# dask

This rule guides Cursor when editing Python code that uses Dask, from client setup to partition sizing. Use it to review distributed data workflows.

- rule · `type=rule` · Cursor · ★ 3580
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/dask.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/dask

## What it does

The rule covers Dask pipeline design: profile serial code before adding distributed work, manage `Client` explicitly, and choose partition sizes with worker memory in mind. It also explains when to use `persist()` for reused results and how to avoid running the same computations again.

For scikit-learn workloads, it recommends the Dask-Joblib backend. For data processing, it favors Dask operations and `map_partitions` over repeated conversions to Pandas or NumPy. The rule targets files matching `**/*.py`. Dask adds overhead, so its guidance calls for checking that distributed computing is needed before introducing it.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/dask.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/dask.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/dask.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/dask.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/dask.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/dask.mdc

## When to use it
- identify bottlenecks before moving work to Dask
- size partitions around worker memory
- reuse intermediate results with `persist()`
- run `GridSearchCV` across a Dask cluster
- avoid repeated computation when mixing Pandas and Dask

## Example prompts
- Review `pipeline.py` and suggest changes based on the Dask rule.
- Show how to manage `Client` and choose partition sizes in this Python file.
- Update `GridSearchCV` training to use the Dask-Joblib backend.

## Source text · dask.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: fb22acb44d20ac49.

BEGIN THIRD-PARTY FILE fb22acb44d20ac49
````text
---
description: This guide provides opinionated, actionable best practices for writing performant, maintainable, and idiomatic Dask code, focusing on real-world scenarios and modern patterns.
globs: **/*.py
---
# Dask Best Practices

Dask is a powerful library for scaling Python, but it introduces new paradigms. Writing effective Dask code requires understanding its lazy execution model and distributed computing overhead. This guide outlines critical best practices to ensure your Dask pipelines are reliable, performant, and easy to maintain.

## Critical Guidelines

### 1. Start Small and Profile First

Before reaching for Dask, ensure your problem genuinely requires distributed computing. Optimize your serial code first. Dask adds overhead; it's not a magic bullet for inefficient algorithms.

**✅ GOOD:** Profile your Pandas/NumPy code to identify bottlenecks. Only then consider Dask for the specific slow parts.

```python
import pandas as pd
import numpy as np
import time

# Simulate a large dataset operation
data = np.random.rand(10_000_000, 10)
df_small = pd.DataFrame(data)

start_time = time.time()
result_small = df_small[df_small[0] > 0.5].groupby(1).mean()
print(f"Pandas execution time: {time.time() - start_time:.2f}s")

# If this is too slow, then consider Dask for the bottleneck.
# Don't start with Dask if Pandas is fast enough.
```

### 2. Explicit Client Management

Always create an explicit `dask.distributed.Client` at the top of your script or notebook. Use it as a context manager to ensure proper shutdown and pass it explicitly to any Dask-aware libraries. Avoid relying on implicit global state.

**❌ BAD:** Implicit client usage or manual `client.close()`.

```python
# Relying on implicit client or forgetting to close
from dask.distributed import Client
client = Client() # Client might not be closed on error
# ... Dask operations ...
# client.close() # Easily forgotten
```

**✅ GOOD:** Use `Client` as a context manager and pass it explicitly.

```python
from dask.distributed import Client, LocalCluster

# For local development, use LocalCluster
with LocalCluster(n_workers=4, threads_per_worker=1, memory_limit='2GB') as cluster:
    with Client(cluster) as client:
        print(f"Dask Dashboard: {client.dashboard_link}")
        # Your Dask operations here, passing 'client' if needed
        import dask.dataframe as dd
        df = dd.read_csv("s3://bucket/data/*.csv", blocksize="64MB")
        # ...
```

### 3. Chunk Data Wisely

Choose chunk (partition) sizes that fit comfortably in worker memory (typically 100 MiB to 1 GiB) and align with your underlying storage layout (e.g., Parquet row groups). Too small chunks lead to high task overhead; too large chunks lead to memory pressure.

**❌ BAD:** Default chunking on large files or arbitrary small chunks.

```python
import dask.dataframe as dd
# Default blocksize might be too large or too small for your data/cluster
df = dd.read_csv("large_file.csv")

# Explicitly small chunks lead to massive task graphs
df = dd.read_csv("large_file.csv", blocksize="1MB")
```

**✅ GOOD:** Tune `blocksize` based on data size, worker memory, and file format.

```python
import dask.dataframe as dd
# Aim for 100MB-1GB chunks. Adjust based on your cluster's memory profile.
# For Parquet, align with row group size if possible.
df = dd.read_parquet("s3://bucket/data/*.parquet", blocksize="256MB")
print(f"Number of partitions: {df.npartitions}")
```

### 4. Persist Intermediate Results

If a Dask collection is reused across multiple steps, `persist()` it to keep its computed results in worker memory. This prevents redundant re-computation of the same task graph. Call `.compute()` only once at the very end of your pipeline.

**❌ BAD:** Re-computing the same graph multiple times.

```python
import dask.dataframe as dd
df = dd.read_parquet("data/*.parquet")
filtered_df = df[df['value'] > 0.5]

# This will re-compute 'filtered_df' twice
mean_val = filtered_df['column_A'].mean().compute()
std_val = filtered_df['column_B'].std().compute()
```
````
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