Data Analysis with Hadoop

Analyzing Data with Hadoop

• Hadoop enables parallel processing, expressing queries as MapReduce jobs.
• Local testing precedes cluster deployment.
• MapReduce uses two phases: map and reduce.
• Each phase handles key-value pairs.
• Data type choices (keys and values) are programmer-defined.
• Map and reduce functions are specified by the programmer.

Map and Reduce Phases

• Input format for the map phase is raw NCDC text data.
• Key is the starting offset, ignored now.
• The map function simplifies, extracting year and temperature.
• Missing, suspect and erroneous data are filtered.
• Map phase output: key-value pairs (year, temperature).
• MapReduce framework processes map output.
• The framework sorts and groups key-value pairs by key.

Java MapReduce Implementation

• Mapper class handles the map operation.
• Input to the Mapper class is a long integer offset and a text value (a line of data).
• Output key is the year and the output value is the air temperature; both as integers.
• Data may be formatted using built-in Java types, but Hadoop provides optimized types for network serialization (org.apache.hadoop.io).
• The map function extracts columns (year, temperature).
• Mapper class writes year and temperature to the context.
• Reducer class processes groups of values associated with the same key.
• Reducer class finds the maximum temperature for each year.

Running the MapReduce Job

• Job specifications include input, output, mapper and reducer classes.
• MapReduce jobs can be run using a Java Virtual Machine (JVM).
• The hadoop command is used to run the job.
• Hadoop creates a Java virtual machine to run the .java code and manage the cluster.

Scaling Out

• MapReduce jobs work best with large datasets.
• Hadoop's distributed file system (DFS) is ideal for large-scale processing.
• Hadoop clusters run using YARN resource manager.
• Input data is divided into smaller chunks.
• Map tasks process these chunks.
• Map outputs handled by the reducer.
• Efficient data transfer within the cluster is crucial.
• Optimal split size is equivalent to the size of an HDFS block (128 MB).

Combiner Functions

• Combiner function processing speeds up data processing by reducing data transferred between map and reduce functions.

• The combiner function is invoked on the outputs from the map.

• If no combiner function or a combiner function that yields the same result as the intended reducer function are present, it may not improve task performance.

• Combiner functions are defined using the Reducer class, which is equivalent to the reduce function implementation except the combiner function is run on the map output.

• MapReduce data is transferred with the "shuffle" in order to group data by its key before the reduce task, however, this data transfer can be costly computationally.

Explore the fundamentals of analyzing data using Hadoop, focusing on the MapReduce paradigm. This quiz covers key concepts like local testing, data processing phases, and Java implementation of Mapper functions. Perfect for anyone looking to deepen their understanding of big data technologies.