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Performance fine-tuning

Reachable services

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 Kong Mesh.

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.

Postgres

If you choose Postgres as a configuration store for Kong Mesh on Universal, please be aware of the following key settings that affect performance of Kong Mesh 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)

KUMA_STORE_POSTGRES_CONNECTION_TIMEOUT

The default value will work well in those cases where both kuma-cp and Postgres database are deployed in the same data center / cloud region.

However, if you’re pursuing a more distributed topology, for example by hosting kuma-cp on premise and using Postgres as a service in the cloud, the default value might no longer be enough.

KUMA_STORE_POSTGRES_MAX_OPEN_CONNECTIONS

The more data planes join your meshes, the more connections to Postgres database Kong Mesh might need to fetch configurations and update statuses.

As of version 1.4.1 the default value is 50.

However, if your Postgres database (for example as a service in the cloud) only permits a small number of concurrent connections, you will have to adjust Kong Mesh configuration respectively.

Snapshot Generation

This is advanced topic describing Kong Mesh implementation internals

The main task of the control plane is to provide config for data planes. When a data plane connects to the control plane, the control plane starts a new Goroutine. This Goroutine runs the reconciliation process with given interval (1s by default). During this process, all data planes and policies are fetched for matching. When matching is done, the Envoy config (including policies and available endpoints of services) for given data plane is generated and sent only if there is an actual change.

• KUMA_XDS_SERVER_DATAPLANE_CONFIGURATION_REFRESH_INTERVAL : interval for re-generating configuration for data planes connected to the control plane (default: 1s)

This process can be CPU intensive with high number of data planes therefore you can control the interval time for a single data plane. You can lower the interval scarifying the latency of the new config propagation to avoid overloading the control plane. For example, changing it to 5 seconds means that when you apply a policy (like MeshTrafficPermission) or the new data plane of the service is up or down, control plane will generate and send new config within 5 seconds.

For systems with high traffic, keeping old endpoints for such a long time (5 seconds) may not be acceptable. To solve this, you can use passive or active health checks provided by Kong Mesh.

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 (1 second 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.

Profiling

Kong Mesh’s control plane ships with pprof endpoints so you can profile and debug the performance of the kuma-cp process.

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:

go tool pprof http://<IP of the CP>:5680/debug/pprof/profile?seconds=30

curl http://<IP of the CP>:5680/debug/pprof/profile?seconds=30 --output prof.out

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.

Kubernetes client

Kubernetes client uses client level throttling to not overwhelm kube-api server. In larger deployments, bigger than 2000 services in a single kubernetes cluster, number of resources updates can hit this throttling. In most cases it’s safe to increase this limit as kube-api has it’s own throttling mechanism. To change client throttling configuration you need to update config.

runtime:
  kubernetes:
    clientConfig:
      qps: ... # Qps defines maximum requests kubernetes client is allowed to make per second.
      burstQps: ... # BurstQps defines maximum burst requests kubernetes client is allowed to make per second

Kubernetes controller manager

Kong Mesh is modifying some Kubernetes resources. Kubernetes calls the process of modification reconciliation. Every resource has its own working queue, and control plane adds reconciliation tasks to that queue. In larger deployments, bigger than 2000 services in a single Kubernetes cluster, size of the work queue for pod reconciliation can grow and slow down pods updates. In this situation you can change the number of concurrent pod reconciliation tasks, by changing configuration:

runtime:
  kubernetes:
    controllersConcurrency:
      podController: ... # PodController defines maximum concurrent reconciliations of Pod resources

Envoy

Envoy concurrency tuning

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.

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
[...]

kuma-dp run \
  [..]
  --concurrency=5