{"id":10903,"date":"2021-03-29T07:00:55","date_gmt":"2021-03-29T14:00:55","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=10903"},"modified":"2025-06-04T02:50:14","modified_gmt":"2025-06-04T09:50:14","slug":"distributed-databases-overview","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/distributed-databases-overview\/","title":{"rendered":"Distributed Database Architecture Explained"},"content":{"rendered":"<div class=\"paragraph\">\n<p>Distributed databases apply the principles of distributed computing to data storage. The simplest example is a database which stores data on two (or more) servers connected by a network. This &#8220;cluster&#8221; can be accessed and managed as if it was a single database server.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>The traditional single server model of a database, in comparison, exists on one server.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>The primary benefits of using distributed database architecture for services include:<\/p>\n<\/div>\n<div class=\"ulist\">\n<ul>\n<li>Clustering to handle load (horizontal scaling)<\/li>\n<li>High availability (if one server goes offline, the remaining servers stay online and available)<\/li>\n<li>Replication (to support high availability, disaster recovery, and geographical distribution)<\/li>\n<\/ul>\n<\/div>\n<div class=\"paragraph\">\n<p>There are many best practices for distributed databases, including different options, topologies, and methods to distribute the load between the servers. So, how do distributed databases work?.<\/p>\n<\/div>\n<div class=\"sect1\">\n<h2 id=\"_horizontal_scaling\">Horizontal Scaling<\/h2>\n<div class=\"sectionbody\">\n<div class=\"paragraph\">\n<p>A single database server for a small set of applications and data has historically worked well. However, when exposed to a large, public user base, the only way to increase capacity of these servers is to upgrade them to a more expensive server.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>To improve capacity, move the database software to another single machine with more memory, more disk space, and more processors. This is &#8220;vertical scaling&#8221;. The drawback to this approach is that it may require downtime. There\u2019s also a ceiling on the performance that can be obtained from a single machine. (See Herb Sutter\u2019s <a href=\"https:\/\/www.gotw.ca\/publications\/concurrency-ddj.htm\">The Free Lunch is Over<\/a>).<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p><span class=\"image\"><img decoding=\"async\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2021\/03\/13101-vertical-scaling.png\" alt=\"Vertical scaling\" \/><\/span><\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>Unfortunately, many databases, especially relational (RDBMS) databases are not designed to be distributed and clustered.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>However, distributed databases are created from the start to support elastic scalability. Need to add more resources to handle more load? Install the database software on 1 or more additional machines and add it to the cluster.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p><span class=\"image\"><img decoding=\"async\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2021\/03\/13102-distributed-database-horizontal-scaling.png\" alt=\"Distributed DB horizontal scaling\" \/><\/span><\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>Then, add inexpensive commodity machines to the cluster when necessary. You can also remove them and scale down if you no longer need them.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"sect1\">\n<h2 id=\"_distributed_databases_architecture\">Distributed Database Architecture<\/h2>\n<div class=\"sectionbody\">\n<div class=\"paragraph\">\n<p>For databases, there are two popular approaches for distributing data: primary\/secondary (historically called <a href=\"https:\/\/en.wikipedia.org\/wiki\/Master\/slave_(technology)\">master\/slave<\/a>) and <a href=\"https:\/\/en.wikipedia.org\/wiki\/Shared-nothing_architecture\">shared-nothing<\/a> (sometimes called masterless).<\/p>\n<\/div>\n<div class=\"sect2\">\n<h3 id=\"_primary_secondary_architecture\">Primary\/Secondary Architecture<\/h3>\n<div class=\"paragraph\">\n<p>In a primary\/secondary architecture, there is a designated &#8220;primary&#8221; server. This server stores all the data and handles all data requests. There are one or more &#8220;secondary&#8221; servers. These servers will receive data updates from the primary in order to stay in sync and store a complete replica of the data.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>If the primary server goes offline (crashes), the remaining servers (and\/or coordination servers) appoint one of the secondary servers to be the new primary.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p><span class=\"image\"><img decoding=\"async\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2021\/03\/13103-primary-secondary.png\" alt=\"Primary\/Secondary aka Master\/Slave\" \/><\/span><\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>Architects use this pattern to provide <a href=\"https:\/\/www.couchbase.com\/blog\/combine-clusters-to-achieve-high-availability\/\">high availability<\/a> to traditional, non-distributed databases. However, this distributed database architecture doesn\u2019t do much to address to issue of increased load. In order to accomplish that, <em>sharding<\/em> must be used.<\/p>\n<\/div>\n<\/div>\n<div class=\"sect2\">\n<h3 id=\"_shared_nothing_distributed_databases\">Shared-nothing distributed databases<\/h3>\n<div class=\"paragraph\">\n<p>Shared-nothing architecture involves splitting the data into partitions usually called &#8220;shards&#8221;. Each shard lives on an individual server (node) in the cluster. For example, if there are 300 records and 3 nodes, each node would (ideally) store 100 records. Each additional node could further partition the data and continue spreading out the load as necessary.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p><span class=\"image\"><img decoding=\"async\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2021\/03\/13104-shared-nothing-distributed-databases.png\" alt=\"Distributed database shared nothing\" \/><\/span><\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>The cluster will also replicate shards between the nodes to maintain high availability. For instance, if Node 1 contains Active Shard A, then Node 2 would contain a Replica Shard A, and so on.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>Then, if Node 3 goes offline, the cluster promotes the replicas from Shard C to Active in order to keep the distributed DB cluster online (as a whole).<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p><span class=\"image\"><img decoding=\"async\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2021\/03\/13105-distributed-database-failover.png\" alt=\"Distributed database high availability\" \/><\/span><\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>The nature of relational databases is to store individual rows of data together in a tightly coupled table. This makes distributed SQL databases difficult. This is why organizations often choose NoSQL where clustering, high-availability, and replication are critical. NoSQL trades off strictly coupled data that cannot exist outside a table in exchange for independent data that can exist in any given shard in a cluster.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"sect1\">\n<h2 id=\"_distributed_database_examples\">Distributed Database Examples<\/h2>\n<div class=\"sectionbody\">\n<div class=\"paragraph\">\n<p>Depending on the distributed DB that you use, sharding may be completely automatic or require considerable effort to plan and maintain.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>Let\u2019s look at two examples of distributed databases that use the popular NoSQL and how they differ:<\/p>\n<\/div>\n<div class=\"sect2\">\n<h3 id=\"_couchbase_server\">Couchbase Server<\/h3>\n<div class=\"paragraph\">\n<p>Couchbase Server is a distributed NoSQL database that stores data as individual pieces of JSON data called documents. Each document has a unique key.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>Each document exists within a shard (called <a href=\"https:\/\/docs.couchbase.com\/server\/current\/learn\/buckets-memory-and-storage\/vbuckets.html\">vBuckets<\/a> in Couchbase), and each shard is assigned to a node. A Couchbase cluster has a fixed amount of 1024 total vBuckets.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p><span class=\"image\"><img decoding=\"async\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2021\/03\/13106-couchbase-shards-vbuckets.png\" alt=\"Couchbase distributed database shards\" \/><\/span><\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>Couchbase <em>automatically<\/em> assigns a document to vBucket based on its key (using a CRC32 hashing algorithm). There is no need for developers, DBAs, or DevOps to create and manage sharding. Each node has a <a href=\"https:\/\/docs.couchbase.com\/server\/current\/learn\/clusters-and-availability\/cluster-manager.html\">cluster manager<\/a> that ensures all the shards remain in balance and the data is evenly distributed. There will be no &#8220;hot spots&#8221;: one node storing much larger amounts of data than other nodes. There is no need for other servers to process query routing or provide a load balancing facade apart from the Couchbase Server cluster.<\/p>\n<\/div>\n<\/div>\n<div class=\"sect2\">\n<h3 id=\"_mongodb\">MongoDB<\/h3>\n<div class=\"paragraph\">\n<p>MongoDB is also a distributed NoSQL database. It also stores data as individual pieces in a format <em>similar<\/em> to JSON called BSON. Each document has a unique key.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>MongoDB takes a different approach to sharding. In order to shard data, you must select a shard key (consisting of one or more pieces of BSON data). For instance, you might consider sharding data by &#8220;last name&#8221; and &#8220;zip code&#8221;.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>Once you\u2019ve defined a shard key, Mongo routes client requests though another server that\u2019s running a &#8220;query router&#8221;. This query router will refer to yet another server called a &#8220;config server&#8221;. It can then determine which data server that the desired shard is on.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p><span class=\"image\"><img decoding=\"async\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2021\/03\/13107-manual-shard.png\" alt=\"Manual sharding\" \/><\/span><\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>The sharding process is not automatic. The development team must choose shard keys carefully and periodically review them in order to avoid bottlenecks and hot spots.<\/p>\n<\/div>\n<\/div>\n<div class=\"sect2\">\n<h3 id=\"_summary\">Summary<\/h3>\n<div class=\"paragraph\">\n<p>These are just two notable distributed database examples. There are many more examples that all take a variety of approaches to architecture.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>What they all have in common is that they store data on network connected servers.<\/p>\n<\/div>\n<div class=\"paragraph\">\n<p>Interested in the automatic sharding approach that Couchbase Server is taking? That\u2019s just scratching the surface of what Couchbase can do. With a built-in memory-first architecture, full text search, distributed SQL querying capabilities, and more, Couchbase is a complete data platform. <a href=\"https:\/\/www.couchbase.com\/downloads\/\">Download Couchbase Server today<\/a> and give it a try.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Distributed databases apply the principles of distributed computing to data storage. The simplest example is a database which stores data on two (or more) servers connected by a network. This &#8220;cluster&#8221; can be accessed and managed as if it was [&hellip;]<\/p>\n","protected":false},"author":71,"featured_media":10989,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1821,1816],"tags":[1311,9144,2311,9197],"ppma_author":[8937],"class_list":["post-10903","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-couchbase-architecture","category-couchbase-server","tag-architecture","tag-distributed","tag-distributed-database","tag-distributed-databases"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Distributed Database Architecture Explained with Examples<\/title>\n<meta name=\"description\" content=\"Distributed databases enhance scalability, availability, and disaster recovery. 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