Hello , @eballinger
Table Metadata Initialization Overhead
When dynamically declaring tables, your loop might be causing additional overhead by reinitializing metadata or creating resources redundantly.
Suggestions:
Ensure that the loop only processes the essential metadata and avoids redundant operations.
Use caching mechanisms for metadata if applicable, to avoid fetching the same information multiple times.
2. Parallel Execution
In the static approach, each table is declared independently and likely allows for better parallel execution. A dynamic loop may serialize operations or block parallelism.
Suggestions:
Use multi-threading or asynchronous execution to declare tables in parallel, especially if the DLT framework supports parallel processing.
Group tables logically and batch their creation.
3. Code Complexity in the Loop
The logic inside the dynamic loop could be more computationally expensive than expected, such as repeated operations, large data manipulations, or complex branching.
Suggestions:
Profile your loop logic to identify bottlenecks (e.g., using Python's cProfile or timeit modules).
Simplify the logic, removing unnecessary operations.
4. Validation and Dependency Checks
DLT may validate each dynamically declared table and check dependencies, which could take longer dynamically compared to static declarations where such checks may be cached.
Suggestions:
Check if DLT provides configuration options to limit validation overhead for dynamic declarations.
If you’re not using all tables, filter the table list to reduce unnecessary declarations.
5. Metadata Table Lookup
If your dynamic approach depends on querying a metadata table or schema, delays may occur due to inefficient database queries.
Suggestions:
Optimize database queries for fetching table metadata (e.g., indexes, caching).
Pre-fetch the metadata into memory if feasible and iterate over it locally.
6. Initialization in DLT
DLT may optimize differently between static and dynamic methods, such as precomputing dependencies for static declarations.
Suggestions:
Review DLT-specific documentation for optimizations when using dynamic declarations.
Check if there are recommended practices for declaring large numbers of tables dynamically.
7. Use Partitioned Runs
If tables can be grouped into logical partitions, split the pipeline initialization into smaller chunks. For instance, initialize 50 tables at a time in separate runs and monitor performance.
Example Optimized Dynamic Declaration:
python
Copy code
import dlt
# Pre-fetch metadata for tables
table_metadata = get_table_metadata() # Replace with your function to fetch metadata
@dlt.pipeline(name="my_pipeline", storage_path="/path/to/storage")
def my_pipeline():
for table in table_metadata:
@dlt.table(
name=table['name'],
schema=table['schema'],
primary_key=table['primary_key'],
)
def load_table():
return read_csv(table['path']) # Replace with your data loading logic
if __name__ == "__main__":
my_pipeline().run()
This ensures minimal overhead by pre-fetching metadata and only declaring what’s necessary.
Next Steps
Profile your dynamic logic to identify bottlenecks.
Implement parallelism where possible.
Optimize metadata fetching and DLT initialization configurations.
Best Regards
