MongoDB™ versus Couchbase - NoSQL Comparison

See how MongoDB™ performs in the NoSQL technical comparison report

Avoid data loss, scale fail, and limited functionality

Critical MongoDB™ deficiencies and how Couchbase overcomes them

Challenge 1:
Elastic capacity and scale-agnostic architecture

MongoDB's™ inflexibility cannot adapt to modern workloads, leaving data at risk

Learn more about MongoDB’s™ architectural weaknesses >

MongoDB™

MongoDB's™ master-slave replication can cause 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 applications function. 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 a masterless, clustered, and replicated distributed database from day one – all nodes can read and write. All users benefit from clustering and rebalance, which improves data safety.

Multi-Dimensional Scaling (MDS) enables independent scaling and complete workload isolation.

Application behavior is unchanged whether growing from a single dev node to 3 test nodes or to 20 production nodes in a cloud, all with scalable performance improvements.

Challenge 2:
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™ page cache capability does not perform well, requiring third-party tools to achieve high performance.

Other factors also degrade database performance including inefficient routing layers which add complexity and latency to requests.

Slow cross-node scatter-gather methods are needed for some queries due to the rigid data and index partitioning limitations. This approach limits query performance especially on large enterprise clusters.

Couchbase

Couchbase has a tightly managed and fully integrated caching layer for both data and indexes, without the need for additional caching products for high performance.

Intelligent routing with direct application-to-node document lookup allows for efficient communications with single network hop access.

Global indexes allow indexes to be partitioned independently of data, minimizing latency for important queries.

Challenge 3:
Integrated enterprise functionality

MongoDB™ limits many features depending on deployment model or third-party requirements

Learn more about integrated Couchbase capabilities >

MongoDB™

MongoDB™ has several capabilities that are bolted on instead of being tightly coupled to the core database, including replication, sharding, limited full-text search, and SQL-based analytics.

Dedicated developers are required because MongoDB™ uses a unique proprietary language for querying that is hard to learn.

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, giving developers easy access to all functionality as well as security and management capabilities.

SQL-based query language leverages traditional database skills for easier adoption.

All functionality is available across all deployment models: on-premises, containers, and cloud.

Couchbase leverages SQL-based syntax to greatly simplify querying

MongoDB™ Query	Couchbase N1QL
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 }}]})	*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

Couchbase N1QL

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 }}]})

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™

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.

Learn more

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

Learn more

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, Nuance found MongoDB™ hard to manage. It chose Couchbase instead for easy, cost-effective performance at scale and bidirectional replication.

Learn more

Staples needed to better manage B2B catalogs using 1.6 billion rules applied in real time. Staples tried MongoDB™, but the 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.

Learn more