Hadoop – Difference between hive,pig,map-reduce use cases

apache-pighadoophivemapreduce

Difference between map-reduce ,hive ,pig

pig : its a data flow language, it can work on any data basically used to convert semi structure ,unstructured data to structure so that can be used in hive advance analytics using windowing function etc.

Hive : Work on structure data and provide sql type query language .

I know at back end both pig and hive uses map -reduces .

I know map-reduce can be good tool for programmer ,hive or pig for sql guy

I just want to know is there any specific use cases where we go for hive,pig and map-reduce

basically we decide that we have to use pig here hive here or we must use map -reduce .

Best Answer

Map-Reduce: Has better performance than pig or hive but requires more development time.

PIg: Less development time but poor performance when compared to map-reduce.

Hve: SQL type language with some good features like partitioning and bucketing to improve performance reads.Also, hive enforces schema on read.

Related Solutions

Hadoop – Running Pig query over data stored in Hive

Here's what I found out: Using HiveColumnarLoader makes sense if you store data as a RCFile. To load table using this you need to register some jars first:

register /srv/pigs/piggybank.jar
register /usr/lib/hive/lib/hive-exec-0.5.0.jar
register /usr/lib/hive/lib/hive-common-0.5.0.jar

a = LOAD '/user/hive/warehouse/table' USING org.apache.pig.piggybank.storage.HiveColumnarLoader('ts int, user_id int, url string');

To load data from Sequence file you have to use PiggyBank (as in previous example). SequenceFile loader from Piggybank should handle compressed files:

register /srv/pigs/piggybank.jar
DEFINE SequenceFileLoader org.apache.pig.piggybank.storage.SequenceFileLoader();
a = LOAD '/user/hive/warehouse/table' USING SequenceFileLoader AS (int, int);

This doesn't work with Pig 0.7 because it's unable to read BytesWritable type and cast it to Pig type and you get this exception:

2011-07-01 10:30:08,589 WARN org.apache.pig.piggybank.storage.SequenceFileLoader: Unable to translate key class org.apache.hadoop.io.BytesWritable to a Pig datatype
2011-07-01 10:30:08,625 WARN org.apache.hadoop.mapred.Child: Error running child
org.apache.pig.backend.BackendException: ERROR 0: Unable to translate class org.apache.hadoop.io.BytesWritable to a Pig datatype
    at org.apache.pig.piggybank.storage.SequenceFileLoader.setKeyType(SequenceFileLoader.java:78)
    at org.apache.pig.piggybank.storage.SequenceFileLoader.getNext(SequenceFileLoader.java:132)
    at org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigRecordReader.nextKeyValue(PigRecordReader.java:142)
    at org.apache.hadoop.mapred.MapTask$NewTrackingRecordReader.nextKeyValue(MapTask.java:448)
    at org.apache.hadoop.mapreduce.MapContext.nextKeyValue(MapContext.java:67)
    at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:143)
    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:639)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:315)
    at org.apache.hadoop.mapred.Child$4.run(Child.java:217)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:396)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1063)
    at org.apache.hadoop.mapred.Child.main(Child.java:211)

How to compile piggybank is described here: Unable to build piggybank -> /home/build/ivy/lib does not exist

Hadoop – When to use Hadoop, HBase, Hive and Pig

MapReduce is just a computing framework. HBase has nothing to do with it. That said, you can efficiently put or fetch data to/from HBase by writing MapReduce jobs. Alternatively you can write sequential programs using other HBase APIs, such as Java, to put or fetch the data. But we use Hadoop, HBase etc to deal with gigantic amounts of data, so that doesn't make much sense. Using normal sequential programs would be highly inefficient when your data is too huge.

Coming back to the first part of your question, Hadoop is basically 2 things: a Distributed FileSystem (HDFS) + a Computation or Processing framework (MapReduce). Like all other FS, HDFS also provides us storage, but in a fault tolerant manner with high throughput and lower risk of data loss (because of the replication). But, being a FS, HDFS lacks random read and write access. This is where HBase comes into picture. It's a distributed, scalable, big data store, modelled after Google's BigTable. It stores data as key/value pairs.

Coming to Hive. It provides us data warehousing facilities on top of an existing Hadoop cluster. Along with that it provides an SQL like interface which makes your work easier, in case you are coming from an SQL background. You can create tables in Hive and store data there. Along with that you can even map your existing HBase tables to Hive and operate on them.

While Pig is basically a dataflow language that allows us to process enormous amounts of data very easily and quickly. Pig basically has 2 parts: the Pig Interpreter and the language, PigLatin. You write Pig script in PigLatin and using Pig interpreter process them. Pig makes our life a lot easier, otherwise writing MapReduce is always not easy. In fact in some cases it can really become a pain.

I had written an article on a short comparison of different tools of the Hadoop ecosystem some time ago. It's not an in depth comparison, but a short intro to each of these tools which can help you to get started. (Just to add on to my answer. No self promotion intended)

Both Hive and Pig queries get converted into MapReduce jobs under the hood.

HTH

Best Answer

Related Solutions

Hadoop – Running Pig query over data stored in Hive

Hadoop – When to use Hadoop, HBase, Hive and Pig

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