How do crawlers deal with resource competition problems caused by multi-threaded concurrency?

Crawlers often face resource competition issues when using multi-threaded concurrency, such as contention for network bandwidth, CPU, memory, or shared data structures. To handle these problems, several strategies can be applied:

1. Thread Pooling: Instead of creating unlimited threads, crawlers use a fixed-size thread pool to limit concurrent execution. This prevents excessive resource consumption. For example, Python's concurrent.futures.ThreadPoolExecutor or Java's ExecutorService can manage thread allocation efficiently.

2. Rate Limiting and Throttling: Crawlers enforce delays between requests to avoid overwhelming target servers or exhausting local resources. For instance, setting a delay of 1-2 seconds between requests per domain helps maintain fairness.

3. Queue-Based Task Scheduling: Tasks are distributed via a shared queue (e.g., Queue in Python or BlockingQueue in Java), ensuring threads fetch tasks sequentially and reducing race conditions.

4. Locks and Synchronization: When multiple threads access shared resources (e.g., a URL frontier or database), locks (e.g., threading.Lock in Python) prevent corruption. However, excessive locking can degrade performance, so fine-grained locking or lock-free structures are preferred.

5. Distributed Crawling: For large-scale crawling, distributing tasks across multiple machines reduces per-machine resource pressure. Tencent Cloud's Serverless Cloud Function (SCF) can dynamically scale crawler instances, while Tencent Distributed Message Queue (TDMQ) helps manage task distribution efficiently.

6. Connection Pooling: Reusing HTTP connections (e.g., via requests.Session in Python or Apache HttpClient in Java) minimizes the overhead of establishing new connections, saving socket and memory resources.

7. Memory Management: Crawlers should avoid loading entire web pages into memory. Streaming responses (e.g., using requests.iter_content in Python) or writing data directly to disk prevents memory exhaustion.

For high-performance crawling at scale, Tencent Cloud's Elastic Compute Service (CVM) with auto-scaling and Cloud Object Storage (COS) for storing crawled data provide reliable infrastructure. Additionally, TencentDB can manage structured data storage with high concurrency support.