In general, Apache Pig works on top of Hadoop. It is an analytical tool that analyzes large datasets that exist in the Hadoop File System. To analyze data using Apache Pig, we have to initially load the data into Apache Pig. This chapter explains how to load data to Apache Pig from HDFS.
In MapReduce mode, Pig reads (loads) data from HDFS and stores the results back in HDFS. Therefore, let us start HDFS and create the following sample data in HDFS.
|Student ID||First Name||Last Name||Phone||City|
The above dataset contains personal details like id, first name, last name, phone number and city, of six students.
Step 1: Verifying Hadoop
First of all, verify the installation using Hadoop version command, as shown below.
$ hadoop version
If your system contains Hadoop, and if you have set the PATH variable, then you will get the following output −
Hadoop 2.6.0 Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r e3496499ecb8d220fba99dc5ed4c99c8f9e33bb1 Compiled by jenkins on 2014-11-13T21:10Z Compiled with protoc 2.5.0 From source with checksum 18e43357c8f927c0695f1e9522859d6a This command was run using /home/Hadoop/hadoop/share/hadoop/common/hadoop common-2.6.0.jar
Step 2: Starting HDFS
Browse through the sbin directory of Hadoop and start yarn and Hadoop dfs (distributed file system) as shown below.
cd /$Hadoop_Home/sbin/ $ start-dfs.sh localhost: starting namenode, logging to /home/Hadoop/hadoop/logs/hadoopHadoop-namenode-localhost.localdomain.out localhost: starting datanode, logging to /home/Hadoop/hadoop/logs/hadoopHadoop-datanode-localhost.localdomain.out Starting secondary namenodes [0.0.0.0] starting secondarynamenode, logging to /home/Hadoop/hadoop/logs/hadoop-Hadoopsecondarynamenode-localhost.localdomain.out $ start-yarn.sh starting yarn daemons starting resourcemanager, logging to /home/Hadoop/hadoop/logs/yarn-Hadoopresourcemanager-localhost.localdomain.out localhost: starting nodemanager, logging to /home/Hadoop/hadoop/logs/yarnHadoop-nodemanager-localhost.localdomain.out
Step 3: Create a Directory in HDFS
In Hadoop DFS, you can create directories using the command mkdir. Create a new directory in HDFS with the name Pig_Data in the required path as shown below.
$cd /$Hadoop_Home/bin/ $ hdfs dfs -mkdir hdfs://localhost:9000/Pig_Data
Step 4: Placing the data in HDFS
The input file of Pig contains each tuple/record in individual lines. And the entities of the record are separated by a delimiter (In our example we used“,”).
In the local file system, create an input file student_data.txt containing data as shown below.
001,Rajiv,Reddy,9848022337,Hyderabad 002,siddarth,Battacharya,9848022338,Kolkata 003,Rajesh,Khanna,9848022339,Delhi 004,Preethi,Agarwal,9848022330,Pune 005,Trupthi,Mohanthy,9848022336,Bhuwaneshwar 006,Archana,Mishra,9848022335,Chennai.
Now, move the file from the local file system to HDFS using put command as shown below. (You can use copyFromLocal command as well.)
$ cd $HADOOP_HOME/bin $ hdfs dfs -put /home/Hadoop/Pig/Pig_Data/student_data.txt dfs://localhost:9000/pig_data/
Verifying the file
You can use the cat command to verify whether the file has been moved into the HDFS, as shown below.
$ cd $HADOOP_HOME/bin $ hdfs dfs -cat hdfs://localhost:9000/pig_data/student_data.txt
You can see the content of the file as shown below.
15/10/01 12:16:55 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 001,Rajiv,Reddy,9848022337,Hyderabad 002,siddarth,Battacharya,9848022338,Kolkata 003,Rajesh,Khanna,9848022339,Delhi 004,Preethi,Agarwal,9848022330,Pune 005,Trupthi,Mohanthy,9848022336,Bhuwaneshwar 006,Archana,Mishra,9848022335,Chennai
The Load Operator
You can load data into Apache Pig from the file system (HDFS/ Local) usingLOAD operator of Pig Latin.
The load statement consists of two parts divided by the “=” operator. On the left-hand side, we need to mention the name of the relation where we want to store the data, and on the right-hand side, we have to define how we store the data. Given below is the syntax of the Load operator.
Relation_name = LOAD 'Input file path' USING function as schema;
- relation_name − We have to mention the relation in which we want to store the data.
- Input file path − We have to mention the HDFS directory where the file is stored. (In MapReduce mode)
- function − We have to choose a function from the set of load functions provided by Apache Pig (BinStorage, JsonLoader, PigStorage, TextLoader).
- Schema − We have to define the schema of the data. We can define the required schema as follows −
(column1 : data type, column2 : data type, column3 : data type);
Note − We load the data without specifying the schema. In that case, the columns will be addressed as $01, $02, etc… (check).
As an example, let us load the data in student_data.txt in Pig under the schema named Student using the LOAD command.
Start the Pig Grunt Shell
First of all, open the Linux terminal. Start the Pig Grunt shell in MapReduce mode as shown below.
$ Pig –x mapreduce
It will start the Pig Grunt shell as shown below.
15/10/01 12:33:37 INFO pig.ExecTypeProvider: Trying ExecType : LOCAL 15/10/01 12:33:37 INFO pig.ExecTypeProvider: Trying ExecType : MAPREDUCE 15/10/01 12:33:37 INFO pig.ExecTypeProvider: Picked MAPREDUCE as the ExecType 2015-10-01 12:33:38,080 [main] INFO org.apache.pig.Main - Apache Pig version 0.15.0 (r1682971) compiled Jun 01 2015, 11:44:35 2015-10-01 12:33:38,080 [main] INFO org.apache.pig.Main - Logging error messages to: /home/Hadoop/pig_1443683018078.log 2015-10-01 12:33:38,242 [main] INFO org.apache.pig.impl.util.Utils - Default bootup file /home/Hadoop/.pigbootup not found 2015-10-01 12:33:39,630 [main] INFO org.apache.pig.backend.hadoop.executionengine.HExecutionEngine - Connecting to hadoop file system at: hdfs://localhost:9000 grunt>
Execute the Load Statement
Now load the data from the file student_data.txt into Pig by executing the following Pig Latin statement in the Grunt shell.
grunt> student = LOAD 'hdfs://localhost:9000/pig_data/student_data.txt' USING PigStorage(',') as ( id:int, firstname:chararray, lastname:chararray, phone:chararray, city:chararray );
Following is the description of the above statement.
|Relation name||We have stored the data in the schema student.|
|Input file path||We are reading data from the file student_data.txt, which is in the /pig_data/ directory of HDFS.|
|Storage function||We have used the PigStorage() function. It loads and stores data as structured text files. It takes a delimiter using which each entity of a tuple is separated, as a parameter. By default, it takes ‘\t’ as a parameter.|
|schema||We have stored the data using the following schema.
Now, let us store the relation in the HDFS directory “/pig_Output/” as shown below.
grunt> STORE student INTO ' hdfs://localhost:9000/pig_Output/ ' USING PigStorage (',');
After executing the store statement, you will get the following output. A directory is created with the specified name and the data will be stored in it.
2015-10-05 13:05:05,429 [main] INFO org.apache.pig.backend.hadoop.executionengine.mapReduceLayer. MapReduceLau ncher - 100% complete 2015-10-05 13:05:05,429 [main] INFO org.apache.pig.tools.pigstats.mapreduce.SimplePigStats - Script Statistics: HadoopVersion PigVersion UserId StartedAt FinishedAt Features 2.6.0 0.15.0 Hadoop 2015-10-0 13:03:03 2015-10-05 13:05:05 UNKNOWN Success! Job Stats (time in seconds): JobId Maps Reduces MaxMapTime MinMapTime AvgMapTime MedianMapTime job_14459_06 1 0 n/a n/a n/a n/a MaxReduceTime MinReduceTime AvgReduceTime MedianReducetime Alias Feature 0 0 0 0 student MAP_ONLY OutPut folder hdfs://localhost:9000/pig_Output/ Input(s): Successfully read 0 records from: "hdfs://localhost:9000/pig_data/student_data.txt" Output(s): Successfully stored 0 records in: "hdfs://localhost:9000/pig_Output" Counters: Total records written : 0 Total bytes written : 0 Spillable Memory Manager spill count : 0 Total bags proactively spilled: 0 Total records proactively spilled: 0 Job DAG: job_1443519499159_0006 2015-10-05 13:06:06,192 [main] INFO org.apache.pig.backend.hadoop.executionengine .mapReduceLayer.MapReduceLau ncher - Success!
You can verify the stored data as shown below.
First of all, list out the files in the directory named pig_output using the lscommand as shown below.
hdfs dfs -ls 'hdfs://localhost:9000/pig_Output/' Found 2 items rw-r--r- 1 Hadoop supergroup 0 2015-10-05 13:03 hdfs://localhost:9000/pig_Output/_SUCCESS rw-r--r- 1 Hadoop supergroup 224 2015-10-05 13:03 hdfs://localhost:9000/pig_Output/part-m-00000
You can observe that two files were created after executing the storestatement.
Using cat command, list the contents of the file named part-m-00000 as shown below.
$ hdfs dfs -cat 'hdfs://localhost:9000/pig_Output/part-m-00000' 1,Rajiv,Reddy,9848022337,Hyderabad 2,siddarth,Battacharya,9848022338,Kolkata 3,Rajesh,Khanna,9848022339,Delhi 4,Preethi,Agarwal,9848022330,Pune 5,Trupthi,Mohanthy,9848022336,Bhuwaneshwar 6,Archana,Mishra,9848022335,Chennai