前言

软件	版本
Python	3.8
Hadoop	3.3.2
Spark	3.3.1
Hudi	0.12.0

Hudi官网demo提供了3种通过Spark操作Hudi的方法:

这里我们选择通过pyspark来操作

一. pyspark连接hudi

pyspark连接hudi:

# Spark 3.3
export PYSPARK_PYTHON=$(which python3)
pyspark \
--packages org.apache.hudi:hudi-spark3.3-bundle_2.12:0.12.0 \
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer' \
--conf 'spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog' \
--conf 'spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension'

设置表名、基本路径和数据生成器:

# pyspark
tableName = "hudi_trips_cow"
basePath = "hdfs://hp5:8020/tmp/hudi_trips_cow"
dataGen = sc._jvm.org.apache.hudi.QuickstartUtils.DataGenerator()

二. 创建表

spark中不需要单独的create table命令。如果表不存在，第一批写入操作将创建该表。

三. 插入数据

生成一些新的trip，将它们加载到DataFrame中，并将DataFrame写入Hudi表中.

# 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': 'upsert',
    '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)

四. 查询数据

# pyspark
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()

五. Time Travel查询

Hudi从0.9.0开始支持Time Travel查询。目前支持三种查询时间格式，如下所示。

#pyspark
spark.read. \
  format("hudi"). \
  option("as.of.instant", "20210728141108"). \
  load(basePath)



# It is equal to "as.of.instant = 2021-07-28 00:00:00"
spark.read. \
  format("hudi"). \
  option("as.of.instant", "2021-07-28"). \
  load(basePath)

六. 更新数据

这类似于插入新数据。使用数据生成器生成现有行程的更新，加载到DataFrame中，并将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)

七. 增量查询

Hudi还提供了获取自给定提交时间戳以来更改的记录流的功能。这可以通过使用Hudi的增量查询来实现，并提供需要流化更改的开始时间。如果我们希望在给定的提交之后进行所有更改(通常是这样)，则不需要指定endTime。

# 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()

八. 基于时间点查询

# 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()

九. 删除数据

Apache Hudi支持两种类型的删除:
(1)软删除:保留记录键，只清除所有其他字段的值(软删除中为空的记录始终保存在存储中，而不会删除);
(2)硬删除:从表中物理删除记录的任何痕迹。详细信息请参见写入数据页面的删除部分。

9.1 软删除

# pyspark
from pyspark.sql.functions import lit
from functools import reduce

spark.read.format("hudi"). \
  load(basePath). \
  createOrReplaceTempView("hudi_trips_snapshot")
# fetch total records count
spark.sql("select uuid, partitionpath from hudi_trips_snapshot").count()
spark.sql("select uuid, partitionpath from hudi_trips_snapshot where rider is not null").count()
# fetch two records for soft deletes
soft_delete_ds = spark.sql("select * from hudi_trips_snapshot").limit(2)

# prepare the soft deletes by ensuring the appropriate fields are nullified
meta_columns = ["_hoodie_commit_time", "_hoodie_commit_seqno", "_hoodie_record_key", \
  "_hoodie_partition_path", "_hoodie_file_name"]
excluded_columns = meta_columns + ["ts", "uuid", "partitionpath"]
nullify_columns = list(filter(lambda field: field[0] not in excluded_columns, \
  list(map(lambda field: (field.name, field.dataType), soft_delete_ds.schema.fields))))

hudi_soft_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': 'upsert',
  'hoodie.datasource.write.precombine.field': 'ts',
  'hoodie.upsert.shuffle.parallelism': 2, 
  'hoodie.insert.shuffle.parallelism': 2
}

soft_delete_df = reduce(lambda df,col: df.withColumn(col[0], lit(None).cast(col[1])), \
  nullify_columns, reduce(lambda df,col: df.drop(col[0]), meta_columns, soft_delete_ds))

# simply upsert the table after setting these fields to null
soft_delete_df.write.format("hudi"). \
  options(**hudi_soft_delete_options). \
  mode("append"). \
  save(basePath)

# reload data
spark.read.format("hudi"). \
  load(basePath). \
  createOrReplaceTempView("hudi_trips_snapshot")

# This should return the same total count as before
spark.sql("select uuid, partitionpath from hudi_trips_snapshot").count()
# This should return (total - 2) count as two records are updated with nulls
spark.sql("select uuid, partitionpath from hudi_trips_snapshot where rider is not null").count()

9.2 硬删除

# 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_hard_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()))
hard_delete_df = spark.sparkContext.parallelize(deletes).toDF(['uuid', 'partitionpath']).withColumn('ts', lit(0.0))
hard_delete_df.write.format("hudi"). \
  options(**hudi_hard_delete_options). \
  mode("append"). \
  save(basePath)

# run the same read query as above.
roAfterDeleteViewDF = spark. \
  read. \
  format("hudi"). \
  load(basePath) 
roAfterDeleteViewDF.createOrReplaceTempView("hudi_trips_snapshot")
# fetch should return (total - 2) records
spark.sql("select uuid, partitionpath from hudi_trips_snapshot").count()

十. 插入覆盖

生成一些新的trip，覆盖输入中出现的所有分区。对于批处理ETL作业，此操作比upsert快，批处理ETL作业一次重新计算整个目标分区(与增量更新目标表相反)。这是因为，我们能够完全绕过索引、预合并和upsert写路径中的其他重分区步骤。

# pyspark
self.spark.read.format("hudi"). \
    load(basePath). \
    select(["uuid", "partitionpath"]). \
    sort(["partitionpath", "uuid"]). \
    show(n=100, truncate=False) 
    
inserts = sc._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(dataGen.generateInserts(10)) 
df = spark.read.json(spark.sparkContext.parallelize(inserts, 2)). \
    filter("partitionpath = 'americas/united_states/san_francisco'")
hudi_insert_overwrite_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_overwrite',
    'hoodie.datasource.write.precombine.field': 'ts',
    'hoodie.upsert.shuffle.parallelism': 2,
    'hoodie.insert.shuffle.parallelism': 2
}
df.write.format("hudi").options(**hudi_insert_overwrite_options).mode("append").save(basePath)
spark.read.format("hudi"). \
    load(basePath). \
    select(["uuid", "partitionpath"]). \
    sort(["partitionpath", "uuid"]). \
    show(n=100, truncate=False)

十一. Spark其它命令

11.1 Alter Table

语法:

-- Alter table name
ALTER TABLE oldTableName RENAME TO newTableName

-- Alter table add columns
ALTER TABLE tableIdentifier ADD COLUMNS(colAndType (,colAndType)*)

-- Alter table column type
ALTER TABLE tableIdentifier CHANGE COLUMN colName colName colType

-- Alter table properties
ALTER TABLE tableIdentifier SET TBLPROPERTIES (key = 'value')

案例:

--rename to:
ALTER TABLE hudi_cow_nonpcf_tbl RENAME TO hudi_cow_nonpcf_tbl2;

--add column:
ALTER TABLE hudi_cow_nonpcf_tbl2 add columns(remark string);

--change column:
ALTER TABLE hudi_cow_nonpcf_tbl2 change column uuid uuid bigint;

--set properties;
alter table hudi_cow_nonpcf_tbl2 set tblproperties (hoodie.keep.max.commits = '10');

11.2 Partition SQL Command

语法:

-- Drop Partition
ALTER TABLE tableIdentifier DROP PARTITION ( partition_col_name = partition_col_val [ , ... ] )

-- Show Partitions
SHOW PARTITIONS tableIdentifier

案例:

--show partition:
show partitions hudi_cow_pt_tbl;

--drop partition：
alter table hudi_cow_pt_tbl drop partition (dt='2021-12-09', hh='10');

参考:

1. https://hudi.apache.org/docs/0.12.0/quick-start-guide