Today’s viewers demand seamless experiences and constant innovation. Finding MongoDB difficult to use and scale, DirecTV chose Couchbase for unparalleled performance at scale, bidirectional cross datacenter replication to keep services available for viewers 24/7, and N1QL for powerful queries.
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See how MongoDB™ compares in the DBaaS performance report
MongoDB vs. Couchbase
Customers who switch from MongoDB to Couchbase – whether on prem or in the cloud – have consistent reasons for doing so. Primarily these complaints center around performance challenges when scaling out, trouble using a proprietary query language, extra work from integrating separate technology capabilities, and challenges with mobile deployments. These difficulties result in wasted time, architectural and operational challenges, and a higher total cost of ownership.
Challenge 1:
An Unfamiliar Query Language
MongoDB's proprietary query language is far more complex and less well known than SQL.
MongoDB
MongoDB provides a query method, find(), to query a collection with simple filtering and projection. For more complex operations, users have to use the aggregation framework to join, process and aggregate multiple documents to return the result.
The MongoDB query method and aggregation framework are both proprietary and procedural, with limited expressive power and poor performance.
Couchbase Capella
Couchbase adopts open standards and extends the SQL query language to support JSON data.
Key benefits over MongoDB’s proprietary methods include:
- Familiarity of SQL
- Declarative
- Expressive power
- Global indexes
- Tunable consistency
Challenge 2:
Flexible, scale-agnostic architecture
MongoDB leaves data at risk because it’s inflexible and can’t adapt to modern workloads
MongoDB
MongoDB’s performance suffers from primary-secondary replication, which causes idle nodes and low hardware utilization.
All MongoDB services share the same resources, making it impossible to isolate workloads.
Single-node and sharded environments require different development patterns and changes to the way your application functions. This discourages the use of sharding by putting the burden on the application and operations teams to know their data and tune it differently for different access patterns.
Couchbase
Couchbase was designed as an all-primary, clustered, and replicated distributed database from day one – all nodes can read and write. All users benefit from clustering and rebalancing, which improves data safety. Built-in capabilities for replication across data centers ensure that your data is always on across the globe.
Multi-dimensional scaling enables independent scaling of services and workload isolation, optimizing performance and costs.
With high performance at scale, application behavior is unchanged whether growing from a single dev node to 3 test nodes or to 20 production nodes in a cloud.
Challenge 3:
Performance at scale
MongoDB performance rapidly degrades as the cluster size or number of users increases
See how Couchbase outperforms MongoDB on enterprise workloads >
MongoDB
MongoDB's performance suffers from limited page cache capability, requiring third-party tools to achieve acceptable norms for an enterprise-scale database.
Another MongoDB disadvantage is that inefficient routing layers add complexity and latency to requests.
Also, rigid data and index partitioning limitations require slow cross-node, scatter-gather methods for some queries. This approach slows query performance, especially on enterprise clusters.
Couchbase
Couchbase has a tightly managed and fully integrated caching layer for both data and indexes and doesn’t need additional caching products for high performance and single-digit latency speeds.
Intelligent routing with direct application-to-node document lookup allows for efficient communications with single network hop access.
Global indexes allow your indexes to be partitioned independently of data, minimizing latency for important queries.
Challenge 4:
Integrated enterprise functionality
MongoDB limits many features depending on deployment model or third-party requirements
MongoDB
MongoDB’s key disadvantage is that some of its most attractive capabilities are bolted on rather than tightly coupled to the core database. These bolt-ons include replication, sharding, limited full-text search and SQL-based analytics.
Because MongoDB uses a proprietary query language that's hard to learn (Figure 1), you must hire developers with specific platform experience in order to get lasting value. (Or else take a long time to get up to speed.)
Finally, sharded environments have functionality limitations. Stitch functions and mobile sync are available only through the hosted Atlas offering.
Couchbase
Couchbase has unified application and management APIs across document lookup, structured query, full-text search, analytics and triggers. This gives developers easy access to all functionality, including security and management capabilities.
Couchbase’s query language extends ANSI SQL, supports multi-document ACID transactions, while still supporting advanced relational syntax. (Figure 1).
All functionality is available across all deployment models: on-premises, containers and cloud.
Challenge 5:
Mobile and edge computing
MongoDB Realm does not allow data storage and sync for the data center of your choice
Learn more about Couchbase vs. MongoDB Realm for mobile and edge computing >
MongoDB Realm
MongoDB Realm sync is cloud-centric and relies on network connectivity, which presents an unpredictable point of failure.
Its reliance on the MongoDB Atlas backend-as-a-service (BaaS) platform to support mobile data sync service means that you can’t deploy the solution on premises, in private data centers, or in distributed cloud environments.
This severely limits options for meeting high availability and responsiveness requirements, as your applications become dependent on an inherently unreliable internet connection.
Couchbase
Couchbase has a flexible platform that supports a variety of advanced topologies for complex edge-to-cloud hierarchies. These include peer-to-peer as well as multi-tier, cloud-to-edge topologies.
You can deploy a robust sync solution anywhere you need it: on premises or hosted on any private or public cloud.
Couchbase offers Capella, a fully managed database-as-a-service, or you can use Couchbase Autonomous Operator for Kubernetes to easily deploy and manage your servers on the cloud or at the edge.
Why enterprises choose Couchbase over MongoDB
Why enterprises choose Couchbase over MongoDB
Pushed to scale at a rate its MongoDB and Redis backend could no longer support, Viber switched its messaging platform to Couchbase. With Couchbase, Viber cut its servers by more than 50%, is robust enough to avoid downtime, and now supports close to a million operations per second and datasets with billions of records.
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Nuance, a speech recognition and imaging software company, had a monolithic all-Oracle environment that was expensive to scale and inflexible for varied data types. As it explored NoSQL, Nuance found MongoDB hard to manage. It chose Couchbase instead for easy, cost-effective performance at scale and bidirectional replication.
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Staples needed to better manage B2B catalogs using 1.6 billion rules applied in real time. Staples tried MongoDB, but its 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 improved database performance and reliability.
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Compare Capella Database Services
Compare Couchbase Capella with other cloud Database-as-a-Service providers like MongoDB’s™ Atlas cloud service and Amazon’s DynamoDB database service. Find out why Couchbase Capella is the best solution for you.
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Couchbase N1QL
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
MongoDB™ Query
db.stocks.aggregate([
{ "$match": {
"$and": [
{"symbol": {
"$in": [
"AAPL",
"GOOG"]}},
{ "value": {
"$gt": 0 }}]}},
{ "$group": {
"_id": {
"symbol": "$symbol" },
"sum(value * volume)": {
"$sum": {
"$multiply": [
"$value",
"$volume"]}}}},
{ "$project": {
"_id": 0,
"sum(value * volume)": "$sum(value * volume)",
"symbol": "$_id.symbol"}}
{ "$sort": {
"sum(value * volume)": -1,
"symbol": 1 }}]})
Figure 1: Couchbase extends ANSI SQL to support the flexible schema of JSON
Altoros Report:
An Analysis of Database Query Languages in MySQL, Couchbase Server, and MongoDB
Additional resources
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