[SPARK-31295][DOC] Supplement version for configuration appear in doc

docs/spark-standalone.md

@@ -192,13 +192,15 @@ SPARK_MASTER_OPTS supports the following system properties:
   <td>
     The maximum number of completed applications to display. Older applications will be dropped from the UI to maintain this limit.<br/>
   </td>
+  <td>0.8.0</td>
 </tr>
 <tr>
   <td><code>spark.deploy.retainedDrivers</code></td>
   <td>200</td>
   <td>
    The maximum number of completed drivers to display. Older drivers will be dropped from the UI to maintain this limit.<br/>
   </td>
+  <td>1.1.0</td>
 </tr>
 <tr>
   <td><code>spark.deploy.spreadOut</code></td>
@@ -208,6 +210,7 @@ SPARK_MASTER_OPTS supports the following system properties:
     to consolidate them onto as few nodes as possible. Spreading out is usually better for
     data locality in HDFS, but consolidating is more efficient for compute-intensive workloads. <br/>
   </td>
+  <td>0.6.1</td>
 </tr>
 <tr>
   <td><code>spark.deploy.defaultCores</code></td>
@@ -219,6 +222,7 @@ SPARK_MASTER_OPTS supports the following system properties:
     Set this lower on a shared cluster to prevent users from grabbing
     the whole cluster by default. <br/>
   </td>
+  <td>0.9.0</td>
 </tr>
 <tr>
   <td><code>spark.deploy.maxExecutorRetries</code></td>
@@ -234,6 +238,7 @@ SPARK_MASTER_OPTS supports the following system properties:
     <code>-1</code>.
     <br/>
   </td>
+  <td>1.6.3</td>
 </tr>
 <tr>
   <td><code>spark.worker.timeout</code></td>
@@ -250,6 +255,7 @@ SPARK_MASTER_OPTS supports the following system properties:
   <td>
     Amount of a particular resource to use on the worker.
   </td>
+  <td>3.0.0</td>
 </tr>
 <tr>
   <td><code>spark.worker.resource.{resourceName}.discoveryScript</code></td>
@@ -258,6 +264,7 @@ SPARK_MASTER_OPTS supports the following system properties:
     Path to resource discovery script, which is used to find a particular resource while worker starting up.
     And the output of the script should be formatted like the <code>ResourceInformation</code> class.
   </td>
+  <td>3.0.0</td>
 </tr>
 <tr>
   <td><code>spark.worker.resourcesFile</code></td>
@@ -317,6 +324,7 @@ SPARK_WORKER_OPTS supports the following system properties:
     enabled).  You should also enable <code>spark.worker.cleanup.enabled</code>, to ensure that the state
     eventually gets cleaned up.  This config may be removed in the future.
   </td>
+  <td>3.0.0</td>
 </tr>
 <tr>
   <td><code>spark.storage.cleanupFilesAfterExecutorExit</code></td>
@@ -329,6 +337,7 @@ SPARK_WORKER_OPTS supports the following system properties:
     all files/subdirectories of a stopped and timeout application.
     This only affects Standalone mode, support of other cluster manangers can be added in the future.
   </td>
+  <td>2.4.0</td>
 </tr>
 <tr>
   <td><code>spark.worker.ui.compressedLogFileLengthCacheSize</code></td>
@@ -490,14 +499,16 @@ ZooKeeper is the best way to go for production-level high availability, but if y
 In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env using this configuration:
 
 <table class="table">
-  <tr><th style="width:21%">System property</th><th>Meaning</th></tr>
+  <tr><th style="width:21%">System property</th><th>Meaning</th><th>Since Version</th></tr>
   <tr>
     <td><code>spark.deploy.recoveryMode</code></td>
     <td>Set to FILESYSTEM to enable single-node recovery mode (default: NONE).</td>
+    <td>0.8.1</td>
   </tr>
   <tr>
     <td><code>spark.deploy.recoveryDirectory</code></td>
     <td>The directory in which Spark will store recovery state, accessible from the Master's perspective.</td>
+    <td>0.8.1</td>
   </tr>
 </table>
 

docs/sql-data-sources-avro.md

@@ -258,21 +258,34 @@ Data source options of Avro can be set via:
 ## Configuration
 Configuration of Avro can be done using the `setConf` method on SparkSession or by running `SET key=value` commands using SQL.
 <table class="table">
-  <tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th></tr>
+  <tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th><th><b>Since Version</b></th></tr>
   <tr>
     <td>spark.sql.legacy.replaceDatabricksSparkAvro.enabled</td>
     <td>true</td>
-    <td>If it is set to true, the data source provider <code>com.databricks.spark.avro</code> is mapped to the built-in but external Avro data source module for backward compatibility.</td>
+    <td>
+      If it is set to true, the data source provider <code>com.databricks.spark.avro</code> is mapped
+      to the built-in but external Avro data source module for backward compatibility.
+    </td>
+    <td>2.4.0</td>
   </tr>
   <tr>
     <td>spark.sql.avro.compression.codec</td>
     <td>snappy</td>
-    <td>Compression codec used in writing of AVRO files. Supported codecs: uncompressed, deflate, snappy, bzip2 and xz. Default codec is snappy.</td>
+    <td>
+      Compression codec used in writing of AVRO files. Supported codecs: uncompressed, deflate,
+      snappy, bzip2 and xz. Default codec is snappy.
+    </td>
+    <td>2.4.0</td>
   </tr>
   <tr>
     <td>spark.sql.avro.deflate.level</td>
     <td>-1</td>
-    <td>Compression level for the deflate codec used in writing of AVRO files. Valid value must be in the range of from 1 to 9 inclusive or -1. The default value is -1 which corresponds to 6 level in the current implementation.</td>
+    <td>
+      Compression level for the deflate codec used in writing of AVRO files. Valid value must be in
+      the range of from 1 to 9 inclusive or -1. The default value is -1 which corresponds to 6 level
+      in the current implementation.
+    </td>
+    <td>2.4.0</td>
   </tr>
 </table>
 

docs/sql-data-sources-orc.md

@@ -27,15 +27,25 @@ serde tables (e.g., the ones created using the clause `USING HIVE OPTIONS (fileF
 the vectorized reader is used when `spark.sql.hive.convertMetastoreOrc` is also set to `true`.
 
 <table class="table">
-  <tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th></tr>
+  <tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th><th><b>Since Version</b></th></tr>
   <tr>
     <td><code>spark.sql.orc.impl</code></td>
     <td><code>native</code></td>
-    <td>The name of ORC implementation. It can be one of <code>native</code> and <code>hive</code>. <code>native</code> means the native ORC support. <code>hive</code> means the ORC library in Hive.</td>
+    <td>
+      The name of ORC implementation. It can be one of <code>native</code> and <code>hive</code>.
+      <code>native</code> means the native ORC support. <code>hive</code> means the ORC library
+      in Hive.
+    </td>
+    <td>2.3.0</td>
   </tr>
   <tr>
     <td><code>spark.sql.orc.enableVectorizedReader</code></td>
     <td><code>true</code></td>
-    <td>Enables vectorized orc decoding in <code>native</code> implementation. If <code>false</code>, a new non-vectorized ORC reader is used in <code>native</code> implementation. For <code>hive</code> implementation, this is ignored.</td>
+    <td>
+      Enables vectorized orc decoding in <code>native</code> implementation. If <code>false</code>,
+      a new non-vectorized ORC reader is used in <code>native</code> implementation.
+      For <code>hive</code> implementation, this is ignored.
+    </td>
+    <td>2.3.0</td>
   </tr>
 </table>

docs/sql-data-sources-parquet.md

@@ -258,7 +258,7 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
 `SET key=value` commands using SQL.
 
 <table class="table">
-<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th><th>Since Version</th></tr>
 <tr>
   <td><code>spark.sql.parquet.binaryAsString</code></td>
   <td>false</td>
@@ -267,6 +267,7 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
     not differentiate between binary data and strings when writing out the Parquet schema. This
     flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
   </td>
+  <td>1.1.1</td>
 </tr>
 <tr>
   <td><code>spark.sql.parquet.int96AsTimestamp</code></td>
@@ -275,6 +276,7 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
     Some Parquet-producing systems, in particular Impala and Hive, store Timestamp into INT96. This
     flag tells Spark SQL to interpret INT96 data as a timestamp to provide compatibility with these systems.
   </td>
+  <td>1.3.0</td>
 </tr>
 <tr>
   <td><code>spark.sql.parquet.compression.codec</code></td>
@@ -287,11 +289,13 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
     Note that <code>zstd</code> requires <code>ZStandardCodec</code> to be installed before Hadoop 2.9.0, <code>brotli</code> requires
     <code>BrotliCodec</code> to be installed.
   </td>
+  <td>1.1.1</td>
 </tr>
 <tr>
   <td><code>spark.sql.parquet.filterPushdown</code></td>
   <td>true</td>
   <td>Enables Parquet filter push-down optimization when set to true.</td>
+  <td>1.2.0</td>
 </tr>
 <tr>
   <td><code>spark.sql.hive.convertMetastoreParquet</code></td>
@@ -300,6 +304,7 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
     When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of the built in
     support.
   </td>
+  <td>1.1.1</td>
 </tr>
 <tr>
   <td><code>spark.sql.parquet.mergeSchema</code></td>
@@ -310,6 +315,7 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
       schema is picked from the summary file or a random data file if no summary file is available.
     </p>
   </td>
+  <td>1.5.0</td>
 </tr>
 <tr>
   <td><code>spark.sql.parquet.writeLegacyFormat</code></td>
@@ -321,5 +327,6 @@ Configuration of Parquet can be done using the `setConf` method on `SparkSession
     example, decimals will be written in int-based format. If Parquet output is intended for use
     with systems that do not support this newer format, set to true.
   </td>
+  <td>1.6.0</td>
 </tr>
 </table>