refine cupsatial demo to make it more clear for customers

examples/UDF-Examples/Spark-cuSpatial/Dockerfile

@@ -39,11 +39,11 @@ RUN conda --version
 RUN conda install -c conda-forge openjdk=8 maven=3.8.1 -y
 
 # install cuDF dependency.
-RUN conda install -c rapidsai-nightly -c nvidia -c conda-forge -c defaults libcuspatial=22.06 python=3.8 -y
+RUN conda install -c rapidsai -c nvidia -c conda-forge -c defaults libcuspatial=22.06 python=3.8 -y
 
 RUN wget --quiet \
     https://github.com/Kitware/CMake/releases/download/v3.21.3/cmake-3.21.3-linux-x86_64.tar.gz \
     && tar -xzf cmake-3.21.3-linux-x86_64.tar.gz \
     && rm -rf cmake-3.21.3-linux-x86_64.tar.gz
 
-ENV PATH="/cmake-3.21.3-linux-x86_64/bin:${PATH}"
+ENV PATH="/cmake-3.21.3-linux-x86_64/bin:${PATH}"

examples/UDF-Examples/Spark-cuSpatial/Dockerfile.awsdb

@@ -18,6 +18,9 @@ FROM nvidia/cuda:11.2.2-devel-ubuntu18.04
 
 ENV DEBIAN_FRONTEND=noninteractive
 
+# update cuda pub key to avoid GPG error
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+
 # See https://github.com/databricks/containers/blob/master/ubuntu/minimal/Dockerfile
 RUN apt-get update && \
     apt-get install --yes --no-install-recommends \

examples/UDF-Examples/Spark-cuSpatial/README.md

@@ -5,18 +5,38 @@ It implements a [RapidsUDF](https://nvidia.github.io/spark-rapids/docs/additiona
 interface to call the cuSpatial functions through JNI. It can be run on a distributed Spark cluster with scalability.
 
 ## Performance
-We got the end-2-end time as below table when running with 2009 NYC Taxi trip pickup location,
-which includes 168,898,952 points, and 3 sets of polygons(taxi_zone, nyct2000, nycd).
-The data can be downloaded from [TLC Trip Record Data](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page) 
-and [NYC Open data](https://www1.nyc.gov/site/planning/data-maps/open-data.page#district_political).
-| Environment | Taxi_zones (263 Polygons) | Nyct2000 (2216 Polygons) | Nycd (71 Complex Polygons)|
+We got the end-2-end hot run times as below table when running with 2009 NYC Taxi trip pickup location,
+which includes 170,896,055 points, and 3 sets of polygons(taxi_zone, nyct2000, nycd Community-Districts).
+The point data can be downloaded from [TLC Trip Record Data](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page).
+The polygon data can be downloaded from [taxi_zone dataset](https://data.cityofnewyork.us/Transportation/NYC-Taxi-Zones/d3c5-ddgc),
+[nyct2000 dataset](https://data.cityofnewyork.us/City-Government/2000-Census-Tracts/ysjj-vb9j) and 
+[nycd Community-Districts dataset](https://data.cityofnewyork.us/City-Government/Community-Districts/yfnk-k7r4)
+
+| Environment | Taxi_zones (263 Polygons) | Nyct2000 (2216 Polygons) | Nycd Community-Districts (71 Complex Polygons)|
 | ----------- | :---------: | :---------: | :---------: |
-| 4-core CPU | 1122.9 seconds | 5525.4 seconds| 6642.7 seconds |
-| 1 GPU(Titan V) on local | 4.5 seconds | 5.7 seconds | 6.6 seconds|
-| 2 GPU(T4) on Databricks | 9.1 seconds | 10.0 seconds | 12.1 seconds |
+| 4-core CPU | 3.9 minutes | 4.0 minutes| 4.1 minutes |
+| 1 GPU(T4) on Databricks | 25 seconds | 27 seconds | 28 seconds|
+| 2 GPU(T4) on Databricks | 15 seconds | 14 seconds | 17 seconds |
+| 4 GPU(T4) on Databricks | 11 seconds | 11 seconds | 12 seconds |
+
+Note: Please update the `x,y` column names to `Start_Lon,Start_Lat` in
+the [notebook](./notebooks/cuspatial_sample_db.ipynb) if you test with the download points.
+
+taxi-zones map:
+
+<img src="../../../docs/img/guides/cuspatial/taxi-zones.png" width="600">
+
+nyct2000 map:
+
+<img src="../../../docs/img/guides/cuspatial/Nyct2000.png" width="600">
+
+nyct-community-districts map:
+
+<img src="../../../docs/img/guides/cuspatial/Nycd-Community-Districts.png" width="600">
 
 ## Build
-You can build the jar file [in Docker](#build-in-docker) with the provided [Dockerfile](Dockerfile)
+First you need to build the UDF Jar file from source code before running this demo.
+You can do it [in Docker](#build-in-docker) with the provided [Dockerfile](Dockerfile).
 or you can build it [in local](#build-in-local) machine after some prerequisites.
 
 ### Build in Docker
@@ -33,6 +53,8 @@ or you can build it [in local](#build-in-local) machine after some prerequisites
      ```
 3. You'll get the jar named like "spark-cuspatial-<version>.jar" in the target folder.
 
+Note: The docker env is just for building the jar, not for running the application.
+
 ### Build in Local:
 1. essential build tools:
     - [cmake(>=3.20)](https://cmake.org/download/),
@@ -43,21 +65,20 @@ or you can build it [in local](#build-in-local) machine after some prerequisites
 4. [cuspatial](https://github.com/rapidsai/cuspatial): install libcuspatial
     ```Bash
     # get libcuspatial from conda
-    conda install -c rapidsai -c nvidia -c conda-forge  -c defaults libcuspatial=22.04
+    conda install -c rapidsai -c nvidia -c conda-forge  -c defaults libcuspatial=22.06
     # or below command for the nightly (aka SNAPSHOT) version.
-    conda install -c rapidsai-nightly -c nvidia -c conda-forge  -c defaults libcuspatial=22.06
+    conda install -c rapidsai-nightly -c nvidia -c conda-forge  -c defaults libcuspatial=22.08
     ```
 5. Get the code, then run "mvn package".
      ```Bash
      git clone https://github.com/NVIDIA/spark-rapids-examples.git
      cd spark-rapids-examples/examples/Spark-cuSpatial/
      mvn package
      ```
-6. You'll get "spark-cuspatial-<version>.jar" in the target folder.      
-
+6. You'll get "spark-cuspatial-<version>.jar" in the target folder.
 
 ## Run
-### Run on-premises clusters: standalone
+### GPU Demo Run on-premises clusters: standalone
 1. Install necessary libraries. Besides `cudf` and `cuspatial`, the `gdal` library that is compatible with the installed `cuspatial` may also be needed.
     Install it by running the command below.
     ```
@@ -69,12 +90,16 @@ or you can build it [in local](#build-in-local) machine after some prerequisites
    * [spark-rapids v22.06.0](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar) or above
 4. Prepare the dataset & jars. Copy the sample dataset from [cuspatial_data](../../../datasets/cuspatial_data.tar.gz) to "/data/cuspatial_data".
     Copy spark-rapids & spark-cuspatial-22.08.0-SNAPSHOT.jar to "/data/cuspatial_data/jars".
+    If you build the spark-cuspatial-22.08.0-SNAPSHOT.jar in docker, please copy the jar from docker to local:
+    ```
+    docker cp your-instance:/root/spark-rapids-examples/examples/UDF-Examples/Spark-cuSpatial/target/spark-cuspatial-<version>.jar ./your-local-path
+    ```
     You can use your own path, but remember to update the paths in "gpu-run.sh" accordingly.
 5. Run "gpu-run.sh"
     ```Bash
     ./gpu-run.sh
     ```
-### Run on AWS Databricks
+### GPU Demo Run on AWS Databricks
 1. Build the customized docker image [Dockerfile.awsdb](Dockerfile.awsdb) and push to dockerhub so that it can be accessible by AWS Databricks.
      ```Bash
      # replace your dockerhub repo, your tag or any other repo AWS DB can access
@@ -103,5 +128,41 @@ or you can build it [in local](#build-in-local) machine after some prerequisites
         points
         polygons
     ```
+   The sample points and polygons are generated by random.
+
+   Sample polygons: 
+
+   <img src="../../../docs/img/guides/cuspatial/sample-polygon.png" width="600">
+
 4. Import the Library "spark-cuspatial-22.08.0-SNAPSHOT.jar" to the Databricks, then install it to your cluster.
+
+   <img src="../../../docs/img/guides/cuspatial/install-jar.png" width="600">    
+
 5. Import [cuspatial_sample.ipynb](notebooks/cuspatial_sample_db.ipynb) to your workspace in Databricks. Attach to your cluster, then run it.
+
+### CPU Demo Run on AWS Databricks
+1. Set up a Databricks cluster with Databricks Runtime Version: Standard Runtime 10.3,
+
+2. Install the Sedona jars and Sedona Python on Databricks using Databricks default web UI. 
+   The Sedona version should be 1.1.1-incubating or higher.
+   * From the Libraries tab install from Maven Coordinates
+    ```Bash
+    org.apache.sedona:sedona-python-adapter-3.0_2.12:1.2.0-incubating
+    org.datasyslab:geotools-wrapper:1.1.0-25.2
+    ```
+   * For enabling python support, from the Libraries tab install from PyPI
+    ```Bash
+    apache-sedona
+    ```
+3. From your cluster configuration (Cluster -> Edit -> Configuration -> Advanced options -> Spark) activate the 
+   Sedona functions and the kryo serializer by adding to the Spark Config
+    ```Bash
+    spark.sql.extensions org.apache.sedona.viz.sql.SedonaVizExtensions,org.apache.sedona.sql.SedonaSqlExtensions
+    spark.serializer org.apache.spark.serializer.KryoSerializer
+    spark.kryo.registrator org.apache.sedona.core.serde.SedonaKryoRegistrator
+    ```
+4. Restart the cluster because the libraries installed via UI are installed after the cluster has already started, 
+   and therefore the classes specified by the config `spark.sql.extensions`, `spark.serializer`, and `spark.kryo.registrator` are not available
+   at startup time.
+
+5. Upload the sample data files to dbfs, attach the [notebook](notebooks/spacial-cpu-apache-sedona_db.ipynb) to the cluster, and run all cells.