读写parquet格式文件的几种方式

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
2.用sparkSql读写hive中的parquet
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; OKleo 27jim 38leo 15jack    22jay 7Time 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;OKCREATE 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.MapredParquetInputFormatimport 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RDDval 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DataFrameval 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;OKSLF4J: Failed to load class 'org.slf4j.impl.StaticLoggerBinder'.SLF4J: Defaulting to no-operation (NOP) logger implementationSLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.leo 27jim 38leo 15jack 22jay 7jim 37jack 36jay 11leo 35leo 33

b.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

@Overridepublic 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/wdFound 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;OKaction  2hadoop  2hello   3in  2presto  1spark   1world   1Time 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_parquetFound 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;OKaction  2hadoop  2hello   3in  2presto  1spark   1world   1Time 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-000001       world1       spark1       presto2       in2       hadoop2       action3       hello