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Last updated : Nov 20, 2019
不在本期内容中
这一条目不在当前版本的技术雷达中。如果它出现在最近几期中,那么它很有可能仍然具有相关参考价值。如果这一条目出现在更早的雷达中,那么它很有可能已经不再具有相关性,我们的评估将不再适用于当下。很遗憾我们没有足够的带宽来持续评估以往的雷达内容。 了解更多
Nov 2019
试验 ? 值得一试。了解为何要构建这一能力是很重要的。企业应当在风险可控的前提下在项目中尝试应用此项技术。

自从Apache Flink在2016年首次进入技术雷达“评估”环以来,越来越多的人开始采用它了。Flink被视为领先的流处理引擎,在批处理与机器学习领域也逐渐成熟。与其他流处理引擎相比,Flink的独特之处在于,它使用了一致的应用状态检查点。当发生错误时,应用可以重启,并从最近的检查点载入状态继续处理,就好像错误从未发生一样,这让我们不必为了容错而不得不构建和操作复杂的外部系统。我们看到越来越多的公司,在使用Flink构建他们的数据处理平台。

Nov 2016
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索

Interest continues to build for Apache Flink, a new-generation platform for scalable distributed batch and stream processing. At the core of Apache Flink is a streaming data-flow engine, with support for tabular (SQL-like), graph-processing and machine learning operations. Apache Flink stands out with feature rich capabilities for stream processing: event time, rich streaming window operations, fault tolerance and exactly-once semantics. The project shows significant ongoing activity, with the latest release (1.1) introducing new datasource/sink integrations as well as improved streaming features.

Apr 2016
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索

Apache Flink is a new-generation platform for scalable distributed batch and stream processing. At its core is a streaming data-flow engine. It also supports tabular (SQL-like), graph-processing and machine-learning operations. Apache Flink stands out with feature-rich capabilities for stream processing: event time, rich streaming window operations, fault tolerance and exactly-once semantics. While it hasn't reached version 1.0, it has raised significant community interest due to innovations in stream processing, memory handling, state management and simplicity of configuration.

已发布 : Apr 05, 2016
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