Custom partitioning is a technique used in analytical data stores. It allows you to partition analytical store data based on specific data fields. This is typically done on fields that are commonly used as filters in analytical queries. The primary goal of implementing custom partitioning is to achieve improved query performance.
Understanding Custom Partitioning
At its core, custom partitioning involves strategically organizing your analytical data. Instead of keeping all the data in one large block, it is divided into smaller, more manageable sections called partitions.
- Data Partitioning: The fundamental process is splitting the analytical data into distinct partitions.
- Selection Criteria: The key characteristic of custom partitioning, according to the reference, is that these partitions are created based on the values within specific data fields.
- Field Choice: The fields chosen for partitioning are those that are frequently used when filtering data during analysis.
Why Partition Data Customly?
The main objective behind custom partitioning is performance enhancement for analytical workloads.
- Faster Queries: By partitioning data based on common filter fields, queries that use these filters can often access only a relevant subset (a specific partition or a few partitions) of the data, rather than scanning the entire dataset.
- Reduced Scan Size: This significantly reduces the amount of data that needs to be read and processed for a query, leading to faster execution times.
How Custom Partitioning Improves Performance
When an analytical query includes a filter condition that matches the field used for custom partitioning (e.g., WHERE Region = 'North America'), the database system can quickly identify and access only the partition(s) that contain data for 'North America'. This avoids the need to scan partitions containing data for other regions, resulting in a much quicker response.
- Identify frequently used filter fields in your analytical queries.
- Configure the analytical store to partition the data based on these chosen fields.
- Analytical queries filtering on these fields will then benefit from reduced data scanning.
In summary, custom partitioning is a targeted method to optimize analytical data stores by aligning the physical data layout with common data access patterns, directly leading to better query speed.
