DNS caching is the temporary storage of domain name resolution records closer to the client — in the OS, the HTTP client, or a local resolver — to eliminate the latency of repeated lookups. For high-throughput scraping pipelines, bypassing the OS default and managing your own DNS cache is mandatory. Relying on public resolvers for 10,000 requests per second guarantees rate limits, artificial latency, and eventual pipeline failure.
Why resolving a hostname to an IP address should happen exactly once per TTL window, not once per HTTP request.
Ask a DataFlirt engineer →DNS caching stores the IP address of a target domain locally for a specified Time-To-Live (TTL). In scraping, managing this cache at the application or worker level prevents DNS timeouts, reduces per-request latency by 20–100ms, and avoids triggering abuse thresholds at upstream resolvers like Cloudflare or Google.
DNS caching is the practice of storing the result of a DNS query (the IP address associated with a domain name) for a set period, known as the Time-To-Live (TTL). When a scraper requests api.target.com, the system checks the cache first. If the record is present and valid, the IP is returned instantly. If not, a full network query is sent to an upstream resolver.
Caching can occur at multiple layers: the browser/HTTP client, the operating system (e.g., systemd-resolved), a local network resolver, or an ISP's public resolver. For scraping, controlling the cache at the application or worker-node level is critical for performance.
When a scraping script initiates a request, the HTTP client asks the OS for the IP. If the OS cache is empty, it queries 1.1.1.1 or 8.8.8.8. The upstream resolver returns the IP along with a TTL (e.g., 300 seconds). For the next 5 minutes, any request to that domain uses the cached IP, reducing lookup time from ~40ms to ~0.1ms.
Once the 300 seconds pass, the record expires. The next request triggers a new upstream query. In high-throughput systems, thousands of concurrent threads might hit this expiration simultaneously, causing a "thundering herd" of DNS queries unless mitigated by prefetching.
DNS caching and connection pooling solve related but distinct problems. Connection pooling (Keep-Alive) keeps the underlying TCP/TLS socket open between requests. If you reuse a socket, you don't need a DNS lookup at all.
However, scrapers frequently rotate proxies, user-agents, or IP addresses to avoid detection. Every time you rotate an IP or open a new session, you must create a new socket, which requires a DNS lookup. This is where DNS caching becomes the primary defense against latency and upstream rate limits.
We do not rely on the host OS for DNS. Every DataFlirt worker node runs a local instance of CoreDNS. This provides three massive advantages:
Standard HTTP libraries in Python (like requests or httpx) and Node.js (like axios) do not implement application-level DNS caching by default. They delegate entirely to the OS. If you run a script inside a minimal Docker container (which often lacks a robust OS-level caching daemon), you are likely performing a full network DNS lookup for every single request your scraper makes.
At scale, DNS resolution time dominates the connection phase. DataFlirt's worker nodes run local caching resolvers to keep cache hit rates above 99.9%.
Trace of a high-concurrency scraper hitting a target domain. The first request pays the lookup penalty; the next 9,999 do not.
Improper DNS caching causes silent failures that look like network drops. Here is what typically goes wrong when scrapers rely on default OS resolvers.
or the resolver controls your throughput.
Default HTTP clients in Python or Node.js rely on the host OS for DNS resolution. At 50 requests per second, this is fine. At 5,000 requests per second, the OS resolver becomes a severe bottleneck, dropping UDP packets and causing artificial timeouts. DataFlirt bypasses the OS entirely, running dedicated caching resolvers on every worker node. We serve stale records during background refreshes, ensuring that a slow upstream DNS server never blocks an active extraction thread.
Local resolver settings for a high-throughput scraping node.
Anti-bot shifts, scraping infrastructure updates, dataset delivery patterns, and business outcomes from our pipelines. Short, technical, no fluff.
Common questions about DNS resolution, TTL management, and avoiding resolver-level blocks during high-volume scraping.
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requests (via urllib3 and the socket module) asks the OS to resolve the hostname for every new connection. If you aren't using connection pooling (Keep-Alive), you are performing a full DNS lookup on every single request.20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous feed across millions of records — we scope, build, and operate the pipeline.