Apache Zeppelin 整合 Spark 和 Hudi

一 环境信息

1.1 组件版本

1.2 环境准备

1. Zeppelin 整合 Spark 参考：Apache Zeppelin 一文打尽
2. Hudi0.14.0编译参考：Hudi0.14.0 最新编译

二 整合 Spark 和 Hudi

2.1 配置

%spark.conf

SPARK_HOME /usr/lib/spark

# set execution mode
spark.master yarn
spark.submit.deployMode client

# --jars
spark.jars /root/app/jars/hudi-spark3.2-bundle_2.12-0.14.0.jar

# --conf
spark.serializer org.apache.spark.serializer.KryoSerializer
spark.sql.catalog.spark_catalog org.apache.spark.sql.hudi.catalog.HoodieCatalog
spark.sql.extensions org.apache.spark.sql.hudi.HoodieSparkSessionExtension
spark.kryo.registrator org.apache.spark.HoodieSparkKryoRegistrar

Specifying yarn-client & yarn-cluster in spark.master is not supported in Spark 3.x any more, instead you need to use spark.master and spark.submit.deployMode together.

2.2 导入依赖

%spark
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.common.table.HoodieTableConfig._
import org.apache.hudi.config.HoodieWriteConfig._
import org.apache.hudi.keygen.constant.KeyGeneratorOptions._
import org.apache.hudi.common.model.HoodieRecord
import spark.implicits._

2.3 插入数据

%spark
val tableName = "trips_table"
val basePath = "hdfs:///tmp/trips_table"
val columns = Seq("ts","uuid","rider","driver","fare","city")
val data =
  Seq((1695159649087L,"334e26e9-8355-45cc-97c6-c31daf0df330","rider-A","driver-K",19.10,"san_francisco"),
    (1695091554788L,"e96c4396-3fad-413a-a942-4cb36106d721","rider-C","driver-M",27.70 ,"san_francisco"),
    (1695046462179L,"9909a8b1-2d15-4d3d-8ec9-efc48c536a00","rider-D","driver-L",33.90 ,"san_francisco"),
    (1695516137016L,"e3cf430c-889d-4015-bc98-59bdce1e530c","rider-F","driver-P",34.15,"sao_paulo"    ),
    (1695115999911L,"c8abbe79-8d89-47ea-b4ce-4d224bae5bfa","rider-J","driver-T",17.85,"chennai"));

var inserts = spark.createDataFrame(data).toDF(columns:_*)
inserts.write.format("hudi").
  option(PARTITIONPATH_FIELD_NAME.key(), "city").
  option(TABLE_NAME, tableName).
  mode(Overwrite).
  save(basePath)

2.3 查询数据

%spark
val tripsDF = spark.read.format("hudi").load(basePath)
tripsDF.createOrReplaceTempView("trips_table")
spark.sql("SELECT uuid, fare, ts, rider, driver, city FROM  trips_table WHERE fare > 20.0").show()

结果：

+--------------------+-----+-------------+-------+--------+-------------+
|                uuid| fare|           ts|  rider|  driver|         city|
+--------------------+-----+-------------+-------+--------+-------------+
|e96c4396-3fad-413...| 27.7|1695091554788|rider-C|driver-M|san_francisco|
|9909a8b1-2d15-4d3...| 33.9|1695046462179|rider-D|driver-L|san_francisco|
|e3cf430c-889d-401...|34.15|1695516137016|rider-F|driver-P|    sao_paulo|
+--------------------+-----+-------------+-------+--------+-------------+