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更新于 : Jul 08, 2014
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
Jul 2014
采纳 ? 我们强烈建议业界采用这些技术,我们将会在任何合适的项目中使用它们。
Hadoop's initial architecture was based on the paradigm of scaling data horizontally and metadata vertically. While data storage and processing were handled by the slave nodes reasonably well, the masters that managed metadata were a single point of failure and limiting for web scale usage. Hadoop 2.0 has significantly re-architected both HDFS and the Map Reduce framework to address these issues. The HDFS namespace can be federated now using multiple name nodes on the same cluster and deployed in a HA mode. MapReduce has been replaced with YARN, which decouples cluster resource management from job state management and eliminates the scale/performance issues with the JobTracker. Most importantly, this change encourages deploying new distributed programming paradigms in addition to MapReduce on Hadoop clusters.
Jan 2014
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。
May 2013
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。
发布于 : May 22, 2013
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