DLT Serverless incremental refresh of materialized view

[L1000]

I have a materialized view that always does a "COMPLETE_RECOMPUTE", but I can't figure out why.
I found how I can get the logs:

SELECT * FROM event_log(pipeline_id)
WHERE event_type = 'planning_information'
ORDER BY timestamp desc;

And for my table I got this info:

{
  "planning_information": {
    "technique_information": [
      {
        "maintenance_type": "MAINTENANCE_TYPE_GENERIC_AGGREGATE",
        "incrementalization_issues": [
          {
            "issue_type": "PLAN_NOT_INCREMENTALIZABLE",
            "prevent_incrementalization": true,
            "operator_name": "Aggregate",
            "plan_not_incrementalizable_sub_type": "AGGREGATE_NOT_TOP_NODE"
          }
        ]
      },
      {
        "maintenance_type": "MAINTENANCE_TYPE_ROW_BASED",
        "incrementalization_issues": [
          {
            "issue_type": "PLAN_NOT_INCREMENTALIZABLE",
            "prevent_incrementalization": true,
            "operator_name": "Join",
            "join_type": "LEFT_OUTER"
          }
        ]
      },
      {
        "maintenance_type": "MAINTENANCE_TYPE_COMPLETE_RECOMPUTE",
        "is_chosen": true,
        "is_applicable": true,
        "cost": 34936819266
      }
    ],
}

It seems like there is an issue with the left outer join?
But normally that is a supported operation:

https://learn.microsoft.com/en-us/azure/databricks/optimizations/incremental-refresh#enzyme-support

[randomquartile]

From the plan it says that aggregation is happening before the join. Can you ensure the join happens first and then the aggregation?  Are you able to share the query?

[L1000]

There is some stuff that happens in separate custom functions, but all the functions consist of supported operations.

The aggregation is .groupBy("Name").count() before anything happens as a way to distinct the data. (because 'distinct' is not supported but 'group by' is).
Afterwards it's a bit of processing and then finally a join on some fields extracted from the 'Name'.

So Materialized Views don't support joins as a final step?

[L1000]

I have split up the materialized view in 3 separate ones:

MV1: deduplicate by grouping:

.table(name="step1", table_properties={"delta.enableRowTracking": "true"})
def step1():
    isolate_names = dlt.read("source_data").select("Name").groupBy("Name").count()
    return isolate_names

MV2:
step2: Use step1 and process data (notable functions: split, slice, sha2, explode -> select expressions)

.table(name="step2", table_properties={"delta.enableRowTracking": "true"})
def step2():
    df = dlt.read(step1).select("Name")
...

MV3:

.table(name="step3", table_properties={"delta.enableRowTracking": "true"})
def step3():
    step2 = dlt.read("step2")

    meta = spark.table("MetaData").alias("meta")

    add_meta = (
        step2.alias("step2")
        .join(
            meta,
            on=[
                f.col("step2.colA") == f.col("meta.col1"),
                f.col("step2.colB") == f.col("meta.col2"),
            ],
            how="left",
        )
        .select("step2.*", "meta.Id")
    )

    return add_meta

for step 3 I get now these incrementalization issues:

{
  "planning_information": {
    "technique_information": [
      {
        "incrementalization_issues": [
          {
            "issue_type": "CDF_UNAVAILABLE",
            "prevent_incrementalization": true,
            "table_information": {
              "table_name": "step2",
            }
          }
        ]
      },
      {
        "maintenance_type": "MAINTENANCE_TYPE_ROW_BASED",
        "incrementalization_issues": [
          {
            "issue_type": "ROW_TRACKING_NOT_ENABLED",
            "prevent_incrementalization": true,
            "table_information": {
              "table_name": "step2",
            }
          },
          {
            "issue_type": "PLAN_NOT_INCREMENTALIZABLE",
            "prevent_incrementalization": true,
            "operator_name": "Join",
            "join_type": "LEFT_OUTER"
          }
        ]
      },
      {
        "maintenance_type": "MAINTENANCE_TYPE_COMPLETE_RECOMPUTE",
        "is_chosen": true,
        "is_applicable": true,
        "cost": 78952
      }
    ],
    "source_table_information": [
      {
        "table_name": "step2",
        "full_size": 3791,
        "is_size_after_pruning": true,
        "is_row_id_enabled": true,
        "is_cdf_enabled": true,
        "is_deletion_vector_enabled": false
      },
      {
        "table_name": "meta",
        "full_size": 1747,
        "is_size_after_pruning": true,
        "is_row_id_enabled": false,
        "is_cdf_enabled": false,
        "is_deletion_vector_enabled": true
      }
    ],
    "target_table_information": {
      "table_name": "step3",
      "full_size": 3943,
      "is_row_id_enabled": true,
      "is_cdf_enabled": true,
      "is_deletion_vector_enabled": false
    }
  }
}

for step1 and step2 I got these messages:
Step1 has been planned in DLT to be executed as GROUP_AGGREGATE. (-> incremental?)
Step2 has been planned in DLT to be executed as COMPLETE_RECOMPUTE. Another option is available:GROUP_AGGREGATE. COMPLETE_RECOMPUTE was chosen in the current run for its optimal performance.

[L1000]

I split up materialized view in 3 separate ones:

step1:

@Dlt.table(name="step1", table_properties={"delta.enableRowTracking": "true"})
def step1():
    isolate_names = dlt.read("soruce").select("Name").groupBy("Name").count()
    return isolate_names

step2:
uses step1 and does some extra processing steps (notable functions: split, explode, slice, ... -> select expressions mostly)

@Dlt.table(name="step2", table_properties={"delta.enableRowTracking": "true"})
def step2():
    asa_telemetry = dlt.read("step1").select("Name")
....

step3:

@Dlt.table(name=step3, table_properties={"delta.enableRowTracking": "true"})
def step3():
    step2 = dlt.read("step2")

    meta = spark.table("MetaData").alias("meta")

    add_id = (
        step2.alias("step2")
        .join(
            meta,
            on=[
                f.col("step2.colA") == f.col("meta.col1"),
                f.col("step2.colB") == f.col("meta.col2"),
            ],
            how="left",
        )
        .select("step2.*", "meta.Id")
    )

    return add_id

For step 1 I get this message:
Flow 'step1' has been planned in DLT to be executed as GROUP_AGGREGATE.

For step2 I get this message:
Flow 'step2' has been planned in DLT to be executed as COMPLETE_RECOMPUTE. Another option is available:GROUP_AGGREGATE. COMPLETE_RECOMPUTE was chosen in the current run for its optimal performance.

So step1 and 2 don't have incrementalisation issues

Step 3 has issues:

{
  "planning_information": {
    "technique_information": [
      {
        "incrementalization_issues": [
          {
            "issue_type": "CDF_UNAVAILABLE",
            "prevent_incrementalization": true,
            "table_information": {
              "table_name": "step2",
            }
          }
        ]
      },
      {
        "maintenance_type": "MAINTENANCE_TYPE_ROW_BASED",
        "incrementalization_issues": [
          {
            "issue_type": "ROW_TRACKING_NOT_ENABLED",
            "prevent_incrementalization": true,
            "table_information": {
              "table_name": "step2",
            }
          },
          {
            "issue_type": "PLAN_NOT_INCREMENTALIZABLE",
            "prevent_incrementalization": true,
            "operator_name": "Join",
            "join_type": "LEFT_OUTER"
          }
        ]
      },
      {
        "maintenance_type": "MAINTENANCE_TYPE_COMPLETE_RECOMPUTE",
        "is_chosen": true,
        "is_applicable": true,
        "cost": 78952
      }
    ],
    "source_table_information": [
      {
        "table_name": "`step2",
        "table_id": "bd21e05c-8011-485a-8f0f-8dc82656d31e",
        "full_size": 3791,
        "is_size_after_pruning": true,
        "is_row_id_enabled": true,
        "is_cdf_enabled": true,
        "is_deletion_vector_enabled": false
      },
      {
        "table_name": "MetaData",
        "full_size": 1747,
        "is_size_after_pruning": true,
        "is_row_id_enabled": false,
        "is_cdf_enabled": false,
        "is_deletion_vector_enabled": true
      }
    ],
    "target_table_information": {
      "table_name": "step3",
      "full_size": 3943,
      "is_row_id_enabled": true,
      "is_cdf_enabled": true,
      "is_deletion_vector_enabled": false
    }
  }
}

So the problem really seems to be the left join?