Github user arunmahadevan commented on a diff in the pull request:
https://github.com/apache/spark/pull/22121#discussion_r212031015
--- Diff: docs/avro-data-source-guide.md ---
@@ -0,0 +1,377 @@
+---
+layout: global
+title: Apache Avro Data Source Guide
+---
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+Since Spark 2.4 release, [Spark
SQL](https://spark.apache.org/docs/latest/sql-programming-guide.html) provides
built-in support for reading and writing Apache Avro data.
+
+## Deploying
+The `spark-avro` module is external and not included in `spark-submit` or
`spark-shell` by default.
+
+As with any Spark applications, `spark-submit` is used to launch your
application. `spark-avro_{{site.SCALA_BINARY_VERSION}}`
+and its dependencies can be directly added to `spark-submit` using
`--packages`, such as,
+
+ ./bin/spark-submit --packages
org.apache.spark:spark-avro_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}}
...
+
+For experimenting on `spark-shell`, you can also use `--packages` to add
`org.apache.spark:spark-avro_{{site.SCALA_BINARY_VERSION}}` and its
dependencies directly,
+
+ ./bin/spark-shell --packages
org.apache.spark:spark-avro_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}}
...
+
+See [Application Submission Guide](submitting-applications.html) for more
details about submitting applications with external dependencies.
+
+## Load and Save Functions
+
+Since `spark-avro` module is external, there is no `.avro` API in
+`DataFrameReader` or `DataFrameWriter`.
+
+To load/save data in Avro format, you need to specify the data source
option `format` as `avro`(or `org.apache.spark.sql.avro`).
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+
+val usersDF =
spark.read.format("avro").load("examples/src/main/resources/users.avro")
+usersDF.select("name",
"favorite_color").write.format("avro").save("namesAndFavColors.avro")
+
+{% endhighlight %}
+</div>
+<div data-lang="java" markdown="1">
+{% highlight java %}
+
+Dataset<Row> usersDF =
spark.read().format("avro").load("examples/src/main/resources/users.avro");
+usersDF.select("name",
"favorite_color").write().format("avro").save("namesAndFavColors.avro");
+
+{% endhighlight %}
+</div>
+<div data-lang="python" markdown="1">
+{% highlight python %}
+
+df =
spark.read.format("avro").load("examples/src/main/resources/users.avro")
+df.select("name",
"favorite_color").write.format("avro").save("namesAndFavColors.avro")
+
+{% endhighlight %}
+</div>
+<div data-lang="r" markdown="1">
+{% highlight r %}
+
+df <- read.df("examples/src/main/resources/users.avro", "avro")
+write.df(select(df, "name", "favorite_color"), "namesAndFavColors.avro",
"avro")
+
+{% endhighlight %}
+</div>
+</div>
+
+## to_avro() and from_avro()
+Spark SQL provides function `to_avro` to encode a struct as a string and
`from_avro()` to retrieve the struct as a complex type.
--- End diff --
does it need to be a struct or any spark sql type?
maybe: `to_avro` to encode spark sql types as avro bytes and `from_avro` to
retrieve avro bytes as spark sql types?
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