Quick Setup Environment

1. Set up a test environment in the Linux local file system

To store data on local disk, only a PostgreSQL database is required.

1.1 Start A Local PostgreSQL DB

The quickest way to start a pg DB is via docker container:

docker run -d --name lakesoul-test-pg -p5432:5432 -e POSTGRES_USER=lakesoul_test -e POSTGRES_PASSWORD=lakesoul_test -e POSTGRES_DB=lakesoul_test -d postgres:14.5

1.2 PG Database Initialization

Init PG database of LakeSoul using script/meta_init.cql. Execute code blow in the LakeSoul base directory:

docker cp script/meta_init.sql lakesoul-test-pg:/

docker exec -i lakesoul-test-pg sh -c "PGPASSWORD=lakesoul_test psql -h localhost -p 5432 -U lakesoul_test -f meta_init.sql"

1.3 Lakesoul PG Database Configuration Description:

By default, the PG database is connected to the local database. The configuration information is as follows,

lakesoul.pg.driver=com.lakesoul.shaded.org.postgresql.Driver
lakesoul.pg.url=jdbc:postgresql://127.0.0.1:5432/lakesoul_test?stringtype=unspecified
lakesoul.pg.username=lakesoul_test
lakesoul.pg.password=lakesoul_test

LakeSoul supports to customize PG database configuration information. Add an environment variable lakesoul_home before starting the Spark program to include the configuration file information.

For example, the PG database configuration information file path name is: /opt/soft/pg.property, you need to add this environment variable before the program starts:

export lakesoul_home=/opt/soft/pg.property

You can put customized database configuration information in this file.

1.4 Install an Apache Spark environment

You could download spark distribution from https://spark.apache.org/downloads.html, and please choose spark version 3.3.0 or above. Note that the official package from Apache Spark does not include hadoop-cloud component. We provide a Spark package with Hadoop cloud dependencies, download it from https://dmetasoul-bucket.obs.cn-southwest-2.myhuaweicloud.com/releases/spark/spark-3.3.2-bin-hadoop3.tgz.

After unpacking spark package, you could find LakeSoul distribution jar from https://github.com/lakesoul-io/LakeSoul/releases. Download the jar file put it into jars directory of your spark environment.

wget https://dmetasoul-bucket.obs.cn-southwest-2.myhuaweicloud.com/releases/spark/spark-3.3.2-bin-hadoop-3.tgz
tar xf spark-3.3.2-bin-hadoop-3.tgz
export SPARK_HOME=${PWD}/spark-3.3.2-bin-hadoop3
wget https://github.com/lakesoul-io/LakeSoul/releases/download/v2.6.2/lakesoul-spark-2.6.2-spark-3.3.jar -P $SPARK_HOME/jars

tip

For production deployment on Hadoop, it's recommended to use spark release without bundled hadoop:

https://dlcdn.apache.org/spark/spark-3.3.2/spark-3.3.2-bin-without-hadoop.tgz

Refer to https://spark.apache.org/docs/latest/hadoop-provided.html on how to setup hadoop classpath.

tip

Since 2.1.0, LakeSoul package all its dependencies into one single jar via maven shade plugin. Before that all jars were packaged into one tar.gz file.

1.4.1 Start spark-shell for testing LakeSoul

cd into the spark installation directory, and start an interactive spark-shell:

./bin/spark-shell --conf spark.sql.extensions=com.dmetasoul.lakesoul.sql.LakeSoulSparkSessionExtension --conf spark.sql.catalog.lakesoul=org.apache.spark.sql.lakesoul.catalog.LakeSoulCatalog --conf spark.sql.defaultCatalog=lakesoul

1.4.2 Write data to object storage service

It is necessary to add information such as object storage access key, secret key and endpoint.

./bin/spark-shell --conf spark.sql.extensions=com.dmetasoul.lakesoul.sql.LakeSoulSparkSessionExtension --conf spark.sql.catalog.lakesoul=org.apache.spark.sql.lakesoul.catalog.LakeSoulCatalog --conf spark.sql.defaultCatalog=lakesoul --conf spark.hadoop.fs.s3a.access.key=XXXXXX --conf spark.hadoop.fs.s3a.secret.key=XXXXXX --conf spark.hadoop.fs.s3a.endpoint=XXXXXX --conf spark.hadoop.fs.s3.impl=org.apache.hadoop.fs.s3a.S3AFileSystem

If it is a storage service compatible with S3 such as Minio, you also need to add --conf spark.hadoop.fs.s3a.path.style.access=true.

LakeSoul Spark Conf Parameters

Before start to use Lakesoul, we should add some paramethers in spark-defaults.conf or Spark Session Builder。

Key	Value	Description
spark.sql.extensions	com.dmetasoul.lakesoul.sql.LakeSoulSparkSessionExtension	extention name for spark sql
spark.sql.catalog.lakesoul	org.apache.spark.sql.lakesoul.catalog.LakeSoulCatalog	plug in LakeSoul's catalog
spark.sql.defaultCatalog	lakesoul	set default catalog for spark

1.5 Setup Flink environment

Download LakeSoul Flink jar: https://github.com/lakesoul-io/LakeSoul/releases/download/v2.6.2/lakesoul-flink-1.17-2.6.2.jar

Download Flink: https://dlcdn.apache.org/flink/flink-1.17.2/flink-1.17.2-bin-scala_2.12.tgz

1.5.1 Start Flink SQL shell

After creating the pg database and lakesoul_home configuration file, place the LakeSoul Flink jars in the FLink directory. Enter the Flink installation directory and execute the following command:

export lakesoul_home=/opt/soft/pg.property && ./bin/start-cluster.sh

export lakesoul_home=/opt/soft/pg.property && ./bin/sql-client.sh embedded -j lakesoul-flink-1.17-2.6.2.jar

1.5.2 Write data to object storage service

Access key, Secret key and Endpoint information need to be added to the Flink configuration file flink-conf.yaml

s3.access-key: XXXXXX
s3.secret-key: XXXXXX
s3.endpoint: XXXXXX

If it is a storage service compatible with S3 such as Minio, you also need to add:

s3.path.style.access: true

And place flink-s3-fs-hadoop.jar and flink-shaded-hadoop-2-uber-2.6.5-10.0.jar under Flink/lib Download flink-s3-fs-hadoop.jar: https://repo1.maven.org/maven2/org/apache/flink/flink-s3-fs-hadoop/1.17.2/flink-s3-fs-hadoop-1.17.2.jar Download flink-shaded-hadoop-2-uber-2.6.5-10.0.jar: https://repo1.maven.org/maven2/org/apache/flink/flink-shaded-hadoop-2-uber/2.6.5-10.0/flink-shaded-hadoop-2-uber-2.6.5-10.0.jar

2. Start on Hadoop, Spark and Flink cluster environments

To deploy LakeSoul in a Hadoop cluster, you only need to add the relevant configuration information to the environment variables and Spark and FLink cluster configurations. For the Spark environment, please refer to 1.3 for installation and deployment. For the Flink environment, please refer to [1.4](#14-flink-Local Environment Construction) for installation and deployment. It is recommended that the environments of Spark and Flink do not include Hadoop dependencies. Use the SPARK_DIST_CLASSPATH and HADOOP_CLASSPATH environment variables to introduce the Hadoop environment to avoid dependence on the Hadoop version.

The detailed configurations are as follows:

2.1 Add the following information to the Spark configuration file spark-defaults.conf

spark.sql.extensions=com.dmetasoul.lakesoul.sql.LakeSoulSparkSessionExtension
spark.sql.catalog.lakesoul=org.apache.spark.sql.lakesoul.catalog.LakeSoulCatalog
spark.sql.defaultCatalog=lakesoul

spark.yarn.appMasterEnv.LAKESOUL_PG_DRIVER=com.lakesoul.shaded.org.postgresql.Driver
spark.yarn.appMasterEnv.LAKESOUL_PG_URL=jdbc:postgresql://127.0.0.1:5432/lakesoul_test?stringtype=unspecified
spark.yarn.appMasterEnv.LAKESOUL_PG_USERNAME=lakesoul_test
spark.yarn.appMasterEnv.LAKESOUL_PG_PASSWORD=lakesoul_test

2.2 Add the following information to the Flink configuration file flink-conf.yaml

containerized.master.env.LAKESOUL_PG_DRIVER: com.lakesoul.shaded.org.postgresql.Driver
containerized.master.env.LAKESOUL_PG_USERNAME: postgres
containerized.master.env.LAKESOUL_PG_PASSWORD: postgres123
containerized.master.env.LAKESOUL_PG_URL: jdbc:postgresql://127.0.0.1:5432/lakesoul_test?stringtype=unspecified
containerized.taskmanager.env.LAKESOUL_PG_DRIVER: com.lakesoul.shaded.org.postgresql.Driver
containerized.taskmanager.env.LAKESOUL_PG_USERNAME: lakesoul_test
containerized.taskmanager.env.LAKESOUL_PG_PASSWORD: lakesoul_test
containerized.taskmanager.env.LAKESOUL_PG_URL: jdbc:postgresql://127.0.0.1:5432/lakesoul_test?stringtype=unspecified

In the above configurations, LakeSoul's PG URL connection address, user name, and password need to be modified accordingly according to the specific deployment of PostgreSQL.

2.3 Configuration Hadoop Environment

Configure global environment variable information on the client machine. Here you need to write the variable information into an env.sh file. Here the Hadoop version is 3.1.4.0-315, the Spark version is spark-3.3.2, and the Flink version is flink-1.17.2. Change Hadoop environment variables according to your Hadoop deployment. If your environment has been pre-configured with Hadoop, you can omit those Hadoop related envs.

export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64
export HADOOP_HOME="/usr/hdp/3.1.4.0-315/hadoop"
export HADOOP_HDFS_HOME="/usr/hdp/3.1.4.0-315/hadoop-hdfs"
export HADOOP_MAPRED_HOME="/usr/hdp/3.1.4.0-315/hadoop-mapreduce"
export HADOOP_YARN_HOME="/usr/hdp/3.1.4.0-315/hadoop-yarn"
export HADOOP_LIBEXEC_DIR="/usr/hdp/3.1.4.0-315/hadoop/libexec"
export HADOOP_CONF_DIR="/usr/hdp/3.1.4.0-315/hadoop/conf"

export SPARK_HOME=/usr/hdp/spark-3.3.2-bin-without-hadoop-ddf
export SPARK_CONF_DIR=/home/lakesoul/lakesoul_hadoop_ci/LakeSoul-main/LakeSoul/script/benchmark/hadoop/spark-conf

export FLINK_HOME=/opt/flink-1.17.2
export FLINK_CONF_DIR=/opt/flink-1.17.2/conf
export PATH=$HADOOP_HOME/bin:$SPARK_HOME/bin:$FLINK_HOME/bin:$JAVA_HOME/bin:$PATH
export HADOOP_CLASSPATH=$(hadoop classpath)
export SPARK_DIST_CLASSPATH=$HADOOP_CLASSPATH
export LAKESOUL_PG_DRIVER=com.lakesoul.shaded.org.postgresql.Driver
export LAKESOUL_PG_URL=jdbc:postgresql://127.0.0.1:5432/lakesoul_test?stringtype=unspecified
export LAKESOUL_PG_USERNAME=lakesoul_test
export LAKESOUL_PG_PASSWORD=lakesoul_test

After configuring the above information, execute the following command, and then you can submit the LakeSoul task to the yarn cluster for running on the client

source env.sh

3. Use Docker Compose to Start a Local Cluster

3.1 Docker Compose Files

We provide a docker compose env to quickly start a local PostgreSQL service and a MinIO S3 Storage service. The docker compose env is located under lakesoul-docker-compose-env.

3.2 Install Docker Compose

To install docker compose, please refer to Install Docker Engine

3.3 Start docker compose

To start the docker compose env, cd into the docker compose env dir, and execute the command:

cd docker/lakesoul-docker-compose-env/
docker compose up -d

Then use docker compose ps to check both services' statuses are running(healthy). The PostgreSQL service would automatically setup the database and tables required by LakeSoul Meta. And the MinIO service would setup a public bucket. You can change the user, password, database name and MinIO bucket name accordingly in the docker-compose.yml file.

3.4 Run LakeSoul Tests in Docker Compose Env

3.4.1 Prepare LakeSoul Properties File

lakesoul.properties

lakesoul.pg.driver=com.lakesoul.shaded.org.postgresql.Driver
lakesoul.pg.url=jdbc:postgresql://lakesoul-docker-compose-env-lakesoul-meta-db-1:5432/lakesoul_test?stringtype=unspecified
lakesoul.pg.username=lakesoul_test
lakesoul.pg.password=lakesoul_test

3.4.2 Prepare Spark Image

You could use bitnami's Spark 3.3 docker image with packaged hadoop denendencies:

docker pull bitnami/spark:3.3.1

3.4.3 Start Spark Shell

docker run --net lakesoul-docker-compose-env_default --rm -ti \
    -v $(pwd)/lakesoul.properties:/opt/spark/work-dir/lakesoul.properties \
    --env lakesoul_home=/opt/spark/work-dir/lakesoul.properties bitnami/spark:3.3.1 \
    spark-shell \
    --packages com.dmetasoul:lakesoul-spark:spark-3.3-2.6.2 \
    --conf spark.sql.extensions=com.dmetasoul.lakesoul.sql.LakeSoulSparkSessionExtension \
    --conf spark.sql.catalog.lakesoul=org.apache.spark.sql.lakesoul.catalog.LakeSoulCatalog \
    --conf spark.sql.defaultCatalog=lakesoul \
    --conf spark.hadoop.fs.s3.impl=org.apache.hadoop.fs.s3a.S3AFileSystem \
    --conf spark.hadoop.fs.s3a.buffer.dir=/opt/spark/work-dir/s3a \
    --conf spark.hadoop.fs.s3a.path.style.access=true \
    --conf spark.hadoop.fs.s3a.endpoint=http://minio:9000 \
    --conf spark.hadoop.fs.s3a.aws.credentials.provider=org.apache.hadoop.fs.s3a.AnonymousAWSCredentialsProvider

3.4.4 Execute LakeSoul Scala APIs

val tablePath= "s3://lakesoul-test-bucket/test_table"
val df = Seq(("2021-01-01",1,"rice"),("2021-01-01",2,"bread")).toDF("date","id","name")
df.write
  .mode("append")
  .format("lakesoul")
  .option("rangePartitions","date")
  .option("hashPartitions","id")
  .option("hashBucketNum","2")
  .save(tablePath)

3.4.5 Verify Data Written Successfully

Open link http://127.0.0.1:9001/buckets/lakesoul-test-bucket/browse/ in your browser to verify that LakeSoul table has been written to MinIO successfully. Use minioadmin1:minioadmin1 to login into MinIO's console.

3.5 Cleanup Meta Tables and MinIO Bucket

To cleanup all contents in LakeSoul meta tables, execute:

docker exec -ti lakesoul-docker-compose-env-lakesoul-meta-db-1 psql -h localhost -U lakesoul_test -d lakesoul_test -f /meta_cleanup.sql

To cleanup all contents in MinIO bucket, execute:

docker run --net lakesoul-docker-compose-env_default --rm -t bitnami/spark:3.3.1 aws --no-sign-request --endpoint-url http://minio:9000 s3 rm --recursive s3://lakesoul-test-bucket/

3.6 Shutdown Docker Compose Env

cd docker/lakesoul-docker-compose-env/
docker compose stop
docker compose down