Introduction

ArkFlow is a high-performance Rust stream processing engine that provides powerful data stream processing capabilities, supporting various input/output sources and processors.

Core Features

• High Performance: Built on Rust and Tokio async runtime, delivering exceptional performance and low latency
• Multiple Data Sources: Support for Kafka, MQTT, HTTP, files, and other input/output sources
• Powerful Processing: Built-in SQL queries, JSON processing, Protobuf encoding/decoding, batch processing, and other processors
• Extensibility: Modular design, easy to extend with new input, output, and processor components

Installation

Building from Source

# Clone repository
git clone https://github.com/arkflow-rs/arkflow.git
cd arkflow

# Build project
cargo build --release

# Run tests
cargo test

Quick Start

1. Create a configuration file config.yaml:

logging:
  level: info
streams:
  - input:
      type: "generate"
      context: '{ "timestamp": 1625000000000, "value": 10, "sensor": "temp_1" }'
      interval: 1s
      batch_size: 10
    buffer:
      type: "memory"
      capacity: 10
      timeout: 10s
    pipeline:
      thread_num: 4
      processors:
        - type: "json_to_arrow"
        - type: "sql"
          query: "SELECT * FROM flow WHERE value >= 10"

    output:
      type: "stdout"
    error_output:
      type: "stdout"

2. Run ArkFlow:

./target/release/arkflow --config config.yaml

Configuration Guide

ArkFlow uses YAML format configuration files and supports the following main configuration items:

Top-level Configuration

logging:
  level: info  # Log levels: debug, info, warn, error

streams: # Stream definition list
  - input:      # Input configuration
    # ...
    pipeline:   # Pipeline configuration
    # ...
    output:     # Output configuration
    # ...
    error_output: # Error output configuration
    # ...
    buffer:     # Buffer configuration
    # ... 

Input Components

ArkFlow supports multiple input sources:

• Kafka: Read data from Kafka topics
• MQTT: Subscribe to messages from MQTT topics
• HTTP: Receive data via HTTP
• File: Reading data from files(Csv,Json, Parquet, Avro, Arrow) using SQL
• Generator: Generate test data
• Database: Query data from databases(MySQL, PostgreSQL, SQLite, Duckdb)

Example:

input:
  type: kafka
  brokers:
    - localhost:9092
  topics:
    - test-topic
  consumer_group: test-group
  client_id: arkflow
  start_from_latest: true

Processors

ArkFlow provides multiple data processors:

• JSON: JSON data processing and transformation
• SQL: Process data using SQL queries
• Protobuf: Protobuf encoding/decoding
• Batch Processing: Process messages in batches

Example:

pipeline:
  thread_num: 4
  processors:
    - type: json_to_arrow
    - type: sql
      query: "SELECT * FROM flow WHERE value >= 10"

Output Components

ArkFlow supports multiple output targets:

• Kafka: Write data to Kafka topics
• MQTT: Publish messages to MQTT topics
• HTTP: Send data via HTTP
• Standard Output: Output data to the console
• Drop: Discard data

Example:

output:
  type: kafka
  brokers:
    - localhost:9092
  topic: 
    type: value
    value: test-topic
  client_id: arkflow-producer

Error Output Components

ArkFlow supports multiple error output targets:

• Kafka: Write error data to Kafka topics
• MQTT: Publish error messages to MQTT topics
• HTTP: Send error data via HTTP
• Standard Output: Output error data to the console
• Drop: Discard error data

Example:

error_output:
  type: kafka
  brokers:
    - localhost:9092
  topic: 
    type: value
    value: error-topic
  client_id: error-arkflow-producer

Buffer Components

ArkFlow provides buffer capabilities to handle backpressure and temporary storage of messages:

• Memory Buffer: Memory buffer, for high-throughput scenarios and window aggregation

Example:

buffer:
  type: memory
  capacity: 10000  # Maximum number of messages to buffer
  timeout: 10s  # Maximum time to buffer messages