Fixed number of partitions

Fortunately, there is a fairly simple solution: create many more partitions than there are nodes, and assign several partitions to each node. For example, a database running on a cluster of 10 nodes may be split into 1,000 partitions from the outset so that approximately 100 partitions are assigned to each node.

Now, if a node is added to the cluster, the new node can steal a few partitions from every existing node until partitions are fairly distributed once again. This process is illustrated in Figure 6-6. If a node is removed from the cluster, the same happens in reverse.

Only entire partitions are moved between nodes. The number of partitions does not change, nor does the assignment of keys to partitions. The only thing that changes is the assignment of partitions to nodes. This change of assignment is not immediate— it takes some time to transfer a large amount of data over the network—so the old assignment of partitions is used for any reads and writes that happen while the transfer is in progress.

Adding a new node to a database cluster with multiple partitions per node

Figure 6-6. Adding a new node to a database cluster with multiple partitions per node.

In principle, you can even account for mismatched hardware in your cluster: by assigning more partitions to nodes that are more powerful, you can force those nodes to take a greater share of the load.

This approach to rebalancing is used in Riak [15], Elasticsearch [24], Couchbase [10], and Voldemort [25].

In this configuration, the number of partitions is usually fixed when the database is first set up and not changed afterward. Although in principle it’s possible to split and merge partitions (see the next section), a fixed number of partitions is operationally simpler, and so many fixed-partition databases choose not to implement partition splitting. Thus, the number of partitions configured at the outset is the maximum number of nodes you can have, so you need to choose it high enough to accommodate future growth. However, each partition also has management overhead, so it’s counterproductive to choose too high a number.

Choosing the right number of partitions is difficult if the total size of the dataset is highly variable (for example, if it starts small but may grow much larger over time). Since each partition contains a fixed fraction of the total data, the size of each partition grows proportionally to the total amount of data in the cluster. If partitions are very large, rebalancing and recovery from node failures become expensive. But if partitions are too small, they incur too much overhead. The best performance is achieved when the size of partitions is “just right,” neither too big nor too small, which can be hard to achieve if the number of partitions is fixed but the dataset size varies.

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