Update docs for 21.08 release

docs/FAQ.md

@@ -64,7 +64,8 @@ Spark driver and executor logs with messages that are similar to the following:
 
 ### What is the right hardware setup to run GPU accelerated Spark?
 
-Reference architectures should be available around Q1 2021.
+GPU accelerated Spark can run on any NVIDIA Pascal or better GPU architecture, including Volta,
+Turing or Ampere. 
 
 ### What parts of Apache Spark are accelerated?
 
@@ -385,6 +386,25 @@ for this issue.
 To fix it you can either disable the IOMMU, or you can disable using pinned memory by setting
 [spark.rapids.memory.pinnedPool.size](configs.md#memory.pinnedPool.size) to 0.
 
+### Why am I getting a buffer overflow error when using the KryoSerializer?
+Buffer overflow will happen when trying to serialize an object larger than
+[`spark.kryoserializer.buffer.max`](https://spark.apache.org/docs/latest/configuration.html#compression-and-serialization),
+and may result in an error such as:
+```
+Caused by: com.esotericsoftware.kryo.KryoException: Buffer overflow. Available: 0, required: 636
+    at com.esotericsoftware.kryo.io.Output.require(Output.java:167)
+    at com.esotericsoftware.kryo.io.Output.writeBytes(Output.java:251)
+    at com.esotericsoftware.kryo.io.Output.write(Output.java:219)
+    at java.base/java.io.ObjectOutputStream$BlockDataOutputStream.write(ObjectOutputStream.java:1859)
+    at java.base/java.io.ObjectOutputStream.write(ObjectOutputStream.java:712)
+    at java.base/java.io.BufferedOutputStream.write(BufferedOutputStream.java:123)
+    at java.base/java.io.DataOutputStream.write(DataOutputStream.java:107)
+    ...
+```
+Try increasing the
+[`spark.kryoserializer.buffer.max`](https://spark.apache.org/docs/latest/configuration.html#compression-and-serialization)
+from a default of 64M to something larger, for example 512M.
+
 ### Is speculative execution supported?
 
 Yes, speculative execution in Spark is fine with the RAPIDS Accelerator plugin.

docs/additional-functionality/qualification-profiling-tools.md

@@ -55,7 +55,7 @@ Optional:
 You do not need to compile the jar yourself because you can download it from the Maven repository directly.
 
 Here are 2 options:
-1. Download the jar file from [Maven repository](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12/21.06.0/)
+1. Download the jar file from [Maven repository](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12/21.08.0/)
 
 2. Compile the jar from github repo
 ```bash

docs/configs.md

@@ -10,7 +10,7 @@ The following is the list of options that `rapids-plugin-4-spark` supports.
 On startup use: `--conf [conf key]=[conf value]`. For example:
 
 ```
-${SPARK_HOME}/bin/spark --jars 'rapids-4-spark_2.12-21.08.0-SNAPSHOT.jar,cudf-21.08.0-SNAPSHOT-cuda11.jar' \
+${SPARK_HOME}/bin/spark --jars 'rapids-4-spark_2.12-21.08.0.jar,cudf-21.08.0-cuda11.jar' \
 --conf spark.plugins=com.nvidia.spark.SQLPlugin \
 --conf spark.rapids.sql.incompatibleOps.enabled=true
 ```

docs/demo/Databricks/generate-init-script-cuda11.ipynb

@@ -1 +1 @@
-{"cells":[{"cell_type":"code","source":["dbutils.fs.mkdirs(\"dbfs:/databricks/init_scripts/\")\n \ndbutils.fs.put(\"/databricks/init_scripts/init.sh\",\"\"\"\n#!/bin/bash\nsudo wget -O /databricks/jars/rapids-4-spark_2.12-21.06.1.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/21.06.1/rapids-4-spark_2.12-21.06.1.jar\nsudo wget -O /databricks/jars/cudf-21.06.1-cuda11.jar https://repo1.maven.org/maven2/ai/rapids/cudf/21.06.1/cudf-21.06.1-cuda11.jar\n\nsudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin\nsudo wget -O ~/cuda-repo-ubuntu1804-11-0-local_11.0.3-450.51.06-1_amd64.deb https://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda-repo-ubuntu1804-11-0-local_11.0.3-450.51.06-1_amd64.deb\nsudo dpkg -i ~/cuda-repo-ubuntu1804-11-0-local_11.0.3-450.51.06-1_amd64.deb\nsudo apt-key add /var/cuda-repo-ubuntu1804-11-0-local/7fa2af80.pub\nsudo apt-get update\nsudo apt -y install cuda-toolkit-11-0\"\"\", True)"],"metadata":{},"outputs":[],"execution_count":1},{"cell_type":"code","source":["%sh\ncd ../../dbfs/databricks/init_scripts\npwd\nls -ltr\ncat init.sh"],"metadata":{},"outputs":[],"execution_count":2},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":3}],"metadata":{"name":"generate-init-script","notebookId":2645746662301564},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","source":["dbutils.fs.mkdirs(\"dbfs:/databricks/init_scripts/\")\n \ndbutils.fs.put(\"/databricks/init_scripts/init.sh\",\"\"\"\n#!/bin/bash\nsudo wget -O /databricks/jars/rapids-4-spark_2.12-21.08.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/21.08.0/rapids-4-spark_2.12-21.08.0.jar\nsudo wget -O /databricks/jars/cudf-21.08.0-cuda11.jar https://repo1.maven.org/maven2/ai/rapids/cudf/21.08.0/cudf-21.08.0-cuda11.jar\n\nsudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin\nsudo wget -O ~/cuda-repo-ubuntu1804-11-0-local_11.0.3-450.51.06-1_amd64.deb https://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda-repo-ubuntu1804-11-0-local_11.0.3-450.51.06-1_amd64.deb\nsudo dpkg -i ~/cuda-repo-ubuntu1804-11-0-local_11.0.3-450.51.06-1_amd64.deb\nsudo apt-key add /var/cuda-repo-ubuntu1804-11-0-local/7fa2af80.pub\nsudo apt-get update\nsudo apt -y install cuda-toolkit-11-0\"\"\", True)"],"metadata":{},"outputs":[],"execution_count":1},{"cell_type":"code","source":["%sh\ncd ../../dbfs/databricks/init_scripts\npwd\nls -ltr\ncat init.sh"],"metadata":{},"outputs":[],"execution_count":2},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":3}],"metadata":{"name":"generate-init-script","notebookId":2645746662301564},"nbformat":4,"nbformat_minor":0}

docs/demo/Databricks/generate-init-script.ipynb

@@ -1 +1 @@
-{"cells":[{"cell_type":"code","source":["dbutils.fs.mkdirs(\"dbfs:/databricks/init_scripts/\")\n \ndbutils.fs.put(\"/databricks/init_scripts/init.sh\",\"\"\"\n#!/bin/bash\nsudo wget -O /databricks/jars/rapids-4-spark_2.12-21.06.1.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/21.06.1/rapids-4-spark_2.12-21.06.1.jar\nsudo wget -O /databricks/jars/cudf-21.06.1-cuda11.jar https://repo1.maven.org/maven2/ai/rapids/cudf/21.06.1/cudf-21.06.1-cuda11.jar\"\"\", True)"],"metadata":{},"outputs":[],"execution_count":1},{"cell_type":"code","source":["%sh\ncd ../../dbfs/databricks/init_scripts\npwd\nls -ltr\ncat init.sh"],"metadata":{},"outputs":[],"execution_count":2},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":3}],"metadata":{"name":"generate-init-script","notebookId":2645746662301564},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","source":["dbutils.fs.mkdirs(\"dbfs:/databricks/init_scripts/\")\n \ndbutils.fs.put(\"/databricks/init_scripts/init.sh\",\"\"\"\n#!/bin/bash\nsudo wget -O /databricks/jars/rapids-4-spark_2.12-21.08.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/21.08.0/rapids-4-spark_2.12-21.08.0.jar\nsudo wget -O /databricks/jars/cudf-21.08.0-cuda11.jar https://repo1.maven.org/maven2/ai/rapids/cudf/21.08.0/cudf-21.08.0-cuda11.jar\"\"\", True)"],"metadata":{},"outputs":[],"execution_count":1},{"cell_type":"code","source":["%sh\ncd ../../dbfs/databricks/init_scripts\npwd\nls -ltr\ncat init.sh"],"metadata":{},"outputs":[],"execution_count":2},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":3}],"metadata":{"name":"generate-init-script","notebookId":2645746662301564},"nbformat":4,"nbformat_minor":0}

docs/dev/adaptive-query.md

@@ -1,3 +1,9 @@
+---
+layout: page
+title: Adaptive Query Execution with the RAPIDS Accelerator for Apache Spark
+nav_order: 1
+parent: Developer Overview
+---
 # Adaptive Query Execution with the RAPIDS Accelerator for Apache Spark
 
 The benefits of AQE are not specific to CPU execution and can provide

docs/dev/nvtx_profiling.md

@@ -1,7 +1,7 @@
 ---
 layout: page
 title: NVTX Ranges
-nav_order: 2
+nav_order: 3
 parent: Developer Overview
 ---
 # Using NVTX Ranges with the RAPIDS Plugin for Spark

docs/dev/testing.md

@@ -1,7 +1,7 @@
 ---
 layout: page
 title: Testing
-nav_order: 1
+nav_order: 2
 parent: Developer Overview
 ---
 An overview of testing can be found within the repository at:

docs/download.md

@@ -18,6 +18,61 @@ cuDF jar, that is either preinstalled in the Spark classpath on all nodes or sub
 that uses the RAPIDS Accelerator For Apache Spark. See the [getting-started
 guide](https://nvidia.github.io/spark-rapids/Getting-Started/) for more details.
 
+## Release v21.08.0
+Hardware Requirements:
+
+The plugin is tested on the following architectures:
+
+	GPU Architecture: NVIDIA V100, T4 and A10/A30/A100 GPUs
+
+Software Requirements:
+
+	OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, CentOS 8
+
+	CUDA & Nvidia Drivers*: 11.0-11.4 & v450.80.02+
+
+	Apache Spark 3.0.1, 3.0.2, 3.0.3, 3.1.1, 3.1.2, Cloudera CDP 7.1.6, 7.1.7, Databricks 7.3 ML LTS or 8.2 ML Runtime, and GCP Dataproc 2.0
+
+	Apache Hadoop 2.10+ or 3.1.1+ (3.1.1 for nvidia-docker version 2)
+
+	Python 3.6+, Scala 2.12, Java 8
+
+*Some hardware may have a minimum driver version greater than v450.80.02+.  Check the GPU spec sheet
+for your hardware's minimum driver version.
+
+### Download v21.08.0
+* Download the [RAPIDS
+  Accelerator for Apache Spark 21.08.0 jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/21.08.0/rapids-4-spark_2.12-21.08.0.jar)
+* Download the [RAPIDS cuDF 21.08.0 jar](https://repo1.maven.org/maven2/ai/rapids/cudf/21.08.0/cudf-21.08.0-cuda11.jar)
+
+This package is built against CUDA 11.2 and has [CUDA forward
+compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html) enabled.  It is tested
+on V100, T4, A30 and A100 GPUs with CUDA 11.0-11.4.  For those using other types of GPUs which
+do not have CUDA forward compatibility (for example, GeForce), CUDA 11.2 is required. Users will
+need to ensure the minimum driver (450.80.02) and CUDA toolkit are installed on each Spark node.
+
+### Release Notes
+New functionality and performance improvements for this release include:
+* Handling data sets that spill out of GPU memory for group by and windowing operations
+* Running window rank and dense rank operations on the GPU
+* Support for the `LEGACY` timestamp
+* Unioning of nested structs
+* Adoption of UCX 1.11 for improved error handling for RAPIDS Spark Accelerated Shuffle
+* Ability to read cached data from the GPU on the supported Databricks runtimes
+* Enabling Parquet writing of array data types from the GPU
+* Optimized reads for small files for ORC
+* Spark Qualification and Profiling Tools
+  * Additional filtering capabilities
+  * Reporting on data types
+  * Reporting on read data formats
+  * Ability to run the qualification tool on Spark 2.x logs
+  * Ability to run the tool on Apache Spark 3.x, AWS EMR 6.3.0, Dataproc 2.0, Microsoft Azure, and
+    Databricks 7.3 and 8.2 logs
+  * Improved qualification tool performance
+
+For a detailed list of changes, please refer to the
+[CHANGELOG](https://github.com/NVIDIA/spark-rapids/blob/main/CHANGELOG.md).
+
 ## Release v21.06.1
 This is a patch release to address an issue with the plugin in the Databricks 7.3 ML LTS runtime.
 
@@ -99,7 +154,7 @@ need to ensure the minimum driver (450.80.02) and CUDA toolkit are installed on
 
 ### Release Notes
 New functionality for this release includes:
-* Support for running on Cloudera CDP 7.1.7 and Databricks 8.2 ML 
+* Support for running on Cloudera CDP 7.1.6, CDP 7.1.7 and Databricks 8.2 ML
 * New functionality related to arrays: 
   * Concatenation of array columns 
   * Casting arrays of floats to arrays of doubles

docs/get-started/Dockerfile.cuda

@@ -50,8 +50,8 @@ COPY spark-3.0.2-bin-hadoop3.2/examples /opt/spark/examples
 COPY spark-3.0.2-bin-hadoop3.2/kubernetes/tests /opt/spark/tests
 COPY spark-3.0.2-bin-hadoop3.2/data /opt/spark/data
 
-COPY cudf-21.08.0-SNAPSHOT-cuda11.jar /opt/sparkRapidsPlugin
-COPY rapids-4-spark_2.12-21.08.0-SNAPSHOT.jar /opt/sparkRapidsPlugin
+COPY cudf-21.08.0-cuda11.jar /opt/sparkRapidsPlugin
+COPY rapids-4-spark_2.12-21.08.0.jar /opt/sparkRapidsPlugin
 COPY getGpusResources.sh /opt/sparkRapidsPlugin
 
 RUN mkdir /opt/spark/python

docs/get-started/getting-started-databricks.md

@@ -48,8 +48,8 @@ cluster.
    version:
     - [Databricks 7.3 LTS
 ML](https://docs.databricks.com/release-notes/runtime/7.3ml.html#system-environment) runs CUDA 10.1
-Update 2. Users wishing to try 21.06.1 on Databricks 7.3 LTS ML will need to install the CUDA
-11.0 toolkit on the cluster.  This can be done with the [generate-init-script-cuda11.ipynb
+Update 2. Users wishing to try 21.06.1 or later on Databricks 7.3 LTS ML will need to install the
+CUDA 11.0 toolkit on the cluster.  This can be done with the [generate-init-script-cuda11.ipynb
 ](../demo/Databricks/generate-init-script-cuda11.ipynb) init script, which installs both the RAPIDS
 Spark plugin and the CUDA 11 toolkit. 
     - [Databricks 8.2
@@ -110,7 +110,7 @@ Spark plugin and the CUDA 11 toolkit.
     ```bash
     spark.rapids.sql.python.gpu.enabled true
     spark.python.daemon.module rapids.daemon_databricks
-    spark.executorEnv.PYTHONPATH /databricks/jars/rapids-4-spark_2.12-21.06.1.jar:/databricks/spark/python
+    spark.executorEnv.PYTHONPATH /databricks/jars/rapids-4-spark_2.12-21.08.0.jar:/databricks/spark/python
     ```
 
 7. Once you’ve added the Spark config, click “Confirm and Restart”.

docs/get-started/getting-started-gcp.md

@@ -131,8 +131,7 @@ submitted as a Dataproc job.  The mortgage examples we use above are also availa
 application](https://github.com/NVIDIA/spark-xgboost-examples/tree/spark-3/examples/apps/scala).
 After [building the jar
 files](https://github.com/NVIDIA/spark-xgboost-examples/blob/spark-3/getting-started-guides/building-sample-apps/scala.md)
-they are available through maven `mvn package -Dcuda.classifier=cuda11-0`. In the 21.06 release, 
-CUDA 11.0/11.2 will be supported. 
+they are available through maven `mvn package -Dcuda.classifier=cuda11-0`. 
 
 Place the jar file `sample_xgboost_apps-0.2.2.jar` under the `gs://$GCS_BUCKET/scala/` folder by
 running `gsutil cp target/sample_xgboost_apps-0.2.2.jar gs://$GCS_BUCKET/scala/`.  To do this you

docs/get-started/getting-started-on-prem.md

@@ -55,17 +55,17 @@ CUDA and will not run on other versions. The jars use a maven classifier to keep
 - CUDA 11.0/11.1/11.2 => classifier cuda11
 
 For example, here is a sample version of the jars and cudf with CUDA 11.0 support:
-- cudf-21.08.0-SNAPSHOT-cuda11.jar
-- rapids-4-spark_2.12-21.08.0-SNAPSHOT.jar
+- cudf-21.08.0-cuda11.jar
+- rapids-4-spark_2.12-21.08.0.jar
 jar that your version of the accelerator depends on.
 
 
 For simplicity export the location to these jars. This example assumes the sample jars above have
 been placed in the `/opt/sparkRapidsPlugin` directory:
 ```shell 
 export SPARK_RAPIDS_DIR=/opt/sparkRapidsPlugin
-export SPARK_CUDF_JAR=${SPARK_RAPIDS_DIR}/cudf-21.08.0-SNAPSHOT-cuda11.jar
-export SPARK_RAPIDS_PLUGIN_JAR=${SPARK_RAPIDS_DIR}/rapids-4-spark_2.12-21.08.0-SNAPSHOT.jar
+export SPARK_CUDF_JAR=${SPARK_RAPIDS_DIR}/cudf-21.08.0-cuda11.jar
+export SPARK_RAPIDS_PLUGIN_JAR=${SPARK_RAPIDS_DIR}/rapids-4-spark_2.12-21.08.0.jar
 ```
 
 ## Install the GPU Discovery Script
@@ -313,6 +313,11 @@ and application.
    of the job will run on the GPU then often you can run with less executor heap memory than would
    be needed for the corresponding Spark job on the CPU.
 
+In case of a "com.esotericsoftware.kryo.KryoException: Buffer overflow" error it is advisable to
+increase the
+[`spark.kryoserializer.buffer.max`](https://spark.apache.org/docs/latest/configuration.html#compression-and-serialization)
+setting to a value higher than the default.
+
 ### Example Command Running on YARN
 ```shell 
 $SPARK_HOME/bin/spark-shell --master yarn \

docs/supported_ops.md

@@ -20899,6 +20899,7 @@ as `a` don't show up in the table. They are controlled by the rules for
 <td> </td>
 <td> </td>
 <td> </td>
+</tr>
 <tr>
 <td>SinglePartition$</td>
 <td>Single partitioning</td>
@@ -20922,6 +20923,7 @@ as `a` don't show up in the table. They are controlled by the rules for
 <td> </td>
 <td> </td>
 <td> </td>
+</tr>
 </table>
 
 ## Input/Output

integration_tests/README.md

@@ -134,7 +134,7 @@ individually, so you don't risk running unit tests along with the integration te
 http://www.scalatest.org/user_guide/using_the_scalatest_shell
 
 ```shell 
-spark-shell --jars rapids-4-spark-tests_2.12-21.08.0-SNAPSHOT-tests.jar,rapids-4-spark-udf-examples_2.12-21.08.0-SNAPSHOT,rapids-4-spark-integration-tests_2.12-21.08.0-SNAPSHOT-tests.jar,scalatest_2.12-3.0.5.jar,scalactic_2.12-3.0.5.jar
+spark-shell --jars rapids-4-spark-tests_2.12-21.08.0-tests.jar,rapids-4-spark-udf-examples_2.12-21.08.0,rapids-4-spark-integration-tests_2.12-21.08.0-tests.jar,scalatest_2.12-3.0.5.jar,scalactic_2.12-3.0.5.jar
 ```
 
 First you import the `scalatest_shell` and tell the tests where they can find the test files you
@@ -157,7 +157,7 @@ If you just want to verify the SQL replacement is working you will need to add t
 example assumes CUDA 11.0 is being used.
 
 ```
-$SPARK_HOME/bin/spark-submit --jars "rapids-4-spark_2.12-21.08.0-SNAPSHOT.jar,rapids-4-spark-udf-examples_2.12-21.08.0-SNAPSHOT.jar,cudf-21.08.0-SNAPSHOT-cuda11.jar" ./runtests.py
+$SPARK_HOME/bin/spark-submit --jars "rapids-4-spark_2.12-21.08.0.jar,rapids-4-spark-udf-examples_2.12-21.08.0.jar,cudf-21.08.0-cuda11.jar" ./runtests.py
 ```
 
 You don't have to enable the plugin for this to work, the test framework will do that for you.
@@ -256,7 +256,7 @@ To run cudf_udf tests, need following configuration changes:
 As an example, here is the `spark-submit` command with the cudf_udf parameter on CUDA 11.0:
 
 ```
-$SPARK_HOME/bin/spark-submit --jars "rapids-4-spark_2.12-21.08.0-SNAPSHOT.jar,rapids-4-spark-udf-examples_2.12-21.08.0-SNAPSHOT.jar,cudf-21.08.0-SNAPSHOT-cuda11.jar,rapids-4-spark-tests_2.12-21.08.0-SNAPSHOT.jar" --conf spark.rapids.memory.gpu.allocFraction=0.3 --conf spark.rapids.python.memory.gpu.allocFraction=0.3 --conf spark.rapids.python.concurrentPythonWorkers=2 --py-files "rapids-4-spark_2.12-21.08.0-SNAPSHOT.jar" --conf spark.executorEnv.PYTHONPATH="rapids-4-spark_2.12-21.08.0-SNAPSHOT.jar" ./runtests.py --cudf_udf
+$SPARK_HOME/bin/spark-submit --jars "rapids-4-spark_2.12-21.08.0.jar,rapids-4-spark-udf-examples_2.12-21.08.0.jar,cudf-21.08.0-cuda11.jar,rapids-4-spark-tests_2.12-21.08.0.jar" --conf spark.rapids.memory.gpu.allocFraction=0.3 --conf spark.rapids.python.memory.gpu.allocFraction=0.3 --conf spark.rapids.python.concurrentPythonWorkers=2 --py-files "rapids-4-spark_2.12-21.08.0.jar" --conf spark.executorEnv.PYTHONPATH="rapids-4-spark_2.12-21.08.0.jar" ./runtests.py --cudf_udf
 ```
 
 ## Writing tests

sql-plugin/src/main/scala/com/nvidia/spark/rapids/RapidsConf.scala

@@ -1280,7 +1280,7 @@ object RapidsConf {
         |On startup use: `--conf [conf key]=[conf value]`. For example:
         |
         |```
-        |${SPARK_HOME}/bin/spark --jars 'rapids-4-spark_2.12-21.08.0-SNAPSHOT.jar,cudf-21.08.0-SNAPSHOT-cuda11.jar' \
+        |${SPARK_HOME}/bin/spark --jars 'rapids-4-spark_2.12-21.08.0.jar,cudf-21.08.0-cuda11.jar' \
         |--conf spark.plugins=com.nvidia.spark.SQLPlugin \
         |--conf spark.rapids.sql.incompatibleOps.enabled=true
         |```

sql-plugin/src/main/scala/com/nvidia/spark/rapids/TypeChecks.scala

@@ -1603,6 +1603,7 @@ object SupportedOpsDocs {
           TypeEnum.values.foreach { _ =>
             println(NotApplicable.htmlTag)
           }
+          println("</tr>")
           totalCount += 1
         }
       }