Let’s face it. MongoDB is a popular NoSQL database upon which to build applications.
But just like many “beginner” data analysis projects that start in a spreadsheet, when it’s time to get serious, it’s also time to upgrade. And when companies’ applications move from “general purpose” to enterprise-scale, they often find that MongoDB is not equipped to meet their needs.
When we talk to enterprise customers about why they moved from MongoDB to Couchbase. Here are the three most common reasons customers of MongoDB switched databases.
1. Performance & Scalability
One of Couchbase’s key advantages is exceptional throughput and low latency at scale. This is driven by a memory-first architecture.
All of Couchbase’s key-value data retrieval and processing operations happen in memory, resulting in sub-millisecond performance. Plus, all nodes within a cluster are active – there’s no single, primary node chokepoint that hands-off to “secondary nodes” and slows things down. All nodes perform queries and writes directly.
In addition, write processing is separated from read and query activities, meaning that writes never interfere with read and query actions. Finally, Couchbase delivers powerful indexing options to speed queries and with the imminent release of 7.0, performance increases by 10-100x in some cases.
Couchbase delivers high availability and reliability by leveraging several technologies working together to ensure your data and databases are “always on,” even under extreme conditions. Many of these capabilities, like continuous replication, auto-failover, speedy rebalancing, cross data center replication (XDCR) across clusters, and auto-sharding happen by default in the background or are manageable without taking down the system.
MongoDB’s architecture is focused on a single node setup, and many important features such as JOINs via its
$lookup function, or grouping, perform poorly on multi-node and multi-shard configurations. By the time you discover these issues, it might be too late to correct them. For example:
- Couchbase easily scales by simply adding necessary nodes one at a time, while MongoDB needs to add at least three new nodes at a time in order to introduce a new shard to the cluster, which drives up your total cost of ownership (TCO). Meanwhile Couchbase automatically creates shards and rebalances distribution to all available nodes.
- The Couchbase auto-failover is robust and quick, based on multiple signals of liveness. MongoDB’s failover technology, on the other hand, is slower and limited as it’s based only on heartbeats between replica-set nodes.
Don’t Just Take Our Word for It
I recently spoke to Microlise, a Couchbase customer who empowers fleet and logistics operators in 195 countries to manage and optimize their operations and deliveries. They process ~100M events per day!
Like others who first explore NoSQL technology, this team began with MongoDB for their microservices applications. As the demand and mission-criticality of these applications grew, the development team switched to Couchbase.
Many of our microservices were first developed on top of MongoDB, but as memory issues and database crashes increased, we were failing to meet our service levels. Our DBA, Support and Dev teams were unhappy. Our internal benchmarking with millions of records showed Couchbase beating MongoDB in meeting these SLAs.”
–Wayne Chandler, Data Architect at Microlise
Find out more about Couchbase’s performance at scale and high availability in this whitepaper: Couchbase vs. MongoDB™ for Scale-Out and High Availability.
2. N1QL, the Couchbase Query Language
The second reason many enterprises change from MongoDB to Couchbase is because of N1QL, the Couchbase query language.
N1QL (pronounced “nickel”) feels familiar because it is SQL – but extended to support the JSON document syntax. SQL is both expressly powerful and universally known, so the Couchbase team based N1QL on the ANSI SQL92 standard to support the SQL syntax that developers already know. Of course, it’s not just developers who know SQL, but your whole team: DBAs, analysts, architects, BI teams, and others. The two query languages are so similar that we sometimes refer to N1QL as “SQL++”.
Just compare the MongoDB query language with Couchbase N1QL side by side in the image below. The difference is remarkable.
The team I spoke to also loved the N1QL query language:
“Couchbase’s query language [N1QL] and backup management were so much easier for our teams. All of our critical microservice applications have been moved from MongoDB to Couchbase. And in the long term, we plan to standardize on Couchbase.”
–Wayne Chandler, Data Architect at Microlise
Here are just a few of the challenges that users say they face with MongoDB’s query language:
- Proprietary: MongoDB’s query language breaks away from the powerful and familiar SQL database language, forcing users to learn and master a whole new language.
- Complex: While MongoDB’s procedural methods are effective for targeted queries (i.e., to locate documents with specific filter conditions), it becomes complex for queries that require document JOINs and aggregations. That might be okay for some developers, but the community of people who either manage or access your data is much larger, making this a company-wide problem. Additionally, MongoDB has very limited support for joining documents across sharded collections because the
$lookupfunction does not support that functionality.
- Application Intensive: Applications need to compensate for database shortcomings by doing complex data processing on the client side, which increases complexity and usually results in poor performance once deployed.
If you’re interested in learning more about N1QL and Couchbase, I encourage you to try it out for yourself with this free online N1QL tutorial. Or, compare N1QL to MongoDB’s query language in this third-party evaluation.
3. Integrated Services for Flexible Development & Data Access
Another key factor that Couchbase customers love is the multiple built-in services that the platform offers to access and manage data without using additional databases. For developers that means less to learn, code, integrate and maintain. For DevOps teams, the result is fewer tools to license, deploy and support.
The Couchbase data platform provides multiple integrated ways to store, query and manipulate data with one uniform data model and a single programming interface, including:
- The universal model of Couchbase – a document database with the schema integrity options of a relational database – covers a broad set of use cases that address the performance, transactionality and data availability issues required by today’s modern applications.
- The in-memory key-value managed cache delivers millisecond performance without needing a separate caching product.
- The N1QL query language means developers, DBAs, analysts and others with SQL knowledge ramp up fast with “SQL++”.
- Even novice users can query the database using Full-Text Search, with geospatial capabilities.
- Eventing comes available out of the box with Couchbase, no outside vendor integration needed.
- Real-time data analytics can be performed in a massively parallel process without affecting other services and without ETL to another database
- Unlike most mobile databases, we offer bidirectional sync between the server and devices with peer-to-peer sync for an offline-first strategy
If you’re ready to move beyond a “general purpose” database and build robust enterprise applications, then you’re ready to try out Couchbase for yourself.