When developing your application, the database you choose can affect the speed of data retrieval, scalability, and overall performance. MongoDB and Redis are popular choices in modern database management.
They’re both scalable, open-source NoSQL databases that offer highly comparable results in ease and speed. Each has unique strengths that suit different developer needs.
This article will explore the similarities and differences between MongoDB and Redis to help determine which is more suitable for your project. Making the right decision now can prevent some tremendous headaches in the future.
MongoDB vs Redis: Different Storage for Different Needs
Different applications demand different database architectures, meaning that MongoDB and Redis may best accommodate unique scenarios. The most significant difference between the two is their respective storage models, which can affect their data retrieval speed, storage volume, and risk of data loss and crashes.
MongoDB stores data on-disk as collections of Binary JSON (BSON) documents by default. This on-disk storage offers greater storage volume and less risk of system crashes. Storing data as BSON documents enables MongoDB to accommodate the many data types that normal JSON cannot parse. Additionally, it can store data in-memory or on the cloud.
Furthermore, MongoDB has no schema or fixed data structure and doesn’t require the creation of document structures. This makes it more accessible for beginners and faster at processing large quantities of data over time.
MongoDB also offers superior scalability, with an outstanding range of horizontal, vertical, and elastic options. These points make the database easily adaptable to subsequent changes, making it a better fit for startups and businesses needing to account for potential developments and growth.
However, if your project involves rapidly changing data, Redis is the better option. The versatility of Redis comes from storing data in key/value formats that support a wide range of data types. It can also function as a message broker and cache.
Unlike MongoDB, Redis uses in-memory storage with on-disk persistence, enabling it to process inflowing data more rapidly on RAM than MongoDB. Additionally, its in-memory storage makes it better suited for real-time analytics, such as ad targeting, social media analytics, Apache Kafka, and other streaming solutions.
Database architecture dictates the rules for the collection, storage, retrieval, enhancement, and integration of data within a specific database management system. A solid understanding of database architecture is essential because it impacts database performance and scalability.
MongoDB uses document-oriented architecture and an unstructured query language, meaning it doesn’t need to store data in rows and columns. Documents have a flexible schema and structure that allows for inserting or removing fields over time. It supports hierarchical and nested data perfectly.
MongoDB also uses BSON, meaning data storage can support many file types generally unsupported by SQL databases. But it requires a lot of storage space due to the high rate of data replication. It also lacks foreign keys and joins.
However, Redis relies heavily on key/value stores that hold data in a distinct collection containing two items: a key and the value assigned to it. It uses a unique identifier (a key) to store and retrieve specific data. These keys are stored in a dictionary and require a parser to enable them to process multiple values (because each key can only contain a single value by default).
Redis also supports several data structures and values, such as hashes, strings, sets, lists, sets, sorted sets, streams, geospatial indexes, and bitmaps. But their storage is restricted by the available storage space on the RAM, and stored data is not as scalable or easily accessible over time.
MongoDB vs Redis: Key Differences
The table below shows the key differences and similarities between MongoDB and Redis:
|Schema-less structure allows high speed when large volumes of data are stored on-disk.
|Much faster than MongoDB, except when large amounts of data are in its in-memory storage.
|Built-in sharding enables scaling out across multiple geographical regions and nodes.
Hashed sharding, range sharding, zone sharding, and cross-shard operations are enabled.
Consistent multi-cloud backups are easily accessed with MongoDB Atlas.
Multi-language feature is supported by all community and official drivers.
|Redis cluster enables scalability.
Hashed sharding alone. Shards are maintained manually.
No consistency in shard backup.
Driver support is limited.
|Transactional Data Integrity
|Multi-document ACID transactions support with multi-statement syntax.
|Multi-command and multi-record transaction support.
No default rollbacks support.
|On-disk storage. Consumes high memory of about 1 GB per 100,000 assets.
|In-memory storage. Requires about 4 GB RAM.
Higher memory usage than MongoDB.
|Index creation is easy and diverse.
MongoDB Atlas’ Performance Advisor can recommend new indexes to users.
Secondary indexes can easily be used to build applications to process data in various ways.
|Secondary indexes are not easily built and are manually maintained.
|High availability through replication.
|High availability through primary-secondary replication across multiple nodes and service centers.
|MongoDB Query API queries documents with text searches, ranges, and single or multiple keys.
It creates materialized views of retrieved data (on demand), and performs geospatial queries, and graph traversals.
|Key-value queries alone. Lower query functionality which can be enhanced with external Redis modules.
|Persistent volume feature in Kubernetes achieves data/storage persistence.
|Append-Only File data persistence and Snapshot aid persistent storage.
|Single purpose, map reduce function, and aggregation pipeline enable data aggregation.
MongoDB Atlas uses the aggregation pipeline builder to build and process aggregation pipelines.
|Map reduce function and aggregation pipelines are used.
MongoDB vs Redis: Ideal Usages
While it is essential to compare specifications when deciding between any two technologies, the best database system for you most strongly depends on the type of application you’re developing.
If high speed and low latency are the deciding factors, then Redis is the top contender for optimum performance. It can handle the workload of applications such as fraud detection and modern game development that need to process large amounts of ever-changing, real-time data quickly and effectively.
Meanwhile, MongoDB is better in terms of scalability and reliability, making it ideal for applications that store large volumes of data over a long period. Examples include ecommerce websites, photo-sharing applications, and employee benefits programs.
Redis with Kinsta
The Redis add-on from Kinsta is a persistent object cache used to enhance the page caching function of highly dynamic websites, such as discussion boards, membership sites, e-Commerce sites, forums, and highly active blogs.
It can also help your WordPress projects by enabling the persistent storage of values generated by WordPress’s native object cache. Persistent storage will allow your project to reuse cached objects instead of querying the MySQL database twice (or more) for the same object. Ultimately, this reduces the response time of a website and the load on its MySQL database while enhancing the capability to process traffic.
MongoDB and Redis are both excellent database management options. Their differences in storage, model, architecture, and features mean that the right choice for you depends on your project’s specific needs.
MongoDB is stable, reliable, and best suited for general-purpose projects. However, its on-disk storage model makes it relatively slower at processing real-time data.
On the other hand, Redis’ in-memory storage makes it much better at handling massive volumes of real-time data. However, it does not scale nearly as well and has a higher barrier of entry for newer developers.
The Redis add-on from Kinsta allows you to comfortably integrate Redis into your project without worrying about the complexities of managing it. It also increases your productivity by focusing on other business problems.