Big Data Processing in C#: From LINQ to Parallel Streams

Why C# is Your Secret Tool for Big Data?

When it comes to big data processing, Python with its Pandas, Scala with Spark, or Java with Hadoop immediately come to mind. But let's be honest: C# often remains undeservedly in the shadows. And that's a shame. The .NET platform gives us such powerful tools as PLINQ, MemoryMappedFile, Parallel.For, and asynchronous streams, which allow you to process gigabytes of data on ordinary hardware without any hassle.

In this article, we won't just theorize — we'll get straight to business. You'll learn how to get the most out of your CPU when processing big data in C#, how to avoid OutOfMemoryException, and how to process files tens of gigabytes in size as if they were just a large book.

The Main Problem: How Not to Kill Memory?

Imagine: you have a 50 GB CSV file with server logs. You open it with a regular StreamReader and read it line by line. Everything is fine? No. If you try to load it entirely into a List<string> — hello, OutOfMemoryException. Even if you have 64 GB of RAM, .NET can fragment memory so that a large object simply won't fit in the heap.

Solution number one: streaming and chunking. Don't load everything at once — chew it in pieces.

public async IAsyncEnumerable<string[]> ReadCsvChunksAsync(string filePath, int chunkSize = 10000)\n{\n    using var reader = new StreamReader(filePath);\n    string? line;\n    var buffer = new List<string>(chunkSize);\n\n    while ((line = await reader.ReadLineAsync()) != null)\n    {\n        buffer.Add(line);\n        if (buffer.Count >= chunkSize)\n        {\n            yield return buffer.ToArray();\n            buffer.Clear();\n        }\n    }\n\n    if (buffer.Count > 0)\n        yield return buffer.ToArray();\n}\n

Now you can process gigabytes without worrying about memory:

await foreach (var chunk in ReadCsvChunksAsync("logs.csv"))\n{\n    ProcessChunk(chunk); // your logic\n}\n

Parallel Processing: Using All CPU Cores

Okay, we're reading data in chunks. But if you have a 16-core processor and the data is processed sequentially — you're using only 6% of its power. Time to enable parallel computing in C#.

PLINQ — Parallel LINQ

The simplest magic is to add .AsParallel() to your LINQ query. But with big data, you need to be careful: PLINQ decides when to parallelize on its own, which can lead to excessive fragmentation.

var numbers = Enumerable.Range(1, 10_000_000);\n\n// Regular LINQ (slow)\nvar sum = numbers.Where(n => n % 2 == 0).Sum();\n\n// PLINQ (fast, but careful)\nvar parallelSum = numbers\n    .AsParallel()\n    .WithDegreeOfParallelism(Environment.ProcessorCount)\n    .Where(n => n % 2 == 0)\n    .Sum();\n\nConsole.WriteLine($"Result: {parallelSum}");\n

But PLINQ is not a silver bullet. For complex big data processing in C#, it's better to use Parallel.ForEach with manual partitioning.

Parallel.ForEach with Range Partitioning

var allLines = File.ReadLines("huge_dataset.csv");\nvar processedCount = 0;\nvar lockObject = new object();\n\nParallel.ForEach(\n    allLines,\n    new ParallelOptions { MaxDegreeOfParallelism = Environment.ProcessorCount },\n    line =>\n    {\n        var result = ProcessLine(line); // your heavy function\n        lock (lockObject)\n        {\n            processedCount++;\n            if (processedCount % 100_000 == 0)\n                Console.WriteLine($"Processed: {processedCount}");\n        }\n    });\n

MemoryMappedFile: Working with Files as if They Were RAM

When a file is too large to load into RAM, but you need