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This article is about how to use Apache Pulsar and Apache Flink for flexible data processing. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

Flexible data processing of batch and stream integration using Apache Pulsar and Apache Flink

More and more applications use stream computing to provide low-latency data processing. A particularly attractive feature of stream computing is that it conceptually unifies batch processing (bounded static historical data) and continuous near real-time data processing (unbounded streams and event data).

Flink achieves batch flow unification in terms of computing framework and programming logic; however, in practice, it is not easy to achieve a real batch flow unified data architecture. Because the near real-time stream and event data are usually stored by message queue and log storage system, while the static data needed by batch processing is usually stored by file system and object storage. This means that data scientists still need to write two different sets of computing logic to access data stored in different storage systems.

Apache Pulsar, the next generation of Yahoo open source distributed messaging system, graduated from the Apache Software Foundation in September to become a top project. Pulsar's unique hierarchical and fragmented architecture not only ensures the performance and throughput of big data's message flow system, but also provides high availability, high scalability and easy maintenance. The sharding architecture reduces the storage granularity of message flow data from partition to fragmentation, and the corresponding hierarchical storage, which makes Pulsar the best choice for unbounded streaming data storage. This enables Pulsar to better match and adapt to the batch-stream integrated computing model of Flink.

We will briefly introduce what is the hierarchical and sharding architecture of Pulsar,Pulsar, and why this architecture of Pulsar can better adapt to Flink's batch-flow integrated computing framework, and introduce how Pulsar and Flink are combined to carry out batch-flow integrated computing.

The next generation of big data processing engine king-- Apache Flink

Apache Flink is recognized as the most likely to become the leader and king of the next generation of big data computing engine. Once it came out, it was defined as "subverter", "dark horse" and "future".

Fifteen years ago, Google's "troika" first appeared on the stage, and the subsequent emergence of Hadoop opened the prelude to the development of open source big data. Now, with the increasing requirements for timeliness of data, and the rise of artificial intelligence, Apache Flink (hereinafter referred to as Flink) has emerged as a new force in the field of big data.

Flink is like a "clear stream" in the field of big data, which shows amazing potential as soon as it appears: it can not only ensure data consistency "Exactly Once", but also deal with massive data in real time and quickly. The inherent Watermark function enables it to deal with complex data disorder scenarios freely, which fully embodies the perfect combination of "batch" and "flow" and represents the harmonious unity of "flow" and "table".

In the face of the pain point of massive data processing, it can help enterprises and developers easily gain insight in a variety of stream-based computing; it can not only achieve real-time data analysis, but also analyze massive historical data. and greatly simplify the data processing process. Not only that, Flink also has complete semantics and powerful performance, which makes application development easy, and its architecture makes application maintenance extremely easy.

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