If I remember correctly, the major audience of the "observe" API is Structured Streaming, micro-batch mode. From the example, the abstraction in 2 isn't something working with Structured Streaming. It could be still done with callback, but it remains the question how much complexity is hidden from abstraction.
I see you're focusing on PySpark - I'm not sure whether there's intention on not exposing query listener / streaming query listener to PySpark, but if there's some valid reason to do so, I'm not sure we do like to expose them to PySpark in any way. 2 isn't making sense to me with PySpark - how do you ensure all the logic is happening in the JVM and you can leverage these values from PySpark? (I see there's support for listeners with DStream in PySpark, so there might be reasons not to add the same for SQL/SS. Probably a lesson learned?) On Mon, Mar 15, 2021 at 6:59 PM Enrico Minack <m...@enrico.minack.dev> wrote: > Hi Spark-Devs, > > the observable metrics that have been added to the Dataset API in 3.0.0 > are a great improvement over the Accumulator APIs that seem to have much > weaker guarantees. I have two questions regarding follow-up contributions: > > *1. Add observe to Python **DataFrame* > > As I can see from master branch, there is no equivalent in the Python API. > Is this something planned to happen, or is it missing because there are not > listeners in PySpark which renders this method useless in Python. I would > be happy to contribute here. > > > *2. Add Observation class to simplify result access * > > The Dataset.observe method requires users to register listeners > <https://spark.apache.org/docs/latest/api/scala/org/apache/spark/sql/Dataset.html#observe(name:String,expr:org.apache.spark.sql.Column,exprs:org.apache.spark.sql.Column*):org.apache.spark.sql.Dataset[T]> > with QueryExecutionListener or StreamingQUeryListener to obtain the > result. I think for simple setups, this could be hidden behind a common > helper class here called Observation, which reduces the usage of observe > to three lines of code: > > // our Dataset (this does not count as a line of code)val df = Seq((1, "a"), > (2, "b"), (4, "c"), (8, "d")).toDF("id", "value") > // define the observation we want to makeval observation = > Observation("stats", count($"id"), sum($"id")) > // add the observation to the Dataset and execute an action on itval cnt = > df.observe(observation).count() > // retrieve the resultassert(observation.get === Row(4, 15)) > > The Observation class can handle the registration and de-registration of > the listener, as well as properly accessing the result across thread > boundaries. > > With *2.*, the observe method can be added to PySpark without introducing > listeners there at all. All the logic is happening in the JVM. > > Thanks for your thoughts on this. > > Regards, > Enrico >
