摘要
本文将介绍常用parquet文件读写的几种方式
1.用spark的hadoopFile api读取hive中的parquet格式文件
2.用sparkSql读写hive中的parquet格式
3.用新旧MapReduce读写parquet格式文件
读parquet文件
首先创建hive表，数据用tab分隔
create table test(name string,age int)
row format delimited
fields terminated by '\t';
加载数据
load data local inpath '/home/work/test/ddd.txt' into table test;数据样例格式：
hive> select * from test limit 5;
OK
leo 27
jim 38
leo 15
jack 22
jay 7
Time taken: 0.101 seconds, Fetched: 5 row(s)
创建parquet格式表
create table test_parquet(name string,age int) stored as parquet查看表结构
hive> show create table test_parquet;
OK
CREATE TABLE `test_parquet`(
`name` string,
`age` int)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
STORED AS INPUTFORMAT
'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
LOCATION
'hdfs://localhost:9000/user/hive/warehouse/test_parquet'
TBLPROPERTIES (
'transient_lastDdlTime'='1495038003')
可以看到数据的inputFormat是MapredParquetInputFormat，之后我们将用这个类来解析数据文件
往parquet格式表中插入数据
insert into table test_parquet select * from test;a.用spark中hadoopFile api解析hive中parquet格式文件
如果是用spark-shell中方式读取文件一定要将hive-exec-0.14.0.jar加入到启动命令行中（MapredParquetInputFormat在这个jar中），还有就是要指定序列化的类，启动命令行如下
spark-shell --master spark://xiaobin:7077 --jars /home/xiaobin/soft/apache-hive-0.14.0-bin/lib/hive-exec-0.14.0.jar
--conf spark.serializer=org.apache.spark.serializer.KryoSerializer
具体读取代码如下
scala> import org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat
import org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat
scala> import org.apache.hadoop.io.{ArrayWritable, NullWritable, Text}
import org.apache.hadoop.io.{ArrayWritable, NullWritable, Text}
scala> val file =sc.hadoopFile("hdfs://localhost:9000/user/hive/warehouse/test_parquet/000000_0",
| classOf[MapredParquetInputFormat],classOf[Void],classOf[ArrayWritable])
file: org.apache.spark.rdd.RDD[(Void, org.apache.hadoop.io.ArrayWritable)] =
hdfs://localhost:9000/user/hive/warehouse/test_parquet/000000_0 HadoopRDD[0] at hadoopFile at <console>:29
scala> file.take(10).foreach{case(k,v)=>
| val writables = v.get()
| val name = writables(0)
| val age = writables(1)
| println(writables.length+" "+name+" "+age)
| }用MapredParquetInputFormat解析hive中parquet格式文件，每行数据将会解析成一个key和value，这里的key是空值，value是一个ArrayWritable，value的长度和表的列个数一样，value各个元素对应hive表中行各个字段的值
b.用spark DataFrame 解析parquet文件
val conf = new SparkConf().setAppName("test").setMaster("local")
val sc = new SparkContext(conf)
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val parquet: DataFrame =
sqlContext.read.parquet("hdfs://192.168.1.115:9000/user/hive/warehouse/test_parquet")
parquet.printSchema()
parquet.select(parquet("name"), parquet("age") + 1).show()
root
|-- name: string (nullable = true)
|-- age: integer (nullable = true)
+----+---------+
|name|(age + 1)|
+----+---------+
| leo| 28|
| jim| 39|
| leo| 16|
|jack| 23|
| jay| 8|
| jim| 38|
|jack| 37|
| jay| 12|
c.用hivesql直接读取hive表
在local模式下没有测试成功，打包用spark-submit测试，代码如下
val conf = new SparkConf().setAppName("test")
val sc = new SparkContext(conf)
val hiveContext = new HiveContext(sc)
val sql: DataFrame = hiveContext.sql("select * from test_parquet limit 10")
sql.take(10).foreach(println)
[leo,27]
[jim,38]
[leo,15]
[jack,22]
[jay,7]
[jim,37]
[jack,36]
[jay,11]
[leo,35]
[leo,33]
提交任务命令行
spark-submit --class quickspark.QuickSpark02 --master spark://192.168.1.115:7077 sparkcore-1.0-SNAPSHOT.jar写parquet文件
a.用spark写parquet文件
val conf = new SparkConf().setAppName("test").setMaster("local")
val sc = new SparkContext(conf)
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
// 读取文件生成RDD
val file = sc.textFile("hdfs://192.168.1.115:9000/test/user.txt")
//定义parquet的schema，数据字段和数据类型需要和hive表中的字段和数据类型相同，否则hive表无法解析
val schema = (new StructType)
.add("name", StringType, true)
.add("age", IntegerType, false)
val rowRDD = file.map(_.split("\t")).map(p => Row(p(0), Integer.valueOf(p(1).trim)))
// 将RDD装换成DataFrame
val peopleDataFrame = sqlContext.createDataFrame(rowRDD, schema)
peopleDataFrame.registerTempTable("people")
peopleDataFrame.write.parquet("hdfs://192.168.1.115:9000/user/hive/warehouse/test_parquet/")用hivesql读取用spark DataFrame生成的parquet文件
hive> select * from test_parquet limit 10;
OK
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
leo 27
jim 38
leo 15
jack 22
jay 7
jim 37
jack 36
jay 11
leo 35
leo 33b.用MapReduce写parquet文件
用MR读写parquet文件，刚开始打算使用hive中指定的org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat这个类，但是这个类的getRecordWriter方法没实现，直接抛出异常
@Override
public RecordWriter<Void, ArrayWritable> getRecordWriter(
final FileSystem ignored,
final JobConf job,
final String name,
final Progressable progress
) throws IOException {
throw new RuntimeException("Should never be used");
}所以使用官方提供的parquet解析方式，github地址：https://github.com/apache/parquet-mr/，导入依赖
<dependency>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-common</artifactId>
<version>1.8.1</version>
</dependency>
<dependency>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-encoding</artifactId>
<version>1.8.1</version>
</dependency>
<dependency>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-column</artifactId>
<version>1.8.1</version>
</dependency>
<dependency>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-hadoop</artifactId>
<version>1.8.1</version>
</dependency>Parquet读写有新旧两个版本，主要是新旧MR api之分，我们用新旧老版本的MR实现下parquet文件的读写
旧版本如下
package com.fan.hadoop.parquet;
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.parquet.hadoop.example.GroupWriteSupport;
import org.apache.parquet.example.data.Group;
import org.apache.parquet.example.data.simple.SimpleGroupFactory;
import org.apache.parquet.hadoop.mapred.DeprecatedParquetOutputFormat;
import org.apache.parquet.schema.MessageTypeParser;
/**
* Created by fanlegefan.com on 17-7-17.
*/
public class ParquetMR {
public static class Map extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output,
Reporter reporter) throws IOException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
output.collect(word, one);
}
}
}
public static class Reduce extends MapReduceBase implements
Reducer<Text, IntWritable, Void, Group> {
private SimpleGroupFactory factory;
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Void, Group> output,
Reporter reporter) throws IOException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
Group group = factory.newGroup()
.append("name", key.toString())
.append("age", sum);
output.collect(null,group);
}
@Override
public void configure(JobConf job) {
factory = new SimpleGroupFactory(GroupWriteSupport.getSchema(job));
}
}
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(ParquetMR.class);
conf.setJobName("wordcount");
String in = "hdfs://localhost:9000/test/wordcount.txt";
String out = "hdfs://localhost:9000/test/wd";
String writeSchema = "message example {\n" +
"required binary name;\n" +
"required int32 age;\n" +
"}";
conf.setMapOutputKeyClass(Text.class);
conf.setMapOutputValueClass(IntWritable.class);
conf.setOutputKeyClass(NullWritable.class);
conf.setOutputValueClass(Group.class);
conf.setMapperClass(Map.class);
conf.setReducerClass(Reduce.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(DeprecatedParquetOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(in));
DeprecatedParquetOutputFormat.setWriteSupportClass(conf, GroupWriteSupport.class);
GroupWriteSupport.setSchema(MessageTypeParser.parseMessageType(writeSchema), conf);
DeprecatedParquetOutputFormat.setOutputPath(conf, new Path(out));
JobClient.runJob(conf);
}
}
生成的文件：
hadoop dfs -ls /test/wd
Found 2 items
-rw-r--r-- 3 work supergroup 0 2017-07-18 17:41 /test/wd/_SUCCESS
-rw-r--r-- 3 work supergroup 392 2017-07-18 17:41 /test/wd/part-00000-r-00000.parquet将生成的文件复制到hive表test_parquet的路径下：
hadoop dfs -cp /test/wd/part-00000-r-00000.parquet /user/work/warehouse/test_parquet/测试hive表读取parquet文件
hive> select * from test_parquet limit 10;
OK
action 2
hadoop 2
hello 3
in 2
presto 1
spark 1
world 1
Time taken: 0.056 seconds, Fetched: 7 row(s)新版本如下
新版本的MR读写Parquet和老版本有点区别，schema必须用在conf中设置，其他的区别不大
conf.set("parquet.example.schema",writeSchema);还是贴下完整的代码
package com.fan.hadoop.parquet;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.parquet.example.data.Group;
import org.apache.parquet.example.data.simple.SimpleGroupFactory;
import org.apache.parquet.hadoop.ParquetOutputFormat;
import org.apache.parquet.hadoop.example.GroupWriteSupport;
import java.io.IOException;
import java.util.StringTokenizer;
/**
* Created by fanglegefan.com on 17-7-18.
*/
public class ParquetNewMR {
public static class WordCountMap extends
Mapper<LongWritable, Text, Text, IntWritable> {
private final IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer token = new StringTokenizer(line);
while (token.hasMoreTokens()) {
word.set(token.nextToken());
context.write(word, one);
}
}
}
public static class WordCountReduce extends
Reducer<Text, IntWritable, Void, Group> {
private SimpleGroupFactory factory;
public void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
Group group = factory.newGroup()
.append("name", key.toString())
.append("age", sum);
context.write(null,group);
}
@Override
protected void setup(Context context) throws IOException, InterruptedException {
super.setup(context);
factory = new SimpleGroupFactory(GroupWriteSupport.getSchema(context.getConfiguration()));
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String writeSchema = "message example {\n" +
"required binary name;\n" +
"required int32 age;\n" +
"}";
conf.set("parquet.example.schema",writeSchema);
Job job = new Job(conf);
job.setJarByClass(ParquetNewMR.class);
job.setJobName("parquet");
String in = "hdfs://localhost:9000/test/wordcount.txt";
String out = "hdfs://localhost:9000/test/wd1";
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputValueClass(Group.class);
job.setMapperClass(WordCountMap.class);
job.setReducerClass(WordCountReduce.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(ParquetOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(in));
ParquetOutputFormat.setOutputPath(job, new Path(out));
ParquetOutputFormat.setWriteSupportClass(job, GroupWriteSupport.class);
job.waitForCompletion(true);
}
}查看生成的文件
hadoop dfs -ls /user/work/warehouse/test_parquet
Found 4 items
-rw-r--r-- 1 work work 0 2017-07-18 18:27 /user/work/warehouse/test_parquet/_SUCCESS
-rw-r--r-- 1 work work 129 2017-07-18 18:27 /user/work/warehouse/test_parquet/_common_metadata
-rw-r--r-- 1 work work 275 2017-07-18 18:27 /user/work/warehouse/test_parquet/_metadata
-rw-r--r-- 1 work work 392 2017-07-18 18:27 /user/work/warehouse/test_parquet/part-r-00000.parquet将生成的文件复制到hive表test_parquet的路径下：
hadoop dfs -cp /test/wd/part-00000-r-00000.parquet /user/work/warehouse/test_parquet/
测试hive
hive> select name,age from test_parquet limit 10;
OK
action 2
hadoop 2
hello 3
in 2
presto 1
spark 1
world 1
Time taken: 0.036 seconds, Fetched: 7 row(s)用mapreduce读parquet文件
package com.fan.hadoop.parquet;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.parquet.example.data.Group;
import org.apache.parquet.hadoop.ParquetInputFormat;
import org.apache.parquet.hadoop.api.DelegatingReadSupport;
import org.apache.parquet.hadoop.api.InitContext;
import org.apache.parquet.hadoop.api.ReadSupport;
import org.apache.parquet.hadoop.example.GroupReadSupport;
import java.io.IOException;
import java.util.*;
/**
* Created by fanglegefan.com on 17-7-18.
*/
public class ParquetNewMRReader {
public static class WordCountMap1 extends
Mapper<Void, Group, LongWritable, Text> {
protected void map(Void key, Group value,
Mapper<Void, Group, LongWritable, Text>.Context context)
throws IOException, InterruptedException {
String name = value.getString("name",0);
int age = value.getInteger("age",0);
context.write(new LongWritable(age),
new Text(name));
}
}
public static class WordCountReduce1 extends
Reducer<LongWritable, Text, LongWritable, Text> {
public void reduce(LongWritable key, Iterable<Text> values,
Context context) throws IOException, InterruptedException {
Iterator<Text> iterator = values.iterator();
while(iterator.hasNext()){
context.write(key,iterator.next());
}
}
}
public static final class MyReadSupport extends DelegatingReadSupport<Group> {
public MyReadSupport() {
super(new GroupReadSupport());
}
@Override
public org.apache.parquet.hadoop.api.ReadSupport.ReadContext init(InitContext context) {
return super.init(context);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String readSchema = "message example {\n" +
"required binary name;\n" +
"required int32 age;\n" +
"}";
conf.set(ReadSupport.PARQUET_READ_SCHEMA, readSchema);
Job job = new Job(conf);
job.setJarByClass(ParquetNewMRReader.class);
job.setJobName("parquet");
String in = "hdfs://localhost:9000/test/wd1";
String out = "hdfs://localhost:9000/test/wd2";
job.setMapperClass(WordCountMap1.class);
job.setReducerClass(WordCountReduce1.class);
job.setInputFormatClass(ParquetInputFormat.class);
ParquetInputFormat.setReadSupportClass(job, MyReadSupport.class);
ParquetInputFormat.addInputPath(job, new Path(in));
job.setOutputFormatClass(TextOutputFormat.class);
FileOutputFormat.setOutputPath(job, new Path(out));
job.waitForCompletion(true);
}
}查看生成的文件
hadoop dfs -cat /test/wd2/part-r-00000
1 world
1 spark
1 presto
2 in
2 hadoop
2 action
3 hello