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See how MongoDB™ fails to perform at scale on enterprise workloads

Avoid scale fail, intermittent availability, and potential data loss

5 top challenges with MongoDB™ and how the Couchbase Data Platform avoids them

Challenge 1: Performance at scale

MongoDB™ is not only difficult to manage, but its performance also rapidly degrades as the cluster size or number of users increases


MongoDB's™ master-slave architecture limits its ability to efficiently support many concurrent users with a single node – and its performance rapidly degrades beyond a few dozen concurrent users per node. Adding more hardware resources complicates database management, but doesn’t improve performance proportionally.


Couchbase’s memory-first, masterless distributed architecture is elegantly simple and delivers predictable and consistent performance at any scale. Couchbase easily supports hundreds of concurrent users on a single node and scales horizontally across multiple nodes. Setting up a multi-node Couchbase cluster takes only a few minutes, and adding or removing nodes is a simple push-button process.

Challenge 2:

MongoDB™’ lacks a native mobile solution


MongoDB™ does not have a complete solution to support mobile applications. You either have to write your own code to synchronize data on the mobile device with data in a remote server, or you have to forego synchronization and instead pull data from the remote server for every request. This means your app always needs a reliable internet connection to work properly, and it may suffer performance problems due to network latency.


Couchbase provides a complete mobile solution – Couchbase Mobile – which includes an embedded JSON database (Couchbase Lite) and a pre-built synchronization solution (Sync Gateway). That means you can easily build mobile apps that always work, with or without an internet connection.

Challenge 3:
High availability and disaster recovery

MongoDB™ can’t perform all writes locally and is significantly susceptible to data loss and inconsistency


When deployed across multiple datacenters, MongoDB™ can only perform writes in the primary datacenter. This means it has to perform writes from the “secondaries” to remote locations, which can increase latency and degrade application performance. And 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.


Couchbase’s masterless distributed architecture, along with its built-in support for bidirectional cross datacenter replication (XDCR), allows all writes to be performed locally rather than to a remote location. This capability minimizes latency and helps optimize application performance. Couchbase also provides an entirely different approach to replication and failover that significantly reduces the potential of data loss and inconsistencies.

Challenge 4:
Integrated cache

MongoDB™ requires a third-party cache to help it perform


Because MongoDB™ cannot handle many connections and many concurrent users per node, companies often need to add a dedicated third-party cache to help MongoDB™ meet their performance requirements. This requirement can add both cost and complexity to a MongoDB™ deployment.


Couchbase has a fully integrated managed object cache that completely removes the need for a third-party cache. In fact, Couchbase leverages the memcached protocol, which is widely regarded as a de facto standard and the industry-leading high-performance cache.

Challenge 5:
Enterprise-ready query

MongoDB™ struggles to run complex queries


MongoDB™ limits developers to simple data models due to constraints on how data is modeled and stored. A proprietary API leads to additional data transfers as well as more hardware and network overhead. Additionally, developers need to shape their code to the API and make ongoing code changes as the API evolves. This makes it much more challenging to rely on MongoDB™ in enterprise environments where adapting to changing business requirements and running ad hoc reporting are both key to success.


Couchbase allows you to use any data model, whether simple or complex. N1QL offers a complete declarative language with industry standard ANSI joins for querying JSON data. And N1QL expresses itself in natural human language like SQL, allowing non-developers like business analysts to easily describe the queries they need. With Couchbase, logic is pushed down to the database layer. The result? Fewer lines of code and higher performance as the system automatically determines the optimal execution plan.

1/2 the code in 1/2 the time

MongoDB Query
Couchbase N1QL
        { "$match": {
                "$and": [
                    {"symbol": {
                            "$in": [
                    { "value": {
                            "$gt": 0 }}]}},
        { "$group": {
                "_id": {
                    "symbol": "$symbol" },
                "sum(value * volume)": {
                    "$sum": {
                        "$multiply": [
        { "$project": {
                "_id": 0,
                "sum(value * volume)": "$sum(value * volume)",
                "symbol": "$_id.symbol"}}
        { "$sort": {
                "sum(value * volume)": -1,
                "symbol": 1 }}]})
SELECT SUM(value * volume) AS val, symbol
FROM   db.stocks
WHERE  symbol IN ( "AAPL", "GOOG" ) AND value > 0
GROUP  BY symbol
ORDER  BY val DESC, symbol ASC

Why enterprises choose Couchbase over MongoDB™

directv logo

Today’s viewers demand seamless experiences and constant innovation. Finding MongoDB™ difficult to use and scale, DirectTV chose Couchbase for unparalleled performance at scale, bi-directional XDCR to keep services available for viewers 24/7, and N1QL for powerful queries.

Learn more
viber logo

Pushed to scale at a rate its MongoDB™ and Redis backend could no longer support, messaging platform Viber switched to Couchbase. Supporting close to a million operations per second, datasets with billions of records, and robust enough to avoid downtime, Couchbase helped Viber cut servers by more than 50%.

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nuance logo

Nuance, a speech-recognition and imaging software company, knew its monolithic, all-Oracle environment was expensive to scale and inflexible for varied data types. As it explored NoSQL, it found MongoDB™ hard to manage. It chose Couchbase for easy, cost-effective performance at scale and bi-directional replication.

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staples logo

Staples needed to better manage B2B catalogs using 1.6 billion rules applied in real time. While it explored MongoDB™, an inability to scale easily and affordably led to canceled projects. Couchbase not only enabled Staples to simplify its catalog management using N1QL and JSON, but also increased their database performance and reliability.

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Webinar: Switching From MongoDB to Couchbase for Scalability and Performance

If you want to create engaging customer experiences, you need a highly performant and scalable data platform. MongoDB might be a good fit for small-scale applications running on a few nodes, but try scaling it to support more users and bigger workloads and you’ll quickly hit a wall.


In this webinar you’ll learn all about:

  • The common operational challenges with MongoDB
  • Significant architectural differences between MongoDB and Couchbase
  • How Couchbase supports multiple datacenters with active/active configurations
  • Why a leading office supply retailer made the move from MongoDB to Couchbase

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).