kingswanwho commented on a change in pull request #2286:
URL: https://github.com/apache/drill/pull/2286#discussion_r685057034
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File path: _docs/zh/tutorials/050-analyzing-highly-dynamic-datasets.md
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@@ -5,9 +5,9 @@ parent: "教程"
lang: "zh"
---
-Today’s data is dynamic and application-driven. The growth of a new era of
business applications driven by industry trends such as web, social, mobile,
and Internet of Things are generating datasets with new data types and new data
models. These applications are iterative, and the associated data models
typically are semi-structured, schema-less and constantly evolving.
Semi-structured data models can be complex/nested, schema-less, and capable of
having varying fields in every single row and of constantly evolving as fields
get added and removed frequently to meet business requirements.
+大数据是动态的并应用驱动的。互联网时代商业软件的发展由不同的产业端所驱动，如网页端，媒体端，移动端，物联网。他们所生成的数据集，包含了新的数据类型和模型。这些应用都是交互式的，他们所关联的数据模型一般都是半结构化，schema-less，以及持续变化。半结构化数据模型可以是复杂/嵌套或
schema-less，并且能够在每一行中含有不同的字段，为满足业务需求，字段会频繁修改。
-This tutorial shows you how to natively query dynamic datasets, such as JSON,
and derive insights from any type of data in minutes. The dataset used in the
example is from the Yelp check-ins dataset, which has the following structure:
+本教程将向你展示如何查询动态原生数据集，例如 JSON，并在几分钟内从任意类型的数据中获得有效信息。示例中使用的数据集来自 Yelp 签到数据集，其结构如下：
Review comment:
resolved
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File path: _docs/zh/tutorials/050-analyzing-highly-dynamic-datasets.md
##########
@@ -23,32 +23,32 @@ This tutorial shows you how to natively query dynamic
datasets, such as JSON, an
}, # if there was no checkin for a hour-day block it will not be in
the dataset
}
-It is worth repeating the comment at the bottom of this snippet:
+请特别注意此段代码底部的注释：
If there was no checkin for a hour-day block it will not be in the
dataset.
-The element names that you see in the `checkin_info` are unknown upfront and
can vary for every row. The data, although simple, is highly dynamic data. To
analyze the data there is no need to first represent this dataset in a
flattened relational structure, as you would using any other SQL on Hadoop
technology.
+你在 `checkin_info` 中看到的元素名称预先未知，并且每行都可能不同。数据虽然简单，但却是高度动态的数据。想要分析数据，无需在 Hadoop
平台上那样，需要先以扁平结构表示数据集，然后才能使用 SQL 类工具。
Review comment:
resolved
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File path: _docs/zh/tutorials/050-analyzing-highly-dynamic-datasets.md
##########
@@ -58,13 +58,13 @@ First, let’s take a look at the dataset:
|
{"6-6":2,"6-5":1,"7-6":1,"7-5":1,"8-5":2,"10-5":1,"9-3":1,"12-5":1,"15-3":1,"15-5":1,"15-6":1,"16-3":1,"10-0":1,"15-4":1,"10-4":1,"8-2":1}
| checkin | uGykseHzyS5xAMWoN6YUqA |
|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|------------------------|
-{% include startnote.html %}This document aligns Drill output for example
purposes. Drill output is not aligned in this case.{% include endnote.html %}
+{% include startnote.html %}本文档为了展示方便对齐了 Drill 的输出。实际上 Drill 的输出不会这样对齐。{%
include endnote.html %}
-You query the data in JSON files directly. Schema definitions in Hive store
are not necessary. The names of the elements within the `checkin_info` column
are different between the first and second row.
+你可以直接查询 JSON 文件中的数据。 不必给 Hive 中的数据定义 schema。`checkin_info`
列中，第一行和第二行之间的元素名称是不同的。
-Drill provides a function called KVGEN (Key Value Generator) which is useful
when working with complex data that contains arbitrary maps consisting of
dynamic and unknown element names such as checkin_info. KVGEN turns the dynamic
map into an array of key-value pairs where keys represent the dynamic element
names.
+Drill 提供了一个名为 KVGEN（键值生成器）的函数，该函数在处理包含由动态和未知元素名称（例如
checkin_info）组成的任意映射的复杂数据时非常有用。 KVGEN 将动态映射转换为键值对数组，其中键表示动态元素名称。
Review comment:
resolved
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