The error you're encountering occurs because Delta Live Tables (DLT) append flows currently do not support streaming aggregations or other transformations on streaming DataFrames unless a watermark is applied properly to handle late data. Based on your query and the provided context, letโs carefully address the issues and clarify some points:
1. Flow Planning in Append Mode: The error indicates that DLT is unable to compute a plan for append mode because multiple flows are writing to the same target table. However, the target table needs clearly defined appendable data that streaming transformations can process incrementally.
2. Watermarking: The error message indicates the necessity to use watermarks effectively to support append mode for cases involving streaming aggregations. Watermarks are essential to manage event-time-based processing in order to drop late events and enable structured streaming to process data incrementally.
Points to Address: Dynamic Function in DLT Append Flows - The example in the documentation supports looping through multiple topics for Kafka sources, and similar patterns should also apply to non-Kafka sources. Here's how to dynamically create
@Dlt.append_flow for each source, adapting your logic for non-Kafka scenarios.
#### Recommendations: 1. Ensure Watermark is Applied to Source Streams: Add .withWatermark(event_time_column, "time_limit") to your streaming DataFrame of each source to manage late-arrival data. Watermarking is critical for streaming aggregations.
-
Refactor Code for Dynamic @append_flow Definitions:
- Move your logic into individual
@append_flow-decorated functions for each source, ensuring they return a DataFrame compatible with append mode.
-
Avoid Multiple Writers to the Same Output Using @append_flow:
- Ensure each
@append_flow writes to distinct portions of data in the target table, or refactor the pipeline to combine streams upstream if possible.
Example Code for Non-Kafka Source Streams: Below is the revised version of your pipeline, incorporating watermarks and avoiding the issue of multiple flows writing to the same target without clear partitioning logic:
```python import dlt from pyspark.sql.functions import col
# Define the catalog, schema, and output table catalog = "<catalog>" bronze_schema = "<bronze_schema>" stat_table = f"{catalog}.{bronze_schema}.output"
# Create the target streaming table dlt.create_streaming_table(name=stat_table)
# Function to create dynamic flows with watermarks for each source list = ['table1', 'table2', 'table3'] # Replace this with your actual sources
for source in list:
@Dlt.append_flow(target=stat_table, name=f"{source}_flow") def process_source(): return dlt.read_stream(f"{source}").withWatermark("event_time_column", "10 minutes").select("*")
``
Here are the key highlights of this approach:
- A withWatermark
is applied to every source, using an appropriate column (e.g., event_time_column
) and a time limit like "10 minutes" to ensure support for append mode.
- Each flow has a unique name (e.g., "{source}_flow"`), ensuring that checkpoints are distinct and consistent with the flow definition.
Additional Notes: - The provided solution is scalable, as each additional source automatically generates a new @append_flow without conflict. - If your sources donโt have event_time columns, consider generating a pseudo-event time column for them based on ingestion timestamps.
Reference Documentation: - Delta Live Tables append flow documentation mentions the importance of watermarks and dynamic @append_flow processing. - Examples of handling multiple flows to a single target are also described in other contexts.
.
Hope this helps, Big Roux.
