数据湖-Apache Hudi

Hudi特性

• 数据湖处理非结构化数据、日志数据、结构化数据

• 支持较快upsert/delete, 可插入索引

• Table Schema

• 小文件管理Compaction

• ACID语义保证,多版本保证 并具有回滚功能

• savepoint 用户数据恢复的保存点

• 支持多种分析引擎 spark、hive、presto

编译Hudi

git clone https://github.com/apache/hudi.git && cd hudi
mvn clean package -DskipTests

hudi 高度耦合spark

执行spark-shell测试Hudi

bin/spark-shell  --packages org.apache.spark:spark-avro_2.11:2.4.5   --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer' --jars /Users/macwei/IdeaProjects/hudi-master/packaging/hudi-spark-bundle/target/hudi-spark-bundle_2.11-0.6.1-SNAPSHOT.jar

hudi 写入数据

// spark-shell
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 = "file:///tmp/hudi_trips_cow"
val dataGen = new DataGenerator

// spark-shell
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)

读取hudi数据:

  val 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()
  
  +------------------+-------------------+-------------------+-------------+
|              fare|          begin_lon|          begin_lat|           ts|
+------------------+-------------------+-------------------+-------------+
| 64.27696295884016| 0.4923479652912024| 0.5731835407930634|1609771934700|
| 93.56018115236618|0.14285051259466197|0.21624150367601136|1610087553306|
| 33.92216483948643| 0.9694586417848392| 0.1856488085068272|1609982888463|
| 27.79478688582596| 0.6273212202489661|0.11488393157088261|1610187369637|
|34.158284716382845|0.46157858450465483| 0.4726905879569653|1610017361855|
|  43.4923811219014| 0.8779402295427752| 0.6100070562136587|1609795685223|
| 66.62084366450246|0.03844104444445928| 0.0750588760043035|1609923236735|
| 41.06290929046368| 0.8192868687714224|  0.651058505660742|1609838517703|
+------------------+-------------------+-------------------+-------------+


spark.sql("select _hoodie_commit_time, _hoodie_record_key, _hoodie_partition_path, rider, driver, fare from  hudi_trips_snapshot").show()


+-------------------+--------------------+----------------------+---------+----------+------------------+
|_hoodie_commit_time|  _hoodie_record_key|_hoodie_partition_path|    rider|    driver|              fare|
+-------------------+--------------------+----------------------+---------+----------+------------------+
|     20210110225218|3c7ef0e7-86fb-444...|  americas/united_s...|rider-213|driver-213| 64.27696295884016|
|     20210110225218|222db9ca-018b-46e...|  americas/united_s...|rider-213|driver-213| 93.56018115236618|
|     20210110225218|3fc72d76-f903-4ca...|  americas/united_s...|rider-213|driver-213|19.179139106643607|
|     20210110225218|512b0741-e54d-426...|  americas/united_s...|rider-213|driver-213| 33.92216483948643|
|     20210110225218|ace81918-0e79-41a...|  americas/united_s...|rider-213|driver-213| 27.79478688582596|
|     20210110225218|c76f82a1-d964-4db...|  americas/brazil/s...|rider-213|driver-213|34.158284716382845|
|     20210110225218|73145bfc-bcb2-424...|  americas/brazil/s...|rider-213|driver-213|  43.4923811219014|
|     20210110225218|9e0b1d58-a1c4-47f...|  americas/brazil/s...|rider-213|driver-213| 66.62084366450246|
|     20210110225218|b8fccca1-9c28-444...|    asia/india/chennai|rider-213|driver-213|17.851135255091155|
|     20210110225218|6144be56-cef9-43c...|    asia/india/chennai|rider-213|driver-213| 41.06290929046368|
+-------------------+--------------------+----------------------+---------+----------+------------------+

对比

数据导入至hadoop方案: maxwell、canal、flume、sqoop

hudi是通用方案

• hudi 支持presto、spark sql下游查询

• hudi存储依赖hdfs

• hudi可以当作数据源或数据库,支持PB级别

概念

Timeline: 时间戳

state：即时状态

原子写入操作

compaction： 后台协调hudi中差异数据

rollback： 回滚

savepoint： 数据还原

任何操作都有以下状态:

• Requested 已安排操作行为,但是没有开始
• Inflight 正在执行当前操作
• Completed 已完成操作

hudi提供两种表类型:

• CopyOnWrite 适用全量数据,列式存储,写入过程执行同步合并重写文件
• MergeOnRead 增量数据,基于列式(parquet)和行式(avro)存储,更新记录到增量文件(日志文件),压缩同步和异步生成新版本文件,延迟更低

hudi查询类型:

• 快照查询 查询最新快照表数据,如果是MergeOnRead表,动态合并最新版本基本数据和增量数据用于显示查询;如果是CopyOnWrite,直接查询Parquet表,同时提供upsert、delete操作
• 增量查询 只能看到写入表的新数据
• 优化读查询 给定时间段的一个查询

资料参考

• Docker Demo: https://hudi.apache.org/docs/docker_demo.html Hudi 官方建议代码测试可在Docker进行, 如果在Docker运行有问题也可以进行Remote Debugger
• Hudi 目前代码写的很多实现其实有点不太好, 如果有想贡献提交PR的可参考: 如何进行开源贡献