DistributedCache is a Hadoop MapReduce service that provides a mechanism to cache files (like text, archives, or JARs) needed by applications. Once you put files in the cache, Hadoop framework will make them available on each data node where map/reduce tasks are running, eliminating the need to fetch them from a central server or file system, thus enhancing the performance of the application.

Understanding DistributedCache

DistributedCache is used primarily for caching data across all nodes in a Hadoop cluster. For instance, if your MapReduce application needs a static lookup table or configuration files across all mappers and reducers, you can use DistributedCache instead of copying files into HDFS or fetching them from a remote server repeatedly.

However, certain confusions might arise when using DistributedCache, one of which is the third party JAR not found issue. Let's delve deeper into this problem and explore ways to resolve it within the Hadoop ecosystem.

Third Party JAR Not Found Issue

This issue occurs when your MapReduce job depends on third-party JARs, which are not part of Hadoop's default classpath but are crucial for your job's execution. If these JARs are not properly distributed or available on all nodes, your job might fail or not work as expected.

Traditionally, any third-party dependencies should be specified within the job configuration, or preferably using the DistributedCache to distribute the required JARs across all nodes.

Step-by-Step Resolution Process

Here’s a step-by-step guide to address this issue:

Step 1: Placement of the JAR

First, ensure that the JAR file is accessible. You can place the JAR in HDFS for accessibility across all nodes:

hadoop fs -put path_to_local_third_party.jar /path_in_hdfs/

Step 2: Adding JAR to DistributedCache

To add the JAR to DistributedCache, modify your Hadoop job configuration:

1import org.apache.hadoop.filecache.DistributedCache;
2import org.apache.hadoop.mapreduce.Job;
3
4public class CacheAddition {
5    public static void main(String[] args) throws Exception {
6        Job job = new Job();
7        // Assuming '/path_in_hdfs/third_party.jar' is the path to your jar in HDFS
8        DistributedCache.addArchiveToClassPath(new Path("/path_in_hdfs/third_party.jar"), job.getConfiguration());
9    }
10}
11

Step 3: Verifying the Classpath

Ensure that the classpath used by your Hadoop job configurations includes the JAR file. This can be typically checked by accessing the classpath programmatically or by debugging a running job.

Other Considerations and Best Practices

• Version Compatibility: Ensure that the versions of any third-party JARs are compatible with your Hadoop cluster.
• Resource Management: Adding too many or very large JARs to DistributedCache can consume significant disk space and potentially slow down your MapReduce job initiations. Keep an eye on overall resource usage.
• Permission Issues: Ensure that the JAR files in HDFS are readable by the user under which the Hadoop tasks run.

Summary Table: Key Points in Handling Third Party JARs

By following these detailed steps, users should be able to seamlessly integrate third-party JARs into their Hadoop jobs, thus avoiding common pitfalls like the 'third-party jar not found' error, and aligning with best practices in the MapReduce application development lifecycle.