Function Mesh: Serverless Innovation in Message and Streaming Data Scenarios
The talk by Zha Jia showcases StreamNative’s open‑source Function Mesh, which builds on Apache Pulsar’s unified messaging and streaming platform and its lightweight Pulsar Functions, using Kubernetes CRDs to simplify deployment, management, and scaling of serverless data processing while highlighting the project’s rapid community growth.
This article is an organized transcript of a speech by Zha Jia, Co-founder of StreamNative and Tencent Cloud TVP, at the Techo TVP Developer Summit ServerlessDays China 2021. The speech focuses on Function Mesh, a new open-source project by StreamNative, and its innovative practices in the message and streaming data scenarios.
The article begins by introducing Apache Pulsar, a unified data platform that provides unified operation and management, reduces operational pressure, and improves resource utilization. Pulsar's cloud-native architecture, node-to-node architecture, and dedicated storage engine Apache BookKeeper enable it to support both MQ and streaming scenarios.
The article then delves into Pulsar Functions, a lightweight computing framework that allows users to create complex processing logic based on single messages without deploying a separate system. Pulsar Functions provide three semantic supports and built-in state storage, making it easier for users to use and manage.
Finally, the article introduces Function Mesh, a tool that provides better management and makes it easier for users to use Pulsar Functions. Function Mesh uses Kubernetes CRD as its core, describing the relationship between input and output, and providing better management and usage experience for users.
The article also mentions the growth of the Pulsar community, with 403 contributors and nearly 9000 GitHub stars. It highlights the contributions of Tencent Cloud and other users to the Pulsar community.
In conclusion, the article provides a comprehensive overview of Pulsar, Pulsar Functions, and Function Mesh, and their innovative practices in the message and streaming data scenarios.
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