alexott commented on a change in pull request #558:
URL: https://github.com/apache/incubator-sedona/pull/558#discussion_r738224510
##########
File path: docs/download/databricks.md
##########
@@ -4,13 +4,66 @@ You just need to install the Sedona jars and Sedona Python on
Databricks using D
## Advanced editions
-If you are not using the free version of Databricks, there is an issue with
the path where Sedona Python looks for the jar. Thanks to the report from
Sedona user @amoyrand.
+### Databricks DBR 7.x (Recommended)
-Two steps to fix this:
+If you are using the commercial version of Databricks up to version 7.x you
can install the Sedona jars and Sedona Python using the Databricks default web
UI and everything should work.
-1. Upload the jars in /dbfs/FileStore/jars/
-2. Add this line to the config `.config("spark.jars",
"/dbfs/FileStore/jars/sedona-python-adapter-3.0_2.12-{{ sedona.current_version
}}.jar") \`
+### Databricks DBR 8.x, 9.x, 10.x
+
+If you are not using the free version of Databricks, there are currently some
compatibility issues with DBR 8.x+. Specifically, the `ST_intersect` join query
will throw a `java.lang.NoSuchMethodError` exception.
+
+
+## Install Sedona from the web UI
+
+1) From the Libraries tab install from Maven Coordinates
+ ```
+ org.apache.sedona:sedona-python-adapter-3.0_2.12:{{ sedona.current_version
}}
+ org.datasyslab:geotools-wrapper:{{ sedona.current_geotools }}
+ ```
+
+2) From the Libraries tab install from PyPI
+ ```
+ apache-sedona
+ ```
+
+3) (Optional) You can speed up the serialization of geometry types by adding
to your spark configurations (`Cluster` -> `Edit` -> `Configuration` ->
`Advanced options`) the following lines:
+
+ ```
+ spark.serializer org.apache.spark.serializer.KryoSerializer
+ spark.kryo.registrator org.apache.sedona.core.serde.SedonaKryoRegistrator
+ ```
+
+ *This options are not compatible with the commercial Databricks DBR
versions (8.x+).*
+
+## Initialise
+
+After you have installed the libraries and started the cluster, you can
initialize the Sedona `ST_*` functions and types by running from your code:
+
+(scala)
+```Scala
+import org.apache.sedona.sql.utils.SedonaSQLRegistrator
+SedonaSQLRegistrator.registerAll(sparkSession)
+```
+
+(or python)
+```Python
+from sedona.register.geo_registrator import SedonaRegistrator
+SedonaRegistrator.registerAll(spark)
+```
## Pure SQL environment
+
+In order to use the Sedona `ST_*` functions from SQL, you need to register the
Sedona bindings. There are two ways to do that:
+
+1) Insert a python (or scala) cell at the beginning of your SQL notebook to
activate the bindings
+
+ ```Python
+ %python
+ from sedona.register.geo_registrator import SedonaRegistrator
+ SedonaRegistrator.registerAll(spark)
+ ```
+
+2) Install the sedona libraries from the [cluster
init-scripts](https://docs.databricks.com/clusters/init-scripts.html) and
activate the bindings by adding `spark.sql.extensions
org.apache.sedona.viz.sql.SedonaVizExtensions,org.apache.sedona.sql.SedonaSqlExtensions`
to your cluster's spark configuration. This way you can activate the Sedona
bindings without typing any python or scala code.
Review comment:
I suggest to add the init script example, for example, to install
necessary libraries from the DBFS. This will simplify adoption by users
##########
File path: docs/download/databricks.md
##########
@@ -4,13 +4,66 @@ You just need to install the Sedona jars and Sedona Python on
Databricks using D
## Advanced editions
-If you are not using the free version of Databricks, there is an issue with
the path where Sedona Python looks for the jar. Thanks to the report from
Sedona user @amoyrand.
+### Databricks DBR 7.x (Recommended)
-Two steps to fix this:
+If you are using the commercial version of Databricks up to version 7.x you
can install the Sedona jars and Sedona Python using the Databricks default web
UI and everything should work.
-1. Upload the jars in /dbfs/FileStore/jars/
-2. Add this line to the config `.config("spark.jars",
"/dbfs/FileStore/jars/sedona-python-adapter-3.0_2.12-{{ sedona.current_version
}}.jar") \`
+### Databricks DBR 8.x, 9.x, 10.x
+
+If you are not using the free version of Databricks, there are currently some
compatibility issues with DBR 8.x+. Specifically, the `ST_intersect` join query
will throw a `java.lang.NoSuchMethodError` exception.
+
+
+## Install Sedona from the web UI
+
+1) From the Libraries tab install from Maven Coordinates
+ ```
+ org.apache.sedona:sedona-python-adapter-3.0_2.12:{{ sedona.current_version
}}
+ org.datasyslab:geotools-wrapper:{{ sedona.current_geotools }}
+ ```
+
+2) From the Libraries tab install from PyPI
+ ```
+ apache-sedona
+ ```
+
+3) (Optional) You can speed up the serialization of geometry types by adding
to your spark configurations (`Cluster` -> `Edit` -> `Configuration` ->
`Advanced options`) the following lines:
+
+ ```
+ spark.serializer org.apache.spark.serializer.KryoSerializer
+ spark.kryo.registrator org.apache.sedona.core.serde.SedonaKryoRegistrator
+ ```
+
+ *This options are not compatible with the commercial Databricks DBR
versions (8.x+).*
+
+## Initialise
+
+After you have installed the libraries and started the cluster, you can
initialize the Sedona `ST_*` functions and types by running from your code:
+
+(scala)
+```Scala
+import org.apache.sedona.sql.utils.SedonaSQLRegistrator
+SedonaSQLRegistrator.registerAll(sparkSession)
+```
+
+(or python)
+```Python
+from sedona.register.geo_registrator import SedonaRegistrator
+SedonaRegistrator.registerAll(spark)
+```
## Pure SQL environment
+
+In order to use the Sedona `ST_*` functions from SQL, you need to register the
Sedona bindings. There are two ways to do that:
Review comment:
I'm not sure that the `Sedona bindings` is correct name - maybe
something like: "Sedona functions" or something like this
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