Over the years we have built Octopus to enable reliable and repeatable deployments, but that doesn't necessarily mean it has to be slow. In fact, Octopus can scale with you as you grow. Octopus is a complex system with a core component allowing you to run your own custom scripts. We work hard to ensure all the parts we control work quickly and efficiently leaving as many resources as possible for running your deployments. That being said, there are many things you can do to ensure your Octopus deployments execute efficiently.
This page is intended to help you tune and maintain your deployment processes and troubleshoot problems as they occur.
Want to tune your Octopus Server for optimum performance? Read our detailed guide on optimizing your server.
By the time your deployment starts, the Octopus HTTP API and database are no longer the bottleneck. The key concerns are now:
- The throughput and reliability of the connection from Octopus Server to your deployment targets.
- The speed and reliability of your deployment targets themselves.
- The load your deployment targets are under while the deployment is taking place.
- The number of steps in your deployment process.
- The size and number of packages you are deploying.
- How your packages are acquired/transferred to your deployment targets.
- How many packages you keep in your package feed and how you configure retention policies.
- The amount and size of log messages you write during deployment.
- How many deployment targets acquire packages in parallel.
- How many deployment targets you deploy to in parallel.
- Whether your steps run serially (one-after-the-other) or in parallel (at the same time).
- How much of the work in the deployment steps is done on the Octopus Server.
- The number and size of your variables.
- Other processes on the deployment target interfering with your deployment.
We don't offer a one-size-fits-all approach to optimizing your deployments using Octopus: every deployment scenario is unique. Instead we recommend taking an experimental approach to optimization: measure-then-cut. Record your deployments, make an adjustment, then measure again, etc. These tips should give you enough information to get started.
Optimize the connection to your deployment targets
If you have a reliable, high throughput connection between your Octopus Server and deployment targets, your deployments can go fast. Unfortunately the opposite is also true:
- Low throughput connections make package acquisition and deployment orchestration slow.
- An unreliable connection can make deployments slow through dropped connections and unnecessary retries.
A reliable connection to your deployment targets is the foundation of reliable and fast deployments. If bandwidth/throughput is genuinely an invariant, you may want to consider a way of acquiring your packages prior to the deployment starting, leaving the bandwidth available for deployment orchestration.
Optimize your deployment targets
Fast and reliable deployment targets are also a foundation for fast and reliable deployments. This is one area where every deployment is different, but the key considerations are similar:
- If your deployment does a lot of work on the disk(s) make sure your disks have sufficient throughput/IOPS.
- If your deployment does a lot of work in the CPU make sure your CPU has enough throughput/cores.
- If your deployment does anything, make sure your deployment target isn't already saturated running your applications, or choose a time of lower usage.
If a particular operation seems slow during deployment, test that single operation on your deployment target without Octopus in the mix. Octopus adds as little overhead as possible to your deployments, so there's a good chance that operation is slow because of some kind of bottleneck on the deployment target itself.
Reduce load on your deployment targets during deployment
If the applications running on your deployment targets are busy, this will slow down your deployment. Similarly, when you run a deployment it will be taking resources from your running applications. Octopus does not perform any kind of throttling on the deployment target - it will attempt to run your deployment process on your targets as fast as possible.
One of the best ways to reduce load on your deployment targets is to temporarily remove them from the active pool of servers. For example, with web applications you can do this by removing the server from your load balancer, perhaps using a rolling deployment.
If you don't want to take this kind of approach, you can safely deploy your application to an active server, but you should take some time to understand the impact this has on your running applications and the speed of your deployments.
Optimize the size of your deployment process
By their very nature, each step in your deployment process comes with an overhead. On one hand a deployment process with more steps can be easier to understand at a high level, and easier to manage over time. On the other hand, more steps results in more system variables, more communication overhead, more startup/teardown cost, and more contention on Octopus Server resources.
A deployment process with a single giant step might be the most efficient approach in your scenario. Imagine a single complex step which deploys all the required packages, running a single hand-crafted script to complete your deployment all in one hit. This approach would eliminate a lot of the costs we discussed earlier, but at the cost of making your deployment process harder to understand and maintain over time.
There is typically a happy balance you can strike for each of your projects. The most common problem related to performance is having too many steps, where "too many" depends on your specific situation, but we typically consider an average project to use 10-20 steps, and we have many customers deploying projects with 50-80 steps. If your projects have hundreds of steps, perhaps you should consider modeling your deployments differently?
- If your project could be broken down into logical components which ship on their own cadence, do it! Make each component its own project.
- If your project could be broken down into logical components which ship at the same time, you can do that too! Consider breaking your deployment into multiple logical projects and coordinate their deployments.
- If your project cannot be broken down logically, consider combining some of your steps together into a single step. For example, you may be able to run your custom scripts as a pre- or post- activity.
Consider the size of your packages
Size really does matter when it comes to your packages:
- Larger packages require more network bandwidth to transfer to your deployment targets.
- Larger packages take more resources to unpack on your deployment targets.
- When using delta compression for package transfers, larger packages require more CPU and disk IOPS on the Octopus Server to calculate deltas - this is a tradeoff you can determine through testing.
- Larger packages usually result in larger file systems on your deployment targets, making any steps which scan files much slower. For example, substituting variables in files can be configured to scan every file extracted from your package.
Consider whether one large package is better in your scenario, or perhaps you could split your application into multiple smaller packages, one for each deployable component.
Consider how you transfer your packages
Octopus provides two primary methods for transferring your packages to your deployment targets:
- Push from the Octopus Server to your targets.
- Pull from an external feed to your targets.
Each option provides different performance benefits, depending on your specific scenario:
- If network bandwidth is the limiting factor, consider:
- pushing the package from the Octopus Server to your targets using delta compression for package transfers; or
- using custom package feed in the same network as your deployment targets and download the packages directly on the agent.
- If network bandwidth is not a limiting factor consider downloading the packages directly on the agent. This alleviates a lot of resource contention on the Octopus Server.
- If Octopus Server CPU and disk IOPS is a limiting factor, avoid using delta compression for package transfers. Instead, consider downloading the packages directly on the agent. This alleviates a lot of resource contention on the Octopus Server.
Consider retention policies for your package feeds
Imagine if you keep every package you've ever built or deployed. Over time your package feed will get slower and slower to index, query, and stream packages for your deployments.
If you are using another feed, you should configure its retention policies yourself, making sure to cater for packages you may want to deploy.
Consider the size of your task logs
Larger task logs put the entire Octopus pipeline under more pressure. A good rule of thumb is to keep your log files under 20MB. We recommend printing messages required to understand progress and the reason for any deployment failures. The rest of the information should be streamed to a file, then published as a deployment artifact.
Consider how many targets acquire packages in parallel
Imagine you have 1,000 deployment targets configured to stream packages from the Octopus Server and you configure your deployment so all of the packages are acquired across all of your deployment targets in parallel. This can put a lot of strain on your Octopus Server as the constraint in this mix.
Alternatively, imagine if you have 1,000 deployment targets configured to download packages directly from a package feed or a file share. Now the package feed or file share becomes the constraint.
Firstly, consider how you transfer your packages and then consider the degree of parallelism will suit your scenario best. Octopus ships with a sensible and stable default configuration of
10, and you can tune the degree of parallelism by setting the
Octopus.Acquire.MaxParallelism variable as an unscoped/global value in your project. Start by slowly increasing this number until you reach a constraint for your scenario, and continue to tune from there.
Consider how many targets you deploy to in parallel
Imagine you have step in your deployment process which runs across all deployment targets with a specific role, and that results in 1,000 targets. Octopus will attempt to run that step simultaneously across all 1,000 deployment targets. This will cause your deployment to go slower since the Octopus Server spends most of its time task dealing with all the concurrent noise rather than being productive.
Alternatively if you constrain your process to a single deployment target at a time, the Octopus Server and your deployment targets will be bored, and your deployments will take longer.
Consider using a rolling deployment to deploy to a subset of these deployment targets at any one time. Rolling deployments allow you to define a "window" which is the maximum number of deployment targets which will run the step at any one time.
This default behavior makes a lot of sense for smaller installations, but it is an unsafe default for larger installations. We are looking to change this default behavior in a future version of Octopus.
Consider how many steps you run in parallel
Steps and actions in your deployment process can be configured to start after the previous step has succeeded (the default) or you can configure them to start at the same time (run in parallel).
Similarly to parallel targets, running too many steps in parallel can cause your Octopus Server to become the bottleneck and make your deployments take longer overall.
Consider how much deployment work the Octopus Server is doing
Some steps, like Azure deployments and AWS steps, run on a worker. By default, thats the built-in worker in the Octopus Server. That means the step invokes a (or many) Calamari processes on the server machine to do the deployment work. That workload can be shifted off the server and onto workers. See this blog post for a way to begin looking at workers for performance.
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