Doris Database vs MySQL: Unveiling the Similarities and Differences between Two Major Databases

# 1. Overview of Doris and MySQL

Doris and MySQL are two widely popular database systems, each with distinct features and applications.

**Doris** is a distributed analytical database based on columnar storage, specifically designed for big data analytics and real-time queries. It employs a columnar storage format, enabling efficient compression and processing of vast amounts of data, while supporting rapid, interactive queries.

**MySQL** is a traditional row-based relational database, extensively used in Online Transaction Processing (OLTP) scenarios. It is renowned for its reliability, scalability, and rich feature set.

# 2. Data Model Comparison

### 2.1 Columnar Storage vs. Row-Based Storage

#### Columnar Storage

Columnar storage groups together data from the same column, rather than storing all the data from a row together. This organization is highly efficient for analytical queries as it allows for the reading of necessary column data in one go, without needing to read the entire row.

**Advantages:**

- **High Query Performance:** Columnar storage significantly boosts query performance for analytical queries, as it reads necessary columns in one go, without needing the entire row.
- **Data Compression:** Similar values in the same column can be compressed efficiently.
- **Scalability:** Columnar storage is easy to scale, as it allows adding or removing columns with ease.

**Disadvantages:**

- **Low Update Performance:** Columnar storage has lower performance for update operations, as all affected columns need to be updated.
- **Low Random Read Performance:** For random read operations, columnar storage performs poorly, as the entire column must be read to retrieve the necessary data.

#### Row-Based Storage

Row-based storage stores data from each row together. This organization is highly efficient for transactional processing queries, as it allows for quick access to entire row data.

**Advantages:**

- **High Update Performance:** Row-based storage performs well for update operations, as only the affected row needs to be updated.
- **High Random Read Performance:** For random read operations, row-based storage performs well, as the entire row data can be accessed swiftly.

**Disadvantages:**

- **Low Query Performance:** Row-based storage performs poorly for analytical queries, as the entire row must be read to obtain necessary data.
- **Data Compression:** Data compression in row-based storage is less efficient, as different columns typically have different values.
- **Scalability:** Row-based storage has poorer scalability, as adding or removing columns requires reorganizing the entire table.

### 2.2 Data Partitioning and Indexing

#### Data Partitioning

Data partitioning is a technique for dividing data in a table into smaller, more manageable chunks. Partitions can be based on time, geographical location, or other criteria.

**Advantages:**

- **Optimized Query Performance:** Partitioning can optimize query performance, as only the relevant partitions are scanned.
- **Simplified Data Management:** Partitioning simplifies data management, as each partition can be managed individually.
- **Scalability:** Partitioning can improve scalability, as data can be distributed across multiple nodes.

#### Indexing

Indexing is a data structure used to quickly locate data. Indexes can be based on columns or expressions in a table.

**Advantages:**

- **Optimized Query Performance:** Indexing can greatly enhance query performance, as data can be located quickly without needing to scan the entire table.
- **Data Integrity:** Indexes can help ensure data integrity, as they can prevent duplicate data.
- **Scalability:** Indexes can improve scalability, as they reduce the amount of data that needs to be scanned.

# 3. Query Performance Analysis

### 3.1 Aggregate Query Optimization

Doris has significant advantages