Improving Query Efficiency: Index Design and Optimization in Doris Database

# 1. Overview of Doris Database Indexes

Indexes are an essential data structure in databases, designed to speed up data retrieval. Doris Database supports a variety of index types, including Bitmap, Bloom Filter, and Rowkey indexes. These indexes can be selected and optimized according to different data characteristics and query patterns to enhance query performance.

The design of Doris Database indexes follows certain principles, such as the minimum indexing principle, the covering index principle, and the principle of locality. By adhering to these principles, unnecessary indexes can be avoided, and indexes can effectively support queries.

# 2. Index Design Principles and Types

### 2.1 Principles of Index Design

When designing indexes, the following principles should be followed:

- **Selectivity Principle:** The indexed columns should have high selectivity, meaning the proportion of different values in the column to the total number of rows in the table is high. Indexes with high selectivity can effectively reduce the amount of data to be scanned.
- **Covering Principle:** Indexes should include columns frequently used in queries to avoid the need for table lookups during query execution. Covering indexes can improve query efficiency.
- **Leftmost Prefix Principle:** For composite indexes, the leftmost prefix columns of the index should be used in queries. For example, for the index `(a, b, c)`, the query `WHERE a = 1 AND b = 2` can use the index, while the query `WHERE b = 2 AND a = 1` cannot.
- **Avoid Redundancy Principle:** Do not create indexes that duplicate other indexes. For instance, if the index `(a, b)` already exists, there is no need to create `(b, a)`.

### 2.2 Types of Indexes

Doris Database supports various types of indexes, including:

#### 2.2.1 Bitmap Index

Bitmap index is a type of bitmap index that maps each column value to a bitmap. When querying a column, the Bitmap index can quickly locate the positions of rows containing that value. Bitmap indexes are suitable for columns with low cardinality (fewer different values).

**Example Code:**

CREATE BITMAP INDEX idx_gender ON table_name(gender);

**Logical Analysis:**

This code creates a Bitmap index named `idx_gender` for the `gender` column in the `table_name` table.

**Parameter Explanation:**

- `idx_gender`: Index name
- `table_name`: Table name
- `gender`: Indexed column

#### 2.2.2 Bloom Filter Index

The Bloom Filter index is a probabilistic data structure that can quickly determine if an element is in a set. In Doris Database, Bloom Filter indexes are used to determine if a row meets query conditions. Bloom Filter indexes are suitable for columns with high cardinality (more different values).

**Example Code:**

CREATE BLOOM FILTER INDEX idx_user_id ON table_name(user_id);

**Logical Analysis:**

This code creates a Bloom Filter index named `idx_user_id` for the `user_id` column in the `table_name` table.

**Parameter Explanation:**

- `idx_user_id`: Index name
- `table_name`: Table name
- `user_id`: Indexed column

#### 2.2.3 Rowkey Index

Rowkey index is a special type of index in Doris Dat