{"id":14436,"date":"2023-06-05T12:23:02","date_gmt":"2023-06-05T19:23:02","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=14436"},"modified":"2025-06-13T20:19:27","modified_gmt":"2025-06-14T03:19:27","slug":"couchbase-capella-outshines-dynamodb-mongodb-redis-speed-functionality-tco","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/couchbase-capella-outshines-dynamodb-mongodb-redis-speed-functionality-tco\/","title":{"rendered":"Couchbase Capella Outshines DynamoDB, MongoDB, Redis in Speed, Functionality, and TCO"},"content":{"rendered":"<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/resources.couchbase.com\/capella\/altoros-report-eval-nosql-dbaas\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-14455 alignright\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/05\/image_2023-05-31_121949585-768x998.png\" alt=\"\" width=\"426\" height=\"554\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/05\/image_2023-05-31_121949585-768x998.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/05\/image_2023-05-31_121949585-231x300.png 231w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/05\/image_2023-05-31_121949585-788x1024.png 788w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/05\/image_2023-05-31_121949585-1182x1536.png 1182w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/05\/image_2023-05-31_121949585-300x390.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/05\/image_2023-05-31_121949585-1320x1715.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/05\/image_2023-05-31_121949585.png 1482w\" sizes=\"auto, (max-width: 426px) 100vw, 426px\" \/><\/a>Altoros, a renowned IT consulting organization with an extensive history in database comparison, has published their most recent independent benchmark, commissioned by Couchbase. The benchmark evaluates the DBaaS solutions offered by Couchbase, Redis, Amazon, and MongoDB. This study utilizes the widely accepted YCSB standard for benchmarking NoSQL databases. This blog presents a short summary of their report, which demonstrates that Couchbase Capella surpasses MongoDB Atlas, Redis Enterprise Cloud, and Amazon DynamoDB in terms of features, performance, and total cost of ownership (TCO) across diverse workloads and cluster sizes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/github.com\/brianfrankcooper\/YCSB\/wiki\">YCSB<\/a> (Yahoo! Cloud Serving Benchmark) is a standardized framework used for benchmarking the performance of cloud-based database systems. It consists of a variety of workload tests. Here are the workloads chosen for this study:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Workload A<\/b><span style=\"font-weight: 400;\">, 50% read and 50% update: designed to simulate a write-heavy workload scenario where the database system primarily handles read operations with occasional updates.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Workload C<\/b><span style=\"font-weight: 400;\">, 100% read: simulates a scenario where the database system primarily performs read operations, providing insights into the system\u2019s performance and scalability specifically for read-intensive workloads.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Pagination workload<\/b><span style=\"font-weight: 400;\">: emulates a scenario involving paginated data retrieval. In this workload, the database system is evaluated based on its ability to efficiently fetch a subset of data from a larger dataset, typically through a combination of read and seek operations. It helps assess the system\u2019s performance in handling pagination and data retrieval operations commonly encountered in applications that require displaying data in smaller chunks, like a leaderboard or implementing pagination mechanisms for long series of ordered results.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Workload E<\/b><span style=\"font-weight: 400;\">, 95% read, 5% update: designed to assess the performance and scalability of a database system under a workload where the majority of operations are reads, with a smaller proportion of updates.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2><b>Benchmark Highlights<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">For the results, make sure to check out the detailed Altoros report. Note that the new Magma storage engine was used for this set of benchmarks. (Magma has efficiency benefits and reduced memory requirements for large data storage, but for pure speed <\/span><a href=\"https:\/\/docs.couchbase.com\/server\/current\/learn\/buckets-memory-and-storage\/storage-engines.html#couchstore-and-magma-at-a-glance\"><span style=\"font-weight: 400;\">Couchstore<\/span><\/a><span style=\"font-weight: 400;\"> will often be the better choice.) Here are some highlights.<\/span><\/p>\n<h3><b>Performance<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">As in previous years, Couchbase does extraordinarily well in an update-heavy use case, especially as the need for large scale increases. For example, here\u2019s Workload A results:<\/span><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2023\/05\/image_2023-05-26_114744585.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-14437 size-large\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2023\/05\/image_2023-05-26_114744585-1024x335.png\" alt=\"\" width=\"900\" height=\"294\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_114744585-1024x335.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_114744585-300x98.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_114744585-768x251.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_114744585-1536x503.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_114744585-2048x671.png 2048w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_114744585-1320x432.png 1320w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">In these graphs, you can see that Capella\u2019s throughput is the highest of all 4 DBaaS offerings. Due to Capella\u2019s memory-first architecture and automatic sharding, the latency is also the lowest among the 4 competitors.<\/span><\/p>\n<h3><b>Functionality<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Workload C is worth highlighting for a &#8220;pure cache&#8221; use case. Capella and Redis Enterprise Cloud both perform well for this workload due to their memory-first designs, which can cache results for faster response. MongoDB and DynamoDB need to read data from disk more often, and therefore do not perform as well. We\u2019ve seen many designs where MongoDB is augmented with a Redis-like system to act as its cache, while Couchbase has this capability built-in.<\/span><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2023\/05\/image_2023-05-26_115127566.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-14438\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2023\/05\/image_2023-05-26_115127566-1024x339.png\" alt=\"\" width=\"900\" height=\"298\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115127566-1024x339.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115127566-300x99.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115127566-768x254.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115127566-1536x509.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115127566-2048x678.png 2048w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115127566-1320x437.png 1320w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Once again, high throughput and low latency.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, while Redis performs very well in the case of a pure cache, it struggles with another workload: pagination.<\/span><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2023\/05\/image_2023-05-26_115319799.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-14439\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2023\/05\/image_2023-05-26_115319799-1024x291.png\" alt=\"\" width=\"900\" height=\"256\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115319799-1024x291.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115319799-300x85.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115319799-768x218.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115319799-1536x437.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115319799-2048x582.png 2048w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115319799-1320x375.png 1320w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">As in previous years, DynamoDB is left out of this workload, since it does not have any pagination capabilities in its query language. Redis does have pagination capabilities, but they perform very poorly. In a typical Redis deployment, this type of processing would be pawned off on a secondary database. Capella handles this workload very well: high throughput, low latency, and easy as SQL syntax of <\/span><span style=\"font-weight: 400;\">SELECT . . . LIMIT . . . OFFSET<\/span><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">On the other hand, DynamoDB performs well in Workload E. However, there are some other factors to consider with Workload E in Dynamo: see more detail in the report about how DynamoDB was provisioned, the money required, and DynamoDB\u2019s behavior with regards to throttling and scaling up.<\/span><\/p>\n<h3><b>Total Cost of Ownership (TCO)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">In the case of a &#8220;pure cache&#8221;, Redis is often used in conjunction with other databases. You will get good cache performance, but at the price of running two separate databases. (For instance, Redis and Mongo, Redis and Oracle, etc).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In regards to &#8220;bang for your buck&#8221;, there are charts included that summarize the cost of each database deployment in &#8220;dollars per billion operations&#8221;. For example, here is Workload A\u2019s chart (shorter bars means less expensive):<\/span><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2023\/05\/image_2023-05-26_115402347.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-14440\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2023\/05\/image_2023-05-26_115402347-1024x658.png\" alt=\"\" width=\"900\" height=\"578\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115402347-1024x658.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115402347-300x193.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115402347-768x494.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115402347-1536x987.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115402347-1320x848.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_115402347.png 1895w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">When you read the full report in detail, you will see that Couchbase doesn\u2019t always win every matchup. However, what it does win is the bigger picture: an ideal combination of low cost, sophisticated functionality, and performance (<\/span><a href=\"https:\/\/resources.couchbase.com\/c\/broadjump-case-study?x=s9hNYZ\"><span style=\"font-weight: 400;\">the &#8220;triple threat&#8221; as our customer Broadjump has stated<\/span><\/a><span style=\"font-weight: 400;\">).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Couchbase Capella does very well for the majority of workload+cluster combinations. In addition, Capella is a fast, extremely versatile multi-model database that includes:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Key-value<\/strong> &#8211; direct lookup at memory speeds<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Built-in caching<\/strong> &#8211; low latency, fast lookup<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>JSON Document<\/strong> &#8211; flexible data<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SQL++<\/strong> &#8211; flexible querying<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Full Text Search<\/strong> &#8211; text, ranking, geospatial searches (without needing a separate tool like Solr)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Time Series<\/strong> &#8211; stored in TS_arrays in JSON with built-in formatting functions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Mobile Sync<\/strong> &#8211; automatic and offline-first with client to client sync, too!<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Automatic sharding\/partitioning<\/strong> &#8211; easy for developers and great for growth<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Automatic replication and rebalancing<\/strong> &#8211; easy for ops<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Analytics with SQL++<\/strong> &#8211; complex query options for BI<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Eventing<\/strong> &#8211; processing logic at the database level<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Change-data-capture with Kafka<\/strong> integration to stream document change logs<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Taking all these factors into consideration, Couchbase Capella provides the best total package of high performance, sophisticated functionality, and lower total cost of ownership.<\/span><\/p>\n<h2><b>Next Steps<\/b><\/h2>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span style=\"font-weight: 400;\">Make sure to read the <\/span><a href=\"https:\/\/resources.couchbase.com\/capella\/altoros-report-eval-nosql-dbaas\"><span style=\"font-weight: 400;\">final report from Altoros<\/span><\/a><\/li>\n<li><span style=\"font-weight: 400;\">Sign-up for a free trial of <\/span><a href=\"https:\/\/www.couchbase.com\/products\/capella\/\"><span style=\"font-weight: 400;\">Couchbase Capella<\/span><\/a><\/li>\n<li><span style=\"font-weight: 400;\">Got questions? Check out the <\/span><a href=\"https:\/\/www.couchbase.com\/blog\/couchbase-on-discord\/\"><span style=\"font-weight: 400;\">Couchbase Discord<\/span><\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Altoros, a renowned IT consulting organization with an extensive history in database comparison, has published their most recent independent benchmark, commissioned by Couchbase. The benchmark evaluates the DBaaS solutions offered by Couchbase, Redis, Amazon, and MongoDB. This study utilizes the [&hellip;]<\/p>\n","protected":false},"author":71,"featured_media":14442,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1821,2225,9417],"tags":[9632,9817,9694,9818,2278],"ppma_author":[8937],"class_list":["post-14436","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-couchbase-architecture","category-cloud","category-performance","tag-database-tco","tag-dynamodb","tag-mongodb-atlas","tag-redis","tag-ycsb"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.8 (Yoast SEO v25.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Couchbase Capella Outshines DynamoDB, MongoDB, Redis in Speed, Functionality, and TCO<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.couchbase.com\/blog\/couchbase-capella-outshines-dynamodb-mongodb-redis-speed-functionality-tco\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Couchbase Capella Outshines DynamoDB, MongoDB, Redis in Speed, Functionality, and TCO\" \/>\n<meta property=\"og:description\" content=\"Altoros, a renowned IT consulting organization with an extensive history in database comparison, has published their most recent independent benchmark, commissioned by Couchbase. The benchmark evaluates the DBaaS solutions offered by Couchbase, Redis, Amazon, and MongoDB. This study utilizes the [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/couchbase-capella-outshines-dynamodb-mongodb-redis-speed-functionality-tco\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:published_time\" content=\"2023-06-05T19:23:02+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-14T03:19:27+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2023\/05\/image_2023-05-26_120537602.png\" \/>\n\t<meta property=\"og:image:width\" content=\"2657\" \/>\n\t<meta property=\"og:image:height\" content=\"1435\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Matthew Groves\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@mgroves\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" 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