Should I always use a parallel stream when possible?

java

parallel-streams

thread-safety

performance-optimization

byAnton Shumikhin·Dec 10, 2024

The use of parallel streams is not a rule of thumb, but more of a strategic option. They are primarily useful for processing large datasets and intensive computations, thanks to their concurrent processing abilities. However, for smaller datasets or simpler operations, the overhead of parallelism may lead to inefficiency. Thus, parallelStream() is ideally suited for:

Crunching massive amounts of data.
Dealing with CPU-bound tasks.
Situations with sufficient CPU resources.

A quick illustrative example:

// It's crunch time!
largeList.parallelStream()
         .map(this::intenseComputation)
         .toList(); // Java 16 and onwards shorthand

Prior to transitioning to parallel operation, ensure to measure your code's performance with the actual load. Blindly switching to parallel can lead to counterproductive results.

Understanding parallel streams

Parallel streams can optimize resource use for high-load computations in multi-core processors. However, the overhead associated with task management may negate their benefits—imagine several chefs in a small kitchen, everyone’s cooking but also colliding constantly.

Key notes to remember:

Synchronized resource access can lead to bottlenecks, chef fights over the only stove.
Thread safety is needed for parallel stream operations; Don't risk chefs chopping off each other's fingers.
Execution sequence is not predictable in a parallel stream as it is in a sequential stream.

Parallel stream wisdom

Adding parallel streams in scenarios with many running threads can hamper performance due to context-switching, analogous to chefs frequently swapping jobs.
Data sources like LinkedList that are hard to split may result in inefficient parallel streams. Instead, use array-based data structures such as ArrayList or HashMap that can be split more efficiently.
Code with good locality of reference—data grouped closely in memory—is crucial for efficient parallel streaming. Like a kitchen arranged by food types.
For potentially unbounded or lengthy stream operations, use safeguards to avoid memory overflow—like setting a timer on cooking to prevent burning the dish.

Tailoring streams

Large data processing

When N (the size of the data) and Q (the processing cost per item) suggest a high workload, a parallel stream could enhance throughput. Informally, if N x Q is above 10,000, go for parallel streams.

CPU-bound scenarios

Parallel streams perform best when dealing with compute-intensive tasks that can be divided and processed independently across cores.

I/O-intensive tasks

For I/O-intensive tasks, parallelism can often lead to performance degradation due to resource contention. Sequential execution is usually better in such cases.

Benchmark with reality

Techniques such as JMH (Java Microbenchmark Harness) or simple stopwatch strategies using System.nanoTime() can provide accurate performance measures before and after wrapping your code with a parallel stream. Data-driven decisions are key to maximizing code efficiency.

Thread-safe collections

If shared structures are concurrently modified, using concurrent collections such as ConcurrentHashMap is recommended. They are designed for high concurrency levels without paying the price of cumbersome synchronization blocks.

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