筆者從事大數(shù)據(jù)行業(yè)，最近對Rust語言比較感興趣挎袜，特地關(guān)注了一下Rust在大數(shù)據(jù)生態(tài)中的建設(shè)情況只厘，以下是一些由Rust編寫的大數(shù)據(jù)框架乘综，感興趣的同學(xué)可以關(guān)注相關(guān)項(xiàng)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.
總結(jié)來說就是以下3點(diǎn)：
- Rust避免了GC符隙，效率更高
- 純列式存儲
- 采用Arrow內(nèi)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.
總結(jié)來說是以下3點(diǎn)：
- Serverless暖途，更加適用與云生態(tài)
- 高性能SQL
- 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.
總結(jié)來說是以下3點(diǎn)：
- 云友好
- 高性能+低成本
- 豐富的數(shù)據(jù)支持和管理
- 開源
