Real-time Processing of Massive Data: Applications of Doris Database in the Internet Industry

# 1. Overview of Doris Database

Doris is an open-source distributed MPP (Massively Parallel Processing) database designed specifically for big data analytics and real-time queries. It employs columnar storage format and supports high-concurrency queries, enabling it to process massive amounts of data swiftly. Doris is widely used in internet industries, such as log analysis, user behavior analysis, and recommendation systems.

The core technologies of the Doris database include the distributed storage engine, columnar storage format, and high-concurrency query engine. The distributed storage engine is responsible for data storage and management, using data sharding and replication mechanisms to ensure data reliability and availability. The columnar storage format stores data by column, significantly enhancing query efficiency. The high-concurrency query engine employs a query optimizer and execution engine, capable of quickly handling complex query requests.

# 2. Core Technologies of Doris Database

The core technologies of Doris database encompass the distributed storage engine, columnar storage format, and high-concurrency query engine. These technologies collectively contribute to Doris's high performance and high availability.

### 2.1 Distributed Storage Engine

#### 2.1.1 Data Sharding and Replication Mechanism

Doris employs a distributed storage engine that shreds data across different nodes. Each data shard has multiple replicas to ensure data reliability and availabil***

***mon sharding strategies include:

- Hash sharding: Distributes data based on the hash values to different nodes.
- Range sharding: Distributes data based on a range of values to different nodes.
- Composite sharding: Combines hash and range sharding for a more flexible data sharding strategy.

The replication mechanism ensures that data can still be retrieved from other replicas even if a node fails. Doris supports various replication strategies, including:

- Single replica: Each data shard is stored with a single replica.
- Multiple replicas: Each data shard has multiple replicas.
- Remote multiple replicas: Each data shard is stored with multiple replicas in different locations.

#### 2.1.2 Data Compression and Encoding Techniques

Doris suppor***monly used compression algorithms include:

- Snappy: A fast lossless compression algorithm.
- LZ4: A lossless compression algorithm with a higher compression ratio than Snappy.
- ZSTD: ***

***mon encoding techniques include:

- RLE: Run-Length Encoding, which encodes repeated data.
- Dict: Dictionary encoding, which replaces repeated values with indexes in the dictionary.
- BitPacking: Bit packing, which packs multiple Boolean values or small integers into one or more bytes.

### 2.2 Columnar Storage Format

#### 2.2.1 Advantages of Columnar Storage

Compared to traditional row-based storage formats, columnar storage has the following advantages:

- High query performance: Columnar storage stores data of the same column together, allowing only those columns to be read when queries only involve certain columns, reducing IO overhead.
- High compression ratio: Columnar storage can compress each column individually, achieving higher compression rates.
- Good scalability: Columnar storage can easily add new columns without reorganizing the entire table.

#### 2.2.2 Implementation of Columnar Storage in Doris

Doris database uses a columnar storage format known as "Hybrid Columnar Storage." Hybrid Columnar Storage divides the data in the table into two parts:

- **Sparse Columns:** Columns with a large number of null values.
- **Dense Columns:** Columns with a small number of null values.

Sparse columns use RLE encoding for compression, while dense columns use Dict encoding for compression. This hybrid storage approach balances query performance and storage space.

### 2.3 High-Concurrency Query Engine

#### 2.3.1 Query Optimizer

The Doris database query optimizer is responsible for converting SQL queries into efficient execution plans. The query optimizer considers factors such as:

- Data sharding strategy
- Data compression and encoding technologies
- Query patterns
- Cluster resources

The query optimizer generates an execution plan that specifies how to retrieve data from different data shards and how to process the data.

#### 2.3.2 Execution Engine

The Doris database execution engine is responsible for executing the query plan. The execution engine utilizes parallel execution, fully utilizing cluster resources.

The execution engine supports the following parallel execution techniques:

- Data parallelism: Assigns data shards to different executors for parallel processing.
- Operator parallelism: Assigns operators within queries to different executors for parallel execution.
- Cross parallelism: Combines data parallelism and operator parallelism for finer-grained parallel execution.

# 3.1 Real-time Log Analysis

#### 3.1.1 Log Collection and Preprocessing

The first step in real-time log analysis is collecting and preprocessing log data. Log data typically comes from various sources, such as applications, server