blocks|key|235018|text|我不知道德鲁伊，可以根据我在网站上看到的评论。在存储解决方案领域，Druid似乎与Impala、Redshift、Vertica、Cassandra和Hadoop进行了比较。它似乎是为时间序列优化的存储查询，其中的“主键”是时间对象。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|235019|Esper不存储数据，而是在数据到达时进行分析，因此可能实现更低的延迟和更高的吞吐量，因为它永远不会进入磁盘(除非具有高可用性)。Druid似乎能够分析存储的时间序列数据，而这将需要事件从另一个存储重放到Esper。|235020|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

I don't know Druid and can comment based on what I see on the web site. Druid seems to compare itself with Impala, Redshift, Vertica, Cassandra and Hadoop in the field of storage solution. It seems a store-then-query optimized for time series, with the 'primary key' being time objects. 

Esper does not store and instead analyzes as data arrives, therefore presumably achieving lower latency and higher throughput as it never goes to disk (unless with the high-availability). Druid seems to be able to analyze stored time series data, while this would require event replay from another store into Esper.

blocks|key|235110|text|Druid集群由不同类型的节点组成，因此，虽然有些节点是“存储然后查询”，但也有所谓的“实时”节点。来自德鲁伊文献|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|235111|实时节点提供实时索引。通过这些节点建立索引的数据可立即用于查询。实时节点将周期性地构建表示它们在一段时间内收集的数据的段，并将这些段转移到历史节点。|blockquote|235112|Druid文档和博客中有关于如何在其中流数据并立即查询数据的示例。因此，从这个意义上说，它可以执行与Esper类似的操作，但也可以将数据存储在历史节点中，以供以后查询。它还可以通过Hadoop+(显然不是实时处理)摄取数据，这意味着流数据可以根据校正或丢失的数据进行修改。|235113|entityMap|0|LINK|mutability|MUTABLE|url|http://druid.io/docs/0.6.158/Realtime.html^0|1G|5|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$]]|$1|G|3|H|5|6|7|V|8|@]|9|@]|C|$]]|$1|I|3|-4|5|6|7|W|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

A Druid cluster is composed of different types of nodes, so while some are "store-then-query," there are also what are called "real-time" nodes. From the <a href="http://druid.io/docs/0.6.158/Realtime.html" rel="nofollow">Druid documentation</a>:

<blockquote>
 Realtime nodes provide a realtime index. Data indexed via these nodes is immediately available for querying. Realtime nodes will periodically build segments representing the data they’ve collected over some span of time and transfer these segments off to Historical nodes.
</blockquote>

There are examples in the Druid documentation and blogs on how to stream data in and immediately query it. So in that sense it can do similar things to Esper, but can also store the data in historical nodes for later querying. It can also ingest data via Hadoop (obviously not real-time processing), which means that the streamed data can be amended as corrected or missing data comes in.

I have been reading a little about Druid last few days and wondering how is this different from Esper. I have been using Esper for realtime event processing. It feels Druid does that with its simple JSON like queries and much simpler interface.

Can someone please correct me and share more light?

Edit 
Can both co-exist?

DRUID.io vs Esper CEP

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我最近几天读到了一些关于德鲁伊的书，想知道这和埃斯波有什么不同。我一直在使用Esper进行实时事件处理。它觉得Druid使用简单的JSON查询和更简单的接口就可以做到这一点。有人能纠正我，多分享点光吗？编辑两者能共存吗？

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