Big Data Analytics

MapReduce Data Flow

If have gone through our previous postthe then we have seen the components that make up a basic MapReduce job, we can see how everything works together at a higher level:

MapReduce inputs typically come from input files loaded onto our processing cluster in HDFS. These files are evenly distributed across all our nodes. Running a MapReduce program involves running mapping tasks on many or all of the nodes in our cluster. Each of these mapping tasks is equivalent: no mappers have particular "identities" associated with them. Therefore, any mapper can process any input file. Each mapper loads the set of files local to that machine and processes them.

When the mapping phase has completed, the intermediate (key, value) pairs must be exchanged between machines to send all values with the same key to a single reducer. The reduce tasks are spread across the same nodes in the cluster as the mappers. This is the only communication step in MapReduce. Individual map tasks do not exchange information with one another, nor are they aware of one another's existence. Similarly, different reduce tasks do not communicate with one another. The user never explicitly marshals information from one machine to another; all data transfer is handled by the Hadoop MapReduce platform itself, guided implicitly by the different keys associated with values. This is a fundamental element of Hadoop MapReduce's reliability. If nodes in the cluster fail, tasks must be able to be restarted. If they have been performing side-effects, e.g., communicating with the outside world, then the shared state must be restored in a restarted task. By eliminating communication and side-effects, restarts can be handled more gracefully.

A Closer Look

The previous figure described the high-level view of Hadoop MapReduce. From this diagram, you can see where the mapper and reducer components of the Word Count application fit in, and how it achieves its objective. We will now examine this system in a bit closer detail.

shows the pipeline with more of its mechanics exposed. While only two nodes are depicted, the same pipeline can be replicated across a very large number of nodes. The next several paragraphs describe each of the stages of a MapReduce program more precisely.

Input files: This is where the data for a MapReduce task is initially stored. While this does not need to be the case, the input files typically reside in HDFS. The format of these files is arbitrary; while line-based log files can be used, we could also use a binary format, multi-line input records, or something else entirely. It is typical for these input files to be very large -- tens of gigabytes or more.

InputFormat: How these input files are split up and read is defined by the InputFormat. An InputFormat is a class that provides the following functionality:

* Selects the files or other objects that should be used for input
* Defines the InputSplits that break a file into tasks
* Provides a factory for RecordReader objects that read the file

Several InputFormats are provided with Hadoop. An abstract type is called FileInputFormat; all InputFormats that operate on files inherit functionality and properties from this class. When starting a Hadoop job, FileInputFormat is provided with a path containing files to read. The FileInputFormat will read all files in this directory. It then divides these files into one or more InputSplits each. You can choose which InputFormat to apply to your input files for a job by calling the setInputFormat() method of the JobConf object that defines the job. A table of standard InputFormats is given below.

The default InputFormat is the TextInputFormat. This treats each line of each input file as a separate record, and performs no parsing. This is useful for unformatted data or line-based records like log files. A more interesting input format is the KeyValueInputFormat. This format also treats each line of input as a separate record. While the TextInputFormat treats the entire line as the value, the KeyValueInputFormat breaks the line itself into the key and value by searching for a tab character. This is particularly useful for reading the output of one MapReduce job as the input to another, as the default OutputFormat (described in more detail below) formats its results in this manner. Finally, the SequenceFileInputFormat reads special binary files that are specific to Hadoop. These files include many features designed to allow data to be rapidly read into Hadoop mappers. Sequence files are block-compressed and provide direct serialization and deserialization of several arbitrary data types (not just text). Sequence files can be generated as the output of other MapReduce tasks and are an efficient intermediate representation for data that is passing from one MapReduce job to anther.

InputSplits: An InputSplit describes a unit of work that comprises a single map task in a MapReduce program. A MapReduce program applied to a data set, collectively referred to as a Job, is made up of several (possibly several hundred) tasks. Map tasks may involve reading a whole file; they often involve reading only part of a file. By default, the FileInputFormat and its descendants break a file up into 64 MB chunks (the same size as blocks in HDFS). You can control this value by setting the mapred.min.split.size parameter in hadoop-site.xml, or by overriding the parameter in the JobConf object used to submit a particular MapReduce job. By processing a file in chunks, we allow several map tasks to operate on a single file in parallel. If the file is very large, this can improve performance significantly through parallelism. Even more importantly, since the various blocks that make up the file may be spread across several different nodes in the cluster, it allows tasks to be scheduled on each of these different nodes; the individual blocks are thus all processed locally, instead of needing to be transferred from one node to another. Of course, while log files can be processed in this piece-wise fashion, some file formats are not amenable to chunked processing. By writing a custom InputFormat, you can control how the file is broken up (or is not broken up) into splits. Custom input formats are described in Module 5.

The InputFormat defines the list of tasks that make up the mapping phase; each task corresponds to a single input split. The tasks are then assigned to the nodes in the system based on where the input file chunks are physically resident. An individual node may have several dozen tasks assigned to it. The node will begin working on the tasks, attempting to perform as many in parallel as it can. The on-node parallelism is controlled by the parameter.

RecordReader: The InputSplit has defined a slice of work, but does not describe how to access it. The RecordReader class actually loads the data from its source and converts it into (key, value) pairs suitable for reading by the Mapper. The RecordReader instance is defined by the InputFormat. The default InputFormat, TextInputFormat, provides a LineRecordReader, which treats each line of the input file as a new value. The key associated with each line is its byte offset in the file. The RecordReader is invoke repeatedly on the input until the entire InputSplit has been consumed. Each invocation of the RecordReader leads to another call to the map() method of the Mapper.

Mapper: The Mapper performs the interesting user-defined work of the first phase of the MapReduce program. Given a key and a value, the map() method emits (key, value) pair(s) which are forwarded to the Reducers. A new instance of Mapper is instantiated in a separate Java process for each map task (InputSplit) that makes up part of the total job input. The individual mappers are intentionally not provided with a mechanism to communicate with one another in any way. This allows the reliability of each map task to be governed solely by the reliability of the local machine. The map() method receives two parameters in addition to the key and the value:

* The OutputCollector object has a method named collect() which will forward a (key, value) pair to the reduce phase of the job.
* The Reporter object provides information about the current task; its getInputSplit() method will return an object describing the current InputSplit. It also allows the map task to provide additional information about its progress to the rest of the system. The setStatus() method allows you to emit a status message back to the user. The incrCounter() method allows you to increment shared performance counters. You may define as many arbitrary counters as you wish. Each mapper can increment the counters, and the JobTracker will collect the increments made by the different processes and aggregate them for later retrieval when the job ends.

Partition & Shuffle: After the first map tasks have completed, the nodes may still be performing several more map tasks each. But they also begin exchanging the intermediate outputs from the map tasks to where they are required by the reducers. This process of moving map outputs to the reducers is known as shuffling. A different subset of the intermediate key space is assigned to each reduce node; these subsets (known as "partitions") are the inputs to the reduce tasks. Each map task may emit (key, value) pairs to any partition; all values for the same key are always reduced together regardless of which mapper is its origin. Therefore, the map nodes must all agree on where to send the different pieces of the intermediate data. The Partitioner class determines which partition a given (key, value) pair will go to. The default partitioner computes a hash value for the key and assigns the partition based on this result. Custom partitioners are described in more detail in Module 5.

Sort: Each reduce task is responsible for reducing the values associated with several intermediate keys. The set of intermediate keys on a single node is automatically sorted by Hadoop before they are presented to the Reducer.

Reduce: A Reducer instance is created for each reduce task. This is an instance of user-provided code that performs the second important phase of job-specific work. For each key in the partition assigned to a Reducer, the Reducer's reduce() method is called once. This receives a key as well as an iterator over all the values associated with the key. The values associated with a key are returned by the iterator in an undefined order. The Reducer also receives as parameters OutputCollector and Reporter objects; they are used in the same manner as in the map() method.

OutputFormat: The (key, value) pairs provided to this OutputCollector are then written to output files. The way they are written is governed by the OutputFormat. The OutputFormat functions much like the InputFormat class described earlier. The instances of OutputFormat provided by Hadoop write to files on the local disk or in HDFS; they all inherit from a common FileOutputFormat. Each Reducer writes a separate file in a common output directory. These files will typically be named part-nnnnn, where nnnnn is the partition id associated with the reduce task. The output directory is set by the FileOutputFormat.setOutputPath() method. You can control which particular OutputFormat is used by calling the setOutputFormat() method of the JobConf object that defines your MapReduce job.

Hadoop provides some OutputFormat instances to write to files. The basic (default) instance is TextOutputFormat, which writes (key, value) pairs on individual lines of a text file. This can be easily re-read by a later MapReduce task using the KeyValueInputFormat class, and is also human-readable. A better intermediate format for use between MapReduce jobs is the SequenceFileOutputFormat which rapidly serializes arbitrary data types to the file; the corresponding SequenceFileInputFormat will deserialize the file into the same types and presents the data to the next Mapper in the same manner as it was emitted by the previous Reducer. The NullOutputFormat generates no output files and disregards any (key, value) pairs passed to it by the OutputCollector. This is useful if you are explicitly writing your own output files in the reduce() method, and do not want additional empty output files generated by the Hadoop framework.

RecordWriter: Much like how the InputFormat actually reads individual records through the RecordReader implementation, the OutputFormat class is a factory for RecordWriter objects; these are used to write the individual records to the files as directed by the OutputFormat.

The output files written by the Reducers are then left in HDFS for your use, either by another MapReduce job, a separate program, for for human inspection.