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7 frequent challenges with Redis/MongoDB and how the Couchbase Data Platform avoids them
Challenge 1: Architecture
Redis/MongoDB is very difficult to scale from a single replica set to a fully sharded environment.
To scale Redis, which is single-threaded, you have to manually partition your data based on IDs embedded in the keys, hash of keys, or some combination of the two. MongoDB struggles with the same scalability challenges. Scaling the Redis/MongoDB stack requires a complex process and manual configuration to scale from a single replica set to a fully sharded environment.
Couchbase has a modern, masterless, elastic architecture, where both the integrated object cache and document database are supported with a single node type. Scaling Couchbase on demand is a simple, push-button process – add nodes and rebalance data with a few clicks, and you’re done.
Challenge 2: Cost
MongoDB’s inability to scale increases operational costs.
Both Redis and MongoDB use a master-slave architecture, that limits the ability to efficiently support many concurrent users with a single node. Beyond a few dozen concurrent users per node with MongoDB, performance rapidly degrades. Some businesses try to optimize MongoDB database operations by designing and implementing Redis as an in-memory caching solution in front of MongoDB. This could conceivably alleviate the performance challenges of MongoDB, for certain applications. But the only way to effectively deal with performance degradation is to add more hardware resources manually, which increases operational cost substantially.
Couchbase is purpose-built as a memory-first architecture with key-value and blazing-fast cache integrated with a document database that is designed to efficiently handle large numbers of concurrent users. Couchbase easily supports hundreds of concurrent users on a single node with little to no impact on performance.
Challenge 3: Replication and failover
MongoDB is significantly susceptible to data loss and inconsistency.
The way MongoDB handles replication and failover (by promoting “secondaries” to “primaries” automatically) can result in lost writes and inconsistent reads. So if a primary goes down, MongoDB is likely to suffer data loss or inconsistency. This risk is one reason why MongoDB deployments need to be closely monitored at all times.
The Couchbase Data Platform, which was purpose-built for distributed environments, has an entirely different approach to replication and failover, with cross datacenter replication (XDCR) that significantly reduces the potential of data loss and inconsistency. During a failover, Couchbase prevents two distinct nodes from accepting simultaneous reads or writes of the same data.
Challenge 4: Operational overhead
Redis/MongoDB deployments require operations teams with mastery of disparate skill sets.
Because MongoDB cannot handle many connections and many concurrent users per node, companies often need to add a dedicated third-party cache like Redis, with Redis-capable engineers to help MongoDB engineers meet their performance requirements for internet-scale enterprise applications. The need for a third-party cache can add both cost and complexity to a Redis/MongoDB deployment.
The Couchbase Data Platform is self-managing for most applications. It has a fully integrated managed object cache that completely removes the need for a third-party cache like Redis. The Couchbase Engagement Database combines a key-value store and blazing-fast cache with an open source document database, eliminating the need to hire operations teams with knowledge of multiple technologies.
Challenge 5: Multi-data center deployment
When deployed in multiple geographies, MongoDB can’t perform all writes locally.
Due to its master-slave architecture, and its use of “primaries” and “secondaries,” MongoDB cannot perform all writes locally when it’s deployed across multiple data centers in different geographies. This means it has to perform at least some writes to remote locations, which can increase latency and degrade application performance.
The Couchbase Data Platform’s masterless, memory-first distributed architecture, and its built-in support for cross datacenter replication (XDCR), means that all writes can be performed locally rather than to a remote location. This capability minimizes latency to help optimize application performance.
Challenge 6: Mobile
Redis/MongoDB lacks a mobile solution.
Redis/MongoDB does not have a complete solution to support mobile applications. As a result, you have to write custom services for your mobile apps to access data in Redis/MongoDB. And you either have to write your own code to synchronize data on the mobile device with data in a remote server, or forego synchronization and instead pull data from the remote server for every request. This means that your app always needs a reliable internet connection to work properly and may suffer performance problems due to network latency and repeated network round-trips.
The Couchbase Data Platform provides a complete mobile solution – Couchbase Mobile – that includes an embedded JSON database (Couchbase Lite) and a pre-built synchronization solution (Sync Gateway). You can use Couchbase to easily build mobile apps that always work, with or without an internet connection.
Challenge 7: Time
Redis/MongoDB is time-consuming to manage and maintain.
Due to its master-slave architecture, the topology of a MongoDB deployment can be complex and increasingly complicated as you try to scale it. Deploying MongoDB requires lots of manual configurations and includes numerous components: routers, config servers, arbiters, primary instances, and secondary instances.
Couchbase’s masterless, distributed architecture is elegantly simple. Installing Couchbase and setting up a cluster is a fast and easy process, with minimal components and configuration requirements. A multi-node Couchbase cluster can be set up in minutes, and adding or removing nodes is a simple, push-button process.
Viber switched to Couchbase and for less than half the number of Redis/MongoDB nodes
Many companies have outgrown the Redis/MongoDB stack and switched to Couchbase, such as electronics and internet communications giant Rakuten Viber. Viber is a cross-platform instant messaging and voice-over application used by over 800 million subscribers, and competes with Facebook Messenger and WhatsApp for both audience share and revenues. Viber needed a single robust data platform that could perform millions of operations per second with consistent low latency on huge data sets; a database that could scale easily without any performance impact, and was easy and inexpensive to monitor and manage. To power potential user growth and support hundreds of millions of subscribers, Viber chose to switch from its MongoDB and Redis system to the Couchbase Data Platform. In doing so it moved from an estate of 150 MongoDB servers and 100 Redis servers to one of 120 Couchbase servers, because Couchbase takes care of the caching duties originally performed by Redis. In Couchbase, Viber found a single platform that provides internet-scale performance, elastic scalability, five nines availability, manageability, and lower cost of operations.Learn more
Register for the webinar Couchbase Data Platform vs. Redis/MongoDB stack
MongoDB is sometimes a good fit for small-scale applications running on a few nodes, but it presents major challenges when you need to support more users and larger workloads on clusters with multiple nodes. Combining MongoDB with Redis can improve performance and scalability, but it also adds cost and complexity. In this webinar, we’ll compare the Couchbase Data Platform versus the Redis/MongoDB stack and discuss how the different solutions handle common operational challenges, including: architecture, data consistency, deployments, data reads, writes, and queries, support for multiple data centers, support for offline mobile use cases, SQL on JSON, and agility and time-to-market for internet-scale applications. Join us, and see for yourself how Couchbase reduces cost, complexity, and database sprawl with a purpose-built data platform that includes caching, cross datacenter replication, Full Text Search, analytics, multi-dimensional scaling, and N1QL (SQL for JSON).Register For US Register For EMEA