Optimization of Doris Table Design (such as partitioned tables and index selection)

Table design in Doris is a key step to optimize performance and storage space. Here are some commonly used table design optimization methods:

Partitioned Tables

Partitioned tables divide large tables into several smaller sub-tables, with each sub-table containing an index. Data can be searched faster through these indexes.

What is Partitioning?

Partitioning is the process of classifying and organizing data in a table according to a certain method, such as by time, region, or other attributes.

Advantages

1. Improve lookup performance: Partitioning can accelerate the search for specific data from the table.

2. Reduce storage space: Since each sub-table contains an index, storage space can be utilized effectively.

Index Selection

Indexes are key to helping quickly find data. Doris offers several types of indexes to choose from:

1. B-tree Index

A B-tree index is a balanced binary search tree, suitable for indexing large tables.

2. Hash Index

A Hash index is an index built based on a hashing function and is more applicable for small tables or cases with uniform data distribution.

3. Composite Index

A Composite index is a joint index on multiple columns and can effectively improve retrieval performance.

Table Design Principles

1. Choose appropriate storage engines: Doris provides several storage engines, such as Rowstore and Columnstore, each prioritizing different usage scenarios.

2. Use reasonable data types: Choosing appropriate data types can reduce storage space and improve performance.

3. Partition reasonably: Design table structure partitioning based on specific business logic, which can effectively utilize storage space and improve performance.

Precautions for Table Design

1. Consider data distribution: Since Doris has different data distribution methods, index types and partitioning strategies should be selected based on actual conditions.

2. Avoid over-normalization: Over-normalized tables may have multiple relationships, which could degrade query performance.

3. Keep table structure simple: Simple table structures can improve performance and are easier to maintain.