Introduction
When anyone can generate enormous volumes of data in a matter of seconds, the Hadoop platform becomes indispensable. Here are some of Hadoop’s challenges, along with solutions. Explore more in our Hadoop course syllabus.
Big Data Hadoop Challenges and Solutions
Reading over all of Hadoop’s challenges and solutions will undoubtedly make it easier for you to operate with Hadoop.
Small File Challenge
Hadoop was designed to hold a limited number of huge files. Hadoop, however, is unable to handle a large number of tiny files.
Challenge: Files that are significantly smaller than the Hadoop block size are referred to as small files. Within NameNode’s memory, each file, directory, and block takes up a memory element.
- This memory element is typically 150 bytes in size.
- Therefore, a block containing 10 million files would need 1.39 GB of memory. With existing gear, scaling beyond this level is not feasible.
- Small file retrieval in Hadoop is incredibly inefficient.
- It results in several disks searching and bouncing between data nodes at the backend. It takes a long time.
Solution 1: One solution to the challenge of small files is Hadoop Archives, often known as HAR files. Over Hadoop, Hadoop archives serve as an additional file system layer.
- We may create HAR files using the Hadoop archive function.
- This command packs the archived files into a limited number of HDFS files by doing a map-reduce task at the backend.
- However, reading HAR files is not as efficient as reading HDFS files.
- This is because it needs to access the data file after two index files.
Solution 2: Sequence File: Here, we create software that combines several little files into a single sequence file. Next, we do streaming processing on this sequence file. Because we can split the sequence file, map-reduce can process it in parallel by breaking it up into chunks.
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Slow Processing Speed Challenge
Challenge: The MapReduce function in Hadoop reads and writes data to and from the disk. Data is read from the disk and written to the disk at each processing step.
- The entire process is extremely slow because this disk search takes time.
- Hadoop is relatively slow for processing tiny amounts of data. It works well with big data sets.
- Because Hadoop is built on a batch processing engine, its real-time processing performance is poor.
- Compared to more recent technologies like Spark and Flink, Hadoop is slow.
Solution 1: Map-reduce’s slow processing speed can be addressed with Spark.
- It is 100 times faster than Hadoop since it does computations in memory.
- Spark is a quick processing tool because it reads data from RAM and writes it to RAM while processing.
Solution 2: Another technique that performs calculations in memory and is quicker than Hadoop map-reduce is Flink.
- Spark is slower than Flink. This is because, unlike Spark, which has a batch-processing engine, Spark has a stream-processing engine at its core.
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Absence of Real-Time Processing
Challenge: Real-time data cannot be processed using Hadoop’s Map-Reduce structure. Data is processed in batches via Hadoop.
- The file is first loaded into HDFS by the user.
- The user then uses the file as input for a map-reduce process.
- It processes data according to the ETL cycle.
- The data is taken from the source by the user.
After that, the data is changed to satisfy the needs of the business and moved into the data warehouse at last. From this data, the users can produce insights. Businesses use this information to improve their operations.
Solution 1: Spark has emerged as a solution to the above challenges.
- Spark can process data in real-time.
- By creating micro-batches and performing calculations on them, it handles the incoming data streams.
Solution 2: Another option for slow processing speed is Flink.
- With a stream processing engine at its heart, it is even faster than Spark.
- Flink is a real streaming engine that allows you to control throughput and latency.
- It makes use of streaming runtime through a wide range of APIs.
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No Iterative Processing Challenge
Challenge: Iterative processing is not supported by Hadoop. A cyclical data flow is necessary for iterative processing.
- In this case, the output of one stage is used as an input for the subsequent stage.
- Batch processing is possible with Hadoop Map-Reduce.
- It operates on the write-once-read-many premise.
- Once the data is written to the disk, and then read a few times to gain understanding.
At the heart of Hadoop’s Map-reduce is a batch processing engine. It cannot iterate through the data.
Solution 1: Iterative processing is supported by Spark. Every iteration in Spark must be scheduled and carried out independently.
- It uses a Directed Acyclic Graph (DAG) to achieve iterative processing.
- Resilient Distributed Datasets (RDDs) are a feature of Spark.
- These are a group of components divided among the node cluster. Spark uses HDFS files to generate RDDs.
- We can cache them so that RDDs can be reused.
- Data is subjected to repeated manipulations by the iterative algorithms.
- RDDs caching over iterations is hence advantageous to them.
Solution 2: Iterative processing is also supported by Flink. Flink uses a streaming architecture to iterate data.
- To increase performance, we can tell Flink to process just the data that changes.
- Flink defines a step function to implement iterative algorithms.
- The step functions are included in a unique iteration operator.
- This operation has two variations: iterate and delta iterate.
Until they reach a termination condition, each of these operators repeatedly applies the step function. Explore various big data projects to learn them comprehensively.
Complexity in Implementation
Challenge: Every operation in Hadoop requires hand coding. This has two problems.
- The first is that it’s hard to utilize. Secondly, the quantity of lines of code is increased.
- Hadoop MapReduce does not have an interactive mode. It operates in batch mode.
- Debugging becomes challenging.
- The jar file, input, and output file location must all be specified in this mode.
- It is challenging to identify the faulty code if the program crashes in the middle.
Solution 1: Compared to Hadoop, Spark is easier for users to utilize. Its numerous Java, Scala, Python, and Spark SQL APIs are the reason for this.
- On the same cluster, Spark carries out machine learning, batch processing, and stream processing. Users’ lives are made easier by this.
- They can utilize the same infrastructure for different workloads.
Solution 2: There are a lot of high-level operators in Flink. By doing this, fewer lines of code are needed to accomplish the same goal.
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Security Challenge in Hadoop
Challenge: Both the storage and network levels of encryption and decryption are not implemented by Hadoop. As a result, it lacks security. Hadoop uses Kerberos authentication for security, which is challenging to manage.
Solution: Temporary data written to the local disk is encrypted by Spark.
- Applications that generate output data using APIs like saveAsHadoopFile or saveASTable are not supported for encryption.
- For RPC connections, Spark uses AES-based encryption.
- To enable encryption, we must enable RPC authentication. It needs to be set up correctly.
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Conclusion
Spark and Flink were developed in response to the challenges of Hadoop and its MapReduce engine. Reshape your career by enrolling in our big data Hadoop training in Chennai.
