But what is MapReduce, and how does it work in distributed file systems? You’ll find out in this post.
What Is MapReduce?
MapReduce is a data engineering model applied to programs or applications that process big data logic within parallel clusters of servers or nodes. It distributes a processing logic across several data nodes and aggregates the results into the client-server.
MapReduce ensures that the processing is fast, memory-efficient, and reliable, regardless of the size of the data.
Hadoop File System (HDFS), Google File System (GFS), Apache Kafka, GlusterFS, and more are examples of distributed big data file systems that use the MapReduce algorithm.
What Is a Distributed File System?
A distributed file system (DFS) is a method of storage in computing that involves splitting large data files into smaller chunks and spreading them over several servers within the system. It allows clients from various sources to write and read data, share, and run programmable logic on data—right from anywhere.
A distributed file system typically consists of the primary server (also called a NameNode in Hadoop), parallel clusters, and several nodes or servers containing replicated data chunks, all in a data center. However, each cluster within the distributed file system holds hundreds to thousands of these nodes.
The primary server automatically detects changes within the clusters. So it can assign roles accordingly to each node.
When the primary server receives a data file, it sends it to the clusters within the DFS. These clusters chunk and distribute the data into each node within them. Each node then replicates the data into what’s called data blocks to form a chain. At this point, each node becomes a chunk server.
In addition to managing access to the data, the primary server holds a metadata annotation on each file. That way, it knows which node handles which file in each cluster.
How Does MapReduce Work in Distributed File Systems?
As mentioned earlier, big data is available in several chunk servers in a DFS. One way to perform programmable logic on these data files is to aggregate them into one. You can then pull them into a single server, which now handles the logic.
While that’s a conventional way of querying data, the problem is the data becomes a whole again inside the single server. So a single server will still have to manage logic on several petabytes of data at once. Unfortunately, this was the problem the system intended to solve at first. So it’s not a best practice, after all.
Further, such an aggregation into a single server poses several performance risks. These might range from a server crash, poor calculation efficiency, high latency, high memory consumption, and vulnerabilities to more.
But another way to run the programmable logic is to leave the data in chunks inside each distributed server. And then inject the logic function into each server. It means each chunk server within a cluster now handles its calculation. Using this approach means there’s no need to aggregate or pull data into a single server.
That there is the MapReduce concept in a distributed data file system. It ensures that a single server doesn’t need to pull data from the source. Instead, it disperses the processing function (MapReduce) into several chunk nodes in separate clusters, so each node within each cluster handles the logic individually without overloading a single server.
Consequently, several servers handle logic on bits of data concurrently. This distribution of labor among servers results in optimum performance and higher security, among other positivities.
How Is the MapReduce Result Processed in a DFS?
Here’s how the entire MapReduce processing works in a DFS:
The primary server receives a big data query (MapReduce function) from the client. It then sends this to each cluster to spread it across each node within it. Each node processes the MapReduce function and cumulates its result. Another server collates the results from each node and sends them back to the primary server. The primary server then sends the result as a response to the client.
Thus, the only job of a primary server is to send a readily-computed result to the client, listen to changes, and manage access to the data. It doesn’t perform any calculations. This is why most cloud computing applications are impressively fast despite the amount of data they process.
What Exactly Is the Map and Reduce in MapReduce?
MapReduce uses two programming logic to process big data in a distributed file management system (DFS). These are a map and reduce function.
The map function does the processing job on each of the data nodes in each cluster of a distributed file system. The reduce function then aggregates the results returned by each chunk server and passes it to another server within the DFS for result aggregation. The receiving server sends this calculation to the primary server, which posts the returned value to the client-side server.
What Happens When a Chunk Server Goes Down?
Servers within a distributed file system (DFS) might experience downtime sometimes. You might think this will break the entire system, but it doesn’t.
There’s a system in computing that prevents such impending breakdown. It’s called fault tolerance.
Hence, even when a server goes off during data processing, fault tolerance ensures that the primary server detects it immediately. And since there’s a replica of the data chunks across the nodes, the primary server instantly transfers the processing job to another server. That way, server downtime within the DFS doesn’t affect data processing.
MapReduce Eases Big Data Processing
MapReduce is an essential model that makes computing easy in distributed file systems. Because it allows several nodes to run a calculation concurrently, it’s a speedy method used by various tech giants to solve many of the problems that accompany big data analysis.
