![]() ![]() ![]() This is way more than a standard Linux config can cope with. To give a concrete example, one large SaaS provider we worked with had a set of memcached servers running on bare metal servers (not virtualized or containerized) each handling 50k+ short-lived connections per second. For other scenarios, you need to bypass conntrack for the offending traffic. For some scenarios tuning conntrack may be sufficient to meet your needs by increasing the conntrack table size or reducing conntrack timeouts (but if you get this tuning wrong it can lead to a lot of pain). In both cases, conntrack can become the limiting bottleneck in your system. In addition, if you’re in a hostile environment then flooding your server with lots of half-open connections can be used as a denial-of-service attack. There are some niche workload types fall into these categories. For example, if your conntrack table is configured to be 128k entries, and you are trying to handle 1,100 connections per second, that’s going to exceed the conntrack table size even if the connections are very short-lived (128k / 120s = 1092 connections/s). Even if the connections are short-lived, connections continue to be tracked by Linux for a short timeout period (120s by default). The slightly less obvious case is if your server handles an extremely high number of connections per second.For example, if your conntrack table is configured to be 128k entries but you have >128k simultaneous connections, you’ll definitely hit issues! The most obvious case is if your server handles an extremely high number of simultaneously active connections.However, there are a few scenarios where the conntrack table needs a bit more thought: For most workloads, there’s plenty of headroom in the table and this will never be an issue. The conntrack table has a configurable maximum size and, if it fills up, connections will typically start getting rejected or dropped. However, conntrack has its limits… So, where does it break down? See the “ Comparing kube-proxy modes” blog for one example of this in action. In addition, conntrack normally improves performance (reduced CPU and reduced packet latencies) since only the first packet in a flow needs to go through the full network stack processing to work out what to do with it. (Without this you would have to add the much less secure rule “allow packets to my pod from any IP”.) This allows you to write a network policy that says “allow my pod to connect to any remote IP” without needing to write policy to explicitly allow the response traffic. ![]() Stateful firewalls, such as Calico, rely on the connection tracking information to precisely whitelist “response” traffic.It is conntrack that records that for a particular connection, packets to the service IP should all be sent to the same backend pod, and that packets returning from backend pod should be un-NATed back to the source pod. For example, when a pod accesses a Kubernetes service, kube-proxy’s load balancing uses NAT to redirect the connection to a particular backend pod. NAT relies on the connection tracking information so it can translate all of the packets in a flow in the same way.It allows the kernel to keep track of all logical network connections or flows, and thereby identify all of the packets which make up each flow so they can be handled consistently together.Ĭonntrack is an important kernel feature that underpins some key mainline use cases: CalicoCon + Cloud-Native Security SummitĬonnection tracking (“conntrack”) is a core feature of the Linux kernel’s networking stack.Compare Products Open source, Cloud and Enterprise.Calico Enterprise Zero trust security for Kubernetes.Calico Cloud Security for containers and Kubernetes.Calico Open Source eBPF-based networking and security. ![]()
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