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https://issues.apache.org/jira/browse/BEAM-10983?focusedWorklogId=523974&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-523974
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ASF GitHub Bot logged work on BEAM-10983:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 14/Dec/20 16:27
Start Date: 14/Dec/20 16:27
Worklog Time Spent: 10m
Work Description: davidcavazos commented on a change in pull request
#12963:
URL: https://github.com/apache/beam/pull/12963#discussion_r542520456
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File path: website/www/site/content/en/get-started/from-spark.md
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+---
+title: "Getting started from Apache Spark"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Getting started from Apache Spark
+
+{{< localstorage language language-py >}}
+
+If you already know [_Apache Spark_](http://spark.apache.org/),
+learning _Apache Beam_ is familiar.
+The Beam and Spark APIs are similar, so you already know the basic concepts.
+
+Spark stores data _Spark DataFrames_ for structured data,
+and in _Resilient Distributed Datasets_ (RDD) for unstructured data.
+We are using RDDs for this guide.
+
+A Spark RDD represents a collection of elements,
+while in Beam it's called a _Parallel Collection_ (PCollection).
+A PCollection in Beam does _not_ have any ordering guarantees.
+
+Likewise, a transform in Beam is called a _Parallel Transform_ (PTransform).
+
+Here are some examples of common operations and their equivalent between
PySpark and Beam.
+
+## Overview
+
+Here's a simple example of a PySpark pipeline that takes the numbers from one
to four,
+multiplies them by two, adds all the values together, and prints the result.
+
+{{< highlight py >}}
+import pyspark
+
+sc = pyspark.SparkContext()
+result = (
+ sc.parallelize([1, 2, 3, 4])
+ .map(lambda x: x * 2)
+ .reduce(lambda x, y: x + y)
+)
+print(result)
+{{< /highlight >}}
+
+In Beam you _pipe_ your data through the pipeline using the
+_pipe operator_ `|` like `data | beam.Map(...)` instead of chaining
+methods like `data.map(...)`, but they're doing the same thing.
+
+Here's what an equivalent pipeline looks like in Beam.
+
+{{< highlight py >}}
+import apache_beam as beam
+
+with beam.Pipeline() as pipeline:
+ result = (
+ pipeline
+ | beam.Create([1, 2, 3, 4])
+ | beam.Map(lambda x: x * 2)
+ | beam.CombineGlobally(sum)
+ | beam.Map(print)
+ )
+{{< /highlight >}}
+
+> ℹ️ Note that we called `print` inside a `Map` transform.
+> That's because we can only access the elements of a PCollection
+> from within a PTransform.
+
+Another thing to note is that Beam pipelines are constructed _lazily_.
+This means that when you pipe `|` data you're only _decalring_ the
Review comment:
Thanks, fixed
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Issue Time Tracking
-------------------
Worklog Id: (was: 523974)
Time Spent: 4.5h (was: 4h 20m)
> Have a getting started for Spark users
> --------------------------------------
>
> Key: BEAM-10983
> URL: https://issues.apache.org/jira/browse/BEAM-10983
> Project: Beam
> Issue Type: New Feature
> Components: website
> Reporter: David Cavazos
> Assignee: David Cavazos
> Priority: P2
> Time Spent: 4.5h
> Remaining Estimate: 0h
>
> Have a friendlier getting started experience for users who already know Spark.
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