Azure Synapse Analytics: Partition vs Distribution

While using Azure Synapse Analytics dedicated SQL pools, sometimes it is confusing the purpose of partitioning and distribution. They are both used to manage data for performance optimization, but serve different purposes.

Distribution

Distribution refers to how data is spread across 60 physical nodes (called distributions) in a dedicated SQL pool. When you create a table, you choose a distribution strategy that defines how data is distributed across these nodes:

• Hash distribution: Distributes rows based on the hash value of a specified column. This is effective for large tables involved in joins or aggregations on that column, as it minimizes data movement during queries.

• Round-robin distribution: Distributes rows evenly across all nodes, without regard to column values. This is useful for smaller tables or those not frequently joined.

• Replicated distribution: Creates a full copy of the table on each node. This is suitable for small, dimension-type tables (e.g., lookup tables) and helps avoid data movement during joins.

The primary goal of distribution is parallel processing—breaking down data so that each distribution (node) processes a part of the workload simultaneously, which enhances performance, especially for large datasets.

Partitioning

Partitioning organizes data within each distribution based on a specific column, such as a date or transaction month. Each distribution (node) holds multiple partitions based on the chosen partition key. For example, if you partition by TransactionMonth, each distribution will have separate partitions for each month.

Partitioning works well when you need to manage very large tables (hundreds of millions or billions of rows) with queries frequently filtered by the partitioned column (e.g., querying specific months or years).

The main goal of partitioning is to optimize data scanning and query performance by allowing the SQL pool to quickly eliminate irrelevant data partitions based on the filter conditions in queries. This reduces the amount of data read, saving time and resources.

Key Differences

Distribution would apply to how the table is spread across nodes, and partitioning by date range or month would organize the data within each node to improve filtering and query performance.