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commit a069dd8ff2ba6565dab5831d519d1f08bff047df
Author: buildbot <[email protected]>
AuthorDate: Sun Nov 14 14:35:55 2021 +0000
Automatic Site Publish by Buildbot
---
output/feed.xml | 4 +-
.../index.html | 145 ++++++++-------------
output/zh/feed.xml | 4 +-
3 files changed, 59 insertions(+), 94 deletions(-)
diff --git a/output/feed.xml b/output/feed.xml
index 020953a..2bda3a7 100644
--- a/output/feed.xml
+++ b/output/feed.xml
@@ -6,8 +6,8 @@
</description>
<link>/</link>
<atom:link href="/feed.xml" rel="self" type="application/rss+xml"/>
- <pubDate>Sun, 14 Nov 2021 11:29:15 +0000</pubDate>
- <lastBuildDate>Sun, 14 Nov 2021 11:29:15 +0000</lastBuildDate>
+ <pubDate>Sun, 14 Nov 2021 14:33:13 +0000</pubDate>
+ <lastBuildDate>Sun, 14 Nov 2021 14:33:13 +0000</lastBuildDate>
<generator>Jekyll v3.9.1</generator>
<item>
diff --git
a/output/zh/docs/lesson-3-run-queries-on-complex-data-types/index.html
b/output/zh/docs/lesson-3-run-queries-on-complex-data-types/index.html
index 056be48..511fbe5 100644
--- a/output/zh/docs/lesson-3-run-queries-on-complex-data-types/index.html
+++ b/output/zh/docs/lesson-3-run-queries-on-complex-data-types/index.html
@@ -1430,28 +1430,23 @@
<div class="int_text" align="left">
- <h2 id="goal">Goal</h2>
+ <h2 id="目标">目标</h2>
-<p>This lesson focuses on queries that exercise functions and operators on
self-
-describing data and complex data types. Drill offers intuitive SQL extensions
-to work with such data and offers high query performance with an architecture
-built from the ground up for complex data.</p>
+<p>本课重点介绍在 self-describing 数据和复杂数据类型上运用函数和运算符的查询。Drill 通过直观的 SQL
扩展来处理此类数据，并通过专用架构为复杂数据类型提供高查询性能。</p>
-<h2 id="queries-in-this-lesson">Queries in This Lesson</h2>
+<h2 id="本课中的查询示例">本课中的查询示例</h2>
-<p>Now that you have run ANSI SQL queries against different tables and files
with
-relational data, you can try some examples including complex types.</p>
+<p>之前已经对含有关系数据的表和文件运行了 ANSI SQL 查询，针对复杂数据类型再做一些示例查询：</p>
<ul>
- <li>Access directories and subdirectories of files in a single SELECT
statement.</li>
- <li>Demonstrate simple ways to access complex data in JSON files.</li>
- <li>Demonstrate the repeated_count function to aggregate values in an
array.</li>
+ <li>在单个 SELECT 语句中访问文件的目录和子目录。</li>
+ <li>轻松访问 JSON 文件中复杂数据的方法。</li>
+ <li>通过 repeat_count 函数来聚合数组中的值。</li>
</ul>
-<h2 id="query-partitioned-directories">Query Partitioned Directories</h2>
+<h2 id="查询分区目录">查询分区目录</h2>
-<p>You can use special variables in Drill to refer to subdirectories in your
-workspace path:</p>
+<p>您可以在 Drill 中使用特殊变量来引用文件路径中的子目录：</p>
<ul>
<li>dir0</li>
@@ -1459,13 +1454,11 @@ workspace path:</p>
<li>…</li>
</ul>
-<p>Note that these variables are dynamically determined based on the
partitioning
-of the file system. No up-front definitions are required on what partitions
-exist. Here is a visual example of how this works:</p>
+<p>请注意，这些变量是根据文件系统的分区动态确定的。不需要预先定义存在哪些分区。以下是其工作原理的示例：</p>
<p><img src="/images/docs/example_query.png" alt="drill query flow" /></p>
-<h3 id="set-workspace-to-dfslogs">Set workspace to dfs.logs:</h3>
+<h3 id="将工作区设置为-dfslogs">将工作区设置为 dfs.logs：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> use dfs.logs;
|-------|---------------------------------------|
@@ -1476,7 +1469,7 @@ exist. Here is a visual example of how this works:</p>
1 row selected
</code></pre></div></div>
-<h3 id="query-logs-data-for-a-specific-year">Query logs data for a specific
year:</h3>
+<h3 id="查询特定年份的日志数据">查询特定年份的日志数据：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select * from logs where dir0='2013'
limit 10;
|-------|-------|-----------|-------------|-----------|----------|---------|--------|----------|-----------|----------|-------------|
@@ -1496,14 +1489,11 @@ exist. Here is a visual example of how this works:</p>
10 rows selected
</code></pre></div></div>
-<p>This query constrains files inside the subdirectory named 2013. The variable
-dir0 refers to the first level down from logs, dir1 to the next level, and so
-on. So this query returned 10 of the rows for February 2013.</p>
+<p>此查询将文件限制在名为 2013 的子目录中。变量 dir0 指的是从日志目录向下的第一级，dir1 到下一级，依此类推。所以这个查询返回了 2013
年文件夹中的前 10 个文件。</p>
-<h3
id="further-constrain-the-results-using-multiple-predicates-in-the-query">Further
constrain the results using multiple predicates in the query:</h3>
+<h3 id="在查询中使用多个条件进一步限制结果">在查询中使用多个条件进一步限制结果：</h3>
-<p>This query returns a list of customer IDs for people who made a purchase via
-an IOS5 device in August 2013.</p>
+<p>此查询返回 2013 年 8 月通过 IOS5 设备有过购买记录的客户 ID 数据。</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select dir0 as yr, dir1 as mth,
cust_id from logs
where dir0='2013' and dir1='8' and device='IOS5' and purch_flag='true'
@@ -1519,7 +1509,7 @@ order by `date`;
...
</code></pre></div></div>
-<h3 id="return-monthly-counts-per-customer-for-a-given-year">Return monthly
counts per customer for a given year:</h3>
+<h3 id="返回特定年份中每个客户每月的复购次数">返回特定年份中每个客户每月的复购次数：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select cust_id, dir1 month_no,
count(*) month_count from logs
where dir0=2014 group by cust_id, dir1 order by cust_id, month_no limit 10;
@@ -1540,16 +1530,13 @@ where dir0=2014 group by cust_id, dir1 order by
cust_id, month_no limit 10;
10 rows selected
</code></pre></div></div>
-<p>This query groups the aggregate function by customer ID and month for one
-year: 2014.</p>
+<p>此查询指定在2014年中根据客户 ID 和月份使用聚合函数进行分组。</p>
-<h2 id="query-complex-data">Query Complex Data</h2>
+<h2 id="查询复杂数据">查询复杂数据</h2>
-<p>Drill provides some specialized operators and functions that you can use to
-analyze nested data natively without transformation. If you are familiar with
-JavaScript notation, you will already know how some of these extensions
work.</p>
+<p>Drill 提供了一些专门的操作符和函数，无需转换即可用来分析嵌套数据。如果用户熟悉 JSON，就可以轻松掌握这些扩展功能。</p>
-<h3 id="set-the-workspace-to-dfsclicks">Set the workspace to dfs.clicks:</h3>
+<h3 id="将工作区设置为-dfsclicks">将工作区设置为 dfs.clicks：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> use dfs.clicks;
|-------|-----------------------------------------|
@@ -1560,11 +1547,9 @@ JavaScript notation, you will already know how some of
these extensions work.</p
1 row selected
</code></pre></div></div>
-<h3 id="explore-clickstream-data">Explore clickstream data:</h3>
+<h3 id="探索点击流数据">探索点击流数据：</h3>
-<p>Note that the user_info and trans_info columns contain nested data: arrays
and
-arrays within arrays. The following queries show how to access this complex
-data.</p>
+<p>请注意，user_info 和 trans_info 列包含嵌套数据：数组和嵌套数组。以下查询展示了如何访问此类复杂数据。</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select * from `clicks/clicks.json`
limit 5;
|-----------|-------------|-----------|---------------------------------------------------|---------------------------------------------------------------------------|
@@ -1579,7 +1564,7 @@ data.</p>
5 rows selected
</code></pre></div></div>
-<h3 id="unpack-the-user_info-column">Unpack the user_info column:</h3>
+<h3 id="解析-user_info-列">解析 user_info 列：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select t.user_info.cust_id as
custid, t.user_info.device as device,
t.user_info.state as state
@@ -1596,19 +1581,16 @@ from `clicks/clicks.json` t limit 5;
5 rows selected (0.171 seconds)
</code></pre></div></div>
-<p>This query uses a simple table.column.column notation to extract nested
column
-data. For example:</p>
+<p>此查询使用简单的 table.column.column 表示法来提取嵌套列数据。例如：</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>t.user_info.cust_id
</code></pre></div></div>
-<p>where <code class="language-plaintext highlighter-rouge">t</code> is the
table alias provided in the query, <code class="language-plaintext
highlighter-rouge">user_info</code> is a top-level
-column name, and <code class="language-plaintext
highlighter-rouge">cust_id</code> is a nested column name.</p>
+<p>其中 <code class="language-plaintext highlighter-rouge">t</code>
是查询中提供的表别名，<code class="language-plaintext highlighter-rouge">user_info</code>
是顶级列名，<code class="language-plaintext highlighter-rouge">cust_id</code>
是嵌套列名。</p>
-<p>The table alias is required; otherwise column names such as <code
class="language-plaintext highlighter-rouge">user_info</code> are
-parsed as table names by the SQL parser.</p>
+<p>表别名是必需的；否则，“user_info” 之类的列名会被 SQL 解析器解析为表名。</p>
-<h3 id="unpack-the-trans_info-column">Unpack the trans_info column:</h3>
+<h3 id="解析-trans_info-列">解析 trans_info 列：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select t.trans_info.prod_id as
prodid, t.trans_info.purch_flag as
purchased
@@ -1625,29 +1607,25 @@ from `clicks/clicks.json` t limit 5;
5 rows selected
</code></pre></div></div>
-<p>Note that this result reveals that the prod_id column contains an array of
IDs
-(one or more product ID values per row, separated by commas). The next step
-shows how you to access this kind of data.</p>
+<p>请注意，此结果表明 prod_id 列包含一组 ID（每行一个或多个产品 ID 值，以逗号分隔）。下一步将展示如何访问此类数据。</p>
-<h2 id="query-arrays">Query Arrays</h2>
+<h2 id="查询数组">查询数组</h2>
-<p>Now use the [n] notation, where n is the position of the value in an array,
-starting from position 0 (not 1) for the first value. You can use this
-notation to write interesting queries against nested array data.</p>
+<p>现在使用 [n] 表示法，其中 n 是数组中值的位置，从第一个值的位置 0（不是 1）开始。用户可以使用此表示法针对嵌套数组数据进行的查询。</p>
-<p>For example:</p>
+<p>比如:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>trans_info.prod_id[0]
</code></pre></div></div>
-<p>refers to the first value in the nested prod_id column and</p>
+<p>指嵌套的 prod_id 列中的第一个值和</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>trans_info.prod_id[20]
</code></pre></div></div>
-<p>refers to the 21st value, assuming one exists.</p>
+<p>指到第 21 个值，假设存在第21个值。</p>
-<h3 id="find-the-first-product-that-is-searched-for-in-each-transaction">Find
the first product that is searched for in each transaction:</h3>
+<h3 id="查找每笔交易中搜索的第一个产品">查找每笔交易中搜索的第一个产品：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select t.trans_id,
t.trans_info.prod_id[0] from `clicks/clicks.json` t limit 5;
|------------|------------|
@@ -1662,7 +1640,7 @@ notation to write interesting queries against nested
array data.</p>
5 rows selected
</code></pre></div></div>
-<h3
id="for-which-transactions-did-customers-search-on-at-least-21-products">For
which transactions did customers search on at least 21 products?</h3>
+<h3 id="哪些交易客户搜索了至少-21-种产品">哪些交易客户搜索了至少 21 种产品？</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select t.trans_id,
t.trans_info.prod_id[20]
from `clicks/clicks.json` t
@@ -1680,10 +1658,9 @@ order by trans_id limit 5;
5 rows selected
</code></pre></div></div>
-<p>This query returns transaction IDs and product IDs for records that contain
a
-non-null product ID at the 21st position in the array.</p>
+<p>对于在数组的第 21 个位置包含非空产品 ID 的交易记录，此查询将返回此交易的交易 ID 和产品 ID。</p>
-<h3 id="return-clicks-for-a-specific-product-range">Return clicks for a
specific product range:</h3>
+<h3 id="返回特定产品范围的点击次数">返回特定产品范围的点击次数：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select * from (select t.trans_id,
t.trans_info.prod_id[0] as prodid,
t.trans_info.purch_flag as purchased
@@ -1703,12 +1680,11 @@ order by sq.prodid;
...
</code></pre></div></div>
-<p>This query assumes that there is some meaning to the array (that it is an
-ordered list of products purchased rather than a random list).</p>
+<p>此查询假定数组具有某种意义（即它是已购买产品的有序表而不是随机列表）。</p>
-<h2 id="perform-operations-on-arrays">Perform Operations on Arrays</h2>
+<h2 id="对数组执行操作">对数组执行操作</h2>
-<h3
id="rank-successful-click-conversions-and-count-product-searches-for-each-session">Rank
successful click conversions and count product searches for each session:</h3>
+<h3 id="对会话中成功的点击转化和搜索的产品数量进行排名">对会话中成功的点击转化和搜索的产品数量进行排名：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select t.trans_id, t.`date` as
session_date, t.user_info.cust_id as
cust_id, t.user_info.device as device, repeated_count(t.trans_info.prod_id) as
@@ -1726,17 +1702,13 @@ where t.trans_info.purch_flag = 'true' order by
prod_count desc;
...
</code></pre></div></div>
-<p>This query uses an SQL extension, the repeated_count function, to get an
-aggregated count of the array values. The query returns the number of products
-searched for each session that converted into a purchase and ranks the counts
-in descending order. Only clicks that have resulted in a purchase are
counted.</p>
+<p>此查询使用 SQL 扩展，通过 repeat_count
函数来获取数组值的聚合统计。该查询返回每个会话中搜索的产品数量，按降序排列。仅统计转化为购买的点击次数。</p>
-<h2 id="store-a-result-set-in-a-table-for-reuse-and-analysis">Store a Result
Set in a Table for Reuse and Analysis</h2>
+<h2 id="将结果集存储在表中方便重用和分析">将结果集存储在表中方便重用和分析</h2>
-<p>To facilitate additional analysis on this result set, you can easily and
-quickly create a Drill table from the results of the query.</p>
+<p>为了便于对此结果集进行额外分析，用户可以根据查询结果快捷地创建 Drill 表。</p>
-<h3 id="continue-to-use-the-dfsclicks-workspace">Continue to use the
dfs.clicks workspace</h3>
+<h3 id="继续使用-dfsclicks-工作区">继续使用 dfs.clicks 工作区：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> use dfs.clicks;
|-------|-----------------------------------------|
@@ -1747,7 +1719,7 @@ quickly create a Drill table from the results of the
query.</p>
1 row selected (1.61 seconds)
</code></pre></div></div>
-<h3 id="return-product-searches-for-high-value-customers">Return product
searches for high-value customers:</h3>
+<h3 id="返回高价值客户的产品搜索">返回高价值客户的产品搜索：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select o.cust_id, o.order_total,
t.trans_info.prod_id[0] as prod_id
from
@@ -1770,10 +1742,9 @@ where o.order_total > (select avg(inord.order_total)
107,482 rows selected (14.863 seconds)
</code></pre></div></div>
-<p>This query returns a list of products that are being searched for by
customers
-who have made transactions that are above the average in their states.</p>
+<p>此查询返回一个产品列表，这些产品被客户搜索的产品的交易额高于其所在州的平均水平。</p>
-<h3 id="materialize-the-result-of-the-previous-query">Materialize the result
of the previous query:</h3>
+<h3 id="保存上一个查询的结果">保存上一个查询的结果：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> create table product_search as
select o.cust_id, o.order_total, t.trans_info.prod_id[0] as prod_id
from
@@ -1791,16 +1762,11 @@ where o.order_total > (select avg(inord.order_total)
1 row selected (3.488 seconds)
</code></pre></div></div>
-<p>This example uses a CTAS statement to create a table based on a correlated
-subquery that you ran previously. This table contains all of the rows that the
-query returns (107,482) and stores them in the format specified by the storage
-plugin (Parquet format in this example). You can create tables that store data
-in csv, parquet, and json formats.</p>
+<p>此示例使用 CTAS 语句将上一个查询的结果创建为表。该表包含查询返回的所有行 (107,482)，并以存储插件指定的格式（本例中为 Parquet
格式）存储。用户可以创建以 csv、parquet 和 json 格式存储的表。</p>
-<h3 id="query-the-new-table-to-verify-the-row-count">Query the new table to
verify the row count:</h3>
+<h3 id="查询新表以验证行数">查询新表以验证行数：</h3>
-<p>This example simply checks that the CTAS statement worked by verifying the
-number of rows in the table.</p>
+<p>此示例仅通过验证表中的行数来检查 CTAS 语句是否有效。</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>0: jdbc:drill:> select count(*) from product_search;
|---------|
@@ -1811,7 +1777,7 @@ number of rows in the table.</p>
1 row selected (0.155 seconds)
</code></pre></div></div>
-<h3 id="find-the-storage-file-for-the-table">Find the storage file for the
table:</h3>
+<h3 id="找到表的存储文件">找到表的存储文件：</h3>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>[root@maprdemo product_search]# cd
/mapr/demo.mapr.com/data/nested/product_search
[root@maprdemo product_search]# ls -la
@@ -1821,17 +1787,16 @@ drwxr-xr-x. 4 root root 2 Sep 15 13:41 ..
-rwxr-xr-x. 1 mapr mapr 460715 Sep 15 13:41 0_0_0.parquet
</code></pre></div></div>
-<p>Note that the table is stored in a file called <code
class="language-plaintext highlighter-rouge">0_0_0.parquet</code>. This file is
-stored in the location defined by the dfs.clicks workspace:</p>
+<p>请注意，该表存储在名为 “0_0_0.parquet” 的文件中。这个文件存储在 dfs.clicks 工作区定义的位置：</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>"location": "http://demo.mapr.com/data/nested";
</code></pre></div></div>
-<p>There is a subdirectory that has the same name as the table you created.</p>
+<p>有一个子目录与您创建的表同名。</p>
-<h2 id="whats-next">What’s Next</h2>
+<h2 id="下一步">下一步</h2>
-<p>Complete the tutorial with the <a href="/zh/docs/summary">Summary</a>.</p>
+<p>完成教程并 <a href="/zh/docs/summary">总结</a>。</p>
diff --git a/output/zh/feed.xml b/output/zh/feed.xml
index 82c99e3..9c53b83 100644
--- a/output/zh/feed.xml
+++ b/output/zh/feed.xml
@@ -6,8 +6,8 @@
</description>
<link>/</link>
<atom:link href="/zh/feed.xml" rel="self" type="application/rss+xml"/>
- <pubDate>Sun, 14 Nov 2021 11:29:15 +0000</pubDate>
- <lastBuildDate>Sun, 14 Nov 2021 11:29:15 +0000</lastBuildDate>
+ <pubDate>Sun, 14 Nov 2021 14:33:13 +0000</pubDate>
+ <lastBuildDate>Sun, 14 Nov 2021 14:33:13 +0000</lastBuildDate>
<generator>Jekyll v3.9.1</generator>
<item>
