Teleport
Scaling
Version preview- Older Versions
This section explains the recommended configuration settings for large-scale self-hosted deployments of Teleport.
Teleport Enterprise Cloud takes care of this setup for you so you can provide secure access to your infrastructure right away.
Get started with a free trial of Teleport Enterprise Cloud.
Prerequisites
- Teleport v16.4.7 Open Source or Enterprise.
Hardware recommendations
Set up Teleport with a High Availability configuration.
Scenario | Max Recommended Count | Proxy Service | Auth Service | AWS Instance Types |
---|---|---|---|---|
Teleport SSH Nodes connected to Auth Service | 10,000 | 2x 4 vCPUs, 8GB RAM | 2x 8 vCPUs, 16GB RAM | m4.2xlarge |
Teleport SSH Nodes connected to Auth Service | 50,000 | 2x 4 vCPUs, 16GB RAM | 2x 8 vCPUs, 16GB RAM | m4.2xlarge |
Teleport SSH Nodes connected to Proxy Service through reverse tunnels | 10,000 | 2x 4 vCPUs, 8GB RAM | 2x 8 vCPUs, 16+GB RAM | m4.2xlarge |
Auth Service and Proxy Service Configuration
Upgrade Teleport's connection limits from the default connection limit of 15000
to 65000
.
# Teleport Auth Service and Proxy Service
teleport:
connection_limits:
max_connections: 65000
max_users: 1000
Agent configuration
Agents cache roles and other configuration locally in order to make access-control decisions quickly.
By default agents are fairly aggressive in trying to re-initialize their caches if they lose connectivity
to the Auth Service. In very large clusters, this can contribute to a "thundering herd" effect,
where control plane elements experience excess load immediately after restart. Setting the max_backoff
parameter to something in the 8-16 minute range can help mitigate this effect:
teleport:
cache:
enabled: yes
max_backoff: 12m
Kernel parameters
Tweak Teleport's systemd unit parameters to allow a higher amount of open files:
[Service]
LimitNOFILE=65536
Verify that Teleport's process has high enough file limits:
cat /proc/$(pidof teleport)/limitsLimit Soft Limit Hard Limit Units
Max open files 65536 65536 files
DynamoDB configuration
When using Teleport with DynamoDB, we recommend using on-demand provisioning. This allow DynamoDB to scale with cluster load.
For customers that can not use on-demand provisioning, we recommend at least 250 WCU and 100 RCU for 10k clusters.
etcd
When using Teleport with etcd, we recommend you do the following.
- For performance, use the fastest SSDs available and ensure low-latency network connectivity between etcd peers. See the etcd Hardware recommendations guide for more details.
- For debugging, ingest etcd's Prometheus metrics and visualize them over time using a dashboard. See the etcd Metrics guide for more details.
During an incident, we may ask you to run etcdctl
, test that you can run the
following command successfully.
etcdctl \ --write-out=table \ --cacert=/path/to/ca.cert \ --cert=/path/to/cert \ --key=/path/to/key.pem \ --endpoints=127.0.0.1:2379 \ endpoint status
Supported Load
The tests below were performed against a Teleport Cloud tenant which runs on instances with 8 vCPU and 32 GiB memory and has default limits of 4CPU and 4Gi memory.
Concurrent Logins
Resource Type | Login Command | Logins | Failure |
---|---|---|---|
SSH | tsh login | 2000 | Auth CPU Limits exceeded |
Application | tsh app login | 2000 | Auth CPU Limits exceeded |
Database | tsh db login | 2000 | Auth CPU Limits exceeded |
Kubernetes | tsh kube login && tsh kube credentials | 2000 | Auth CPU Limits exceeded |
Sessions Per Second
Resource Type | Sessions | Failure |
---|---|---|
SSH | 1000 | Auth CPU Limits exceeded |
Application | 2500 | Proxy CPU Limits exceeded |
Database | 40 | Proxy CPU Limits exceeded |
Kubernetes | 50 | Proxy CPU Limits exceeded |
Windows Desktop Service
Windows Desktop sessions can vary greatly in resource usage depending on the applications being used. The primary factor affecting resource usage per session is how often the screen is updated. For example, a session playing a video in full screen mode will consume significantly more resources than a session where the user is typing in a text editor.
We measured the resource usage of sessions playing fullscreen videos to get the worst-case estimate for resource requirements. We then inferred resource requirements for more standard use cases on the basis of those measurements.
Worst Case:
- 1/12 vCPU per concurrent session
- 42 MB RAM per concurrent session
Typical Case:
- 1/240 vCPU per concurrent session
- 2 MB RAM per concurrent session
From these estimates we calculated the following table of recommendations based on the expected maximum number of concurrent sessions:
Concurrent users | CPU (vCPU, low to high) | Memory (GB, low to high) |
---|---|---|
1 | 1 | 0.5 |
10 | 1 | 0.5 to 1 |
100 | 1 to 8 | 1 to 8 |
1000 | 4 to 96 | 4 to 64 |
To avoid service interruptions, we recommend leaning towards the higher end of the recommendations to start while monitoring your resource usage, and then scaling resources based on measured outcomes.
Note that you are not limited to a single windows_desktop_service
, and can connect multiple to your cluster in order to
spread resources out over multiple logical machines.