Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/22121#discussion_r211011718
--- Diff: docs/avro-data-source-guide.md ---
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+---
+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/Save Functions
+
+Since `spark-avro` module is external, there is not such API as `.avro` in
+`DataFrameReader` or `DataFrameWriter`.
+To load/save data in Avro format, you need to specify the data source
option `format` as short name `avro` or full name `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>
+
+## Data Source Options
+
+Data source options of Avro can be set using the `.option` method on
`DataFrameReader` or `DataFrameWriter`.
+<table class="table">
+ <tr><th><b>Property
Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th><th><b>Scope</b></th></tr>
+ <tr>
+ <td><code>avroSchema</code></td>
+ <td>None</td>
+ <td>Optional Avro schema provided by an user in JSON format.</td>
+ <td>read and write</td>
+ </tr>
+ <tr>
+ <td><code>recordName</code></td>
+ <td>topLevelRecord</td>
+ <td>Top level record name in write result, which is required in Avro
spec.</td>
+ <td>write</td>
+ </tr>
+ <tr>
+ <td><code>recordNamespace</code></td>
+ <td>""</td>
+ <td>Record namespace in write result.</td>
+ <td>write</td>
+ </tr>
+ <tr>
+ <td><code>ignoreExtension</code></td>
+ <td>true</td>
+ <td>The option controls ignoring of files without <code>.avro</code>
extensions in read. If the option is enabled, all files (with and without
<code>.avro</code> extension) are loaded.</td>
+ <td>read</td>
+ </tr>
+ <tr>
+ <td><code>compression</code></td>
+ <td>snappy</td>
+ <td>The <code>compression</code> option allows to specify a
compression codec used in write. Currently supported codecs are
<code>uncompressed</code>, <code>snappy</code>, <code>deflate</code>,
<code>bzip2</code> and <code>xz</code>. If the option is not set, the
configuration <code>spark.sql.avro.compression.codec</code> config is taken
into account.</td>
+ <td>write</td>
+ </tr>
+</table>
+
+## Supported types for Avro -> Spark SQL conversion
+Currently Spark supports reading all [primitive
types](https://avro.apache.org/docs/1.8.2/spec.html#schema_primitive) and
[complex types](https://avro.apache.org/docs/1.8.2/spec.html#schema_complex) of
Avro.
+<table class="table">
+ <tr><th><b>Avro type</b></th><th><b>Spark SQL type</b></th></tr>
+ <tr>
+ <td>boolean</td>
+ <td>BooleanType</td>
+ </tr>
+ <tr>
+ <td>int</td>
+ <td>IntegerType</td>
+ </tr>
+ <tr>
+ <td>long</td>
+ <td>LongType</td>
+ </tr>
+ <tr>
+ <td>float</td>
+ <td>FloatType</td>
+ </tr>
+ <tr>
+ <td>double</td>
+ <td>DoubleType</td>
+ </tr>
+ <tr>
+ <td>string</td>
+ <td>StringType</td>
+ </tr>
+ <tr>
+ <td>enum</td>
+ <td>StringType</td>
+ </tr>
+ <tr>
+ <td>fixed</td>
+ <td>BinaryType</td>
+ </tr>
+ <tr>
+ <td>bytes</td>
+ <td>BinaryType</td>
+ </tr>
+ <tr>
+ <td>record</td>
+ <td>StructType</td>
+ </tr>
+ <tr>
+ <td>array</td>
+ <td>ArrayType</td>
+ </tr>
+ <tr>
+ <td>map</td>
+ <td>MapType</td>
+ </tr>
+ <tr>
+ <td>union</td>
+ <td>See below</td>
+ </tr>
+</table>
+
+In addition to the types listed above, it supports reading `union` types.
The following three types are considered basic `union` types:
+
+1. `union(int, long)` will be mapped to LongType.
+2. `union(float, double)` will be mapped to DoubleType.
+3. `union(something, null)`, where something is any supported Avro type.
This will be mapped to the same Spark SQL type as that of something, with
nullable set to true.
+All other union types are considered complex. They will be mapped to
StructType where field names are member0, member1, etc., in accordance with
members of the union. This is consistent with the behavior when converting
between Avro and Parquet.
+
+It also supports reading the following Avro [logical
types](https://avro.apache.org/docs/1.8.2/spec.html#Logical+Types):
+
+<table class="table">
+ <tr><th><b>Avro logical type</b></th><th><b>Avro
type</b></th><th><b>Spark SQL type</b></th></tr>
+ <tr>
+ <td>date</td>
+ <td>int</td>
+ <td>DateType</td>
+ </tr>
+ <tr>
+ <td>timestamp-millis</td>
+ <td>long</td>
+ <td>TimestampType</td>
+ </tr>
+ <tr>
+ <td>timestamp-micros</td>
+ <td>long</td>
+ <td>TimestampType</td>
+ </tr>
+ <tr>
+ <td>decimal</td>
+ <td>bytes</td>
+ <td>DecimalType</td>
+ </tr>
+ <tr>
+ <td>decimal</td>
+ <td>bytes</td>
+ <td>DecimalType</td>
+ </tr>
+</table>
+At the moment, it ignores docs, aliases and other properties present in
the Avro file.
+
+## Supported types for Spark SQL -> Avro conversion
+Spark supports writing of all Spark SQL types into Avro. For most types,
the mapping from Spark types to Avro types is straightforward (e.g. IntegerType
gets converted to int); however, there are a few special cases which are listed
below:
+
+<table class="table">
+<tr><th><b>Spark SQL type</b></th><th><b>Avro type</b></th><th><b>Avro
logical type</b></th></tr>
+ <tr>
+ <td>ByteType</td>
+ <td>int</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>ShortType</td>
+ <td>int</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>BinaryType</td>
+ <td>bytes</td>
+ <td></td>
+ </tr>
+ <tr>
+ <td>Date</td>
+ <td>int</td>
+ <td>date</td>
+ </tr>
+ <tr>
+ <td>TimestampType</td>
+ <td>long</td>
+ <td>timestamp-micros</td>
+ </tr>
+ <tr>
+ <td>DecimalType</td>
+ <td>fixed</td>
+ <td>decimal</td>
+ </tr>
+</table>
+
+You can also specify the whole output Avro schema with the option
`avroSchema`, so that Spark SQL types can be converted into other Avro types.
The following conversions is not by default and require user specified Avro
schema:
--- End diff --
`is not` -> `are not applied`?
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