How to overcome Hadoop Drawbacks

Big Data is at the focal point of the digital world as tons of data is generated and collected through various processes regulated by the company. Big Data is of great use to companies as it has embedded patterns that can be used to improve processes. As data involves the feedback from customers, it becomes vital to the company and can not be discarded.

Usually, a whole of data is not used as some of it is redundant. Therefore, the useful part needs to be differentiated from the others. Various platforms are used to perform this task, such as Hadoop. Though Hadoop can efficiently analyze and extract data, still; there are some challenges associated with it.

This post describes those challenges and presents you solutions to solve Hadoop related problems.

A Small File is a Significant Problem 

Hadoop is not suitable for small data. Due to the high capacity design of Hadoop distributed file system, it does not support the random reading of small files efficiently. A small file is a big constraint in HDFS as it is lower than the HDFS block size-default 128MB.

HDFS cannot handle a large number of small files as it works correctly with large datasets rather than small files. NameNode will get overloaded when there are so many small files, as it stores the namespace of HDFS. Solve this problem by merging the small files to create bigger files and then copy them to HDFS.

Moreover, the introduction of HAR files (Hadoop Archives) has reduced the problem of higher pressure on the namenode’s memory. HAR files build a layered filesystem on the top of HDFS. Developing of HAR with the help of Hadoop archive command runs a MapReduce job to aggregate the files being archived into a small number of HDFS files.

Reading files in HDFS is better than reading through HAR. Access to an HAR file requires two index files read along with a data file to read which makes it slower to use.

Iterative Processing

Apache Hadoop and iterative processing do not run parallel to each other as Hadoop does not support cyclic data flow which means a chain of stages in which the previous stage output acts as the input to the next stage.

How does Spark solve this problem? Apache Spark accesses data from RAM rather than the Disk which significantly enhances the efficiency of an iterative algorithm that accesses the same dataset every time. Each iteration has to be scheduled and executed separately in Apache Spark for iterative processing.


MapReduce in Hadoop supports the different format, structured and a massive amount of data which makes it slower to use. Map converts a data set into another set where every element is divided into a key-value pair.

Reduce receives the output from the map but uses it as input to process further. This increases latency as MapReduce requires a lot of time to perform. Apache Spark is a solution. It is the batch system, but it is faster as compared to MapReduce as it caches the input data on memory by RDD. Apache Flink delivers low latency and high throughput.

Hard to use

A MapReduce developer has to hand code for every operation in Hadoop which makes it tough to use. MapReduce has no interactive mode in Hadoop, but the addition of hive and pig makes easier for developers to work with MapReduce.

While many argue over the benefits of Hadoop vs Spark, one cannot deny that in this case, Spark has been able to overcome the issue of ease of use through its interactive mode. Therefore,  developers and users get intermediate solutions to queries and other activities as well. Programming spark is easy as it has tons of high-level operators. Developers can even use Apache Flink for high-level operators.


The limitations of Hadoop has triggered the use of Spark and Flink. Developers are in a quest for advanced solutions to develop systems efficient to play with a huge amount of data; therefore, they enrol themselves in courses, certifications, and training like Big Data Hadoop Certifications Training to master the technicalities of Hadoop.

Apache Spark increases the processing speed by improving in-memory processing of data. Flink offers single run-time for batch processing as well as streaming thus improving performance throughout its use. Spark is a security bonus.

The limitations as mentioned above of Hadoop can be resolved by using other big data technologies like Flink, and Apache Spark. We hope this post solves your doubts or problems you might face with Hadoop. Remember, you can overcome Hadoop’s limitations by implementing Flink or Apache Spark.