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Deep Dive into the Internals of the Database

Different Partitioning Strategies in Databases

Understand how databases partition data across distributed systems using key range and key hash methods. Learn the advantages and disadvantages of each, and explore consistent hashing for balanced load distribution and smooth scaling. This lesson helps you grasp different partitioning strategies to optimize query performance and avoid hotspots.

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This section will discuss two strategies to partition the dataset in a distributed environment:

  • Partition by a key range

  • Partition by key hash

Partition by a key range

In this strategy, we divide a continuous range of keys into buckets. Then, we assign each bucket to a partition. A single host instance can hold multiple partitions. The range of keys assigned to a bucket may or may not be continuous. Within each partition, they store keys in sorted order, thus facilitating range scan queries.

In the example above:

  • We have 3 host instances namely Node 1, Node 2, and Node 3.

  • Node 1 has 2 partitions.

    • Partition 1 includes keys starting from A, B, C, D, E.

    • Partition 2 includes keys starting from F, G, H, I, J.

  • Node 2 has 2 partitions.

    • Partition 3 includes keys starting from K , L , M, N, O.

    • Partition 4 includes keys starting from P, Q, ...