大数据Hadoop之—Apache Hudi 数据湖实战操作

# 启动
./hudi-cli/hudi-cli.sh

cd $SPARK_HOME
hdfs dfs -mkdir /tmp/
hdfs dfs -put README.md /tmp/
hdfs dfs -text /tmp/README.md

# 启动spark-shell
./bin/spark-shell --master local[2]

val datasRDD = sc.textFile("/tmp/README.md")
# 行数
datasRDD.count()
# 读取第一行数据
datasRDD.first()
val dataframe = spark.read.textFile("/tmp/README.md")
dataframe.printSchema
dataframe.show(10,false)

### 启动spark-shell，使用spark-shell操作hudi数据湖
### 第一种方式
./bin/spark-shell \
  --master local[2] \
  --packages org.apache.hudi:hudi-spark3.2-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'

### 上述命令需要联网，基于ivy下载下载相关jar包到本地，然后加载到CLASSPATH，其中包含三个jar包。

### 第二种方式，使用--jars
cd /opt/apache
wget https://repo1.maven.org/maven2/org/apache/spark/spark-avro_2.12/3.3.0/spark-avro_2.12-3.3.0.jar

cd $SPARK_HOME
./bin/spark-shell \
--master local[2] \
--jars  /opt/apache/hudi-0.12.0/packaging/hudi-spark-bundle/target/hudi-spark3.2-bundle_2.12-0.12.0.jar,/opt/apache/hudi-0.12.0/hudi-examples/hudi-examples-spark/target/lib/unused-1.0.0.jar,/opt/apache/spark-avro_2.12-3.3.0.jar \
--conf 'spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog' \
--conf "spark.serializer=org.apache.spark.serializer.KryoSerializer"

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._
import org.apache.hudi.common.model.HoodieRecord

val tableName = "hudi_trips_cow"
# 存储到HDFS
val basePath = "hdfs://hadoop-hadoop-hdfs-nn:9000/tmp/hudi_trips_cow"
# 存储到本地
# val basePath = "file:///tmp/hudi_trips_cow"

##构建DataGenerator对象，用于模拟生成10条Trip乘车数据
val dataGen = new DataGenerator
 
val inserts = convertToStringList(dataGen.generateInserts(10))

##转成df
val df = spark.read.json(spark.sparkContext.parallelize(inserts,2))

##查看数据结构
df.printSchema()
##查看数据
df.show()
# 指定字段查询
df.select("rider","begin_lat","begin_lon","driver","end_lat","end_lon","fare","partitionpath","ts","uuid").show(10,truncate=false)

# 将数据保存到hudi表中，由于Hudi诞生时基于Spark框架，所以SparkSQL支持Hudi数据源，直接通过format指定数据源Source，设置相关属性保存数据即可，注意，hudi不是正真存储数据，而是管理数据。

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)

## 重要参数说明
#参数：getQuickstartWriteConfigs，设置写入/更新数据至Hudi时，Shuffle时分区数目
#参数：PRECOMBINE_FIELD_OPT_KEY，数据合并时，依据主键字段
#参数：RECORDKEY_FIELD_OPT_KEY，每条记录的唯一id，支持多个字段
#参数：PARTITIONPATH_FIELD_OPT_KEY，用于存放数据的分区字段

### 1、下载软件包
wget https://dlcdn.apache.org/flink/flink-1.14.6/flink-1.14.6-bin-scala_2.12.tgz
tar -xf flink-1.14.6-bin-scala_2.12.tgz
export FLINK_HOME=/opt/apache/flink-1.14.6

### 2、设置HADOOP_CLASSPATH
# HADOOP_HOME is your hadoop root directory after unpack the binary package.
export HADOOP_CLASSPATH=`$HADOOP_HOME/bin/hadoop classpath`
export HADOOP_CONF_DIR='/opt/apache/hadoop/etc/hadoop'

### 3、启动单节点flink 集群
# Start the Flink standalone cluster，这里先修改slot数量，默认是1，这里改成4
# taskmanager.numberOfTaskSlots: 4
cd $FLINK_HOME
./bin/start-cluster.sh

# 测试可用性
./bin/flink run  examples/batch/WordCount.jar

# 【第一种方式】指定jar包
./bin/sql-client.sh embedded -j ../hudi-0.12.0/packaging/hudi-flink-bundle/target/hudi-flink1.14-bundle-0.12.0.jar shell

# 【第二种方式】还可以将jar包放在$FINK_HOME/lib目录下

-- sets up the result mode to tableau to show the results directly in the CLI
SET 'sql-client.execution.result-mode' = 'tableau';

CREATE TABLE t1(
  uuid VARCHAR(20) PRIMARY KEY NOT ENFORCED,
  name VARCHAR(10),
  age INT,
  ts TIMESTAMP(3),
  `partition` VARCHAR(20)
)
PARTITIONED BY (`partition`)
WITH (
  'connector' = 'hudi',
  'path' = 'hdfs://hadoop-hadoop-hdfs-nn:9000/tmp/flink-hudi-t1',
  'table.type' = 'MERGE_ON_READ' -- this creates a MERGE_ON_READ table, by default is COPY_ON_WRITE
);

INSERT INTO t1 VALUES ('id1','Danny',23,TIMESTAMP '1970-01-01 00:00:01','par1');
-- insert data using values
INSERT INTO t1 VALUES
  ('id1','Danny',23,TIMESTAMP '1970-01-01 00:00:01','par1'),
  ('id2','Stephen',33,TIMESTAMP '1970-01-01 00:00:02','par1'),
  ('id3','Julian',53,TIMESTAMP '1970-01-01 00:00:03','par2'),
  ('id4','Fabian',31,TIMESTAMP '1970-01-01 00:00:04','par2'),
  ('id5','Sophia',18,TIMESTAMP '1970-01-01 00:00:05','par3'),
  ('id6','Emma',20,TIMESTAMP '1970-01-01 00:00:06','par3'),
  ('id7','Bob',44,TIMESTAMP '1970-01-01 00:00:07','par4'),
  ('id8','Han',56,TIMESTAMP '1970-01-01 00:00:08','par4');

select * from t1;

-- this would update the record with key 'id1'
insert into t1 values
  ('id1','Danny',27,TIMESTAMP '1970-01-01 00:00:01','par1');

CREATE TABLE t2(
  uuid VARCHAR(20) PRIMARY KEY NOT ENFORCED,
  name VARCHAR(10),
  age INT,
  ts TIMESTAMP(3),
  `partition` VARCHAR(20)
)
PARTITIONED BY (`partition`)
WITH (
  'connector' = 'hudi',
  'path' = 'hdfs://hadoop-hadoop-hdfs-nn:9000/tmp/flink-hudi-t1',
  'table.type' = 'MERGE_ON_READ',
  'read.streaming.enabled' = 'true',  -- this option enable the streaming read
  'read.start-commit' = '20210316134557', -- specifies the start commit instant time
  'read.streaming.check-interval' = '4' -- specifies the check interval for finding new source commits, default 60s.
);

-- Then query the table in stream mode
select * from t2;