Rust和大数据

笔者从事大数据行业,最近对Rust语言比较感兴趣,特地关注了一下Rust在大数据生态中的建设情况,以下是一些由Rust编写的大数据框架,感兴趣的同学可以关注相关项目:

Apache Arrow Ballista

VS Spark

Although Ballista is largely inspired by Apache Spark, there are some key differences.

  • The choice of Rust as the main execution language means that memory usage is deterministic and avoids the overhead of GC pauses.
  • Ballista is designed from the ground up to use columnar data, enabling a number of efficiencies such as vectorized processing (SIMD and GPU) and efficient compression. Although Spark does have some columnar support, it is still largely row-based today.
  • The combination of Rust and Arrow provides excellent memory efficiency and memory usage can be 5x - 10x lower than Apache Spark in some cases, which means that more processing can fit on a single node, reducing the overhead of distributed compute.
  • The use of Apache Arrow as the memory model and network protocol means that data can be exchanged between executors in any programming language with minimal serialization overhead.

总结来说就是以下3点:

  1. Rust避免了GC,效率更高
  2. 纯列式存储
  3. 采用Arrow内存模型更高效

arroyo

VS Flink:

  • Serverless operations: Arroyo pipelines are designed to run in modern cloud environments, supporting seamless scaling, recovery, and rescheduling
  • High performance SQL: SQL is a first-class concern, with consistently excellent performance
  • Designed for non-experts: Arroyo cleanly separates the pipeline APIs from its internal implementation. You don’t need to be a streaming expert to build real-time data pipelines.

总结来说是以下3点:

  1. Serverless,更加适用与云生态
  2. 高性能SQL
  3. 易上手

Databend

VS Snowflake*

  • Cloud-Friendly: Seamlessly integrates with various cloud storages like AWS S3, Azure Blob, Google Cloud, and more.
  • High Performance: Built in Rust, utilizing SIMD and vectorized processing for rapid analytics. See ClickBench.
  • Cost-Efficient Elasticity: Innovative design for separate scaling of storage and computation, optimizing both costs and performance.
  • Easy Data Management: Integrated data preprocessing during ingestion eliminates the need for external ETL tools.
  • Data Version Control: Offers Git-like multi-version storage, enabling easy data querying, cloning, and reverting from any point in time.
  • Rich Data Support: Handles diverse data formats and types, including JSON, CSV, Parquet, ARRAY, TUPLE, MAP, and JSON.
  • AI-Enhanced Analytics: Offers advanced analytics capabilities with integrated AI Functions.
  • Community-Driven: Benefit from a friendly, growing community that offers an easy-to-use platform for all your cloud analytics.

总结来说是以下3点:

  1. 云友好
  2. 高性能+低成本
  3. 丰富的数据支持和管理
  4. 开源
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