In this section you will find EURA NOVA’s latest news and activities.
Early October 2019, 6 EURA NOVA engineers travelled to Berlin to attend the Flink Forward Conference, dedicated to Apache Flink users and stream processing communities.
In this article, they will give you their opinion about Ververica’s’ main announcement, the impact of Ververica acquisition by Alibaba, the big trends, and a selection of their favourite talks.
This is the first Flink Forward conference since the acquisition of Ververica (formerly known as data Artisans) by Alibaba, which has been one of the largest users of Flink and second-largest contributor for years. Our R&D director Sabri Skhiri says: “The only significant impact of this acquisition on the conference is that the venue is now at the Berlin Business Center instead of the Kulturbrauerei. There, we could see that the Apache Flink user’s community has grown significantly as well as their commits on Flink. This edition was a bit more business and enterprise-oriented than previous ones, although it still had its technical DNA and a lot of technical talks. All in all, this was a very good mix. Alibaba folks are deeply committed to open source and creating technology impact. We saw a lot of activities from them such as the integration of the Blink SQL runner, the hive integration or the new scheduling model. In summary, a great event.”
During the first keynote, Ververica took the opportunity to announce the launch of Stateful Functions (statefun.io), an open-source framework built on top of Flink to run stateful serverless functions. It bridges the gap between Function as a Service and stream processing.
Sabri says: ”Last year, they announced their streaming ledger that brings ACID transactions between states to stream processing applications. This year, they announced the launch of Stateful Functions, a framework that reduces the complexity of building and orchestrating stateful applications at scale. In the streaming world, this announcement does not change a lot of things. However, in the microservice community, this opens new doors in terms of design patterns, especially in the way data feeding and stateful operations can be designed more flexibly.”
You can find the video of the presentation here.
1. Unified batch and streaming
A significant trend of this edition is the “Unified Stream and Batch” moto. Our R&D engineer Syrine Ferjaoui says: “Flink currently features different APIs, the DataSet API for batch processing and the DataStream API for stream processing. In addition, the Table API is already a unified API on top of both (DataSet and DataStream) with declarative-style programming. Now, they are working on a solution to unify truly the batch and streaming APIs.”
Sabri adds: “In Flink 1.9, they released the State API with which a state created in batch can be used in a stream application – interesting for bootstrapping/backfilling states. But the community is going further by proposing in Flink 2.0 a unique Data API that will merge DataSet and DataStream while still taking advantage of the batch properties to optimise the execution.”
Every talk was exploring in a way or another how this unification can be pushed forward. For instance, in the Pulsar talk, they were thinking about using Pulsar as a back end to transparently bootstrap a state and then switch on stream using (1) pulsar capability in terms of segment storage and (2) unified data stream API in Flink.”
Flink is moving clearly toward an “enterprise-grade” technology. Sabri says: “The first signal is that Cloudera adopted Apache Flink into its Data Platform. Also, AWS Kinetics now integrates Flink as a client. Adoption by such big players goes to show that Flink is well on the way to gain enterprise-grade support. The second signal is the release of the Ververica Platform that highly facilitates enterprise-grade operations. Thirdly, the integration of the Hive Metastore with the pluggable catalogue architecture is a significant step towards better governance and metadata management. Finally, there were many talks about lowering the barrier to deploying Flink in prod. The topics included APIs, configuration, memory management, K8S operators, etc.”
3.The ML path
Finally, regarding ML/AI, there is still a lot of work to get over the gap with the Spark ecosystem. However, the Alibaba folks are working hard on this topic and we can already see the first results. Sabri says: “The refactoring of the Flink ML interface to work on Flink Table APIs is excellent. There is an excellent vision of integrating Flink as a data prep engine for ML and serving layer; and the roadmap looks great.”
A personal selection by Charles & Christophe of interesting talks to check out :
For Charles, our data architect:
For Christophe, our software engineer: