使用spark3操作hudi数据湖初探

环境：

• hadoop 3.2.0
• spark 3.0.3-bin-hadoop3.2
• hudi 0.8.0

本文基于上述组件版本使用spark插入数据到hudi数据湖中。为了确保以下各步骤能够成功完成，请确保hadoop集群正常启动。

确保已经配置环境变量HADOOP_CLASSPATH

对于开源版本hadoop，HADOOP_CLASSPATH配置为：

export HADOOP_CLASSPATH=$HADOOP_CLASSPATH:$HADOOP_HOME/share/hadoop/client/*:$HADOOP_HOME/share/hadoop/common/*:$HADOOP_HOME/share/hadoop/hdfs/*:$HADOOP_HOME/share/hadoop/mapreduce/*:$HADOOP_HOME/share/hadoop/tools/*:$HADOOP_HOME/share/hadoop/yarn/*:$HADOOP_HOME/etc/hadoop/*

本文使用的hdfs地址为：hdfs://hadoop2:9000

本地安装spark集群

1 spark下载

wget https://dlcdn.apache.org/spark/spark-3.0.3/spark-3.0.3-bin-hadoop3.2.tgz
tar zxvf spark-3.0.3-bin-hadoop3.2.tgz

2 下载hudi相关jar包到spark-3.0.3-bin-hadoop3.2/jars目录下。需要下载spark_avro_2.12-3.0.3.jar以及hudi-spark3-bundle_2.12-0.8.0.jar

cd spark-3.0.3-bin-hadoop3.2/jars
wget https://repo1.maven.org/maven2/org/apache/spark/spark-avro_2.12/3.0.3/spark-avro_2.12-3.0.3.jar
wget https://repo1.maven.org/maven2/org/apache/hudi/hudi-spark3-bundle_2.12/0.8.0/hudi-spark3-bundle_2.12-0.8.0.jar

3 修改配置文件

cp conf/spark-env.sh.template conf/spark-env.sh
cp conf/slaves.template conf/slaves
cp conf/spark-defaults.conf.template conf/spark-defaults.conf

vi conf/spark-env.sh

新增如下内容，指明JAVA_HOME目录，否则worker无法启动

JAVA_HOME=/data/jdk8

slaves为spark worker的地址，本地执行可只填localhost，无需修改

spark-defaults.conf为spark相关配置，可根据需要修改，本文都是用默认配置，未做修改。

4 启动spark集群

sbin/start-all.sh

启动spark-shell并attach本地spark集群

执行以下命令启动（需要指定spark.serializer）：

./bin/spark-shell --master spark://hadoop1:7077   --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'

使用spark-shell操作hudi数据湖

1 导入依赖包以及指定表名、hdfs路径、数据生成器

import org.apache.hudi.QuickstartUtils._
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._

val tableName = "hudi_trips_cow"
val basePath =  "hdfs://hadoop2:9000/tmp/spark_hudi_test"
val dataGen = new DataGenerator

其中，DataGenerator可以用于生成测试数据，用来完成后续操作。具体可参考相关源码：DataGenerator

2 生成数据并写入hudi中

val inserts = convertToStringList(dataGen.generateInserts(10))
val df = spark.read.json(spark.sparkContext.parallelize(inserts, 2))
df.write.format("hudi").
  options(getQuickstartWriteConfigs).
  option(PRECOMBINE_FIELD_OPT_KEY, "ts").
  option(RECORDKEY_FIELD_OPT_KEY, "uuid").
  option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
  option(TABLE_NAME, tableName).
  mode(Overwrite).
  save(basePath)

其中生成数据如下所示：

查看hdfs相关路径

3 查询数据

// spark-shell
val tripsSnapshotDF = spark.
  read.
  format("hudi").
  load(basePath + "/*/*/*/*")
//load(basePath) use "/partitionKey=partitionValue" folder structure for Spark auto partition discovery
tripsSnapshotDF.createOrReplaceTempView("hudi_trips_snapshot")

spark.sql("select fare, begin_lon, begin_lat, ts from  hudi_trips_snapshot where fare > 20.0").show()
spark.sql("select _hoodie_commit_time, _hoodie_record_key, _hoodie_partition_path, rider, driver, fare from  hudi_trips_snapshot").show()

相关结果

4 更新数据

// spark-shell
val updates = convertToStringList(dataGen.generateUpdates(10))
val df = spark.read.json(spark.sparkContext.parallelize(updates, 2))
df.write.format("hudi").
  options(getQuickstartWriteConfigs).
  option(PRECOMBINE_FIELD_OPT_KEY, "ts").
  option(RECORDKEY_FIELD_OPT_KEY, "uuid").
  option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
  option(TABLE_NAME, tableName).
  mode(Append).
  save(basePath)

其中第一行为生成更新数据，详细操作如下：

5 增量查询

// spark-shell
// reload data
spark.
  read.
  format("hudi").
  load(basePath + "/*/*/*/*").
  createOrReplaceTempView("hudi_trips_snapshot")

val commits = spark.sql("select distinct(_hoodie_commit_time) as commitTime from  hudi_trips_snapshot order by commitTime").map(k => k.getString(0)).take(50)
val beginTime = commits(commits.length - 2) // commit time we are interested in

// incrementally query data
val tripsIncrementalDF = spark.read.format("hudi").
  option(QUERY_TYPE_OPT_KEY, QUERY_TYPE_INCREMENTAL_OPT_VAL).
  option(BEGIN_INSTANTTIME_OPT_KEY, beginTime).
  load(basePath)
tripsIncrementalDF.createOrReplaceTempView("hudi_trips_incremental")

spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from  hudi_trips_incremental where fare > 20.0").show()

查询结果如下：

6 查询指定时间。具体的时间可以通过将endTime指向特定的提交时间，将beginTime指向“000”(表示可能最早的提交时间)来表示。

// spark-shell
val beginTime = "000" // Represents all commits > this time.
val endTime = commits(commits.length - 2) // commit time we are interested in

//incrementally query data
val tripsPointInTimeDF = spark.read.format("hudi").
  option(QUERY_TYPE_OPT_KEY, QUERY_TYPE_INCREMENTAL_OPT_VAL).
  option(BEGIN_INSTANTTIME_OPT_KEY, beginTime).
  option(END_INSTANTTIME_OPT_KEY, endTime).
  load(basePath)
tripsPointInTimeDF.createOrReplaceTempView("hudi_trips_point_in_time")
spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from hudi_trips_point_in_time where fare > 20.0").show()

查询结果如下：

7 根据HoodieKeys删除数据

// spark-shell
// fetch total records count
spark.sql("select uuid, partitionpath from hudi_trips_snapshot").count()
// fetch two records to be deleted
val ds = spark.sql("select uuid, partitionpath from hudi_trips_snapshot").limit(2)

// issue deletes
val deletes = dataGen.generateDeletes(ds.collectAsList())
val df = spark.read.json(spark.sparkContext.parallelize(deletes, 2))

df.write.format("hudi").
  options(getQuickstartWriteConfigs).
  option(OPERATION_OPT_KEY,"delete").
  option(PRECOMBINE_FIELD_OPT_KEY, "ts").
  option(RECORDKEY_FIELD_OPT_KEY, "uuid").
  option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
  option(TABLE_NAME, tableName).
  mode(Append).
  save(basePath)

// run the same read query as above.
val roAfterDeleteViewDF = spark.
  read.
  format("hudi").
  load(basePath + "/*/*/*/*")

roAfterDeleteViewDF.registerTempTable("hudi_trips_snapshot")
// fetch should return (total - 2) records
spark.sql("select uuid, partitionpath from hudi_trips_snapshot").count()

相关结果如下：

8 插入并覆盖表数据

生成一些新的数据，在Hudi元数据级别逻辑上覆盖表。 Hudi清理器最终会清理上一个表快照的文件组。 这比删除旧表并在覆盖模式下重新创建要快。

spark.
  read.format("hudi").
  load(basePath + "/*/*/*/*").
  select("uuid","partitionpath").
  show(10, false)

val inserts = convertToStringList(dataGen.generateInserts(10))
val df = spark.read.json(spark.sparkContext.parallelize(inserts, 2))
df.write.format("hudi").
  options(getQuickstartWriteConfigs).
  option(OPERATION_OPT_KEY,"insert_overwrite_table").
  option(PRECOMBINE_FIELD_OPT_KEY, "ts").
  option(RECORDKEY_FIELD_OPT_KEY, "uuid").
  option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
  option(TABLE_NAME, tableName).
  mode(Append).
  save(basePath)

// Should have different keys now, from query before.
spark.
  read.format("hudi").
  load(basePath + "/*/*/*/*").
  select("uuid","partitionpath").
  show(10, false)

相关结果如下

9 插入覆盖

生成一些新数据，覆盖输入中的所有分区。对于批处理ETL作业来说，这个操作比upsert更快，批处理ETL作业一次重新计算整个目标分区(而不是增量地更新目标表)。这是因为，我们可以在upsert写路径中完全绕过索引、预组合和其他重分区步骤。

// spark-shell
spark.
  read.format("hudi").
  load(basePath + "/*/*/*/*").
  select("uuid","partitionpath").
  sort("partitionpath","uuid").
  show(100, false)

val inserts = convertToStringList(dataGen.generateInserts(10))
val df = spark.
  read.json(spark.sparkContext.parallelize(inserts, 2)).
  filter("partitionpath = 'americas/united_states/san_francisco'")
df.write.format("hudi").
  options(getQuickstartWriteConfigs).
  option(OPERATION_OPT_KEY,"insert_overwrite").
  option(PRECOMBINE_FIELD_OPT_KEY, "ts").
  option(RECORDKEY_FIELD_OPT_KEY, "uuid").
  option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
  option(TABLE_NAME, tableName).
  mode(Append).
  save(basePath)

// Should have different keys now for San Francisco alone, from query before.
spark.
  read.format("hudi").
  load(basePath + "/*/*/*/*").
  select("uuid","partitionpath").
  sort("partitionpath","uuid").
  show(100, false)

相关结果如下：