README.md

dataLake-template

1. Introduction

Some demos of using Spark to write MySQL and Kafka data to data lake,such as Delta,Hudi,Iceberg.

2. Version info

SPARK 3.1.2

DELTA 1.0.0

HUDI 0.10.1

ICEBERG 0.12.1

MYSQL 8.0.24

3 Kafka to Data Lake

3.1 read Kafka

use kafka.py to generate data. kafka.bootstrap.servers and subscribe is necessary

import sparkSession.implicits._
val df: DataFrame = sparkSession
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", kafkaConsumer)
  .option("subscribe", kafkaTopic)
  .option("startingOffsets", startingOffsets)
  .option("failOnDataLoss", failOnDataLoss)
  .option("maxOffsetsPerTrigger", maxOffsetsPerTrigger)
  .load()

val frame: Dataset[Row] = df.select(from_json('value.cast("string"), schema) as "value").select($"value.*")

3.2 write to Delta

val query: StreamingQuery = frame
  .writeStream
  .format("delta")
  .option("path", deltaPath)
  .option("checkpointLocation", deltaPath)
  .partitionBy(partitionColumns:_*)
  .start()

3.3 write to Hudi

val query: StreamingQuery = frame
  .writeStream
  .format("hudi")
  .option(RECORDKEY_FIELD.key, "user_id")
  .option(PRECOMBINE_FIELD.key, "user_id")
  .option(PARTITIONPATH_FIELD.key(), partitionFields)
  .option(HoodieWriteConfig.TBL_NAME.key, hoodieTableName)
  .outputMode("append")
  .option("path", lakePath)
  .option("checkpointLocation", checkpointLocation)
  .trigger(Trigger.ProcessingTime(10,TimeUnit.SECONDS))
  .start()

3.4 write to Iceberg

create iceberg table first

//The table should be created in prior to start the streaming query.
//Refer SQL create table on Spark page to see how to create the Iceberg table.
sparkSession.sql("create table czl_iceberg.demo.kafka_spark_iceberg (" +
 "user_id bigint, station_time string, score integer, local_time timestamp" +
  ") using iceberg")

val query: StreamingQuery = frame
  .writeStream
  .format("iceberg")
  //Iceberg supports append and complete output modes:
  //append: appends the rows of every micro-batch to the table
  //complete: replaces the table contents every micro-batch
  .outputMode("append")
  .trigger(Trigger.ProcessingTime(10, TimeUnit.SECONDS))
  .option("path", icebergPath)
  .option("checkpointLocation", checkpointPath)
  .start()

4 Mysql to Data Lake

4.1 read Mysql

val readMysql: DataFrame = sparkSession
  .read
  .format("jdbc")
  .option("url", url)
  .option("driver", "com.mysql.cj.jdbc.Driver")
  .option("dbtable", tableName)
  .option("user", username)
  .option("password", password).load()

4.2 write to Delta

readMysql
  .write
  .format("delta")
  .mode(SaveMode.Overwrite)
  .partitionBy(partitionColumns:_*)
  .save(path)

4.3 write to Hudi

readMysql
  .write
  .format("hudi")
  .option(RECORDKEY_FIELD.key, "id")
  .option(PRECOMBINE_FIELD.key, "id")
  .option(HoodieWriteConfig.TBL_NAME.key, hoodieTableName)
  .mode(SaveMode.Overwrite)
  .partitionBy(partitionColumns:_*)
  .save(path)

4.4 write to Iceberg

readMysql
  .writeTo(path)
  .create()