Change is the only constant! Especially in the era of big data analytics and data science, the contemporary solution to managing distributed databases is changing from traditional databases to NoSQL databases.
The major features that are attracting an increasing number of developers toward NoSQL databases include:
- Capability to manage large volumes of data
- Straightforward scalability
- Agile development
- Fail-over safety
- Great CPU and memory skills
- Faster time to market
- No database breakage
The two governing technologies in this arena are HBase and MongoDB. With the differences between the two being important, software architects are finding it difficult to select the right one for their projects.
Before we explore this further by comparing the two leading technologies, let’s take a glance at their individual capabilities.
What Is Apache HBase?
According to Wikipedia’s definition, “HBase is an open-source, non-relational distributed database modeled after Google's Bigtable and written in Java.”
Apache HBase is best utilized when there is a necessity for random and real-time read/write approach to Big Data databases, and massive amounts of structured data. It provides Bigtable-like skills on top of HDFS and Hadoop—compression, in-memory operation, Bloom filters etc.
HBase is a scalable, column-dependent database for structured data that enables effective and precise management of enormous sets of data that are distributed among various servers.
As it is created in Java, it provides support for different APIs. It is mainly constructed to manage real-time queries in big tables that have many rows and columns and perform across a cluster of hardware. It works on a four-dimensional data model with utmost scalability and fault tolerance.
- Linear scalability and modularity
- Aid in exporting metrics through Hadoop metrics subsystem
- Automated failover support and sharding of tables
- Effortlessly usable Java API for clients
- Regularity in reads and writes
- Data storage as key or values
- Best fit for range-based scan
There are many companies and organizations using HBase, such as Adobe, Hubspot, Vinted, JVM Stack, Flurry, Tumblr, Pinterest, Celer Technologies, Yahoo!, etc. For more information, consider HBase: The Definitive Guide: Random Access to Your Planet-Size Data.
What Is MongoDB?
As Wikipedia informs us, “MongoDB is a source-available, cross-platform, document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas.”
In more detail, MongoDB is a general purpose, document-based, distributed database created for modern-day application developers and for the cloud era. It is utilized by many developers to empower the world's most advanced products and build highly scalable APIs.
It has the capacity to serve multiple Fortune 500 and global 500 organizations across different industry segments, like healthcare, education, eCommerce, and finance.
Key Features of MongoDB
- Decreased I/O overload and dynamic schema for easy data structures
- Replication, support for several storage engines
- Schema-less database, quicker query handling via indexes
- Horizontal scaling, distributed storage and superior availability
- Elasticity, real-time view of data, nested object structure
- Indexable array attributes, on-desk encryption in enterprise version
Among the many organizations using MongoDB, it’s worth mentioning Facebook, Adobe, Foursquare, Google, SAP, Shutterfly, Cisco, Forbes, eBay, and Paypal. For more information on MongoDB, consider reading MongoDB: The Definitive Guide: Powerful and Scalable Data Storage.
A Comprehensive Evaluation of MongoDB vs HBase
As we tend to compare the two databases, here are certain similarities that are observed in both:
- Possesses master-slave replication
- Supports sharding partitioning method
- Does not support foreign keys
- Durable, concurrent with in-memory capabilities
Here is a detailed comparison between the two:
|Overview||A wide column store, NoSQL database based on Apache Hadoop and fundamentals of BigTable||A document store, NoSQL database accessible as a fully managed cloud-based service and self-managed too|
|Developed By||Apache Software Foundation||MongoDB Inc.|
|Number of Nodes||Ten nodes for masters, data nodes, region servers, etc.||Three nodes for primary, secondary and replication|
|Backup and Recovery||Takes snapshots of data every minute on all nodes of clusters||Uses Ops manager and Atlas for regular backups|
|Latency||High latency operations||Low latency operations|
|Supported Data Types||Converted data to continuous bytes||Multiple data types including floats, strings, timestamps, etc.|
|Secondary Indexes||Not present, materialized views sustained by developers in code||Native indexes having geospatial, text, TTL, etc.|
|Server OS||Linux, Windows, Unix||Linux, Solaris, Windows, OS X|
|Available Editions||Community edition||Community and Enterprise edition|
|Known Use Cases||Hadoop, online log analytics, MapReduce, heavy applications||Operational and real-time intelligence, content management, IoT|
|Replication Factor||Utilizes a selectable replication factor||Utilizes a master-slave replication factor|
|Partitioning||Uses hashing method||Uses has, range, and zone sharding|
|API and Access Methodologies||Java API, Thrift, RESTful HTTP API||Proprietary protocol with JSON|
|Database Type||Distributed database||Decentralized database|
|Distributed System Consistency||Immediate consistency||Immediate and eventual consistency|
|Table and Row||Collection and Document||Table and Column family|
|DBaaS||None||ScaleGrid MongoDB Hosting, MongoDB Atlas, mLab MongoDB|
|Query Model||Possesses a key-value pairing for data||Query model has varied filtering, projections, etc.|
|Text Search Functionality||Data replication for a search engine||Native feature for text indexes|
|Performing Group By||Done with Hadoop traditional Map Reduce||Done with use of aggregation pipeline|
Unique Options for Unique Demands
To each their own! Though there are distinctions between HBase and MongoDB—the two prominent NoSQL databases—both have their share of fame and trustworthiness. Organizations must assess their own factors in detail before selecting which one to take.
HBase is best fit to key-value workloads where there is a high volume of random read/write for organizations who have HDFS as their storage layer, especially because it provides very fast reads/writes.
If there is a broader range of application areas, MongoDB is the right choice since it offers an enriched model for tracking user behavior easily. HBase is scalable and performant for a chosen subset while MongoDB can be used for a wider variety of applications.
It is up to the user to select if they need performance-driven implementation or effective support for a variety of applications. In the world governed by Big Data and BI solutions, it is a difficult choice to make, especially when it is like choosing the better out of the best. Hopefully this post has made your decision simpler and easier!