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Redis Vs MongoDB - Top Key Differences

Redis Vs MongoDB - Top Key Differences

We will also try to differentiate them by different aspects and see which is better for what.

Redis vs MongoDB will be our topic of discussion for today. Let’s try to make a grip on their top key differences.

Making a choice between the two very popular modern database architectures namely, MongoDB and Redis have recently been a crossroads for organizations across the globe. 

Every organization, big or small wants the best performing and working systems to achieve that competitive edge in the market. Let’s make that journey easy by evaluating our options with Redis vs MongoDB. 

Following is the list of contents that we will be exploring through this blog. 

What is MongoDB?

MongoDB is a NoSQL database that stores data in JSON-like documents with optional schemas. From a syntax and structure point of view, it is significantly different from the traditional RDMS. Developed by MongoDB Inc, even those without any prior programming skills can utilize MongoDB rather easily,  with semi-structured processing, even vast amounts of data can be processed.

The MongoDB was designed using Javascript, C, and C++ and is hosted on cloud platforms such as GCP, AWS, and Microsft Azure. MongoDB is known for using binary JSON and MQL, which is an alternative language for SQL.  BSON allows an increased bandwidth for certain data types like long, floating-point, and date. MQL also offers additional capabilities compared to SQL. 

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What is Redis?  

Redis (Remote Dictionary Server) is a key-value store and an in-memory database. You’ll find that the majority of its benefits and drawbacks are encapsulated in its term.

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In-memory store

It means that the data will be stored on the host’s Random Access Memory (RAM), instead of a disk. It is significantly faster to read and write from RAM than any other disk operation. 

However, there is also a disadvantage to the in-memory storage model. Storing large data sets in RAM is not at all cost-effective as it is significantly more expensive than disk storage. Precisely, one’s data set would be limited by the memory allocated for the Redis Process.   

Key-Value store

Data is viewed as a single collection in a key-value store, and each record is thought of as having a pair of keys and values associated with it. Keys need to be distinct because they are used to retrieve values. The data types and structures allowed for keys and values are implementation-specific. Redis lets you utilize binary safe strings with a maximum size of 512 MB as keys. However, you can choose from a wider range of data structures for values. Strings, lists, sets, maps, and streams are among the supported types. 

One of the consequences of using a key-value store like Redis is that there is no query language. On top of that, Redis doesn’t natively support secondary indexes, thus, limiting the flexibility of your data access. If you require an alternative method of accessing the data then the primary key. You must create and maintain your own indexes by putting them in RAM that is already constrained. 

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Use Cases of Redis

In terms of its configuration, Redis can be deployed for different use cases including session management, cache, or even a message broker. It is rarely used as a system of record given its in-memory shortage model. 

Commonly, Redis is used in a combination with an on-disk database like MongoDB. Redis can perform the tasks of a caching layer, and real-time analytics while the on-disk database.

In conjunction with an on-disk database like MongoDB, Redis is frequently utilized. Redis can be used as a cache layer or for real-time analytics, although the on-disk database is still the preferred storage option.

MongoDB vs Redis: Data Storage

As we have already discussed, that the major difference between MongoDB and Redis is its conceptually distinct storage models. However, in spite of being an in-memory store, Redis increases data durability by persisting it on disk so when the Redis process crashes, it can restore the data from the disk on restart. You can find different persistence options provided by Redis and choose a suitable one for your application requirement. With more frequent writes on the disk, the durability of the data also increases, and at the same time, the performance degrades. 

Also, read about Couchbase vs MongoDB – Using Comparison Table

Does MongoDB support in-memory storage?

MongoDB offers support for the in-memory storage engine as part of MongoDB Enterprise. The in-memory storage engine converges the predictable latency benefits of the in-memory storage model with the rich query capabilities of MongoDB.

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Does MongoDB Atlas support in-memory storage?

MongoDB Atlas uses an in-memory storage engine. However, one can use NVMe SSD drives that can offer more predictable latency than regular hard disk storage.

MongoDB vs. Redis: Scalability

A horizontal scale-out architecture is offered by MongoDB. It is achieved by sharding, which enables you to distribute data across numerous nodes. Data is divided among nodes with the help of configured shard key. Redis Cluster is Redis’s solution for scaling out by sharing data across multiple nodes.

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Sharding strategies

Only hashed sharding is supported by Redis Cluster. Ranged, hashed, and zoned sharding is the different shard key schemes that MongoDB offers. You can select the tactic that your system requires the most. You may create shared cluster zones with Global Clusters in MongoDB Atlas to enable geographically distributed applications.

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Consistency and backups

Redis Cluster does not ensure steadiness, which means that in the chances of failure, the cluster could lose data. MongoDB offers one of the strongest data safety and consistency guarantees among databases tested by Jepsen.

Cross-shard backups are not a feature of Redis Cluster. It is necessary to back up each fragment separately. You may execute consistent backups with point-in-time recovery using MongoDB Atlas.

Language support

There are just a few libraries that are community-maintained that enable Redis Cluster. Currently, libraries for Node.js, Ruby, Python, PHP, Java, C#, and Go are actively maintained. Sharding in MongoDB is a language-agnostic feature that is supported by all official and community libraries, covering dozens of languages.

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Businesses across numerous industries utilize the all-purpose persistent database MongoDB as their main storage option. Redis is an in-memory data store that may be set up to operate in a variety of ways based on the requirements of your system. 

Hope, now if opting for one of these databases you will know what to choose according to its scalability.

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About the Author

Technical Reseach Analyst - Data Engineering

Abhijit is a Technical Research Analyst specializing in Deep Learning. He holds a degree in Computer Science with a focus on Data Science. Being proficient in Python, Scala, C++, Dart, and R, he is passionate about new-age technologies. Abhijit crafts insightful analyses and impactful content, bridging the gap between cutting-edge research and practical applications.