Spark K8s Learning

Repository to test and development in Spark on K8s

Requirements

• Docker and Docker Composer
• Kind
• Kubectl
• Helm

DO YOUR "JEITO" #BRHUEHUE

Create kind cluster

make create-kind

# To clean-up
kind delete cluster

Install Grafana and Prometheus

make helm-add
make install-prometheus

Access http://grafana.localhost/ with

user: admin
pass: prom-operator

External Kafka in Docker-Compose

• Note: The new docker compose use docker compose up -d instead docker-compose up -d

make kafka-setup

# To cleanup
# make kafka-destroy

Check with Kowl if data are sent to Kafka:

Create Spark Image and sent to Kind Cluster

# Create Spark Image
make build-spark

# Create Operator Image
make build-operator

# Send image to Kind Cluster
make send-images

Spark Operator

make helm-add
make install-spark

Copy jibaro library and scripts with:

make copy

This command will send files to s3://spark-artifacts.

Test Spark Job

Test Spark submit:

kubectl apply -f tests/spark-test.yaml

Check if job is completed:

kubectl get sparkapplications -n spark

NAME                 STATUS      ATTEMPTS   START                  FINISH                 AGE
spark-yuriniitsuma   COMPLETED   1          2022-05-29T06:43:26Z   2022-05-29T06:43:57Z   49s

Delete job:

kubectl delete sparkapplications spark-yuriniitsuma -n spark

Generate data in postgres database

Generate data in dbserver1.inventory.products:

bash external-services/generate_data.sh

Airflow

You need to install airflow on your host computer to easily access kind cluster. Obviosly you need Python 3 and pip3 installed on your computer.

# Install Airflow
python3 -m venv venv && source venv/bin/activate && \
pip install apache-airflow==2.4.3 \
    psycopg2-binary \
    apache-airflow-providers-cncf-kubernetes \
    apache-airflow-providers-amazon

Run airflow db init for the first time to create the config files.

Change the config below in $HOME/airflow/airflow.cfg:

dags_folder = $LOCAL_TO_REPO/airflow/dags
executor = LocalExecutor
load_examples = False
sql_alchemy_conn = postgresql+psycopg2://postgres:postgres@localhost/airflow
remote_logging = True
remote_log_conn_id = AirflowS3Logs
remote_base_log_folder = s3://airflow-logs/logs

Change config with sed:

sed -i '' 's/^executor = .*/executor = LocalExecutor/g' $HOME/airflow/airflow.cfg
sed -i '' 's/^load_examples = .*/load_examples = False/g' $HOME/airflow/airflow.cfg
sed -i '' 's/^sql_alchemy_conn = .*/sql_alchemy_conn = postgresql+psycopg2:\/\/postgres:postgres@localhost\/airflow/g' $HOME/airflow/airflow.cfg
sed -i '' 's:^dags_folder = .*:dags_folder = '`pwd`'/airflow\/dags:g' $HOME/airflow/airflow.cfg
sed -i '' 's/^remote_logging = .*/remote_logging = True/g' $HOME/airflow/airflow.cfg
sed -i '' 's/^remote_log_conn_id =.*/remote_log_conn_id = AirflowS3Logs/g' $HOME/airflow/airflow.cfg
sed -i '' 's/^remote_base_log_folder =.*/remote_base_log_folder = s3:\/\/airflow-logs\/logs/g' $HOME/airflow/airflow.cfg
sed -i '' 's/^web_server_port = .*/web_server_port = 8000/g' $HOME/airflow/airflow.cfg

Then run:

source venv/bin/activate && airflow db init && \
airflow connections add \
   --conn-type 'aws' \
   --conn-extra '{ "aws_access_key_id": "minio", "aws_secret_access_key": "miniominio", "host": "http://localhost:9000" }' \
   AirflowS3Logs &&\
airflow pools set spark 2 'spark on k8s'

# in one terminal run
source venv/bin/activate && PYTHON_PATH=$PWD/airflow/dags airflow webserver

# in another terminal run the scheduler
source venv/bin/activate && PYTHON_PATH=$PWD/airflow/dags airflow scheduler

Create an admin user:

airflow users create \
    --username admin \
    --password admin \
    --firstname admin \
    --lastname admin \
    --role Admin \
    --email [email protected]

Insert password and go to Airflow UI

And run Pipeline dag:

After the DAG is completed, you can check the output in Minio's datalake bucket http://localhost:9001/buckets/datalake/browse:

TODO

• Export SparkUI with Ingress or a reverse proxy
• Deploy Spark-History-server
• Export SparkUI to bucket to Spark-History-server
• Export metrics to Prometheus
• Documentation of SparkOperator
• Support to spark with .jar files
• Create a lib and send with a .zip file with --pyFiles
• Support ENV
• Support secrets
• Create a new class with family of driver/executors with different configs (CPU/MEM)
• Support with JSON without Schema-Registry
• Support to parse Avro in Key and Value from Kafka
• Try to support Protobuf.
• Support to dynamic 'pip install' of packages
• Automatic compact files in Delta Lake
• Automatic vacuum files in Delta Lake
• Create automatic history in _history folder to inspect metrics like numberoffiles for each version
• Add Trino to connect to Data Lake
• Add hive metastore with postgresql
• Add support to Spark use Hive or Jibaro libray use SQL in Trino.
• Airflow on Kubernetes with Official Helm Chart
• Airflow allow to send spark job to kubernetes
• Compile spark-operator binary in amd64 and arm64 to Apple Silicon
• Update to Spark 3.2.1
• Update to Spark 3.3.x (after the release of Delta Lake)
• SparkOperator delete spark manifest after COMPLETED
• SparkOperator get status error from SparkApplication and output on Airflow UI
• Rebuild Confluent Kafka-Stack in ARM64 to support Apple Silicon (M1)