真香！PySpark整合Apache Hudi实战

Posted 2020-12-15 leesf

1. 准备

Hudi支持Spark-2.x版本，你可以点击如下链接安装Spark，并使用pyspark启动

# pyspark
export PYSPARK_PYTHON=$(which python3)
spark-2.4.4-bin-hadoop2.7/bin/pyspark   --packages org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4   --conf ‘spark.serializer=org.apache.spark.serializer.KryoSerializer‘

• spark-avro模块需要在--packages显示指定
• spark-avro和spark的版本必须匹配
• 本示例中，由于依赖spark-avro_2.11，因此使用的是scala2.11构建hudi-spark-bundle，如果使用spark-avro_2.12，相应的需要使用hudi-spark-bundle_2.12

进行一些前置变量初始化

# pyspark
tableName = "hudi_trips_cow"
basePath = "file:///tmp/hudi_trips_cow"
dataGen = sc._jvm.org.apache.hudi.QuickstartUtils.DataGenerator()

其中DataGenerator可以用来基于行程schema生成插入和删除的样例数据。

2. 插入数据

生成一些新的行程数据，加载到DataFrame中，并将DataFrame写入Hudi表

# pyspark
inserts = sc._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(dataGen.generateInserts(10))
df = spark.read.json(spark.sparkContext.parallelize(inserts, 2))

hudi_options = {
  ‘hoodie.table.name‘: tableName,
  ‘hoodie.datasource.write.recordkey.field‘: ‘uuid‘,
  ‘hoodie.datasource.write.partitionpath.field‘: ‘partitionpath‘,
  ‘hoodie.datasource.write.table.name‘: tableName,
  ‘hoodie.datasource.write.operation‘: ‘insert‘,
  ‘hoodie.datasource.write.precombine.field‘: ‘ts‘,
  ‘hoodie.upsert.shuffle.parallelism‘: 2, 
  ‘hoodie.insert.shuffle.parallelism‘: 2
}

df.write.format("hudi").   options(**hudi_options).   mode("overwrite").   save(basePath)

mode(Overwrite)会覆盖并重新创建数据集。示例中提供了一个主键 (schema中的uuid)，分区字段(region/county/city）和组合字段(schema中的ts) 以确保行程记录在每个分区中都是唯一的。

3. 查询数据

将数据加载至DataFrame

# pyspark
tripsSnapshotDF = spark.   read.   format("hudi").   load(basePath + "/*/*/*/*")

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()

该查询提供读取优化视图，由于我们的分区路径格式为region/country/city)，从基本路径（basepath）开始，我们使用load(basePath + "/*/*/*/*")来加载数据。

4. 更新数据

与插入新数据类似，还是使用DataGenerator生成更新数据，然后使用DataFrame写入Hudi表。

# pyspark
updates = sc._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(dataGen.generateUpdates(10))
df = spark.read.json(spark.sparkContext.parallelize(updates, 2))
df.write.format("hudi").   options(**hudi_options).   mode("append").   save(basePath)

注意，现在保存模式现在为append。通常，除非是第一次尝试创建数据集，否则请始终使用追加模式。每个写操作都会生成一个新的由时间戳表示的commit 。

5. 增量查询

Hudi提供了增量拉取的能力，即可以拉取从指定commit时间之后的变更，如不指定结束时间，那么将会拉取最新的变更。

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

commits = list(map(lambda row: row[0], spark.sql("select distinct(_hoodie_commit_time) as commitTime from  hudi_trips_snapshot order by commitTime").limit(50).collect()))
beginTime = commits[len(commits) - 2] # commit time we are interested in

# incrementally query data
incremental_read_options = {
  ‘hoodie.datasource.query.type‘: ‘incremental‘,
  ‘hoodie.datasource.read.begin.instanttime‘: beginTime,
}

tripsIncrementalDF = spark.read.format("hudi").   options(**incremental_read_options).   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()

这表示查询在开始时间提交之后的所有变更，此增量拉取功能可以在批量数据上构建流式管道。

6. 特定时间点查询

即如何查询特定时间的数据，可以通过将结束时间指向特定的提交时间，将开始时间指向”000”(表示最早的提交时间)来表示特定时间。

# pyspark
beginTime = "000" # Represents all commits > this time.
endTime = commits[len(commits) - 2]

# query point in time data
point_in_time_read_options = {
  ‘hoodie.datasource.query.type‘: ‘incremental‘,
  ‘hoodie.datasource.read.end.instanttime‘: endTime,
  ‘hoodie.datasource.read.begin.instanttime‘: beginTime
}

tripsPointInTimeDF = spark.read.format("hudi").   options(**point_in_time_read_options).   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()

7. 删除数据

删除传入的HoodieKey集合，注意：删除操作只支持append模式

# pyspark
# fetch total records count
spark.sql("select uuid, partitionPath from hudi_trips_snapshot").count()
# fetch two records to be deleted
ds = spark.sql("select uuid, partitionPath from hudi_trips_snapshot").limit(2)

# issue deletes
hudi_delete_options = {
  ‘hoodie.table.name‘: tableName,
  ‘hoodie.datasource.write.recordkey.field‘: ‘uuid‘,
  ‘hoodie.datasource.write.partitionpath.field‘: ‘partitionpath‘,
  ‘hoodie.datasource.write.table.name‘: tableName,
  ‘hoodie.datasource.write.operation‘: ‘delete‘,
  ‘hoodie.datasource.write.precombine.field‘: ‘ts‘,
  ‘hoodie.upsert.shuffle.parallelism‘: 2, 
  ‘hoodie.insert.shuffle.parallelism‘: 2
}

from pyspark.sql.functions import lit
deletes = list(map(lambda row: (row[0], row[1]), ds.collect()))
df = spark.sparkContext.parallelize(deletes).toDF([‘partitionpath‘, ‘uuid‘]).withColumn(‘ts‘, lit(0.0))
df.write.format("hudi").   options(**hudi_delete_options).   mode("append").   save(basePath)

# run the same read query as above.
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()

8. 总结

本篇博文展示了如何使用pyspark来插入、删除、更新Hudi表，有pyspark和Hudi需求的小伙伴不妨一试！