基于 Ubuntu 玩转 Hudi Docker Demo （3）—— Spark写入和查询

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简介

上一篇文章《基于 Ubuntu 玩转 Hudi Docker Demo （2）—— 测试数据写入 Kafka》介绍了如何将测试数据写入到 kafka 集群。
本文介绍如何使用 Spark 消费 Kafka 数据，并将数据写入 HDFS。其中 Hudi 以 Jar 包的方式引入到 Spark。

Hudi 表和查询的类型

表类型	支持的查询类型
Copy On Write (写时复制，简称 cow)	支持快照查询和增量查询
Merge On Read (写时复制，简称 mor)	支持快照查询、增量查询、读优化查询

1. 表类型

Copy On Write (写时复制，简称 cow) ：以列式（e.g parquet）格式存储数据，数据写入的时候同步合并历史数据。
Merge On Read (读时合并，简称 mor)：结合列存（e.g parquet）和行存（e.g avro）方式来存储数据。增量的数据先以行存的方式存储数据，然后以同步或者异步的方式合并数据生成性的列存文件。

权衡	CopyOnWrite	MergeOnRead
数据延迟	高	低
查询延迟	低	高
更新成本	高，需要重写整个 parquet 文件	低，append 方式写增量文件
写放大	大	小，取决于合并策略

2. 查询类型

Snapshot Queries (快照查询）：可以查询到最近一次成功提交或者合并的快照数据。
Incremental Queries （增量查询）：可查询指定提交或者合并后新写入表的数据。
Read Optimized Queries （读优化查询）：仅限于 MergeOnRead 表，可以查询到列存文件的数据。

对于 MergeOnRead 表选择查询类型需做以下权衡：

权衡	Snapshot Queries	Read Optimized Queries
数据延迟	低	高
查询延迟	高	低

具体过程

1. 进入容器 adhoc-2

sudo docker exec -it adhoc-2 /bin/bash

在这里插入图片描述

2. 执行 spark-submit

执行以下spark-submit 命令以启动delta-streamer，从 kafka 集群消费数据，采用 COPY_ON_WRITE 模式写入到HDFS，表名 stock_ticks_cow

spark-submit \
  --class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer $HUDI_UTILITIES_BUNDLE \
  --table-type COPY_ON_WRITE \
  --source-class org.apache.hudi.utilities.sources.JsonKafkaSource \
  --source-ordering-field ts  \
  --target-base-path /user/hive/warehouse/stock_ticks_cow \
  --target-table stock_ticks_cow --props /var/demo/config/kafka-source.properties \
  --schemaprovider-class org.apache.hudi.utilities.schema.FilebasedSchemaProvider

在这里插入图片描述

执行以下spark-submit 命令以启动delta-streamer，从 kafka 集群消费数据，采用 MERGE_ON_READ 模式写入到HDFS，表名 stock_ticks_mor

spark-submit \
  --class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer $HUDI_UTILITIES_BUNDLE \
  --table-type MERGE_ON_READ \
  --source-class org.apache.hudi.utilities.sources.JsonKafkaSource \
  --source-ordering-field ts \
  --target-base-path /user/hive/warehouse/stock_ticks_mor \
  --target-table stock_ticks_mor \
  --props /var/demo/config/kafka-source.properties \
  --schemaprovider-class org.apache.hudi.utilities.schema.FilebasedSchemaProvider \
  --disable-compaction

在这里插入图片描述

3. 查看 hdfs 文件

stock_ticks_cow
stock_ticks_cow以日期分区，分区目录下有一个元数据文件和 parquet 格式的数据文件。
在 .hoodle 目录下可以看见 commit 信息。
在这里插入图片描述

stock_ticks_mor
在这里插入图片描述

4. 同步到 Hive 元数据

/var/hoodie/ws/hudi-sync/hudi-hive-sync/run_sync_tool.sh \
  --jdbc-url jdbc:hive2://hiveserver:10000 \
  --user hive \
  --pass hive \
  --partitioned-by dt \
  --base-path /user/hive/warehouse/stock_ticks_cow \
  --database default \
  --table stock_ticks_cow

/var/hoodie/ws/hudi-sync/hudi-hive-sync/run_sync_tool.sh \
  --jdbc-url jdbc:hive2://hiveserver:10000 \
  --user hive \
  --pass hive \
  --partitioned-by dt \
  --base-path /user/hive/warehouse/stock_ticks_mor \
  --database default \
  --table stock_ticks_mor

4. Spark SQL 查询

进入 spark-shell:

$SPARK_INSTALL/bin/spark-shell \
  --jars $HUDI_SPARK_BUNDLE \
  --master local[2] \
  --driver-class-path $HADOOP_CONF_DIR \
  --conf spark.sql.hive.convertMetastoreParquet=false \
  --deploy-mode client \
  --driver-memory 1G \
  --executor-memory 3G \
  --num-executors 1 \
  --packages org.apache.spark:spark-avro_2.11:2.4.4

stock_ticks_cow 是 CopyOnWrite 表
stock_ticks_mor_ro 是 MergeOnRead 表，用于读优化查询
stock_ticks_mor_rt 是 MergeOnRead 表，用于快照查询

Spark context Web UI available at http://adhoc-2:4040
Spark context available as 'sc' (master = local[2], app id = local-1644547729231).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.4.4
      /_/

Using Scala version 2.11.12 (OpenJDK 64-Bit Server VM, Java 1.8.0_212)
Type in expressions to have them evaluated.
Type :help for more information.

scala> spark.sql("show tables").show(100, false)
+--------+------------------+-----------+
|database|tableName         |isTemporary|
+--------+------------------+-----------+
|default |stock_ticks_cow   |false      |
|default |stock_ticks_mor_ro|false      |
|default |stock_ticks_mor_rt|false      |
+--------+------------------+-----------+

## Run max timestamp query against COW table
scala> spark.sql("select symbol, max(ts) from stock_ticks_cow group by symbol HAVING symbol = 'GOOG'").show(100, false)
+------+-------------------+
|symbol|max(ts)            |
+------+-------------------+
|GOOG  |2018-08-31 10:29:00|
+------+-------------------+

## Projection Query
scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_cow where  symbol = 'GOOG'").show(100, false)
+-------------------+------+-------------------+------+---------+--------+
|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
+-------------------+------+-------------------+------+---------+--------+
|20220211022538859  |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
|20220211022538859  |GOOG  |2018-08-31 10:29:00|3391  |1230.1899|1230.085|
+-------------------+------+-------------------+------+---------+--------+

# Merge-On-Read Queries:
# Run ReadOptimized Query. Notice that the latest timestamp is 10:29
scala> spark.sql("select symbol, max(ts) from stock_ticks_mor_ro group by symbol HAVING symbol = 'GOOG'").show(100, false)
+------+-------------------+
|symbol|max(ts)            |
+------+-------------------+
|GOOG  |2018-08-31 10:29:00|
+------+-------------------+

# Run Snapshot Query. Notice that the latest timestamp is again 10:29
scala> spark.sql("select symbol, max(ts) from stock_ticks_mor_rt group by symbol HAVING symbol = 'GOOG'").show(100, false)
+------+-------------------+
|symbol|max(ts)            |
+------+-------------------+
|GOOG  |2018-08-31 10:29:00|
+------+-------------------+

# Run Read Optimized and Snapshot project queries
scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_ro where  symbol = 'GOOG'").show(100, false)
+-------------------+------+-------------------+------+---------+--------+
|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
+-------------------+------+-------------------+------+---------+--------+
|20220211022707523  |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
|20220211022707523  |GOOG  |2018-08-31 10:29:00|3391  |1230.1899|1230.085|
+-------------------+------+-------------------+------+---------+--------+

scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_rt where  symbol = 'GOOG'").show(100, false)
+-------------------+------+-------------------+------+---------+--------+
|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
+-------------------+------+-------------------+------+---------+--------+
|20220211022707523  |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
|20220211022707523  |GOOG  |2018-08-31 10:29:00|3391  |1230.1899|1230.085|
+-------------------+------+-------------------+------+---------+--------+

5. 写入第二批数据到 kafka

退出 docker 容器，在 Ubuntu 命令行执行

cat docker/demo/data/batch_2.json | kafkacat -b kafkabroker -t stock_ticks -P

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6. 进入容器 adhoc-2，执行 spark-submit 写入第二批数据到 Hudi 表

进入容器 adhoc-2

sudo docker exec -it adhoc-2 /bin/bash

第二批数据到 Hudi CopyOnWrite 表

spark-submit \
  --class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer $HUDI_UTILITIES_BUNDLE \
  --table-type COPY_ON_WRITE \
  --source-class org.apache.hudi.utilities.sources.JsonKafkaSource \
  --source-ordering-field ts \
  --target-base-path /user/hive/warehouse/stock_ticks_cow \
  --target-table stock_ticks_cow \
  --props /var/demo/config/kafka-source.properties \
  --schemaprovider-class org.apache.hudi.utilities.schema.FilebasedSchemaProvider

查看 hdfs 目录：

hdfs dfs -ls -R /user/hive/warehouse/stock_ticks_cow

在这里插入图片描述

第二批数据到 Hudi MergeOnRead 表

spark-submit \
  --class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer $HUDI_UTILITIES_BUNDLE \
  --table-type MERGE_ON_READ \
  --source-class org.apache.hudi.utilities.sources.JsonKafkaSource \
  --source-ordering-field ts \
  --target-base-path /user/hive/warehouse/stock_ticks_mor \
  --target-table stock_ticks_mor \
  --props /var/demo/config/kafka-source.properties \
  --schemaprovider-class org.apache.hudi.utilities.schema.FilebasedSchemaProvider \
  --disable-compaction

查看 hdfs 目录：

hdfs dfs -ls -R /user/hive/warehouse/stock_ticks_mor

在这里插入图片描述

7. Spark SQL 查询

进入 spark-shell:

$SPARK_INSTALL/bin/spark-shell \
  --jars $HUDI_SPARK_BUNDLE \
  --master local[2] \
  --driver-class-path $HADOOP_CONF_DIR \
  --conf spark.sql.hive.convertMetastoreParquet=false \
  --deploy-mode client \
  --driver-memory 1G \
  --executor-memory 3G \
  --num-executors 1 \
  --packages org.apache.spark:spark-avro_2.11:2.4.4

Spark context Web UI available at http://adhoc-2:4040
Spark context available as 'sc' (master = local[2], app id = local-1644571477181).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.4.4
      /_/

Using Scala version 2.11.12 (OpenJDK 64-Bit Server VM, Java 1.8.0_212)
Type in expressions to have them evaluated.
Type :help for more information.

# 1. 快照方式查询 CopyOnWrite 表
scala> spark.sql("select symbol, max(ts) from stock_ticks_cow group by symbol HAVING symbol = 'GOOG'").show(100, false)
+------+-------------------+
|symbol|max(ts)            |
+------+-------------------+
|GOOG  |2018-08-31 10:59:00|
+------+-------------------+

scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_cow where  symbol = 'GOOG'").show(100, false)
+-------------------+------+-------------------+------+---------+--------+
|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
+-------------------+------+-------------------+------+---------+--------+
|20220211022538859  |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
|20220211064632375  |GOOG  |2018-08-31 10:59:00|9021  |1227.1993|1227.215|
+-------------------+------+-------------------+------+---------+--------+

# 2. 增量方式查询 CopyOnWrite 表
scala> import org.apache.hudi.DataSourceReadOptions
scala> val hoodieIncViewDF =  spark.read.format("org.apache.hudi").option(DataSourceReadOptions.QUERY_TYPE_OPT_KEY, DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL).option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY, "20220211064632000").load("/user/hive/warehouse/stock_ticks_cow")

scala> hoodieIncViewDF.registerTempTable("stock_ticks_cow_incr_tmp1")

scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_cow_incr_tmp1 where  symbol = 'GOOG'").show(100, false);
+-------------------+------+-------------------+------+---------+--------+
|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
+-------------------+------+-------------------+------+---------+--------+
|20220211064632375  |GOOG  |2018-08-31 10:59:00|9021  |1227.1993|1227.215|
+-------------------+------+-------------------+------+---------+--------+


# 3. 读优化方式查询 MergeOnRead 表
scala> spark.sql("select symbol, max(ts) from stock_ticks_mor_ro group by symbol HAVING symbol = 'GOOG'").show(100, false)
+------+-------------------+
|symbol|max(ts)            |
+------+-------------------+
|GOOG  |2018-08-31 10:29:00|
+------+-------------------+


scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_ro where  symbol = 'GOOG'").show(100, false)
+-------------------+------+-------------------+------+---------+--------+
|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
+-------------------+------+-------------------+------+---------+--------+
|20220211022538859  |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
|20220211022538859  |GOOG  |2018-08-31 10:29:00|3391  |1230.1899|1230.085|
+-------------------+------+-------------------+------+---------+--------+

# 4. 快照方式查询 MergeOnRead 表
scala> spark.sql("select symbol, max(ts) from stock_ticks_mor_rt group by symbol HAVING symbol = 'GOOG'").show(100, false)
+------+-------------------+
|symbol|max(ts)            |
+------+-------------------+
|GOOG  |2018-08-31 10:59:00|
+------+-------------------+


scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_rt where  symbol = 'GOOG'").show(100, false)
+-------------------+------+-------------------+------+---------+--------+
|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
+-------------------+------+-------------------+------+---------+--------+
|20220211022538859  |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
|20220211064632375  |GOOG  |2018-08-31 10:59:00|9021  |1227.1993|1227.215|
+-------------------+------+-------------------+------+---------+--------+

# 5. 增量方式查询 MergeOnRead 表
scala> val hoodieIncViewDF =  spark.read.format("org.apache.hudi").option(DataSourceReadOptions.QUERY_TYPE_OPT_KEY, DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL).option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY, "20220211064632000").load("/user/hive/warehouse/stock_ticks_mor")


scala> hoodieIncViewDF.registerTempTable("stock_ticks_mor_incr_tmp1")


scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_incr_tmp1 where  symbol = 'GOOG'").show(100, false);
+-------------------+------+-------------------+------+---------+--------+
|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
+-------------------+------+-------------------+------+---------+--------+
|20220211064632375  |GOOG  |2018-08-31 10:59:00|9021  |1227.1993|1227.215|
+-------------------+------+-------------------+------+---------+--------+

其中，对于 MergeOnRead 表，读优化查询和快照查询得到的结果是不一样的。
读优化查询：

+-------------------+------+-------------------+------+---------+--------+
|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
+-------------------+------+-------------------+------+---------+--------+
|20220211022538859  |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
|20220211022538859  |GOOG  |2018-08-31 10:29:00|3391  |1230.1899|1230.085|   <<<<<<<<<<<<<<<<<<
+-------------------+------+-------------------+------+---------+--------+

快照查询：

+-------------------+------+-------------------+------+---------+--------+
|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
+-------------------+------+-------------------+------+---------+--------+
|20220211022538859  |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
|20220211064632375  |GOOG  |2018-08-31 10:59:00|9021  |1227.1993|1227.215|    <<<<<<<<<<<<<<<<<<
+-------------------+------+-------------------+------+---------+--------+

由此可以看出读优化查询与快照查询的区别。

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