Mongodb vs Hadoop Performance - the Best Comparison

The IT community has experienced an interesting phenomenon in recent years. Many new technologies emerged and immediately used big data. A little older technology will also add big data to their own characteristics to avoid overloading the troops, and we notice the marginal blurring between different technologies. If you use MongoDB JSON files, or a set of JSON files in HDFS are stored in a Hadoop cluster. These configurations allow you to do many of the same tasks. In this article I will do the comparison of MongoDb vs Hadoop Performance. Before differentiate them let’s have a little look at the introduction of these technologies.

What is Mongodb?

MongoDB is a cross-platform, document-oriented database. It saves the data as a JSON document. In MongoDB, a document consists of a set of key-value pairs and a collection of a series of documents. It is similar to the RDBMS table. In addition, the documents in this collection have different fields.

In MongoDB each collection contains several documents. The number of fields, the content, and the size of the document can vary from document to document. Therefore it has no structure. There are no complicated links in the relational database.

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With a document-based query language, you can run dynamic queries against a database. An important factor in using a relational database is that the application object must be converted or mapped to a database object so that it can be stored in the database. MongoDB does not need this conversion.

In addition, the internal memory is used to store working sets. Therefore, it has faster data access capabilities.

What is Hadoop?

Hadoop is a framework for building cluster architectures that can retrieve and replicate information in parallel. In addition, Hadoop provides an easy way to add and / or remove cluster nodes to increase scalability and minimize the risk of node outages for distributed data. Hadoop is now the engine of the company’s main applications. But why is this framework attracting professionals?

Of course, Hadoop is an open-source technology that today is tightly linked to the management of volume data and big data applications. The framework was created in 2006, first in Yahoo, and partly inspired by Google’s idea of formatting in technical documentation.

Later, Facebook, Twitter and LinkedIn also raised an important issue, and began to contribute to its development. Today, Hadoop is a complex ecosystem of components and tools, some of which are included in commercial distributions of the framework.

Running on a convenient server cluster, Hadoop provides a cost-effective, yet powerful way to implement architecture for analysis. As these capabilities become more popular, the framework is intervening in the industry to support reporting and analytics applications and to combine structured data with new forms – semi-structured or unstructured data.

This may be data collected while surfing the internet associated with online advertising campaigns, industrial equipment sensors, social media, or other Internet of Things terminals.

Relationship between Mongodb and Hadoop

To compare the MongDb vs Hadoop Performance first we will discuss the difference between them.

Mongodb is a non-relational database widely used in the NoSQI field. Whenever Mongodb users use the sharding feature, the replica set is more likely to be used (sometimes there are not many computers).

Instead of Mongodb’s MapReduce, which replaces the Hadoop HDFS with the Mongodb replica set, there is a Mongo Hadoop master project adapter between Mongodb and Hadoop.

If you only want to save logs, analyze, etc., it is recommended to consult your scenario, and both techniques can be applied.

Mongdodb vs Hadoop Performance

Now let’s have a look at MongoDb vs Hadoop Performance.

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MongoDb Performance

The MapReduce-based fragmentation of MongoDB can do what Hadoop can do. Of course, thanks to many features, we can handle Hadoop (HBase , Hive, Pig, etc.) and query data in a Hadoop cluster in a number of ways.

So can we say that MongoDb vs Hadoop performance is exactly the same? Obviously not! Each tool has its own scenario, but each has great flexibility to manage different roles.

If you need to perform more complex calculations, run server-side scripting on the data, and MapReduce, then MongoDB, or Hadoop will be suitable instead of Elasticsearch.

With MongoDB, you can use an aggregation pipeline to process documents in a MongoDb collection and process them step-by-step through a sequence of pipeline operations. Pipeline operations can generate completely new documents and delete documents from the final results. It’s a powerful data recovery filter, processing, and data transformation feature.

MongoDB also supports the execution of data collection, as well as the mapping and reducing operations for custom js functions. This ensures that MongoDB offers the greatest flexibility in any kind of calculation or conversion of the selected data.

Another extremely powerful feature of MongoDB is called “Capped Collections”. This feature allows the user to set the maximum size of a collection. The collection can be hidden and roll-over the necessary data to get the log and other stream data for analysis.

Hadoop performance

In addition MongoDb vs Hadoop Performance, in this section I will point out the characteristics of Hadoop.

Hadoop is MapReduce, which was supported by MongoDB! If there is a scene dedicated to Hadoop, MongoDB is right.

Yes!

Hadoop is the old MapReduce, which provides the most flexible and powerful environment for processing big data. There is no doubt that it can process scenes that cannot be processed with MongoDB.

To better understand this, take a look at how Hadoop uses HDFS abstract storage in its computation functions. Thanks to the data stored in HDFS, any work on this data can be exploited, written on the main MapReduce API, or programmed directly in a native language using Hadoop streaming technology.

Based on Hadoop 2 and YARN, even the main programming model has been extracted and you will no longer be forced by MapReduce. With YARN you can implement MPI on Hadoop and write tasks in this way.

In addition, the Hadoop ecosystem provides an interlaced collection of HDFS-based tools and MapReduce for data query, analysis and processing.

Hive provides a SQL-like language that enables business analytics to be queried using a user-friendly grammar.

HBASE offers a Hadoop-based column-oriented database.

Pig and Sizzle offer two different programming models for querying Hadoop data. To use the data stored in HDFS, you can use the automatic learning functions of Mahout in your tool set. When using RHadoop, you can use the R statistical language to perform advanced statistical analysis of Hadoop data directly.

Although Hadoop and MongoDB have partially overlapping application scenarios and some useful features (transparent horizontal extension) in common, there are specific scenarios between the two.

Conclusion

If you only want to use keywords and simple analysis, Elasticsearch can do the job. If you need to query the document and have to include a more complex analysis process, MongoDB is a perfect fit. If you have a large amount of data, you need a lot. With complex processing and analysis, Hadoop offers the widest range of tools and flexibility. This comparison of MongoDb vs Hadoop Performance is done for our readers to understand which technology is better for them.