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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Altoros Benchmark: Compare Couchbase Cloud™, MongoDB™ Atlas, and AWS DynamoDB
Critical MongoDB disadvantages and how Couchbase overcomes them
Challenge 1:
Elastic capacity and 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 master-slave replication, which 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.
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 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 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.
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
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 unique proprietary query language that can be hard to learn (Figure 1), you must hire developers with specific platform experience in order to get lasting value.
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 and leverages traditional database skills for easier adoption (Figure 1).
All functionality is available across all deployment models: on premises, containers, and cloud.
Compare Cloud Database Services
Compare Couchbase Cloud™ with other cloud Database-as-a-Service providers like MongoDB’s™ Atlas cloud service and Amazon’s DynamoDB database service. Find out why Couchbase Cloud is the best solution for you.
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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 |
| 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
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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