随着O2O线上线下业务的不断扩展，电商平台也在逐步完善交易侧相关的产品功能。在最近的需求版本中，业务方为进一步提升用户的使用体验，规划了取货码生成及订单核销相关逻辑，目的是让线上的用户在付完款之后能够到店取货或者安排导购派送。
vivo 移动互联网为全球 4 亿 + 智能手机用户提供互联网产品与服务。其中，vivo 分布式消息中间件团队主要为 vivo 所有内外销实时计算业务提供高吞吐、低延时的数据接入、消息队列等服务，覆盖应用商店、短视频、广告等业务。业务集群已达每天十万亿级的数据规模。
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.
