I’m working with a large dataset of molecular structures (approximately 240,000 records) stored in a PostgreSQL database. I need to perform computations on each molecule using RDKit. I’m using Dask for distributed computing and Prefect for workflow management. My main goal is to efficiently distribute this dataset to my Dask workers and compute the results.

Here’s a simplified version of what I’m attempting:

import dask.dataframe as dd
from prefect import flow, task
from prefect_dask import DaskTaskRunner
from rdkit import Chem
from rdkit.Chem import AllChem

@task
def fetch_data():
    return dd.read_sql_table('molecules', engine, index_col='id', npartitions=32)

@task
def process_molecule(smiles):
    mol = Chem.MolFromSmiles(smiles)
    mol = Chem.AddHs(mol)
    AllChem.EmbedMolecule(mol, AllChem.ETKDG())
    # More processing here...
    return processed_data

@flow(task_runner=DaskTaskRunner())
def process_molecules():
    df = fetch_data()
    results = df['smiles'].apply(process_molecule)
    return results.compute()

if __name__ == "__main__":
    process_molecules()

My main questions are:

1. How can I optimize the distribution of this dataset to my Dask workers? Is reading directly from SQL into a Dask DataFrame the most efficient approach?

2. What’s the best way to structure the computation to fully utilize my distributed resources? Should I be using map_partitions instead of apply, or is there a better approach?

3. How can I ensure that the workload is evenly distributed across my Dask workers?

4. Are there any Dask or Prefect-specific optimizations I should consider for this type of large-scale molecular computation?

5. How can I monitor the progress of the computation across the distributed system?

I’m looking for strategies to improve the efficiency of distributing this large dataset to my Dask workers and computing the results. Any insights or examples would be greatly appreciated!