For MongoDB, Altoros configured memory-optimized global secondary indexes for filtering fields, along with index replication on each cluster node.Ĭassandra did not do well on this test. Since the data set for the tests were selected to fit within memory (if not within Cassandra’s cache), Couchbase excelled at this benchmark. The third workload benchmarked represented a pagination workload – specifically a query with a single filtering option to which an offset and a limit are applied – which is more of a BI workload.Ĭouchbase “significantly outperformed” both MongoDB and DataStax on this test, which Altoros attributes to its use of memory-optimized secondary indexes. While latency for Cassandra was higher, the drop in latency for Cassandra as the cluster got bigger was notable, which Altoros attributes to how the coordinator node sends scan requests to nodes responsible for specific token ranges. However, Cassandra “provided better scalability” than the other two databases, Altoros says. Cassandra (or DataStax Enterprise), meanwhile, showed “rather low performance” with the scan operations, Altoros says. MongoDB outperformed the two other databases on the four-node test, but failed to scale much beyond 14,000 operations per second regardless of cluster and data set size, Altoros says.Ĭouchbase “demonstrated great scalability” with near linear growth of throughput from four to 10 and 20 node clusters, with latency remaining around 10 milliseconds. The second workload was a short-range scan workload that contained 95% scans and 5% updates, which simulates threaded conversations. Cassandra also scaled well but still underperformed Couchbase’s throughput by 50% on a 20-node cluster. MongoDB “scaled well” on this first test, and continually decreased processing time, but suffered on smaller nodes due to the background activity of the balancer that tried to balance chunks of data across the shards, the company says. “Couchbase significantly outperformed both MongoDB and Cassandra across all cluster topologies,” Altoros says in its 29-page report. The first benchmark test was an update-heavy workload that was made up of 50% database reads and 50% database updates, which simulated an ecommerce setting. It also turned off any data durability settings, and kept just a single replica copy of data for each data set. Each test was configured so that all the data would fit entirely in memory. YCSB would be used to conduct four separate tests for each of the nine database clusters. The workload to be tested was the Yahoo Cloud Serving Benchmark (YCSB), which is an open source suite of programs used to see how well databases can perform common data retrieval and maintenance functions. The DataStax Enterprise v6 (based on Apache Cassandra) YAML file was configured in a standard manner for resources like memtable heap spaces and cleanup thresholds, row cache sizes, and commit log sizes. Data was sharded across available nodes using a hash-based partitioning scheme. MongoDB v3.6 was configured with a hierarchical topology that included a config server, router services, and shards. The Data Service was given 60% of the RAM and the Index Service 40%. The clusters were deployed on Amazon Web Services, using a storage-optimized extra-large instances, or i3.2xlarge, each of which sports 8 vCPUs, 61GB of RAM, and a single 1,900GB SSD.Īltoros configured Couchbase Server v5.5 with the Data Service, Index Service, and Query Service turned on the Search, Analytics, and Event services were disabled. The company has run tests with these databases other before, and it’s worth noting that it is a contributor to the open source Couchbase Server product.Īltoros set up three separate hardware configurations that were composed of four, 10, and 20 nodes. In late 2018, the company set up a benchmark test to see how the databases from Couchbase, MongoDB, and DataStax compared. The company has also taken it upon itself to shine more light on the inner functions of NoSQL databases, with the goal of helping organizations make better architectural decisions. Figuring out which databases excel in different areas can be tough, but the folks at Altoros aimed to help to narrow the field by benchmarking the three leading NoSQL database solutions, Couchbase, DataStax Enterprise, and MongoDB.Īltoros Systems is a Sunnyvale, California-based engineering company that helps enterprises evaluate technology and implement technological solutions spanning AI, blockchain, and operational databases. Developers have a large number of databases to choose from today, particularly when it comes to newer NoSQL databases.
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