Expanding Database Capabilities: The Ecosystem of Doris Database

# 1. Introduction to Doris Database

Doris is an open-source distributed database designed for interactive analytics, renowned for its high performance, availability, and cost-effectiveness. Utilizing an MPP (Massively Parallel Processing) architecture, Doris distributes data across multiple nodes and processes queries in parallel, achieving high throughput and low latency.

Doris supports various data formats, including CSV, Parquet, and ORC, and offers a rich set of SQL interfaces and APIs for convenient data access and analysis. Additionally, Doris provides a suite of tools and components, such as data import tools, visual analytics tools, and development frameworks, to assist users in easily building and managing data analytics applications.

# 2. Doris Database Ecosystem

### 2.1 Overview of Doris Database Ecosystem

The Doris database ecosystem is a collection of tools and services built around the Doris database, designed to enhance its functionality and simplify its use. This ecosystem includes data integration tools, data analytics tools, and development frameworks, which help users extract more value from their data.

### 2.2 Data Integration Tools

Data integration tools are used to import data from various sources into the Doris database and keep it synchronized. These tools help users easily integrate data from relational databases, non-relational databases, file systems, and streaming data sources into the Doris database.

#### 2.2.1 Data Import Tools

Data import tools are used to import data from external data sources into the Doris database in a single operation. These tools typically support various data formats, such as CSV, JSON, and Parquet.

#### 2.2.2 Data Synchronization Tools

Data synchronization tools are used to continuously synchronize data from external sources to the Doris database. These tools can be configured to trigger synchronization tasks periodically or based on events.

### 2.3 Data Analytics Tools

Data analytics tools are used to query, analyze, and visualize data within the Doris database. These tools help users extract valuable insights and make informed decisions.

#### 2.3.1 SQL Client Tools

SQL client tools are used to query the Doris database using SQL. These tools usually provide code editing, query execution, and result browsing capabilities.

#### 2.3.2 Visual Analytics Tools

Visual analytics tools are used to create interactive data visualizations. These tools help users explore and understand data graphically.

### 2.4 Development Frameworks

Development frameworks are tools for building Doris database applications. These frameworks offer a set of pre-built components and functionalities to help developers quickly develop and deploy applications.

#### 2.4.1 Python Development Framework

The Python development framework is used for building Doris database applications with the Python language. These frameworks provide a set of Python libraries and tools to simplify interaction with the Doris database.

#### 2.4.2 Java Development Framework

The Java development framework is used for building Doris database applications with the Java language. These frameworks provide a set of Java libraries and tools to simplify interaction with the Doris database.

# 3.1 Data Integration Practices

#### 3.1.1 Data Import Practices

**Data Import Tools**

Doris provides a variety of data import tools, including:

- **CSV Import Tool:** Used for importing data in CSV format.
- **Parquet Import Tool:** Used for importing data in Parquet format.
- **JDBC Import Tool:** Used for importing data from relational databases.
- **Kafka Import Tool:** Used for importing data from Kafka message queues.
- **HDFS Import Tool:** Used for importing data from the HDFS file system.

**Data Import Steps**

The data import process typically includes the following steps:

1. **Create a Table:** Create the target table in Doris, specifying the table structure and data types.
2. **Choose an Import Tool:** Select the appropriate import tool based on the data source format.
3. **Configure Import Parameters:** Set import parameters, such as data source path, field mapping, etc.
4. **Execute the Import:** Run the import command to bring data from the source system into the Doris table.

**Code Example**

# Using the CSV