Couchbase Claims Performance Gains Against NoSQL Rivals

Couchbase Claims Performance Gains Against NoSQL Rivals
Couchbase says the coming Multi-Dimensional Scaling feature will bring read/write, indexing, and query advantages over MongoDB and Cassandra.
Companies adopting NoSQL databases are graduating from experiments and early deployments to running scores of production applications, according to Couchbase. Mission-critical demands are going to make Multi-Dimensional Scaling, a new Couchbase feature announced Monday, a competitive differentiator, according to the NoSQL database vendor.

Set for release this summer as part of Couchbase Server 4.0, Multi-Dimensional Scaling promises to give customers the ability to isolate and optimize database read-write, indexing, and query workloads by assigning specific nodes and resources to each task. As a result, Couchbase would be able to support more demanding workloads and a greater diversity of apps without throwing more hardware at the problem.

“Nobody wants to compromise on the performance of basic database reads and writes, but that often happens when you need to build lots of indexes or run intensive queries,” said Bob Wiederhold, Couchbase CEO, in a phone interview with InformationWeek. “With Multi-Dimensional Scaling, you have the option to run those tasks on different servers [in a distributed cluster].”

[ Want more on this topic? Read MongoDB Eyes Bigger, Faster NoSQL Deployments. ]

In an example of how customers might take advantage of the new feature, Wiederhold said Couchbase customers like Neiman Marcus or Nordstrom could expand on read-write-intensive customer-profile applications by adding sophisticated query capabilities.

“If they want to find out how many men between the age of 18 and 25 bought loafers in July east of the Mississippi, they could blend product-purchase data in with the customer-profile data,” Wiederhold said. Specific nodes could be assigned to handle the queries and others to handle indexing of notable attributes without detracting from core read-write performance.

The big win with the new feature is “dramatically higher performance,” according to Wiederhold. Couchbase competes against NoSQL rivals including MongoDB and Cassandra.

“We’ve always had advantages in read-write performance, but now we’re going to substantially extend our lead in terms of indexing and query performance,” Wiederhold said.

A head-to-head test by Avalon Consulting being promoted by Couchbase (above) contends that Couchbase’s current product delivers higher performance than NoSQL rival MongoDB 3.0 with its WiredTiger storage engine.
A head-to-head test by Avalon Consulting being promoted by Couchbase (above) contends that Couchbase’s current product delivers higher performance than NoSQL rival MongoDB 3.0 with its WiredTiger storage engine.

MongoDB in February claimed substantial performance gains with its MongoDB 3.0 release. The upgrade features a pluggable architecture that gives developers options to choose different storage engines for different performance requirements. The first new option available is WiredTiger, which MongoDB says delivers vastly higher database-write performance than the product’s incumbent storage engine.

Taking a swipe at these claims, Couchbase last week released a whitepaper by Avalon Consulting Llc. that shows, among other findings, that “Couchbase Server provided 2.5x the throughput of MongoDB with the same number of concurrent clients.”

Avalon’s head-to-head tests compared Couchbase’s current product to MongoDB 3.0. Thus, performance differences would presumably be even more dramatic once Couchbase 4.0 and the new Milti-Demensional Scaling feature are available sometime this summer.

MongoDB could not be reached in time for publication to comment on the Couchbase announcement or Avalon’s database comparison. We’ll share any statements in the comments area below this article as they become available. [Author’s note: after publication of this article, MongoDB commented that Avalon’s test approach favored Couchbase with three times greater hardware capacity than MongoDB and did not follow MongoDB-recommended deployment and performance-optimizaiton practices. See detailed comments from MongoDB below.]


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