DLT Streaming With Watermark fails, suggesting I should add watermarks

[DaPo]

Hi all,

I have the following Problem: I have two streaming tables containing time-series measurements from different sensor data, each feed by multiple sensors. (Imagine: Multiple Temperature Sensors for the first table, and multiple humidity sensors for the second table). Each sensor has an sensor_id. In addition, I have an table, which sensors should be merged together. I want to use DLT streaming tables for that. However, I get the following error:

com.databricks.pipelines.common.errors.DLTAnalysisException: Failed to start stream temp_and_humidity in either append mode or complete mode.
Append mode error: [STREAMING_OUTPUT_MODE.UNSUPPORTED_OPERATION] Invalid streaming output mode: append. This output mode is not supported for streaming aggregations without watermark on streaming DataFrames/DataSets. SQLSTATE: 42KDE
Complete mode error: Join between two streaming DataFrames/Datasets is not supported in Complete output mode, only in Append output mode;

 Problem is: I added Watermarks, following this guide: https://kb.databricks.com/en_US/streaming/append-output-not-supported-no-watermark so I think the Append mode error should not happen.

My code looks roughly like this (I omitted some details here):

@Dlt.table
def temp_and_humidity():
    temp = spark.readStream.table("LIVE.temp")
    humidity = spark.readStream.table("LIVE.humidity")
    associations = spark.read.table("LIVE.senosr_associations") # note, that this is a VIEW
    # Basically, we want to do an AS OF JOIN here
    # The algorithm works like this:
    #.  1. do a UNION BY NAME between the two data frames
    #.  2. GROUP BY a small window <- here we need a watermark as far as I understand
    # Making sure, that the schemas are compatible
    temp = temp.withColumn("humidity", F.lit(None))
    humidity = humidity.withColumn("temp", F.lit(None)).withColumn("sensor_group_id", "sensor_id")

    # I think, this join is not allowed in complete mode (which I do not want anyway!)
    temp = temp.join(associations, ...).drop(...) # Some column name manipulations, adding the "sensor_group_id" column
 
    # Merging the data
    temp_and_humidity = temp.unionByName(humidity)

    # Finally, we can aggregate, so that measurements "from the same time" are in one row
    # We have prior knowledge, that each sensor sends data once per minute
    returm (
        temp_and_humidity
            .withColumn("time_window", F.window("timestamp", "1 minute"))
            .withWatermark("time_window", "1 minute").    # <- we add the watermark!
            .groupBy("time_window", "sensor_group_id")
            .agg(
                F.first("humidity", ignorenulls=True), 
                F.first("temp", ignorenulls=True)
            )
    )

I have tried different variants (like setting the Watermark on the "timestamp" column instead of the "time_window"), with no success. Can anyone help me out? Some suggestions, what am I missing here?

Greetings, Daniel