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Routing Optimization
Scaling OpenShift Container Platform HAProxy Router
Baseline Performance
The OpenShift Container Platform router is the ingress point for all external traffic destined for OpenShift Container Platform services.
On an public cloud instance of size 4 vCPU/16GB RAM, a single HAProxy router is able to handle between 7000-32000 HTTP keep-alive requests depending on encryption, page size, and the number of connections used. For example, when using TLS edge or re-encryption terminations with large page sizes and a high numbers of connections, expect to see results in the lower range. With HTTP keep-alive, a single HAProxy router is capable of saturating 1 Gbit NIC at page sizes as small as 8 kB.
The table below shows HTTP keep-alive performance on such a public cloud instance with a single HAProxy and 100 routes:
| Encryption | Page size | HTTP(s) requests per second |
|---|---|---|
none |
1kB |
15435 |
none |
4kB |
11947 |
edge |
1kB |
7467 |
edge |
4kB |
7678 |
passthrough |
1kB |
25789 |
passthrough |
4kB |
17876 |
re-encrypt |
1kB |
7611 |
re-encrypt |
4kB |
7395 |
When running on bare metal with modern processors, you can expect roughly twice the performance of the public cloud instance above. This overhead is introduced by the virtualization layer in place on public clouds and holds mostly true for private cloud-based virtualization as well. The following table is a guide on how many applications to use behind the router:
| Number of applications | Application type |
|---|---|
5-10 |
static file/web server or caching proxy |
100-1000 |
applications generating dynamic content |
In general, HAProxy can saturate about 5-1000 applications, depending on the technology in use. The number will typically be lower for applications serving only static content.
Router sharding should be used to serve more routes towards applications and help horizontally scale the routing tier.
Performance Optimizations
Setting the Maximum Number of Connections
One of the most important tunable parameters for HAProxy scalability is the
maxconn parameter, which sets the maximum per-process number of concurrent
connections to a given number. Adjust this parameter by editing the
ROUTER_MAX_CONNECTIONS
environment variable in the OpenShift Container Platform HAProxy router’s deployment
configuration file.
|
A connection includes the frontend and internal backend. This counts as two
connections. Be sure to set |
CPU and Interrupt Affinity
In OpenShift Container Platform, the HAProxy router runs as a single process. The OpenShift Container Platform HAProxy router typically performs better on a system with fewer but high frequency cores, rather than on an symmetric multiprocessing (SMP) system with a high number of lower frequency cores.
Pinning the HAProxy process to one CPU core and the network interrupts to
another CPU core tends to increase network performance. Having processes and
interrupts on the same non-uniform memory access (NUMA) node helps avoid memory
accesses by ensuring a shared L3 cache. However, this level of control is
generally not possible on a public cloud environment. On bare metal hosts,
irqbalance automatically handles peripheral component interconnect (PCI)
locality and NUMA affinity for interrupt request lines (IRQs). On a cloud
environment, this level of information is generally not provided to the
operating system.
CPU pinning is performed either by taskset or by using HAProxy’s cpu-map
parameter. This directive takes two arguments: the process ID and the CPU core
ID. For example, to pin HAProxy process 1 onto CPU core 0, add the following
line to the global section of HAProxy’s configuration file:
To modify the HAProxy configuration file, refer to Deploying a Customized HAProxy Router.
Impacts of Buffer Increases
The OpenShift Container Platform HAProxy router request buffer configuration limits the size
of headers in incoming requests and responses from applications. The HAProxy
parameter tune.bufsize can be increased to allow processing of larger headers
and to allow applications with very large cookies to work, such as those
accepted by load balancers provided by many public cloud providers. However,
this affects the total memory use, especially when large numbers of connections
are open. With very large numbers of open connections, the memory usage will be
nearly proportionate to the increase of this tunable parameter.