Does a file need to be in HDFS in order to use it in distributed cache?
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To fully understand whether a file needs to be in the Hadoop Distributed File System (HDFS) to utilize it in Hadoop’s Distributed Cache, it’s essential to grasp the concepts of both HDFS and the Distributed Cache mechanism.
Understanding HDFS and Distributed Cache
Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream these data sets at high bandwidth to user applications. It functions by breaking data into blocks and distributing these across the cluster’s nodes.
On the other hand, Distributed Cache is a facility provided by the Hadoop framework to cache files (text, archives, jars, etc.) needed by applications. Once you cache a file for your Hadoop job, Hadoop framework will make it available on each data node where your map/reduce tasks are running, saving significant amounts of data transfer which otherwise would take place each time the file is accessed by the task.
Key Question
The question of whether files need to be in HDFS to be used in the Distributed Cache is pivotal because it affects how data is managed and accessed during Hadoop operations.
How Distributed Cache Works
Files to be used in the Distributed Cache can be specified in multiple ways:
- By copying the file to the HDFS and referencing it in your job configuration.
- By keeping the file on the local file system of each node in the Hadoop cluster and using the local file system path.
Not Necessarily in HDFS
While it is a standard approach to place the files in HDFS, it is not strictly necessary. Localizing files in every node's local filesystem could also serve the purpose, especially if the files will be used frequently by various jobs and won't undergo frequent changes.
Benefit of Using HDFS
The advantage of keeping files in HDFS over local file systems includes:
- Redundancy and Fault Tolerance: HDFS provides high fault tolerance and redundancy, which is crucial for important files.
- Simplicity: Managing files becomes more straightforward when centralized in HDFS, rather than managing copies on each node.
- Scalability: As the size and number of nodes grow, HDFS scales more efficiently compared to managing local copies.
Technical Example
Suppose you have a lookup table lookupTbl.csv that your map/reduce tasks frequently access. You can add this file to the Distributed Cache using:
With the file aliased in the HDFS path, each mapper has access to lookupTbl.csv via a local symlink.
Conclusion
While placing files in HDFS is typically the most beneficial and straightforward approach for utilizing Hadoop’s Distributed Cache, doing so is not mandatory. For specific scenarios, especially where files are static and accessed frequently, pre-storing files on each node’s local file systems might be strategically beneficial.
Summary Table
| Feature | HDFS-based Cache Files | Local File System-based Cache Files |
| Fault Tolerance | High | Low |
| Management | Centralized | Decentralized |
| Accessibility | Requires HDFS path setup | Requires file on each node |
| Scalability | High (with cluster expansion) | Limited |
| Recommended Use Case | Large or frequently updated files | Small, rarely changed files |
In conclusion, utilizing HDFS for caching files in Hadoop’s Distributed Cache is not an absolute requirement, but it is often the most practical and reliable method, particularly for larger clusters or critical data tasks.
