互联网的大数据时代的来临，网络爬虫也成了互联网中一个重要行业，它是一种自动获取网页数据信息的爬虫程序，是网站搜索引擎的重要组成部分。通过爬虫，可以获取自己想要的相关数据信息，让爬虫协助自己的工作，进而降低成本，提高业务成功率和提高业务效率。 本文一方面从爬虫与反反爬的角度来说明如何高效的对网络上的公开数据进行爬取，另一方面也会介绍反爬虫的技术手段，为防止外部爬虫大批量的采集数据的过程对服务器造成超负载方面提供些许建议。
Stateful Functions (StateFun) simplifies the building of distributed stateful applications by combining the best of two worlds: the strong messaging and state consistency guarantees of stateful stream processing, and the elasticity and serverless experience of today’s cloud-native architectures and popular event-driven FaaS platforms. Typical StateFun applications consist of functions deployed behind simple services using these modern platforms, with a separate StateFun cluster playing the role
Apache Flink’s checkpoint-based fault tolerance mechanism is one of its defining features. Because of that design, Flink unifies batch and stream processing, can easily scale to both very small and extremely large scenarios and provides support for many operational features like stateful upgrades with state evolution or roll-backs and time-travel.
Apache Flink is a very versatile tool for all kinds of data processing workloads. It can process incoming data within a few milliseconds or crunch through petabytes of bounded datasets (also known as batch processing).
To best understand state and state backends in Flink, it’s important to distinguish between in-flight state and state snapshots. In-flight state, also known as working state, is the state a Flink job is working on. It is always stored locally in memory (with the possibility to spill to disk) and can be lost when jobs fail without impacting job recoverability. State snapshots, i.e., checkpoints and savepoints, are stored in a remote durable storage, and are used to restore the local state
Flink has supported resource management systems like YARN and Mesos since the early days; however, these were not designed for the fast-moving cloud-native architectures that are increasingly gaining popularity these days, or the growing need to support complex, mixed workloads (e.g. batch, streaming, deep learning, web services). For these reasons, more and more users are using Kubernetes to automate the deployment, scaling and management of their Flink applications.
This new release brings remote functions to the front and center of StateFun, making the disaggregated setup that separates the application logic from the StateFun cluster the default. It is now easier, more efficient, and more ergonomic to write applications that live in their own processes or containers. With the new Java SDK this is now also possible for all JVM languages, in addition to Python.
Streaming jobs which run for several days or longer usually experience variations in workload during their lifetime. These variations can originate from seasonal spikes, such as day vs. night, weekdays vs. weekend or holidays vs. non-holidays, sudden events or simply the growing popularity of your product.
