV" == "prod" ]; then + CONTAINER_IMAGE_TAG=${RELEASE} +else + echo "Unknown environment: $ENV" + exit 1 +fi + +CONTAINER_IMAGE=xxxx:${CONTAINER_IMAGE_TAG} + +docker build -f deployment/dataproc/Dockerfile -t ${CONTAINER_IMAGE} . --platform="linux/amd64" +docker push ${CONTAINER_IMAGE} +``` + +In dev workflow: + +```bash +deployment/dataproc/serverless/build_push_docker.sh --env dev +``` + +In prod workflow: + +```bash +deployment/dataproc/serverless/build_push_docker.sh --env prod --release v0.1.2 +``` + +#### Submit pyspark batches + +For dev workflow: + +```bash +python deployment/dataproc/serverless/submit_batches.py \ + --env dev \ + --run-id 20241230-my-kedro-pipeline-dev-run-1 \ + --kedro-run-args="--pipeline=my_kedro_pipeline --params='param1:10,param2=a'" +``` + +For prod workflow: + +During release (one time): + +```bash +python deployment/dataproc/serverless/submit_batches.py \ + --env prod \ + --release v0.1.2 \ + --no-run-trigger +``` + +In subsequent run triggers: + +```bash +python deployment/dataproc/serverless/submit_batches.py \ + --env prod \ + --release v0.1.2 \ + --no-prepare-artifacts \ + --run-id 20241230-my-kedro-pipeline-prod-run-1 \ + --kedro-run-args="--pipeline=my_kedro_pipeline --params='param1:10,param2=a'" +``` + +NOTE +> 1. It is recommended to automate release workflows in the CI/CD pipeline using CI Framework native docker build steps which can benefit from caching. +> 2. The pipeline deployment depicted here is a simplified example. In real world workflows, the pipeline deployment will have a lot more nuances. + + From cdde986cb019fbf265953752734fbe6b43af1fdf Mon Sep 17 00:00:00 2001 From: Abhishek Bhatia Date: Mon, 30 Dec 2024 21:36:52 +0530 Subject: [PATCH 2/7] fix submit_batches.py script Signed-off-by: Abhishek Bhatia --- docs/source/deployment/gcp_dataproc.md | 254 ++++++++++++++----------- 1 file changed, 144 insertions(+), 110 deletions(-) diff --git a/docs/source/deployment/gcp_dataproc.md b/docs/source/deployment/gcp_dataproc.md index 3da9e2a88a..ee90419be4 100644 --- a/docs/source/deployment/gcp_dataproc.md +++ b/docs/source/deployment/gcp_dataproc.md @@ -153,116 +153,6 @@ if KEDRO_RUN_ARGS: subprocess.run(kedro_cmd, check=True, shell=True) ``` -#### Define run submit artifact preparation script - -`deployment/dataproc/prepare_run_submit_artifacts.py` - -```python -import argparse -import subprocess -import sys - -# NOTE: Suggested pattern => Define your own project_utils module to store common functions -from project_utils import load_project_config - -import re - - -def run_subprocess_commands(commands): - for command in commands: - print(f"Running command: {command}") - try: - subprocess.run(command, check=True) - except subprocess.CalledProcessError as e: - print(f"An error occurred: {e}") - sys.exit(1) - - -parser = argparse.ArgumentParser(description='Submit Kedro job to Dataproc') -parser.add_argument('--env', choices=['dev', 'prod']) -parser.add_argument('--release', type=str, help='Release version', required=False) -parser.add_argument('--run-id', type=str, help='run id', required=False) -parser.add_argument('--kedro-run-args', type=str, help='Arguments for kedro run', required=False) - -args = parser.parse_args() - -if args.env == 'dev' and args.release: - parser.error("--release should not be provided when env is 'dev'") -if args.env == 'dev' and not args.run_id: - parser.error("--run-id must be provided when env is 'dev'") -if args.env == 'prod' and not args.release: - parser.error("--release must be provided when env is 'prod'") -if args.env == 'prod' and args.run_id: - parser.error("--run-id should not be provided when env is 'prod'") - -ENV = args.env -RELEASE = args.release -RUN_ID = args.run_id -KEDRO_RUN_ARGS = args.kedro_run_args - -proc = subprocess.Popen(["git", "rev-parse", "--abbrev-ref", "HEAD"], stdout=subprocess.PIPE) -BRANCH_NAME, err = proc.communicate() -BRANCH_NAME = BRANCH_NAME.decode().strip() -BRANCH_NAME_CLEAN = re.sub(r'[^a-zA-Z0-9]', '-', BRANCH_NAME) - -PROJECT_CONFIG = load_project_config()[ENV] - -PROJECT_ID = PROJECT_CONFIG["PROJECT_ID"] -USECASE = PROJECT_CONFIG["USECASE"] -REGION = PROJECT_CONFIG["REGION"] -RUN_SUBMIT_BUCKET = PROJECT_CONFIG["DATAPROC"]["RUN_SUBMIT_BUCKET"] - -if ENV == 'dev': - GCS_RUN_SUBMIT_DIR = f'gs://{RUN_SUBMIT_BUCKET}/{BRANCH_NAME_CLEAN}/{RUN_ID}' -elif ENV == 'prod': - GCS_RUN_SUBMIT_DIR = f'gs://{RUN_SUBMIT_BUCKET}/{RELEASE}' - - -MAIN_ENTRYPOINT_PY = f"{GCS_RUN_SUBMIT_DIR}/entrypoint.py" -GCS_CODE_ARCHIVE_URI = f'{GCS_RUN_SUBMIT_DIR}/code.zip' - -LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR = "dataproc_runs" - -if ENV == 'dev': - CONTAINER_IMAGE_URI = f"{REGION}-docker.pkg.dev/{PROJECT_ID}/{USECASE}/dataproc-serverless:{BRANCH_NAME_CLEAN}" -elif ENV == 'prod': - CONTAINER_IMAGE_URI = f"{REGION}-docker.pkg.dev/{PROJECT_ID}/{USECASE}/dataproc-serverless:{RELEASE}" - - -commands = [ - # create staging directory - ['mkdir', '-p', f'{LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR}'] -] - - -# For dev workflows, we create a new branch for each run with all changes auto-committed -if ENV == 'dev': - - commands += [ - # Stash uncommited changes, checkout new branch, apply stash, commit changes - ["git", "stash"], - ["git", "checkout", '-B', f"pipeline-run/{RUN_ID}"], - ["git", "stash", "apply"], - ["git", "add", "."], - ["git", "commit", '-m', f"pipeline run: {RUN_ID}"] -] - -commands += [ - # Archive code - ['git', 'archive', '-o', f'{LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR}/code.zip', 'HEAD'], - - # Copy run submit artifacts to GCS - ['gsutil', 'cp', '-r', 'scripts/dataproc/main.py', MAIN_ENTRYPOINT_PY], - ['gsutil', 'cp', '-r', f'{LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR}/code.zip', GCS_CODE_ARCHIVE_URI], - - # Return back to original git changes - ["git", "checkout", "-"], - ["git", "stash", "pop"] -] - -run_subprocess_commands(commands) -``` - ## Dataproc Serverless Dataproc Serverless allows you to run Spark jobs without needing to manage the underlying infrastructure. It automatically scales resources up and down based on the job requirements. @@ -418,6 +308,150 @@ deployment/dataproc/serverless/build_push_docker.sh --env prod --release v0.1.2 #### Submit pyspark batches +`deployment/dataproc/serverless/submit_batches.py` + +```python +import argparse +import subprocess +import sys +from datetime import datetime + +# NOTE: Suggested pattern => Define your own project_utils module to store common functions +from project_utils import load_project_config + +import re + + +def run_subprocess_commands(commands): + for command in commands: + print(f"Running command: {command}") + try: + subprocess.run(command, check=True) + except subprocess.CalledProcessError as e: + print(f"An error occurred: {e}") + sys.exit(1) + + +parser = argparse.ArgumentParser(description='Submit Kedro job to Dataproc') +parser.add_argument('--env', choices=['dev', 'prod']) +parser.add_argument('--release', type=str, help='Release version', required=False) +parser.add_argument('--run-id', type=str, help='run id', required=False) +parser.add_argument('--no-prepare-artifacts', action="store_true", help='whether to prepare run submit artifacts. Default: True', required=False) +parser.add_argument('--no-run-trigger', action="store_true", help='whether to trigger run. Default: True', required=False) +parser.add_argument('--kedro-run-args', type=str, help='kedro pipeline run args', required=False) + +args = parser.parse_args() + +if args.env == 'dev' and args.release: + parser.error("--release should not be provided when env is 'dev'") +if args.env == 'dev' and not args.run_id: + parser.error("--run-id must be provided when env is 'dev'") +if args.env == 'prod' and not args.release: + parser.error("--release must be provided when env is 'prod'") +if args.env == 'prod' and args.run_id: + parser.error("--run-id should not be provided when env is 'prod'") + +ENV = args.env +RELEASE = args.release +RUN_ID = args.run_id +PREPARE_ARTIFACTS = not args.no_prepare_artifacts +TRIGGER_RUN = not args.no_run_trigger +KEDRO_RUN_ARGS = args.kedro_run_args + +proc = subprocess.Popen(["git", "rev-parse", "--abbrev-ref", "HEAD"], stdout=subprocess.PIPE) +BRANCH_NAME, err = proc.communicate() +BRANCH_NAME = BRANCH_NAME.decode().strip() +BRANCH_NAME_CLEAN = re.sub(r'[^a-zA-Z0-9]', '-', BRANCH_NAME) + +PROJECT_CONFIG = load_project_config()[ENV] + +PROJECT_ID = PROJECT_CONFIG["PROJECT_ID"] +USECASE = PROJECT_CONFIG["USECASE"] +REGION = PROJECT_CONFIG["REGION"] +RUN_SUBMIT_BUCKET = PROJECT_CONFIG["DATAPROC"]["RUN_SUBMIT_BUCKET"] +RUN_SERVICE_ACCOUNT = PROJECT_CONFIG["DATAPROC"]["RUN_SERVICE_ACCOUNT"] +DATAPROC_RUNTIME_VERSION = PROJECT_CONFIG["DATAPROC"]["SERVERLESS"]["RUNTIME_VERSION"] + +if ENV == 'dev': + GCS_RUN_SUBMIT_DIR = f'gs://{RUN_SUBMIT_BUCKET}/{BRANCH_NAME_CLEAN}/{RUN_ID}' +elif ENV == 'prod': + GCS_RUN_SUBMIT_DIR = f'gs://{RUN_SUBMIT_BUCKET}/{RELEASE}' + + +if ENV == 'dev': + MAIN_ENTRYPOINT_PY = f"{GCS_RUN_SUBMIT_DIR}/entrypoint.py" +elif ENV == 'prod': + MAIN_ENTRYPOINT_PY = "file://home/kedro/code/deployment/dataproc/entrypoint.py" + +GCS_CODE_ARCHIVE_URI = f'{GCS_RUN_SUBMIT_DIR}/code.zip' + +LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR = "dataproc_runs" + +if ENV == 'dev': + CONTAINER_IMAGE_URI = f"{REGION}-docker.pkg.dev/{PROJECT_ID}/{USECASE}/dataproc-serverless:{BRANCH_NAME_CLEAN}" +elif ENV == 'prod': + CONTAINER_IMAGE_URI = f"{REGION}-docker.pkg.dev/{PROJECT_ID}/{USECASE}/dataproc-serverless:{RELEASE}" + + + +if PREPARE_ARTIFACTS: + commands = [ + # create staging directory + ['mkdir', '-p', f'{LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR}'] + ] + + + # For dev workflows, we create a new branch for each run with all changes auto-committed + if ENV == 'dev': + + commands += [ + # Stash uncommited changes, checkout new branch, apply stash, commit changes + ["git", "stash"], + ["git", "checkout", '-B', f"pipeline-run/{RUN_ID}"], + ["git", "stash", "apply"], + ["git", "add", "."], + ["git", "commit", '-m', f"pipeline run: {RUN_ID}"] + ] + + commands += [ + # Archive code + ['git', 'archive', '-o', f'{LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR}/code.zip', 'HEAD'], + + # Copy run submit artifacts to GCS + ['gsutil', 'cp', '-r', 'scripts/dataproc/main.py', MAIN_ENTRYPOINT_PY], + ['gsutil', 'cp', '-r', f'{LOCAL_DATAPROC_RUN_SUBMIT_STAGING_DIR}/code.zip', GCS_CODE_ARCHIVE_URI], + + # Return back to original git changes + ["git", "checkout", "-"], + ["git", "stash", "pop"] + ] + +if TRIGGER_RUN: + + trigger_command = [ + 'gcloud', 'dataproc', 'batches', 'submit', 'pyspark', MAIN_ENTRYPOINT_PY, + '--region', REGION, + '--container-image', CONTAINER_IMAGE_URI, + '--service-account', RUN_SERVICE_ACCOUNT, + '--properties', 'spark.executor.instances=10' # Add more spark conf here + '--version', DATAPROC_RUNTIME_VERSION, + ] + + if ENV == 'dev': + trigger_command += [ + '--archives', f'{GCS_CODE_ARCHIVE_URI}#code' + ] + + if KEDRO_RUN_ARGS: + trigger_command += [ + '--', f'--kedro-run-args={KEDRO_RUN_ARGS}' + ] + + commands += [trigger_command] + +run_subprocess_commands(commands) +``` + For dev workflow: ```bash From eedd1dd4aa3f98eef2c3aa0ad2bbf045d8d4f633 Mon Sep 17 00:00:00 2001 From: Abhishek Bhatia Date: Sun, 5 Jan 2025 16:47:52 +0530 Subject: [PATCH 3/7] add gcp dataproc 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| 102 ++++++++++++++++++++----- 1 file changed, 84 insertions(+), 18 deletions(-) diff --git a/docs/source/deployment/gcp_dataproc.md b/docs/source/deployment/gcp_dataproc.md index ee90419be4..917226c2f6 100644 --- a/docs/source/deployment/gcp_dataproc.md +++ b/docs/source/deployment/gcp_dataproc.md @@ -1,7 +1,59 @@ # GCP Dataproc -The guide details kedro pipeline deployment steps for Dataproc compute engine and Dataproc serverless. +`Dataproc serverless` lets you run Spark workloads without requiring you to provision and manage your own Dataproc cluster. An advantage over `Dataproc compute engine` is that `Dataproc serverless` supports custom containers allowing you package your dependencies at build time. Refer [here](https://cloud.google.com/dataproc-serverless/docs/overview#s8s-compared) for the official comparison between Dataproc serverless and compute engine. +The guide details kedro pipeline deployment steps for `Dataproc serverless`. + +## Overview + +The below diagram details the dataproc serverless dev and prod deployment workflows. + + +### DEV deployment (and experimentation) + +The following are the steps: + +1. **User build**: Each developer branches out from develop, pulls the latest develop dataproc image, and builds their own custom docker image (if required). Note the following: + - `libraries` - Packaged: This includes any python dependencies + any other libraries + - `artifacts` - NOT Packaged + - `code` - NOT Packaged + - `entrypoint.py` - NOT Packaged +2. **Push image**: After successful manual build, the developer pushes it to artifact registry with a custom tag (default: branch_name) +3. **Submit spark batches**: The developer triggers an experimental run with dataproc batches submission python script with parameters + - `--env=dev` + - `--run-id=...` + - `--kedro-run-args=...` +3.1. **Prepare and push artifacts to GCS**: The script packages the active code changes in the branch into an archive, prepares any necessary artifacts and pushes to GCS in `{branch}/{run_id}` namespace. +3.2. **Pull docker image to dataproc**: Dataproc pulls the custom docker image tagged with the branch name from artifact registry +3.3. **Pull artifact archives**: Dataproc pulls the archives containing the artifacts and unzips them on master and all workers in the working directory +3.4. **Run pyspark job**: The main file then triggers the pyspark job as the standard kedro cli command: `kedro run --pipeline=... --params=...` +3.5. *(Optional)* **Run output**: It is recommended to segregate the experimental run outputs into `{branch}/{run_id}` namespace. + + +![GCP DEV deployment workflow](../meta/images/gcp_dataproc_serverless_dev_workflow.png) + +### PROD deployment + +The following are the steps: + +1. **Cut a release from develop**: A release branch is cut from the `develop` branch as `release/v0.2.0` +2. **Prepare release**: Minor fixes, final readiness and release notes are added to prepare the release. +3. **Merge into main**: After all checks passes and necessary approvals, the release branch is merged into main, and the commit is tagged with the version +4. **Deploy docker image**: The docker image is built with release tag version `v0.2.0` and pushed to prod artifact registry. Note the following: + - `libraries` - Packaged + - `artifacts` - Packaged + - `code` - Packaged + - `entrypoint.py` - Packaged +5.1. **Prepare and push artifacts to GCS**: The script prepares any necessary artifacts and pushes to GCS in `{branch}/{run_id}` namespace. +5.2. **Pull docker image to dataproc**: Dataproc pulls the custom docker image tagged with the branch name from artifact registry +5.3. **Pull artifact archives**: Dataproc pulls the archives containing the artifacts and unzips them on master and all workers in the working directory +5.4. **Run pyspark job**: The main file then triggers the pyspark job as the standard kedro cli command: `kedro run --pipeline=... --params=...` +5.5. *(Optional)* **Run output**: It is recommended to segregate the experimental run outputs into `{branch}/{run_id}` namespace. + +![GCP PROD deployment workflow](../meta/images/gcp_dataproc_serverless_prod_workflow.png) + + +**NOTE**: The above describes a simple reference deployment pattern. Please adapt it for your usecase. ## Prerequisite Setup @@ -84,7 +136,7 @@ dataproc-run-submit-{REGION}-{UID}-dev/ ```bash dataproc-run-submit-{REGION}-{UID}-prod/ └── release/ - └── {version}/ # for e.g. v0.1.2 + └── {version}/ # for e.g. v.2.0 ``` #### Dataproc run output bucket @@ -102,7 +154,7 @@ dataproc-run-output-{REGION}-{UID}-dev/ ```bash dataproc-run-output-{REGION}-{UID}-prod/ └── release/ - └── {version}/ # for e.g. v0.1.2 + └── {version}/ # for e.g. v.2.0 └── {run_id} ``` @@ -243,8 +295,10 @@ RUN chmod -R a+w /home/kedro ### Configure Artifact registry +This creates your repository in the artifact registry. + ```bash -gcloud artifacts repositories create my-kedro-project \ +gcloud artifacts repositories create ${ARTIFACT_REPOSITORY} \ --repository-format=docker \ --location=${REGION} \ --description="Kedro project docker repository" @@ -252,26 +306,38 @@ gcloud artifacts repositories create my-kedro-project \ #### Gcloud configure docker auth +This configures gcloud authentication with the artifact registry. + ```bash -gcloud auth configure-docker {region}-docker.pkg.dev +gcloud auth configure-docker ${ARTIFACT_REGISTRY} ``` #### Push Image to Artifact Registry - `deployment/dataproc/serverless/build_push_docker.sh` +- This script builds and pushes the docker image for user dev workflows by tagging each custom build with the branch name (or a custom tag). +- The developer can experiment with any customizations to the docker image in their feature branches. +- It also allows to build and push the docker image for prod workflows by tagging it with the release version. + ```bash #!/usr/bin/env bash set -ex +# NOTE: Specify or fetch project level details here +PROJECT_ID=xxxx +ARTIFACT_REPOSITORY=xxxx +ARTIFACT_REPOSITORY=xxxx + +CONTAINER_IMAGE_NAME="dataproc-serverless" BRANCH_NAME=$(git rev-parse --abbrev-ref HEAD) BRANCH_NAME_CLEAN=$(echo ${BRANCH_NAME} | sed 's/[^a-zA-Z0-9]/-/g') # Parse arguments while [[ "$#" -gt 0 ]]; do case $1 in + --custom-tag) CUSTOM_TAG="$2"; shift ;; --env) ENV="$2"; shift ;; --release) RELEASE="$2"; shift ;; *) echo "Unknown parameter passed: $1"; exit 1 ;; @@ -279,7 +345,9 @@ while [[ "$#" -gt 0 ]]; do shift done -if [ "$ENV" == "dev" ]; then +if [ "$CUSTOM_TAG" ]; then + CONTAINER_IMAGE_TAG=${CUSTOM_TAG} +elif [ "$ENV" == "dev" ]; then CONTAINER_IMAGE_TAG=${BRANCH_NAME_CLEAN} elif [ "$ENV" == "prod" ]; then CONTAINER_IMAGE_TAG=${RELEASE} @@ -288,9 +356,9 @@ else exit 1 fi -CONTAINER_IMAGE=xxxx:${CONTAINER_IMAGE_TAG} +CONTAINER_IMAGE=${ARTIFACT_REGISTRY}/${PROJECT_ID}/${ARTIFACT_REPOSITORY}/${CONTAINER_IMAGE_NAME}:${CONTAINER_IMAGE_TAG} -docker build -f deployment/dataproc/Dockerfile -t ${CONTAINER_IMAGE} . --platform="linux/amd64" +docker build -f deployment/dataproc/serverless/Dockerfile -t ${CONTAINER_IMAGE} . --platform="linux/amd64" docker push ${CONTAINER_IMAGE} ``` @@ -303,7 +371,7 @@ deployment/dataproc/serverless/build_push_docker.sh --env dev In prod workflow: ```bash -deployment/dataproc/serverless/build_push_docker.sh --env prod --release v0.1.2 +deployment/dataproc/serverless/build_push_docker.sh --env prod --release v.2.0 ``` #### Submit pyspark batches @@ -366,11 +434,11 @@ BRANCH_NAME_CLEAN = re.sub(r'[^a-zA-Z0-9]', '-', BRANCH_NAME) PROJECT_CONFIG = load_project_config()[ENV] PROJECT_ID = PROJECT_CONFIG["PROJECT_ID"] -USECASE = PROJECT_CONFIG["USECASE"] REGION = PROJECT_CONFIG["REGION"] RUN_SUBMIT_BUCKET = PROJECT_CONFIG["DATAPROC"]["RUN_SUBMIT_BUCKET"] RUN_SERVICE_ACCOUNT = PROJECT_CONFIG["DATAPROC"]["RUN_SERVICE_ACCOUNT"] DATAPROC_RUNTIME_VERSION = PROJECT_CONFIG["DATAPROC"]["SERVERLESS"]["RUNTIME_VERSION"] +DATAPROC_DOCKER_IMAGE_NAME = PROJECT_CONFIG["DATAPROC"]["SERVERLESS"]["IMAGE_NAME"] if ENV == 'dev': GCS_RUN_SUBMIT_DIR = f'gs://{RUN_SUBMIT_BUCKET}/{BRANCH_NAME_CLEAN}/{RUN_ID}' @@ -457,7 +525,7 @@ For dev workflow: ```bash python deployment/dataproc/serverless/submit_batches.py \ --env dev \ - --run-id 20241230-my-kedro-pipeline-dev-run-1 \ + --run-id 20241230-my-kedro-pipeline-feat-feature-a-dev-run-1 \ --kedro-run-args="--pipeline=my_kedro_pipeline --params='param1:10,param2=a'" ``` @@ -468,7 +536,7 @@ During release (one time): ```bash python deployment/dataproc/serverless/submit_batches.py \ --env prod \ - --release v0.1.2 \ + --release v0.2.0 \ --no-run-trigger ``` @@ -477,14 +545,12 @@ In subsequent run triggers: ```bash python deployment/dataproc/serverless/submit_batches.py \ --env prod \ - --release v0.1.2 \ + --release v0.2.0 \ --no-prepare-artifacts \ - --run-id 20241230-my-kedro-pipeline-prod-run-1 \ + --run-id 20250101-my-kedro-pipeline-v0.2.0-prod-run-1 \ --kedro-run-args="--pipeline=my_kedro_pipeline --params='param1:10,param2=a'" ``` NOTE > 1. It is recommended to automate release workflows in the CI/CD pipeline using CI Framework native docker build steps which can benefit from caching. -> 2. The pipeline deployment depicted here is a simplified example. In real world workflows, the pipeline deployment will have a lot more nuances. - - +> 2. The pipeline deployment depicted here is a simplified example. In real world workflows, the pipeline deployment will have a lot more nuances. \ No newline at end of file From eafdf19c6db057635e57c728684ae5eb5ee1e784 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Juan=20Luis=20Cano=20Rodr=C3=ADguez?= Date: Fri, 10 Jan 2025 14:37:53 +0100 Subject: [PATCH 5/7] Update docs/source/deployment/gcp_dataproc.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Juan Luis Cano Rodríguez --- docs/source/deployment/gcp_dataproc.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/deployment/gcp_dataproc.md b/docs/source/deployment/gcp_dataproc.md index 917226c2f6..25f3402145 100644 --- a/docs/source/deployment/gcp_dataproc.md +++ b/docs/source/deployment/gcp_dataproc.md @@ -57,7 +57,7 @@ The following are the steps: ## Prerequisite Setup -#### Create service accounts +### Create service accounts NOTE: From c8c47f9a36428cbfe6bad66db6a1874bf40e672a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Juan=20Luis=20Cano=20Rodr=C3=ADguez?= Date: Fri, 10 Jan 2025 14:41:46 +0100 Subject: [PATCH 6/7] Update index.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Juan Luis Cano Rodríguez --- docs/source/deployment/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/deployment/index.md b/docs/source/deployment/index.md index 8af43a8e28..bada45dda9 100644 --- a/docs/source/deployment/index.md +++ b/docs/source/deployment/index.md @@ -32,6 +32,7 @@ This following pages provide information for deployment to, or integration with, * [Azure](azure.md) * [Dask](dask.md) * [Databricks](./databricks/index.md) +* [GCP Dataproc](gcp_dataproc.md) * [Kubeflow Workflows](kubeflow.md) * [Prefect](prefect.md) * [Vertex AI](vertexai.md) From a6ad421e5fae5cd286c3b39de9d3cd5ab20c1fd4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Juan=20Luis=20Cano=20Rodr=C3=ADguez?= Date: Fri, 10 Jan 2025 14:44:28 +0100 Subject: [PATCH 7/7] Update index.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Juan Luis Cano Rodríguez --- docs/source/deployment/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/deployment/index.md b/docs/source/deployment/index.md index bada45dda9..6ab18fb767 100644 --- a/docs/source/deployment/index.md +++ b/docs/source/deployment/index.md @@ -59,6 +59,7 @@ aws_step_functions azure dask databricks/index +gcp_dataproc kubeflow prefect vertexai