Dask Parallel and Distributed Computing

Scale pandas/NumPy workflows beyond memory and across clusters.

When to Use

• Datasets exceed available RAM
• Need to parallelize pandas or NumPy operations
• Processing multiple files efficiently (CSVs, Parquet)
• Building custom parallel workflows
• Distributing workloads across multiple cores/machines

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Dask Collections

Collection	Like	Use Case
DataFrame	pandas	Tabular data, CSV/Parquet
Array	NumPy	Numerical arrays, matrices
Bag	list	Unstructured data, JSON logs
Delayed	Custom	Arbitrary Python functions

Key concept: All collections are lazy—computation happens only when you call .compute().

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Lazy Evaluation

Function	Behavior	Use
dd.read_csv()	Lazy load	Large CSVs
dd.read_parquet()	Lazy load	Large Parquet
Operations	Build graph	Chain transforms
.compute()	Execute	Get final result

Key concept: Dask builds a task graph of operations, optimizes it, then executes in parallel. Call .compute() once at the end, not after every operation.

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Schedulers

Scheduler	Best For	Start
threaded	NumPy/Pandas (releases GIL)	Default
processes	Pure Python (GIL bound)	scheduler='processes'
synchronous	Debugging	scheduler='synchronous'
distributed	Monitoring, scaling, clusters	Client()

Distributed Scheduler

Feature	Benefit
Dashboard	Real-time progress monitoring
Cluster scaling	Add/remove workers
Fault tolerance	Retry failed tasks
Worker resources	Memory management

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Chunking Concepts

DataFrame Partitions

Concept	Description
Partition	Subset of rows (like a mini DataFrame)
npartitions	Number of partitions
divisions	Index boundaries between partitions

Array Chunks

Concept	Description
Chunk	Subset of array (n-dimensional block)
chunks	Tuple of chunk sizes per dimension
Optimal size	~100 MB per chunk

Key concept: Chunk size is critical. Too small = scheduling overhead. Too large = memory issues. Target ~100 MB.

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DataFrame Operations

Supported (parallel)

Category	Operations
Selection	filter, loc, column selection
Aggregation	groupby, sum, mean, count
Transforms	apply (row-wise), map_partitions
Joins	merge, join (shuffles data)
I/O	read_csv, read_parquet, to_parquet

Avoid or Use Carefully

Operation	Issue	Alternative
iterrows	Kills parallelism	map_partitions
apply(axis=1)	Slow	map_partitions
Repeated compute()	Inefficient	Single compute() at end
sort_values	Expensive shuffle	Avoid if possible

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Common Patterns

ETL Pipeline

1. scan_* or read_* (lazy load)
2. Chain filters and transforms
3. Single .compute() or .to_parquet()

Multi-File Processing

Pattern	Description
Glob patterns	dd.read_csv('data/*.csv')
Partition per file	Natural parallelism
Output partitioned	to_parquet('output/')

Custom Operations

Method	Use Case
map_partitions	Apply function to each partition
map_blocks	Apply function to each array block
delayed	Wrap arbitrary Python functions

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Best Practices

Practice	Why
Don't load locally first	Let Dask handle loading
Single compute() at end	Avoid redundant computation
Use Parquet	Faster than CSV, columnar
Match partition to files	One partition per file
Check task graph size	len(ddf.__dask_graph__()) < 100k
Use distributed for debugging	Dashboard shows progress

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Common Pitfalls

Pitfall	Solution
Loading with pandas first	Use dd.read_* directly
compute() in loops	Collect all, single compute()
Too many partitions	Repartition to ~100 MB each
Memory errors	Reduce chunk size, add workers
Slow shuffles	Avoid sorts/joins when possible

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vs Alternatives

Tool	Best For	Trade-off
Dask	Scale pandas/NumPy, clusters	Setup complexity
Polars	Fast in-memory	Must fit in RAM
Vaex	Out-of-core single machine	Limited operations
Spark	Enterprise, SQL-heavy	Infrastructure

Resources

• Docs: https://docs.dask.org/
• Best Practices: https://docs.dask.org/en/stable/best-practices.html
• Examples: https://examples.dask.org/