Distibuted Cache in Reduce Hadoop
Master System Design with Codemia
Enhance your system design skills with over 120 practice problems, detailed solutions, and hands-on exercises.
Apache Hadoop is a widely used framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. One of its integral components is the MapReduce programming model, which divides the work into a set of independent tasks that can handle data processing in parallel. Furthermore, to enhance the efficiency and speed of the MapReduce tasks, Hadoop utilizes a feature known as 'Distributed Cache'.
What is Distributed Cache?
Distributed Cache is a facility provided by the Hadoop framework to cache files (text, archives, jar files) needed by applications. Once a file is cached for a job, Hadoop makes it available on each data node where the map/reduce tasks are running, thus preventing the need to fetch it from a central server or a data storage. This caching mechanism significantly speeds up the execution of tasks because the data becomes locally available on the nodes, reducing network transmission and data I/O.
How Does Distributed Cache Work?
When a job is executed, Hadoop framework first copies the necessary files to the local file system (or memory) of each slave node before the execution of any task at that node. This process is transparent to the user; the files are ready by the time they are needed by the task. Here, the files are stored in a read-only manner to prevent tasks from interfering with each other.
Usage Scenarios
The Distributed Cache is particularly useful when dealing with:
- Lookup tables or reference data that are needed by every map and/or reduce task.
- Large configuration files that are not desirable to copy multiple times.
- Libraries or jars needed by your job.
How to Use Distributed Cache
To use Distributed Cache, developers include the files in their job's configuration:
This file will then appear on the local disk of each DataNode executing a task from this job under the name 'alias'. Tasks can then use this file as if it were a local file path.
Performance Implications
Utilizing Distributed Cache effectively allows MapReduce jobs to be more efficient because it reduces the amount of data transported across the network. Here's a brief look at how performance is impacted:
- Reduced Data Transfer: Data files are localized on the nodes, minimizing the data sent over the network.
- Quick Access to Files: Local access speeds up the read operations as opposed to reading from HDFS or another remote location.
- Increased Job Efficiency: Overall time to complete the job is significantly decreased as data fetching time is reduced.
Limitations
While the Distributed Cache provides great benefits, it also has limitations:
- There is a limit to the amount of disk space available on any node, thus limiting the size of files that can be cached.
- Not suitable for files that change frequently since they are read-only after being cached.
Table Summarizing Key Points
| Feature | Description |
| Facility | Caches files across all nodes before task execution. |
| Efficiency | Reduces the time of job as data fetching over the network is minimized. |
| Usage | Useful for storing read-only data such as lookup tables and static configurations. |
| Implementation | Files need to be added to job configuration. Available locally on DataNodes during tasks. |
Conclusion
The Distributed Cache is a powerful feature in Hadoop's MapReduce framework, enabling faster data processing by providing faster access to necessary files. By understanding and utilizing this feature appropriately, developers can enhance the performance of their Hadoop jobs, especially those that depend heavily on external files or libraries. However, careful consideration of the data size and change frequency is crucial to leverage this feature effectively without running into resource allocation issues.
