fixed column width in tables in Getting started from Spark guide (apa…

…che#22770)
cushon · Aug 18, 2022 · db47498 · db47498
1 parent 75eb0b1
commit db47498
Showing 1 changed file with 81 additions and 20 deletions.
diff --git a/website/www/site/content/en/get-started/from-spark.md b/website/www/site/content/en/get-started/from-spark.md
@@ -118,11 +118,11 @@ with beam.Pipeline() as pipeline:
 Here's a comparison on how to get started both in PySpark and Beam.
 
 {{< table >}}
-<table>
+<table style="width:100%">
 <tr>
- <th></th>
- <th>PySpark</th>
- <th>Beam</th>
+ <th style="width:20%"></th>
+ <th style="width:40%">PySpark</th>
+ <th style="width:40%">Beam</th>
 </tr>
 <tr>
  <td><b>Install</b></td>
@@ -180,22 +180,83 @@ Here's a comparison on how to get started both in PySpark and Beam.
 Here are the equivalents of some common transforms in both PySpark and Beam.
 
 {{< table >}}
-| | PySpark | Beam |
-|----------------------------------------------------------------------------------|---------------------------------------|---------------------------------------------------------|
-| [**Map**](/documentation/transforms/python/elementwise/map/) | `values.map(lambda x: x * 2)` | `values | beam.Map(lambda x: x * 2)` |
-| [**Filter**](/documentation/transforms/python/elementwise/filter/) | `values.filter(lambda x: x % 2 == 0)` | `values | beam.Filter(lambda x: x % 2 == 0)` |
-| [**FlatMap**](/documentation/transforms/python/elementwise/flatmap/) | `values.flatMap(lambda x: range(x))` | `values | beam.FlatMap(lambda x: range(x))` |
-| [**Group by key**](/documentation/transforms/python/aggregation/groupbykey/) | `pairs.groupByKey()` | `pairs | beam.GroupByKey()` |
-| [**Reduce**](/documentation/transforms/python/aggregation/combineglobally/) | `values.reduce(lambda x, y: x+y)` | `values | beam.CombineGlobally(sum)` |
-| [**Reduce by key**](/documentation/transforms/python/aggregation/combineperkey/) | `pairs.reduceByKey(lambda x, y: x+y)` | `pairs | beam.CombinePerKey(sum)` |
-| [**Distinct**](/documentation/transforms/python/aggregation/distinct/) | `values.distinct()` | `values | beam.Distinct()` |
-| [**Count**](/documentation/transforms/python/aggregation/count/) | `values.count()` | `values | beam.combiners.Count.Globally()` |
-| [**Count by key**](/documentation/transforms/python/aggregation/count/) | `pairs.countByKey()` | `pairs | beam.combiners.Count.PerKey()` |
-| [**Take smallest**](/documentation/transforms/python/aggregation/top/) | `values.takeOrdered(3)` | `values | beam.combiners.Top.Smallest(3)` |
-| [**Take largest**](/documentation/transforms/python/aggregation/top/) | `values.takeOrdered(3, lambda x: -x)` | `values | beam.combiners.Top.Largest(3)` |
-| [**Random sample**](/documentation/transforms/python/aggregation/sample/) | `values.takeSample(False, 3)` | `values | beam.combiners.Sample.FixedSizeGlobally(3)` |
-| [**Union**](/documentation/transforms/python/other/flatten/) | `values.union(otherValues)` | `(values, otherValues) | beam.Flatten()` |
-| [**Co-group**](/documentation/transforms/python/aggregation/cogroupbykey/) | `pairs.cogroup(otherPairs)` | `{'Xs': pairs, 'Ys': otherPairs} | beam.CoGroupByKey()` |
+<table style="width:100%">
+<tr>
+ <th style="width:20%"></th>
+ <th style="width:40%">PySpark</th>
+ <th style="width:40%">Beam</th>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/elementwise/map/">Map</a></b></td>
+ <td><code>values.map(lambda x: x * 2)</code></td>
+ <td><code>values | beam.Map(lambda x: x * 2)</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/elementwise/filter/">Filter</a></b></td>
+ <td><code>values.filter(lambda x: x % 2 == 0)</code></td>
+ <td><code>values | beam.Filter(lambda x: x % 2 == 0)</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/elementwise/flatmap/">FlatMap</a></b></td>
+ <td><code>values.flatMap(lambda x: range(x))</code></td>
+ <td><code>values | beam.FlatMap(lambda x: range(x))</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/aggregation/groupbykey/">Group by key</a></b></td>
+ <td><code>pairs.groupByKey()</code></td>
+ <td><code>pairs | beam.GroupByKey()</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/aggregation/combineglobally/">Reduce</a></b></td>
+ <td><code>values.reduce(lambda x, y: x+y)</code></td>
+ <td><code>values | beam.CombineGlobally(sum)</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/aggregation/combineperkey/">Reduce by key</a></b></td>
+ <td><code>pairs.reduceByKey(lambda x, y: x+y)</code></td>
+ <td><code>pairs | beam.CombinePerKey(sum)</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/aggregation/distinct/">Distinct</a></b></td>
+ <td><code>values.distinct()</code></td>
+ <td><code>values | beam.Distinct()</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/aggregation/count/">Count</a></b></td>
+ <td><code>values.count()</code></td>
+ <td><code>values | beam.combiners.Count.Globally()</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/aggregation/count/">Count by key</a></b></td>
+ <td><code>pairs.countByKey()</code></td>
+ <td><code>pairs | beam.combiners.Count.PerKey()</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/aggregation/top/">Take smallest</a></b></td>
+ <td><code>values.takeOrdered(3)</code></td>
+ <td><code>values | beam.combiners.Top.Smallest(3)</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/aggregation/top/">Take largest</a></b></td>
+ <td><code>values.takeOrdered(3, lambda x: -x)</code></td>
+ <td><code>values | beam.combiners.Top.Largest(3)</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/aggregation/sample/">Random sample</a></b></td>
+ <td><code>values.takeSample(False, 3)</code></td>
+ <td><code>values | beam.combiners.Sample.FixedSizeGlobally(3)</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/other/flatten/">Union</a></b></td>
+ <td><code>values.union(otherValues)</code></td>
+ <td><code>(values, otherValues) | beam.Flatten()</code></td>
+</tr>
+<tr>
+ <td><b><a href="/documentation/transforms/python/aggregation/cogroupbykey/">Co-group</a></b></td>
+ <td><code>pairs.cogroup(otherPairs)</code></td>
+ <td><code>{'Xs': pairs, 'Ys': otherPairs} | beam.CoGroupByKey()</code></td>
+</tr>
+</table>
 {{< /table >}}
 
 > ℹ️ To learn more about the transforms available in Beam, check the