We have a gRPC application deployed in a cluster (v 1.17.6) with Istio (v 1.6.2) setup. The cluster has istio-ingressgateway setup as the edge LB, with SSL termination. The istio-ingressgateway is fronted by an AWS ELB (classic LB) in passthrough mode. This setup is fully functional and the traffic flows as intended, in general. So the setup looks like:
ELB => istio-ingressgateway => virtual service => app service => [(envoy)pods]
We are running load tests on this setup using GHZ (ghz.sh), running external to the application cluster. From the tests we’ve run, we have observed that each of the app container seems to get about 300 RPS routed to it, no matter the configuration of the GHZ test. For reference, we have tried various combos of --concurrency and --connection settings for the tests. This ~300 RPS is lower than what we expect from the app and, hence, requires a lot more PODs to provide the required throughput.
We are really interested in understanding the details of the physical connection (gRPC/HTTP2) setup in this case, all the way from the ELB to the app/envoy and the details of the load balancing being done. Of particular interest is the the case when the same client, GHZ e.g., opens up multiple connections (specified via the --connection option). We have looked at Kiali and it doesn’t give us the appropriate visibility.
Questions:
How can we get visibility into the physical connections being setup from the ingress gateway to the pod/proxy?
How is the “per request gRPC” load balancing happening?
What options might exist to optimize the various components involved in this setup?
Thanks.
1.How can we get visibility into the physical connections being setup from the ingress gateway to the pod/proxy?
If Kiali doesn't show what exactly you need, maybe you could try with Jaeger?
Jaeger is an open source end to end distributed tracing system, allowing users to monitor and troubleshoot transactions in complex distributed systems.
There is istio documentation about Jaeger.
Additionally Prometheus and Grafana might be helpful here, take a look here.
2.How is the “per request gRPC” load balancing happening?
As mentioned here
By default, the Envoy proxies distribute traffic across each service’s load balancing pool using a round-robin model, where requests are sent to each pool member in turn, returning to the top of the pool once each service instance has received a request.
If you wan't to change the default round-robin model you can use Destination Rule for that. Destination rules let you customize Envoy’s traffic policies when calling the entire destination service or a particular service subset, such as your preferred load balancing model, TLS security mode, or circuit breaker settings.
There is istio documentation about that.
More about load balancing in envoy here.
3.What options might exist to optimize the various components involved in this setup?
I'm not sure if there is anything to optimize in istio components, maybe some custom configuration in Destination Rule?
Additional Resources:
itnext.io
medium.com
programmaticponderings.com
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My architecture looks like this:
Here, the HTTPS requests first go to the route53 service for DNS resolution. Route53 forwards the request to the Network Load balancer. This service redirects the traffic to HAProxy pods running inside a Kubernetes cluster.
The HAProxy servers are required to read a specific request header and based on its value, it will route the traffic to backend. To keep things simple, I have kept a single K8 Backend cluster, but assume that there are more than 1 such backend cluster running.
Considering this architecture:
What is the best place to perform TLS termination? Should we do it at NLB (green box) or implement it at HAProxy (Orange box)?
What are the advantages and disadvantages of each scenario?
As you are using the NLB you can achieve End to end HTTPS also however it forces the service also to use.
You can terminate at the LB level if you have multiple LB backed by clusters, leveraging the AWS cert manage with LB will be an easy way to manage the multiple setups.
There is no guarantee that if anyone that enters in your network won't be able to exploit a bug capable of intercepting traffic between services, Software Defined Network(SDN) in your VPC is secure and protects from spoofing but no guarantee.
So there is an advantage if you use TLS/SSL inside the VPC also.
Recently I have built several microservices within a k8s cluster with Nginx ingress controller and they are working normally.
When dealing with communications among microservices, I attempted gRPC and it worked. Then I discover when microservice A -> gRPC -> microservice B, all requests were only occurred at 1 pod of microservice B (e.g. total 10 pods available for microservice B). In order to load balance the requests to all pods of microservice B, I attempted linkerd and it worked. However, I realized gRPC sometimes will produce internal error (e.g. 1 error out of 100 requests), making me changed to using the k8s DNS way (e.g. my-svc.my-namespace.svc.cluster-domain.example). Then, the requests never fail. I started to hold up gRPC and linkerd.
Later, I was interested in istio. I successfully deployed it to the cluster. However, I observe it always creates its own load balancer, which is not so matching with the existing Nginx ingress controller.
Furthermore, I attempted prometheus and grafana, as well as k9s. These tools let me have better understanding on cpu and memory usage of the pods.
Here I have several questions that I wish to understand:-
If I need to monitor cluster resources, we have prometheus, grafana and k9s. Are they doing the same monitoring role as service mesh (e.g. linkerd, istio)?
if k8s DNS can already achieve load balancing, do we still need service mesh?
if using k8s without service mesh, is it lag behind the normal practice?
Actually I also want to use service mesh every day.
The simple answer is
Service mesh for a kubernetes server is not necessary
Now to answer your questions
If I need to monitor cluster resources, we have prometheus, grafana and k9s. Are they doing the same monitoring role as service mesh (e.g. linkerd, istio)?
K9s is a cli tool that is just a replacement to the kubectl cli tool. It is not a monitor tool. Prometheus and grafana are monitoring tools that will need use the data provided by applications(pods) and builds the time-series data which can be visualized as charts, graphs etc. However the applications have to provide the monitoring data to Prometheus. Service meshes may use a sidecar and provide some default metrics useful for monitoring such as number of requests handled in a second. Your application doesn't need to have any knowledge or implementation of the metrics. Thus service meshes are optional and it offloads the common things such as monitoring or authorization.
if k8s DNS can already achieve load balancing, do we still need service mesh?
Service meshes are not needed for load balancing. When you have multiple services running in the cluster and want to use a single entry point for all your services to simplify maintenance and to save cost, Ingress controllers such as Nginx, Traefik, HAProxy are used. Also, service meshes such as Istio comes with its own ingress controller.
if using k8s without service mesh, is it lag behind the normal practice?
No, there can be clusters that don't have service meshes today and still use Kubernetes.
In the future, Kubernetes may bring some functionalities from service meshes.
Service mesh is not a silver bullet and it doesn't fit into every use case. Service mesh will not do everything for you, it also have bugs and limited features.
You can use Prometheus without Istio and have a very nice app monitoring. Service mesh can simplify some monitoring tasks for you, but it doesn't mean you cannot do it yourself.
Please don't think of DNS as load balancing solution. Kubernetes have Services and Ingresses to do load balancing. Nginx Ingress today is very powerful and have many advanced features.
It heavily depends on your use case.
I've been looking into Kubernetes networking, more specifically, how to serve HTTPS users the most efficient.
I was watching this talk: https://www.youtube.com/watch?v=0Omvgd7Hg1I and from 22:18 he explains what the problem is with a load balancer that is not pod aware. Now, how they solve this in kubernetes is by letting the nodes also act as a 'router' and letting the node pass the request on to another node. (explained at 22:46). This does not seem very efficient, but when looking around SoundCloud (https://developers.soundcloud.com/blog/how-soundcloud-uses-haproxy-with-kubernetes-for-user-facing-traffic) actually seems to do something similar to this but with NodePorts. They say that the overhead costs less than creating a better load balancer.
From what I have read an option might be using an ingress controller. Making sure that there is not more than one ingress controller per node, and routing the traffic to the specific nodes that have an ingress controller. That way there will not be any traffic re-routing needed. However, this does add another layer of routing.
This information is all from 2017, so my question is: is there any pod aware load balancer out there, or is there some other method that does not involve sending the http request and response over the network twice?
Thank you in advance,
Hendrik
EDIT:
A bit more information about my use case:
There is a bare-metal setup with kubernetes. The firewall load balances the incomming data between two HAProxy instances. These HAProxy instances do ssl termination and forward the traffic to a few sites. This includes an exchange setup, a few internal IIS sites and a nginx server for a static web app. The idea is to transform the app servers into kubernetes.
Now my main problem is how to get the requests from HAProxy into kubernetes. I see a few options:
Use the SoundCloud setup. The infrastructure could stay almost the same, the HAProxy server can still operate the way they do now.
I could use an ingress controller on EACH node in the kubernetes cluster and have the firewall load balance between the nodes. I believe it is possible to forward traffic from the ingress controller to server outside the cluster, e.g. exchange.
Some magic load balancer that I do not know about that is pod aware and able to operate outside of the kubernetes cluster.
Option 1 and 2 are relatively simple and quite close in how they work, but they do come with a performance penalty. This is the case when the node that the requests gets forwarded to by the firewall does not have the required pod running, or if another pod is doing less work. The request will get forwarded to another node, thus, using the network twice.
Is this just the price you pay when using Kubernetes, or is there something that I am missing?
How traffic heads to pods depend on whether a managed cluster is used.
Almost all cloud providers can forward traffic in a cloud-native way in their managed K8s clusters. First, you can a managed cluster with some special network settings (e.g. vpc-native cluster of GKE). Then, the only thing you need to do is to create a LoadBalancer typed Service to expose your workload. You can also create Ingresses for your L7 workloads, they are going to be handled by provided IngressControllers (e.g. ALB of AWS).
In an on-premise cluster without any cloud provider(OpenStack or vSphere), the only way to expose workloads is NodePort typed Service. It doesn't mean you can't improve it.
If your cluster is behind reverse proxies (the SoundCloud case), setting externalTrafficPolicy: Local to Services could break traffic forwarding among work nodes. When traffic received through NodePorts, they are forwarded to local Pods or dropped if Pods reside on other nodes. Reserve proxy will mark these NodePort as unhealthy in the backend health check and reject to forward traffic to them. Another choice is to use topology-aware service routing. In this case, local Pods have priorities and traffic is still forwarded between node when no local Pods matched.
For IngressController in on-prem clusters, it is a little different. You may have some work nodes that have EIP or public IP. To expose HTTP(S) services, an IngressController usually deployed on those work nodes through DaemeaSet and HostNetwork such that clients access the IngressController via the well-known ports and EIP of nodes. These work nodes regularly don't accept other workloads (e.g. infra node in OpenShift) and one more forward on the Pod network is needed. You can also deploy the IngressController on all work nodes as well as other workloads, so traffic could be forwarded to a closer Pod if the IngressController supports topology-aware service routing although it can now.
Hope it helps!
I'm having an issue with load balancing persistent tcp connections to my kubernetes replicas.
I have Unity3D clients outside of the kubernetes cluster.
My cluster is a baremetal cluster with metallb installed composed out of 3 nodes: 1 master and 2 workers.
As I have read there are two approaches:
1) client connects to all replicas and each time it needs to send a request it will do so on a random connection out of those that it has previously established. Periodically, it refreshes connections (in case autoscale happened or some of the persistent connections died).
The problem here is, I'm not sure how to access all replicas externally, headless services cannot be exposed externally.
2) service mesh ? I have vaguely read/understood that they might establish persistent tcp on your behalf. So something like this :
unity3d client <----persistent connection ---> controller <---persistent connection----> replicas
However, I'm not sure how to accomplish this and I'm not sure what will happen if the controller itself fails, will all the clients get their connections dropped ? As I see it, it will come down to the same issue as the one from 1), which is allowing a client to connect to multiple different replicas at the same time with a persistent TCP connection.
Part of question comes as a complement to this : https://learnk8s.io/kubernetes-long-lived-connections
In order to enable external traffic to your cluster you need an Ingress Gateway. Your ingress gateway could be the standard nginx Ingress, a gateway provided by a mesh like the Istio Gateway or a more specialized edge gateway like ambassador, traefik, kong, gloo, etc.
There are at least two ways you can perform load balancing in K8s:
Using a Service resource which is just a set of iptables rules managed by the kube-proxy process. This is L4 load balancing only. No L7 application protocols like HTTP2 or gRPC are supported. Depending on your case, this type of LB might not be ideal for long lived connections as connections will rarely be closed.
Using the L7 load balancing offered by any of the ingress controllers which will skip the iptables routing (using a headless Service) and allow for more advanced load balancing algorithms.
In order to benefit from the latter case you still need to ensure that connections are eventually terminated which is often done from the client to the proxy (while reusing connections from the proxy to the upstream). I'm not familiar with Unity3D connections but if terminating them is not an option you won't be able to do much load balancing after all.
When the controller fails, connections will be dropped and your client could either graciously re-attempt the connection or panic. It depends on how you code it.
We have a service with multiple replicas which works with storage without transactions and blocking approaches. So we need somehow to synchronize concurrent requests between multiple instances by some "sharding" key. Right now we host this service in Kubernetes environment as a ReplicaSet.
Don't you know any simple out-of-the-box approaches on how to do this to not implement it from scratch?
Here are several of our ideas on how to do this:
Deploy the service as a StatefulSet and implement some proxy API which will route traffic to the specific pod in this StatefulSet by sharding key from the HTTP request. In this scenario, all requests which should be synchronized will be handled by one instance and it wouldn't be a problem to handle this case.
Deploy the service as a StatefulSet and implement some custom logic in the same service to re-route traffic to the specific instance (or process on this exact instance). As I understand it's not possible to have abstract implementation and it would work only in Kubernetes environment.
Somehow expose each pod IP outside the cluster and implement routing logic on the client-side.
Just implement synchronization between instances through some third-party service like Redis.
I would like to try to route traffic to the specific pods. If you know standard approaches how to handle this case I'll be much appreciated.
Thank you a lot in advance!
Another approach would be to put a messaging queue (like Kafka and RabbitMq) in front of your service.
Then your pods will subscribe to the MQ topic/stream. The pod will decide if it should process the message or not.
Also, try looking into service meshes like Istio or Linkerd.
They might have an OOTB solution for your use-case, although I wasn't able to find one.
Remember that Network Policy is not traffic routing !
Pods are intended to be stateless and indistinguishable from one another, pod-networking.
I recommend to Istio. It has special component which is responsible or routing- Envoy. It is a high-performance proxy developed in C++ to mediate all inbound and outbound traffic for all services in the service mesh.
Useful article: istio-envoy-proxy.
Istio documentation: istio-documentation.
Useful Istio explaination https://www.youtube.com/watch?v=e2kowI0fAz0.
But you should be able to create a Deployment per customer group, and a Service per Deployment. The Ingress nginx should be able to be told to map incoming requests by whatever attributes are relevant to specific customer group Services.
Other solution is to use kube-router.
Kube-router can be run as an agent or a Pod (via DaemonSet) on each node and leverages standard Linux technologies iptables, ipvs/lvs, ipset, iproute2.
Kube-router uses IPVS/LVS technology built in Linux to provide L4 load balancing. Each ClusterIP, NodePort, and LoadBalancer Kubernetes Service type is configured as an IPVS virtual service. Each Service Endpoint is configured as real server to the virtual service. The standard ipvsadm tool can be used to verify the configuration and monitor the active connections.
How it works: service-proxy.