Hadoop Mapreduce joins

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The document discusses different types of joins that can be performed in MapReduce including map-side joins and reduce-side joins. Map-side joins include replicated joins, where a small dataset is copied to each node and joined with the larger dataset, and semi-joins, where an initial large dataset is filtered before the join. Reduce-side joins, also called repartition joins, involve joining datasets in the reduce phase by setting the join key as the map output key so datasets are colocated for joining. Inequality joins are difficult to implement in MapReduce's key-equality paradigm.

• What is join ?
• Where do we prefer to use joins
• Kinds of useful joins we do in Mapreduce
• Map-side join
• Reduce-side join

• Joins are relational constructs which are used to combine
relations together.
• Mapreduce have full support to Equi-join, its very difficult to
implement inequality joins using Mapreduce.
eg:
Consider a join between data sets S and T with an in-equality
condition like S:A <= T:A. Such joins seem inherently difficult
for Mapreduce, because each T-tuple has to be joined not only
with S-tuples that have the same A value, but also those with
different ( smaller) A values. Because Mapreduce is a Key-equality
Paradigm

• In MapReduce joins are applicable in situations where you have
two or more datasets you want to combine.
Eg:-
• An example would be when you want to combine your users
with your log files that contain user activity details.
• Data aggregations based on user demographics (such as
differences in user habits between teenagers and users in their
30s)
• To send an email to users who haven’t used the website for a
prescribed number of days
• A feedback loop that examines a user’s browsing habits,
allowing your system to recommend previously unexplored site
features to the user
All of these scenarios require you to join datasets together

• There are mainly 3 kinds of joins are there in Mapreduce.
 Repartition join—A reduce-side join for situations where
you’re joining two or more large datasets together
 Replication join—A map-side join that works in situations
where one of the datasets is small enough to cache
 Semi-join—Another map-side join where one dataset is
initially too large to fit into memory, but after some filtering
can be reduced down to a size that can fit in memory

• A replicated join is a map-side join, and gets its name from
its function—the smallest of the datasets is replicated to all
the map hosts.
• The replicated join is predicated on the fact that one of the
datasets being joined is small enough to be cached in
memory.
• You’ll use the distributed cache to copy the small dataset to
the nodes running the map tasks, and use the initialization
method of each map task to load the small dataset into a
hashtable.
• Use the key from each record fed to the map function from
the large dataset to look up the small dataset hashtable,
and perform a join between the large dataset record and all
of the records from the small dataset that match the join
value.

• Joins of datasets done in the reduce phase are called reduce side joins.
What's involved..
• The key of the map output, of datasets being joined, has to be the join key
- so they reach the same reducer
• Each dataset has to be tagged with its identity, in the mapper- to help
differentiate between the datasets in the reducer, so they can be processed
accordingly.
• In each reducer, the data values from both datasets, for keys assigned to
the reducer, are available, to be processed as required.
• A secondary sort needs to be done to ensure the ordering of the values
sent to the reducer
• If the input files are of different formats, we would need separate mappers,
and we would need to use MultipleInputs class in the driver to add the
inputs and associate the specific mapper to the same.
 [MultipleInputs.addInputPath( job, (input path n), (inputformat class),
(mapper class n));]

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The document discusses different types of joins that can be performed in MapReduce including reduce-side joins, replicated joins, and composite joins. Reduce-side joins perform the join operation in the reducer by shuffling all the data to the reducers based on the join key. Replicated joins load smaller datasets into memory on the mapper to perform the join without a shuffle. Composite joins require preprocessed and sorted data to perform the join entirely on the mapper without a shuffle for inner and full outer joins.

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This document discusses reduce side joins in MapReduce. It begins by explaining why joins are useful to combine related data from multiple files or tables. It then describes the two types of joins in MapReduce - map side joins and reduce side joins. Reduce side joins are recommended when both datasets are large, as they are more efficient. The document proceeds to explain the MapReduce paradigm and job submission flow. It concludes by describing how reduce side joins work by tagging values with their file identifier and joining the data in the reducers based on common keys.

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Map reduce definition A Programming model and an associated implementation for processing and generating large data sets with a parallel*, distributed* algorithm on a cluster*. A Parallel algorithm is an algorithm which can be executed a piece at a time on many different processing devices, and then combined together again at the end to get the correct result. A distributed algorithm is an algorithm designed to run on computer hardware constructed from interconnected processors. A computer cluster consists of connected computers that work together so that, in many respects, they can be viewed as a single system. Computer clusters have each node set to perform the same task, controlled and scheduled by software. Map reduce - division into two categories map and reduce working of Jobtracker , TaskTracker ,Namenode , Datanode in mapreduce engine of hadoop Fault tolerance in hadoop Box class datatypes Allowable file formats wordcount job explained using animation in hadoop using mapreduce fields where map reduce can be implimented limitations of map reduce

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1) Total order sorting is another kind of sorting technique, where map output keys are sorted across all the reducers. 2) This technique uses, where you want to extract the most popular URLs from a web graph. 1) By default Mapreduce uses HashPartitioner as its Partitioner class, which partitions using a hash of the map output keys. 2) Also HashPartitioner ensures that all records with the same map output key goes to the same reducer, but it doesn’t perform total sorting of the map output keys across all the reducers. 3) For this reason only TotalOrderPartitioner class is introduced, which is by default packed with the Hadoop distribution. 1) If you want to work with Total order sorting, we need to create Partition file, and then we have to run Mapreduce job using TotalOrderPartitioner class. 2) We will create partition file, by using InputSampler class, which is used to do sampling of the whole dataset. 3) There are basically two kinds of samplers that we mostly use. 4) First one is RandomSampler, which is mainly used to pick random samples from the original dataset. And the second one is, IntervalSampler, which is mainly used to pick the sample for every R number of records. In the practical demonstration I have used RandomSampler class to pick the samples from Original dataset. 5) Once all the meaningful samples are extracted from the dataset, it will sort those keys, and pick N-1 keys from those sorted keys where N is number of reducers and it places in a Partition file which is used for Total order sorting. 1) This is an overview of Total Order Sorting, here it show how it generates the Partition file and also it shows how the Mapreduce job uses this Partition file during Total Order Sorting. 1) This is a code Sample for Total Order Sorting, in this we have specified the sampler object as RandomSample class. And we also set the Number of reducers using setNumReduceTasks(). And also we specified the Partionfile location unsing setPartionfile() of TotalOrderPartitioner class. And at last we have used writePartitionFile() of InputSampler class for creating Partition file.

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Flume basicUday Vakalapudi

First slide 1) Apache Flume is a distributed and available service, in which it can collect and move large amount of streaming data from one location to another. 2) Most frequently it will deliver the log data into HDFS. Second slide 1) Event and Client are the logical components of flume. 2) An Event is a Singular unit of data which can be transported by Flume NG from its Source to destination. 3) Typically an Event will be composed of Zero or more headers and a body. Here the headers will be used for contextual routing. This means by using the Header definition we can rout the data to the next eligible destination. 4) Client is an Event generator. It will generate the events and send it to one or more agents. Eg: Apache webservers, which generates continuously a huge amount of log data. Third slide 1) Flume agent is a JVM Daemon service, which holds all Flume-NG components like Sources, Channels, Sinks...etc. 2) Here the Source will send the events to channel and channel will stored it, later the channel will send the events to sink. Fourth slide 1) Source is an active component, which receives data from different locations and places it on one or more Channels. 2) The declaration of source component in “.conf” file of agent “a1” is listed here. In this s1 means Source component, a1 means agent. a1.sources=s1 a1.sources.s1.type=netcat (netcat is one of the Source type) 3) There are different Source types are available like Pollable (Means Auto generating like “tail –F” command and sequencing command), event driven and Netcat. 4) Even we can write our won Source type and specify that Custom class name to source type parameter. Fifth slide 1) A channel is a bridge between Source and Sink. 2) Channel will store the Source events and send it to Sink. 3) There are three different types of Channels like memory channel which is very fast but no guarantee for data loss. And file channel which will store the events in a file system before sending it to sink. And the third one is database channel which will store the events in database. 4) Single Channel can be connected to any number of Sources and Sinks. Sixth slide 1) A sink receives events from one channel only.

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Hadoop Mapreduce joins

3. • What is join ? • Where do we prefer to use joins • Kinds of useful joins we do in Mapreduce • Map-side join • Reduce-side join

4. • Joins are relational constructs which are used to combine relations together. • Mapreduce have full support to Equi-join, its very difficult to implement inequality joins using Mapreduce. eg: Consider a join between data sets S and T with an in-equality condition like S:A <= T:A. Such joins seem inherently difficult for Mapreduce, because each T-tuple has to be joined not only with S-tuples that have the same A value, but also those with different ( smaller) A values. Because Mapreduce is a Key-equality Paradigm

5. • In MapReduce joins are applicable in situations where you have two or more datasets you want to combine. Eg:- • An example would be when you want to combine your users with your log files that contain user activity details. • Data aggregations based on user demographics (such as differences in user habits between teenagers and users in their 30s) • To send an email to users who haven’t used the website for a prescribed number of days • A feedback loop that examines a user’s browsing habits, allowing your system to recommend previously unexplored site features to the user All of these scenarios require you to join datasets together

6. • There are mainly 3 kinds of joins are there in Mapreduce.  Repartition join—A reduce-side join for situations where you’re joining two or more large datasets together  Replication join—A map-side join that works in situations where one of the datasets is small enough to cache  Semi-join—Another map-side join where one dataset is initially too large to fit into memory, but after some filtering can be reduced down to a size that can fit in memory

7. • A replicated join is a map-side join, and gets its name from its function—the smallest of the datasets is replicated to all the map hosts. • The replicated join is predicated on the fact that one of the datasets being joined is small enough to be cached in memory. • You’ll use the distributed cache to copy the small dataset to the nodes running the map tasks, and use the initialization method of each map task to load the small dataset into a hashtable. • Use the key from each record fed to the map function from the large dataset to look up the small dataset hashtable, and perform a join between the large dataset record and all of the records from the small dataset that match the join value.

8. • Joins of datasets done in the reduce phase are called reduce side joins. What's involved.. • The key of the map output, of datasets being joined, has to be the join key - so they reach the same reducer • Each dataset has to be tagged with its identity, in the mapper- to help differentiate between the datasets in the reducer, so they can be processed accordingly. • In each reducer, the data values from both datasets, for keys assigned to the reducer, are available, to be processed as required. • A secondary sort needs to be done to ensure the ordering of the values sent to the reducer • If the input files are of different formats, we would need separate mappers, and we would need to use MultipleInputs class in the driver to add the inputs and associate the specific mapper to the same.  [MultipleInputs.addInputPath( job, (input path n), (inputformat class), (mapper class n));]

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