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Posted to commits@flink.apache.org by mx...@apache.org on 2015/06/24 14:47:46 UTC
flink-web git commit: fix code formatting of table API example
Repository: flink-web
Updated Branches:
refs/heads/asf-site 487049aa0 -> 61864351f
fix code formatting of table API example
Project: http://git-wip-us.apache.org/repos/asf/flink-web/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink-web/commit/61864351
Tree: http://git-wip-us.apache.org/repos/asf/flink-web/tree/61864351
Diff: http://git-wip-us.apache.org/repos/asf/flink-web/diff/61864351
Branch: refs/heads/asf-site
Commit: 61864351f7427ad8128883a38831223a5a29d7c2
Parents: 487049a
Author: Maximilian Michels <mx...@apache.org>
Authored: Wed Jun 24 14:47:15 2015 +0200
Committer: Maximilian Michels <mx...@apache.org>
Committed: Wed Jun 24 14:47:15 2015 +0200
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_posts/2015-06-24-announcing-apache-flink-0.9.0-release.md | 2 ++
content/blog/feed.xml | 8 ++++----
.../2015/06/24/announcing-apache-flink-0.9.0-release.html | 8 ++++----
3 files changed, 10 insertions(+), 8 deletions(-)
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http://git-wip-us.apache.org/repos/asf/flink-web/blob/61864351/_posts/2015-06-24-announcing-apache-flink-0.9.0-release.md
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diff --git a/_posts/2015-06-24-announcing-apache-flink-0.9.0-release.md b/_posts/2015-06-24-announcing-apache-flink-0.9.0-release.md
index b6b9b88..5bf2b26 100644
--- a/_posts/2015-06-24-announcing-apache-flink-0.9.0-release.md
+++ b/_posts/2015-06-24-announcing-apache-flink-0.9.0-release.md
@@ -25,11 +25,13 @@ The fault tolerance mechanism requires data sources that can replay recent parts
Flink’s new Table API offers a higher-level abstraction for interacting with structured data sources. The Table API allows users to execute logical, SQL-like queries on distributed data sets while allowing them to freely mix declarative queries with regular Flink operators. Here is an example that groups and joins two tables:
+```scala
val clickCounts = clicks
.groupBy('user).select('userId, 'url.count as 'count)
val activeUsers = users.join(clickCounts)
.where('id === 'userId && 'count > 10).select('username, 'count, ...)
+```
Tables consist of logical attributes that can be selected by name rather than physical Java and Scala data types. This alleviates a lot of boilerplate code for common ETL tasks and raises the abstraction for Flink programs. Tables are available for both static and streaming data sources (DataSet and DataStream APIs).
http://git-wip-us.apache.org/repos/asf/flink-web/blob/61864351/content/blog/feed.xml
----------------------------------------------------------------------
diff --git a/content/blog/feed.xml b/content/blog/feed.xml
index 53a7665..46caacd 100644
--- a/content/blog/feed.xml
+++ b/content/blog/feed.xml
@@ -28,11 +28,11 @@
<p>Flink’s new Table API offers a higher-level abstraction for interacting with structured data sources. The Table API allows users to execute logical, SQL-like queries on distributed data sets while allowing them to freely mix declarative queries with regular Flink operators. Here is an example that groups and joins two tables:</p>
-<p>val clickCounts = clicks
- .groupBy(‘user).select(‘userId, ‘url.count as ‘count)</p>
+<div class="highlight"><pre><code class="language-scala"><span class="k">val</span> <span class="n">clickCounts</span> <span class="k">=</span> <span class="n">clicks</span>
+ <span class="o">.</span><span class="n">groupBy</span><span class="o">(</span><span class="-Symbol">&#39;user</span><span class="o">).</span><span class="n">select</span><span class="o">(</span><span class="-Symbol">&#39;userId</span><span class="o">,</span> <span class="-Symbol">&#39;url</span><span class="o">.</span><span class="n">count</span> <span class="n">as</span> <span class="-Symbol">&#39;count</span><span class="o">)</span>
-<p>val activeUsers = users.join(clickCounts)
- .where(‘id === ‘userId &amp;&amp; ‘count &gt; 10).select(‘username, ‘count, …)</p>
+<span class="k">val</span> <span class="n">activeUsers</span> <span class="k">=</span> <span class="n">users</span><span class="o">.</span><span class="n">join</span><span class="o">(</span><span class="n">clickCounts</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">where</span><span class="o">(</span><span class="-Symbol">&#39;id</span> <span class="o">===</span> <span class="-Symbol">&#39;userId</span> <span class="o">&amp;&amp;</span> <span class="-Symbol">&#39;count</span> <span class="o">&gt;</span> <span class="mi">10</span><span class="o">).</span><span class="n">select</span><span class="o">(</span><span class="-Symbol">&#39;username</span><span class="o">,</span> <span class="-Symbol">&#39;count</span><span class="o">,</span> <span class="o">...)</span></code></pre&
gt;</div>
<p>Tables consist of logical attributes that can be selected by name rather than physical Java and Scala data types. This alleviates a lot of boilerplate code for common ETL tasks and raises the abstraction for Flink programs. Tables are available for both static and streaming data sources (DataSet and DataStream APIs).</p>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/61864351/content/news/2015/06/24/announcing-apache-flink-0.9.0-release.html
----------------------------------------------------------------------
diff --git a/content/news/2015/06/24/announcing-apache-flink-0.9.0-release.html b/content/news/2015/06/24/announcing-apache-flink-0.9.0-release.html
index 0d1dcc2..60268d7 100644
--- a/content/news/2015/06/24/announcing-apache-flink-0.9.0-release.html
+++ b/content/news/2015/06/24/announcing-apache-flink-0.9.0-release.html
@@ -165,11 +165,11 @@
<p>Flink’s new Table API offers a higher-level abstraction for interacting with structured data sources. The Table API allows users to execute logical, SQL-like queries on distributed data sets while allowing them to freely mix declarative queries with regular Flink operators. Here is an example that groups and joins two tables:</p>
-<p>val clickCounts = clicks
- .groupBy(‘user).select(‘userId, ‘url.count as ‘count)</p>
+<div class="highlight"><pre><code class="language-scala"><span class="k">val</span> <span class="n">clickCounts</span> <span class="k">=</span> <span class="n">clicks</span>
+ <span class="o">.</span><span class="n">groupBy</span><span class="o">(</span><span class="-Symbol">'user</span><span class="o">).</span><span class="n">select</span><span class="o">(</span><span class="-Symbol">'userId</span><span class="o">,</span> <span class="-Symbol">'url</span><span class="o">.</span><span class="n">count</span> <span class="n">as</span> <span class="-Symbol">'count</span><span class="o">)</span>
-<p>val activeUsers = users.join(clickCounts)
- .where(‘id === ‘userId && ‘count > 10).select(‘username, ‘count, …)</p>
+<span class="k">val</span> <span class="n">activeUsers</span> <span class="k">=</span> <span class="n">users</span><span class="o">.</span><span class="n">join</span><span class="o">(</span><span class="n">clickCounts</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">where</span><span class="o">(</span><span class="-Symbol">'id</span> <span class="o">===</span> <span class="-Symbol">'userId</span> <span class="o">&&</span> <span class="-Symbol">'count</span> <span class="o">></span> <span class="mi">10</span><span class="o">).</span><span class="n">select</span><span class="o">(</span><span class="-Symbol">'username</span><span class="o">,</span> <span class="-Symbol">'count</span><span class="o">,</span> <span class="o">...)</span></code></pre></div>
<p>Tables consist of logical attributes that can be selected by name rather than physical Java and Scala data types. This alleviates a lot of boilerplate code for common ETL tasks and raises the abstraction for Flink programs. Tables are available for both static and streaming data sources (DataSet and DataStream APIs).</p>