Why does @dlt.table from a table give different results than from a view?

[PrebenOlsen]

I have some data in silver that I read in as a view using the __apply_changes function on. I create a table based on this, and I then want to create my gold-table, after doing a .groupBy() and .pivot(). The transformations I do in the gold-table aren't giving my any error messages, they simply aren't being done. If I create my gold-table based on a view instead of a table, the transformations are done. See bottom for fabricated example.

Here's my code:

import dlt
from pyspark.sql.functions import col, concat_ws, explode, split, explode_outer, posexplode, concat, lit, expr, first, create_map
 
@dlt.view
def silver_messages():
    """Silver messages table"""
    return spark.readStream.table("eqs_cloud.__apply_changes_storage_silver_messages")
 
@dlt.view
def groups_hierarchy_vw():
    """Get all groups' hierarchy"""
    
    assigned = dlt.read_stream("silver_messages").select("assignedToUnit.*")
    from_unit = dlt.read_stream("silver_messages").select("fromUnit.*")
    to_unit = dlt.read_stream("silver_messages").select("toUnit.*")
    
    groups_hierarchy_vw = assigned.unionByName(from_unit).unionByName(to_unit)
 
    return groups_hierarchy_vw
 
@dlt.table
def groups_hierarchy_tbl():
    return dlt.read_stream("groups_hierarchy_vw")
 
@dlt.table
def groups_hierarchy_tbl_pivoted():
    return(dlt.read("groups_hierarchy_tbl")
        .select(
            "id",
            "name",
            split("path","/").alias("groups_in_path"),
            posexplode(split("path","/")).alias("pos","value"))
        .select(
            "id",
            "name",
            concat(lit("group"),"pos").alias("group_name"),
            expr("groups_in_path[pos]").alias("val"))
        .groupBy("id", "name")
        .pivot("group_name")
        .agg(first("val"))
    )

Is there a way I can use the groups_hierarchy_vw view directly in the gold-table at the bottom? If I enter it with dlt.read_stream("groups_hierarchy_vw") I get an error "pyspark.sql.utils.AnalysisException: Queries with streaming sources must be executed with writeStream.start();"

Here's a fabricated example you can try out.

import pandas as pd
from pyspark.sql.functions import col, concat_ws, explode, split, explode_outer, posexplode, concat, lit, expr, first, create_map
 
pdf = pd.DataFrame({"id": ["1001", "1002", "1003"],
                    "name": ["Dep1", "Dep2", "Dep3"],
                    "path": ["1001", "1001/1002", "1001/1002/1003"]
})
                
df = spark.createDataFrame(pdf)
df.write.mode('overwrite').saveAsTable('fabricated_testtable')
 
@dlt.view
def fabricated_hierarchy_vw():
    return spark.read.table('fabricated_testtable')
 
@dlt.table
def fabricated_tbl_pivoted():
    return (dlt.read('fabricated_hierarchy_vw')
        .select(
            "id",
            "name",
            split("path","/").alias("groups_in_path"),
            posexplode(split("path","/")).alias("pos","value"))
        .select(
            "id",
            "name",
            concat(lit("group"),"pos").alias("group_name"),
            expr("groups_in_path[pos]").alias("val"))
        .groupBy("id", "name")
        .pivot("group_name")
        .agg(first("val"))        
    )

This gives the expected outcome:

 

But if you change line 12 to @dlt.table, the transformations are not performed and the outcome is this:

[PrebenOlsen · Accepted solution]

I have found a temporary solution to solve this.

The .pivot("columnName") should automatically grab all the values it can find, but for some reason it does not. I need to specify the values, using

.pivot("group_name", "group0", "group1", "group2"...)

or

.pivot("group_name", ["group{}".format(i) for i in range(8)])