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@@ -1,48 +1,32 @@
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ifndef::es_build[= placeholder3]
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[[aggregations]]
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-= Aggregations
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+= 聚合
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[partintro]
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--
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-Until this point, this book has been dedicated to search.((("searching", "search versus aggregations")))((("aggregations"))) With search,
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-we have a query and we want to find a subset of documents that
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-match the query. We are looking for the proverbial needle(s) in the
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-haystack.
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+在这之前，本书致力于搜索。((("searching", "search versus aggregations")))((("aggregations"))) 通过搜索，如果我们有一个查询并且希望找到匹配这个查询的文档集，就好比在大海捞针。
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-With aggregations, we zoom out to get an overview of our data. Instead of
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-looking for individual documents, we want to analyze and summarize our complete
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-set of data:
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+通过聚合，我们会得到一个数据的概览。我们需要的是分析和总结全套的数据而不是寻找单个文档：
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// Popular manufacturers? Unusual clumps of needles in the haystack?
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-- How many needles are in the haystack?
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-- What is the average length of the needles?
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-- What is the median length of the needles, broken down by manufacturer?
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-- How many needles were added to the haystack each month?
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-
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-Aggregations can answer more subtle questions too:
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-
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-- What are your most popular needle manufacturers?
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-- Are there any unusual or anomalous clumps of needles?
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-
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-Aggregations allow us to ask sophisticated questions of our data. And yet, while
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-the functionality is completely different from search, it leverages the
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-same data-structures. This means aggregations execute quickly and are
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-_near real-time_, just like search.
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-
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-This is extremely powerful for reporting and dashboards. Instead of performing
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-_rollups_ of your data (_that crusty Hadoop job that takes a week to run_),
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-you can visualize your data in real time, allowing you to respond immediately.
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-Your report changes as your data changes, rather than being pre-calculated, out of
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-date and irrelevant.
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-
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-Finally, aggregations operate alongside search requests.((("aggregations", "operating alongside search requests"))) This means you can
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-both search/filter documents _and_ perform analytics at the same time, on the
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-same data, in a single request. And because aggregations are calculated in the
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-context of a user's search, you're not just displaying a count of four-star hotels--you're displaying a count of four-star hotels that _match their search criteria_.
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-
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-Aggregations are so powerful that many companies have built large Elasticsearch
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-clusters solely for analytics.
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+- 在大海里有多少针？
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+- 针的平均长度是多少？
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+- 按照针的制造商来划分，针的长度中位值是多少？
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+- 每月加入到海中的针有多少？
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+
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+聚合也可以回答更加细微的问题：
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+
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+- 你最受欢迎的针的制造商是什么？
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+- 这里面有异常的针么？
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+
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+聚合允许我们向数据提出一些复杂的问题。虽然功能完全不同于搜索，但它使用相同的数据结构。这意味着聚合的执行速度很快并且就像搜索一样几乎是实时的。
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+
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+这对报告和仪表盘是非常强大的。你可以实时显示你的数据，让你立即回应，而不是对你的数据进行汇总（ _需要一周时间去运行的 Hadoop 任务_ ），您的报告随着你的数据变化而变化，而不是预先计算的、过时的和不相关的。
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+
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+最后，聚合和搜索是一起的。((("aggregations", "operating alongside search requests"))) 这意味着你可以在单个请求里同时对相同的数据进行搜索/过滤和分析。并且由于聚合是在用户搜索的上下文里计算的，你不只是显示四星酒店的数量，而是显示匹配查询条件的四星酒店的数量。
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+
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+聚合是如此强大以至于许多公司已经专门为数据分析建立了大型 Elasticsearch 集群。
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--
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include::301_Aggregation_Overview.asciidoc[]
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songgl