Apache Hudi在阿里巴巴集团、EMIS Health，LinkNovate，Tathastu.AI，腾讯，Uber内使用，并且由Amazon AWS EMR和Google云平台支持，最近Amazon Athena支持了在Amazon S3上查询Apache Hudi数据集的能力，本博客将测试Athena查询S3上Hudi格式数据集。

1. 准备-Spark环境，S3 Bucket

需要使用Spark写入Hudi数据，登陆Amazon EMR并启动spark-shell：

$ export SCALA_VERSION=2.12
$ export SPARK_VERSION=2.4.4
$ spark-shell --packages org.apache.hudi:hudi-spark-bundle_${SCALA_VERSION}:0.5.3,org.apache.spark:spark-avro_${SCALA_VERSION}:${SPARK_VERSION}--conf ‘spark.serializer=org.apache.spark.serializer.KryoSerializer‘
...
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  ‘_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.4.4
      /_/
         
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_242)
Type in expressions to have them evaluated.
Type :help for more information.
scala>

接着使用如下scala代码设置表名，基础路径以及数据生成器来生成数据。这里设置basepath为s3://hudi_athena_test/hudi_trips，以便后面进行查询

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"
val basePath = "s3://hudi_athena_test/hudi_trips"
val dataGen = new DataGenerator

2. 插入数据

生成新的行程数据，导入DataFrame，并将其写入Hudi表

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)

3. 创建Athena数据库/表

Hudi内置表分区支持，所以在创建表后需要添加分区，安装athenareader工具，其提供Athena多个查询和其他有用的特性。

go get -u github.com/uber/athenadriver/athenareader

接着创建hudi_athena_test.sql文件，内容如下

DROP DATABASE IF EXISTS hudi_athena_test CASCADE;
create database hudi_athena_test;
CREATE EXTERNAL TABLE `trips`(
  `begin_lat` double,
  `begin_lon` double,
  `driver` string,
  `end_lat` double,
  `end_lon` double,
  `fare` double,
  `rider` string,
  `ts` double,
  `uuid` string
) PARTITIONED BY (`partitionpath` string) 
ROW FORMAT SERDE ‘org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe‘
STORED AS INPUTFORMAT ‘org.apache.hudi.hadoop.HoodieParquetInputFormat‘ 
OUTPUTFORMAT ‘org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat‘ 
LOCATION ‘s3://hudi_athena_test/hudi_trips‘
ALTER TABLE trips ADD
  PARTITION (partitionpath = ‘americas/united_states/san_francisco‘) LOCATION ‘s3://hudi_athena_test/hudi_trips/americas/united_states/san_francisco‘
  PARTITION (partitionpath = ‘americas/brazil/sao_paulo‘) LOCATION ‘s3://hudi_athena_test/hudi_trips/americas/brazil/sao_paulo‘
  PARTITION (partitionpath = ‘asia/india/chennai‘) LOCATION ‘s3://hudi_athena_test/hudi_trips/asia/india/chennai‘

使用如下命令运行SQL语句

$ athenareader -q hudi_athena_test.sql

4. 使用Athena查询Hudi

如果没有错误，那么说明库和表在Athena中都已创建好，因此可以在Athena中查询Hudi数据集，使用athenareader查询结果如下

athenareader -q "select * from trips" -o markdown

也可以带条件进行查询

athenareader -q "select fare,rider from trips where fare>20" -o markdown

5. 更新Hudi表再次查询

Hudi支持S3中的数据，回到spark-shell并使用如下命令更新部分数据

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)

运行完成后，使用athenareader再次查询

athenareader -q "select * from trips" -o markdown

可以看到数据已经更新了

6. 限制

Athena不支持查询快照或增量查询，Hive/SparkSQL支持，为进行验证，通过spark-shell创建一个快照

spark.
  read.
  format("hudi").
  load(basePath + "/*/*/*/*").
  createOrReplaceTempView("hudi_trips_snapshot")

使用如下代码查询

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)

使用Athena查询将会失败，因为没有物化

$ athenareader -q "select distinct(_hoodie_commit_time) as commitTime from hudi_trips_snapshot order by commitTime"
SYNTAX_ERROR: line 1:57: Table awsdatacatalog.hudi_athena_test.hudi_trips_snapshot does not exist

根据官方文档，Athena支持查询Hudi数据集的Read-Optimized视图，同时，我们可以通过Athena来创建视图并进行查询，使用Athena在Hudi表上创建一个视图

$ athenareader -q "create view fare_greater_than_40 as select * from trips where fare>40" -a

查询视图

$ athenareader -q "select fare,rider from fare_greater_than_40"
 FARE                RIDER     
 43.4923811219014    rider-213 
 63.72504913279929   rider-284 
 90.25710109008239   rider-284 
 93.56018115236618   rider-213 
 49.527694252432056  rider-284 
 90.9053809533154    rider-284 
 98.3428192817987    rider-284

Apache Hudi + AWS S3 + Athena实战