When processing large amounts of data, C# can be efficient through streaming, parallel asynchronous and appropriate data structures. 1. Use streaming processing to read one by one or in batches, such as StreamReader or EF Core's AsAsyncEnumerable to avoid memory overflow; 2. Use parallel (Parallel.ForEach/PLINQ) and asynchronous (async/await Task.Run) reasonably to control the number of concurrency and pay attention to thread safety; 3. Select efficient data structures (such as Dictionary, HashSet) and serialization libraries (such as System.Text.Json, MessagePack) to reduce search time and serialization overhead.
When processing large amounts of data, C# itself is not as "lightweight" as some scripting languages, but with its powerful type system, LINQ and .NET ecosystem, it can be handled efficiently as long as it is done properly. The key is to make rational use of memory, avoid unnecessary operations, and select appropriate data structures and asynchronous mechanisms.
Reduce memory usage with streaming
Loading millions of data into memory at one time can easily lead to OutOfMemoryException. At this time, you should consider using streaming processing to read data one by one or in batches.
For example, use StreamReader to read CSV files one by one instead of .ReadToEnd() directly; or use Entity Framework Core 's AsAsyncEnumerable() to load database records on demand.
Common practices:
- Only the currently required data blocks are processed at a time
- Avoid accumulating result sets in memory, free or write to disk/database after processing
- Enumerator that combines
yield returnto achieve lazy loading
The advantage of this is that even if you face hundreds of millions of data, the program will not crash due to memory explosion.
Rational use of parallel and asynchronously improves throughput
If your task is CPU intensive (such as doing complex calculations for each record), you can consider using Parallel.ForEach or PLINQ for parallel processing. However, if the task involves I/O (such as writing files and accessing databases), it is more suitable to use async/await Task.Run to improve overall efficiency.
A common combination is:
await Parallel.ForEachAsync(dataChunks, async (chunk, ct) => {
var processed = ProcessChunk(chunk);
await WriteToDatabaseAsync(processed, ct);
});Notes:
- Control the number of concurrent and prevent resource scrambling
- Pay attention to thread safety, try to use locks or concurrent collections for shared variables
- For asynchronous database operations, ensure that the connection pool is configured properly
Select the appropriate data structure and serialization method
Many performance bottlenecks actually come from wrong data structure selection or inefficient serialization methods. For example, if you frequently search for records of a certain ID but use List instead of Dictionary, the search time will increase exponentially.
For example, when reading and writing JSON data, the default Newtonsoft.Json is convenient, but its performance is not as good as System.Text.Json in big data scenarios. Especially when combined with JsonSerializer.Deserialize<t>(stream)</t> , it can read and parse while saving, saving a lot of time and memory.
Recommended strategies:
- Use Dictionary or HashSet first when searching frequently
- Use efficient libraries when serializing, such as SpanJson and MessagePack
- If necessary, the serialization process can be manually controlled to skip reflection overhead
summary
In general, the key to using C# to process big data is: don’t load it all at once, but load it as needed; don’t just carry it alone, but reasonably concurrency; don’t blindly use default tools, but choose the appropriate structure and library . These points are not difficult to say, but when it is implemented, you need to pay attention to details, such as resource release, exception handling, log tracking, etc.
The above is the detailed content of Handling Large Datasets Efficiently with C#. For more information, please follow other related articles on the PHP Chinese website!
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