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By default, when transparent proxying is used, every data plane proxy follows every other data plane proxy in the mesh. With large meshes, usually, a data plane proxy connects to just a couple of services in the mesh. By defining the list of such services, we can dramatically improve the performance of Kuma.
The result is that:
- The control plane has to generate a much smaller XDS configuration (just a couple of Clusters/Listeners etc.) saving CPU and memory
- Smaller config is sent over a wire saving a lot of network bandwidth
- Envoy only has to keep a couple of Clusters/Listeners which means much fewer statistics and lower memory usage.
Follow the transparent proxying docs on how to configure it.
If you choose
Postgres as a configuration store for Kuma on Universal,
please be aware of the following key settings that affect performance of Kuma Control Plane.
KUMA_STORE_POSTGRES_CONNECTION_TIMEOUT: connection timeout to the Postgres database (default: 5s)
KUMA_STORE_POSTGRES_MAX_OPEN_CONNECTIONS: maximum number of open connections to the Postgres database (default: unlimited)
The default value will work well in those cases where both
kuma-cp and Postgres database are deployed in the same datacenter / cloud region.
However, if you’re pursuing a more distributed topology, e.g. by hosting
kuma-cp on premise and using Postgres as a service in the cloud, the default value might no longer be enough.
The more dataplanes join your meshes, the more connections to Postgres database Kuma might need to fetch configurations and update statuses.
As of version 1.4.1 the default value is 50.
However, if your Postgres database (e.g., as a service in the cloud) only permits a small number of concurrent connections, you will have to adjust Kuma configuration respectively.
This is advanced topic describing Kuma implementation internals
The main task of the control plane is to provide config for dataplanes. When a dataplane connects to the control plane, the CP starts a new goroutine. This goroutine runs the reconciliation process with given interval (1s by default). During this process, all dataplanes and policies are fetched for matching. When matching is done, the Envoy config (including policies and available endpoints of services) for given dataplane is generated and sent only if there is an actual change.
KUMA_XDS_SERVER_DATAPLANE_CONFIGURATION_REFRESH_INTERVAL: interval for re-generating configuration for Dataplanes connected to the Control Plane (default: 1s)
This process can be CPU intensive with high number of dataplanes therefore you can control the interval time for a single dataplane. You can lower the interval scarifying the latency of the new config propagation to avoid overloading the CP. For example, changing it to 5s means that when you apply a policy (like TrafficPermission) or the new dataplane of the service is up or down, CP will generate and send new config within 5 seconds.
For systems with high traffic, keeping old endpoints for such a long time (5s) may not be acceptable. To solve this, you can use passive or active health checks provided by Kuma.
Additionally, to avoid overloading the underlying storage there is a cache that shares fetch results between concurrent reconciliation processes for multiple dataplanes.
KUMA_STORE_CACHE_EXPIRATION_TIME: expiration time for elements in cache (1s by default).
You can also change the expiration time, but it should not exceed
KUMA_XDS_SERVER_DATAPLANE_CONFIGURATION_REFRESH_INTERVAL, otherwise CP will be wasting time building Envoy config with the same data.
Kuma’s control plane ships with pprof endpoints so you can profile and debug the performance of the
To enable the debugging endpoints, you can set the
KUMA_DIAGNOSTICS_DEBUG_ENDPOINTS environment variable to
true before starting
kuma-cp and use one of the following methods to retrieve the profiling information:
You can retrieve the profiling information with Golang’s
pprof tool, for example:
go tool pprof http://<IP of the CP>:5680/debug/pprof/profile?seconds=30
Then, you can analyze the retrieved profiling data using an application like Speedscope.
After a successful debugging session, please remember to turn off the debugging endpoints since anybody could execute heap dumps on them potentially exposing sensitive data.
Envoy concurrency tunning
Envoy allows configuring the number of worker threads used for processing requests. Sometimes it might be useful to change the default number of worker threads e.g.: high CPU machine with low traffic. Depending on the type of deployment, there are different mechanisms in
kuma-dp to change Envoy’s concurrency level.
By default, Envoy runs with a concurrency level based on resource limit. For example, if you’ve started the
kuma-dp container with CPU resource limit
7000m then concurrency is going to be set to 7. It’s also worth mentioning that concurrency for K8s is set from at least 2 to a maximum of 10 worker threads. In case when higher concurrency level is required it’s possible to change the setting by using annotation
kuma.io/sidecar-proxy-concurrency which allows to change the concurrency level without limits.
apiVersion: apps/v1 kind: Deployment metadata: name: demo-app spec: selector: matchLabels: app: demo-app template: metadata: labels: app: demo-app annotations: kuma.io/sidecar-proxy-concurrency: 55 [...]