Unable to load tables with delta sharing client if share has a dot (.) in its name

[gnmartins]

Loading a table using the delta sharing client fails if the share, schema, or table contains a dot (.) in their names.

>>> spark.read.format("deltaSharing").load("file:///tmp/profile.json#`my.share`.`my.schema`.`my.table`")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/.../py-envs/spark-3.5.1/lib/python3.11/site-packages/pyspark/sql/readwriter.py", line 307, in load
    return self._df(self._jreader.load(path))
                    ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/.../py-envs/spark-3.5.1/lib/python3.11/site-packages/pyspark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__
  File "/Users/.../py-envs/spark-3.5.1/lib/python3.11/site-packages/pyspark/errors/exceptions/captured.py", line 185, in deco
    raise converted from None
pyspark.errors.exceptions.captured.IllegalArgumentException: path file:///tmp/profile.json#`my.share`.`my.schema`.`my.table` is not valid

This issue can be reproduced on the latest delta sharing client version.

The problem comes from path parsing implemented here, which splits the path on . characters, regardless of escaping.

The delta sharing API does not have any restrictions on share, schema, or table names, aside from mentioning they are case-insensitive.
So the expectation is that some sort of escaping could be applied to allow targeting shares with . in the name.

Example JVM stacktrace

java.lang.IllegalArgumentException: path file:///tmp/profile.json#`my.share`.`my.schema`.`my.table` is not valid
	at io.delta.sharing.client.DeltaSharingRestClient$.parsePath(DeltaSharingClient.scala:970)
	at io.delta.sharing.spark.DeltaSharingDataSource.autoResolveBaseRelationForSnapshotQuery(DeltaSharingDataSource.scala:317)
	at io.delta.sharing.spark.DeltaSharingDataSource.createRelation(DeltaSharingDataSource.scala:237)
	at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:346)
	at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:229)
	at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:211)
	at scala.Option.getOrElse(Option.scala:189)
	at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211)
	at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:186)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)
	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.base/java.lang.reflect.Method.invoke(Method.java:569)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
	at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
	at java.base/java.lang.Thread.run(Thread.java:840)