{"id":46539,"date":"2016-11-15T00:00:00","date_gmt":"2016-11-15T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/griddb-and-cassandra-ycsb-benchmarks\/"},"modified":"2025-11-13T12:54:17","modified_gmt":"2025-11-13T20:54:17","slug":"griddb-and-cassandra-ycsb-benchmarks","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/griddb-and-cassandra-ycsb-benchmarks\/","title":{"rendered":"GridDB and Cassandra YCSB Benchmarks"},"content":{"rendered":"<p>Fixstars recently ran a series of benchmarks that compared Cassandra and GridDB on Microsoft Azure using 1, 8, 16, and 32-node clusters with an equal number of YCSB clients and both in-memory (4M records per node) and out-of-memory (12M records per node) data sets.<\/p>\n<p>As the following graphs show, GridDB&#8217;s hybrid storage architecture, in-memory-oriented architecture, outperforms Cassandra both in-memory and in operations required using out-of-memory storage. GridDB accomplishes this while maintaining the same reliability and consistency through twenty-four hours of operation.<\/p>\n<p>The internode communication of GridDB scales significantly better than Cassandra&#8217;s decentralized peer-to-peer system, at least up through 32 nodes. GridDB&#8217;s performance increases by nearly the same factor as the number of nodes added; Cassandra is only able to scale at 50% of that same factor with this particular Azure instance type.<\/p>\n<table class=\"center\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" width=\"50%\">\n        <a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2016\/11\/workloada_4m-e1492646179631.png\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2016\/11\/workloada_4m-e1492646179631.png\" alt=\"\" width=\"480\" height=\"277\" class=\"aligncenter size-full wp-image-622\" srcset=\"\/wp-content\/uploads\/2016\/11\/workloada_4m-e1492646179631.png 480w, \/wp-content\/uploads\/2016\/11\/workloada_4m-e1492646179631-300x173.png 300w\" sizes=\"(max-width: 480px) 100vw, 480px\" \/><\/a>\n      <\/td>\n<td style=\"text-align: center;\" width=\"50%\">\n        <a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2016\/11\/workloada_12m-e1492646216265.png\"><img decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2016\/11\/workloada_12m-e1492646216265.png\" alt=\"\" width=\"480\" height=\"277\" class=\"aligncenter size-full wp-image-623\" srcset=\"\/wp-content\/uploads\/2016\/11\/workloada_12m-e1492646216265.png 480w, \/wp-content\/uploads\/2016\/11\/workloada_12m-e1492646216265-300x173.png 300w\" sizes=\"(max-width: 480px) 100vw, 480px\" \/><\/a>\n      <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u00a0<\/p>\n<table class=\"center\">\n<tbody>\n<tr style=\"vertical-align:top;\">\n<td style=\"text-align: center;\" width=\"50%\">\n        <a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2016\/11\/workloadb_4m-e1492646264302.png\"><img decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2016\/11\/workloadb_4m-e1492646264302.png\" alt=\"\" width=\"480\" height=\"278\" class=\"aligncenter size-full wp-image-624\" srcset=\"\/wp-content\/uploads\/2016\/11\/workloadb_4m-e1492646264302.png 480w, \/wp-content\/uploads\/2016\/11\/workloadb_4m-e1492646264302-300x174.png 300w\" sizes=\"(max-width: 480px) 100vw, 480px\" \/><\/a>\n      <\/td>\n<td style=\"text-align: center;\" width=\"50%\">\n        <a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2016\/11\/workloadb_12m-e1492646296762.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2016\/11\/workloadb_12m-e1492646296762.png\" alt=\"\" width=\"480\" height=\"278\" class=\"aligncenter size-full wp-image-625\" srcset=\"\/wp-content\/uploads\/2016\/11\/workloadb_12m-e1492646296762.png 480w, \/wp-content\/uploads\/2016\/11\/workloadb_12m-e1492646296762-300x174.png 300w\" sizes=\"(max-width: 480px) 100vw, 480px\" \/><\/a>\n      <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>In update-intensive workloads such as Workload A, Cassandra&#8217;s initial results are quite favorable as its log based architecture allows it to quickly mark a row as deleted and then append the new value to the end of the log. Fixstars noticed that over time Cassandra began to slow down.<\/p>\n<p>Fixstars configured an 8-node cluster and loaded 4M and 12M records per node and set operationcount to 2^32-1 and let the test run for twenty-four hours. In both tests, Cassandra&#8217;s throughput is less than 50% of what it was in the twenty fourth hour versus the first. Meanwhile GridDB&#8217;s performance was stable when doing both in and out of memory operations.<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"25%\">\n      <\/td>\n<td width=\"50%\">\n        <a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2016\/11\/24hr_12m-e1492701782645.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2016\/11\/24hr_12m-e1492701782645.png\" alt=\"\" width=\"480\" height=\"327\" class=\"aligncenter size-full wp-image-226\" srcset=\"\/wp-content\/uploads\/2016\/11\/24hr_12m-e1492701782645.png 480w, \/wp-content\/uploads\/2016\/11\/24hr_12m-e1492701782645-300x204.png 300w\" sizes=\"(max-width: 480px) 100vw, 480px\" \/><\/a>\n      <\/td>\n<td width=\"25%\">\n      <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u00a0<\/p>\n<div>\n<h2>\n    More Resources<br \/>\n  <\/h2>\n<p>\n    <img decoding=\"async\" src=\"\/common\/img\/icons\/icon_pdf_download.svg\" alt=\"PDF\" style=\"height:20px\" \/>The full report can be downloaded here:\u00c2\u00a0<a href=\"https:\/\/griddb.net\/en\/docs\/Fixstars_NoSQL_Benchmarks.pdf\">https:\/\/griddb.net\/en\/docs\/Fixstars_NoSQL_Benchmarks.pdf<\/a>\n  <\/p>\n<p>\n    <img decoding=\"async\" src=\"\/common\/img\/icons\/icon_rpm.svg\" alt=\"RPM\" style=\"height:20px\" \/>The GridDB Community Edition (v3.0.1) can be downloaded from <a href=\"https:\/\/github.com\/griddb\/griddb_nosql\/releases\/download\/v3.0.1\/griddb_nosql-3.0.1-1.linux.x86_64.rpm\">GitHub<\/a>.\n  <\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Fixstars recently ran a series of benchmarks that compared Cassandra and GridDB on Microsoft Azure using 1, 8, 16, and 32-node clusters with an equal number of YCSB clients and both in-memory (4M records per node) and out-of-memory (12M records per node) data sets. As the following graphs show, GridDB&#8217;s hybrid storage architecture, in-memory-oriented architecture, [&hellip;]<\/p>\n","protected":false},"author":71,"featured_media":22005,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-46539","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>GridDB and Cassandra YCSB Benchmarks | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"Fixstars recently ran a series of benchmarks that compared Cassandra and GridDB on Microsoft Azure using 1, 8, 16, and 32-node clusters with an equal\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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