Low memory setup nl NL

Configuratie met laag geheugenverbruik

This is exact opposite of high-performance setup and typically you want to follow those tips if you want to decrease ASF's memory usage, for cost of lowering overall performance.

ASF is extremely lightweight on resources by definition, depending on your usage even 128 MB VPS with Linux is capable of running it, although going that low is not recommended and can lead to issues. While being light, ASF is not afraid of asking OS for more memory, if such memory is needed for ASF to operate with optimal speed.

ASF as an application tries to be as much optimized and efficient as possible, which also takes in mind resources being used during execution. When it comes to memory, ASF prefers performance over memory consumption, which can result in temporary memory "spikes", that can be noticed e.g. with accounts having 3+ badge pages, as ASF will fetch and parse first page, read from it total number of pages, then launch fetch task for every extra page, which results in concurrent fetching and parsing of remaining pages. This speeds up execution, for cost of increased memory usage. Similar thing is happening e.g. with parsing active trade offers, ASF is also parsing them all concurrently. On top of all of that, ASF (C# runtime) doesn't return unused memory back to OS immediately. Huh? What's going on?

ASF is extremely well optimized, and makes use of available resources as much as possible. High memory usage of ASF doesn't mean that ASF actively uses that memory and needs it. Very often ASF will keep some memory allocated for some "room" for future actions, as by not asking OS for every memory chunk we're improving performance. The runtime should automatically release unused ASF memory back to OS when OS will truly need it. Remember - unused memory is wasted memory. You run into issues when the memory you need is higher than the memory that is available for you, not when ASF keeps some extra for having free space for functions that will execute in a moment. You run into problems when your Linux kernel is killing ASF process due to OOM (out of memory), not when you see ASF process as top memory consumer in htop.

Garbage collector being used in ASF is smart enough to take into account not only ASF itself, but also your OS. When you have a lot of free memory, ASF will ask for whatever is needed to improve the performance. This can be even as much as 1 GB (with server GC). When your OS memory is close to being full, ASF will automatically release some of it back to the OS to help things settle down, which can result in ASF memory as low as 50 MB. This is why ASF process memory varies from setup to setup, as ASF will do its best to use available resources in as efficient way as possible, and not in a fixed way like it was done during Windows XP times. The actual (real) memory usage that ASF is using can be verified with stats command, and is usually around 4 MB for just a few bots. Keep in mind that memory returned by stats command includes free memory that hasn't been reclaimed by garbage collector yet. Everything else is shared runtime memory (around 40-50 MB) and room for execution (vary). This is also why the same ASF can use as little as 50 MB in low-memory VPS environment, while using even up to 1 GB on your desktop.

Of course, there are a lot of ways how you can help point ASF at the right direction in terms of the memory you expect to use. In general if you don't need to do it, it's best to let garbage collector work in peace and do whatever it considers is best. But this is not always possible, for example if your Linux server is also hosting several websites, MySQL database and PHP workers, then you can't really afford ASF shrinking itself when you run close to OOM, as it's usually too late and performance degradation comes sooner. This is usually when you might be interested in further tuning, and therefore reading this page.

Below suggestions are divided into a few categories, with varied difficulty.

ASF setup (easy)

Below tricks do not affect performance negatively and can be safely applied to all setups.

Never run more than one ASF instance. ASF is meant to handle unlimited number of bots all at once, and unless you're binding every ASF instance to different interface/IP address, you should have exactly one ASF process, with multiple bots (if needed).
Make use of ShutdownOnFarmingFinished. Active bot takes more resources than deactivated one. It's not a significant save, as the state of bot still needs to be kept, but you're saving some amount of resources, especially all resources related to networking, such as TCP sockets. You need only one active bot to keep ASF instance running, and you can always bring up other bots if needed.
Keep your bots number low. Not Enabled bot instance takes less resources, as ASF doesn't bother starting it. Also keep in mind that ASF has to create a bot for each of your configs, therefore if you don't need to start given bot and you want to save some extra memory, you can temporarily rename Bot.json to e.g. Bot.json.bak in order to avoid creating state for your disabled bot instance in ASF. This way you won't be able to start it without renaming it back, but ASF also won't bother keeping state of this bot in memory, leaving room for other things (very small save, in 99.9% cases you shouldn't bother with it, just keep your bots with Enabled of false).
Fine-tune your configs. Especially global ASF config has many variables to adjust, for example by increasing LoginLimiterDelay you'll bring up your bots slower, which will allow already started instance to fetch badges in the meantime, as opposed to bringing up your bots faster, which will take more resources as more bots will do major work (such as parsing badges) at the same time. The less work that has to be done at the same time - the less memory used.

Those are a few things you can keep in mind when dealing with memory usage. However, those things don't have any "crucial" matter on memory usage, because memory usage comes mostly from things ASF has to deal with, and not from internal structures used for cards farming.

The most resources-heavy functions are:

Badge page parsing
Inventory parsing

Which means that memory will spike the most when ASF is dealing with reading badge pages, and when it's dealing with its inventory (e.g. sending trade or working with STM). This is because ASF has to deal with really huge amount of data - the memory usage of your favourite browser launching those two pages will not be any lower than that. Sorry, that's how it works - decrease number of your badge pages, and keep number of your inventory items low, that can for sure help.

Runtime tuning (advanced)

Below tricks involve performance degradation and should be used with caution.

ArchiSteamFarm.runtimeconfig.json allows you to tune ASF runtime, especially allowing you to switch between server GC and workstation GC.

The garbage collector is self-tuning and can work in a wide variety of scenarios. You can use a configuration file setting to set the type of garbage collection based on the characteristics of the workload. The CLR provides the following types of garbage collection:

Workstation garbage collection, which is for all client workstations and stand-alone PCs. This is the default setting for the element in the runtime configuration schema.

Server garbage collection, which is intended for server applications that need high throughput and scalability. Server garbage collection can be non-concurrent or background.

More can be read at fundamentals of garbage collection.

ASF is already using workstation GC, but you can ensure that it's truly the case by checking if System.GC.Server property of ArchiSteamFarm.runtimeconfig.json is set to false.

In addition to verifying that workstation GC is active, there are also interesting configuration knobs that you can use - gcTrimCommitOnLowMemory and GCLatencyLevel.

`GCLatencyLevel`

Specifies the GC latency level that you want to optimize for.

This works exceptionally well by limiting size of GC generations and in result make GC purge them more frequently and more aggressively. Default (balanced) latency level is 1, we'll want to use 0, which will tune for memory usage.

`gcTrimCommitOnLowMemory`

When set we trim the committed space more aggressively for the ephemeral seg. This is used for running many instances of server processes where they want to keep as little memory committed as possible.

This offers little improvement, but might make GC even more aggressive when system will be low on memory.

You can enable both by setting appropriate COMPlus_ environment variables. For example, on Linux:

export COMPlus_GCLatencyLevel=0
export COMPlus_gcTrimCommitOnLowMemory=1
./ArchiSteamFarm

Or on Windows:

SET COMPlus_GCLatencyLevel=0
SET COMPlus_gcTrimCommitOnLowMemory=1
.\ArchiSteamFarm.exe

Especially GCLatencyLevel will come very useful as we verified that the runtime indeed optimizes code for memory and therefore drops average memory usage significantly, even with server GC. It's one of the best tricks that you can apply if you want to significantly lower ASF memory usage while not degrading performance too much with OptimizationMode.

ASF tuning (intermediate)

Below tricks involve serious performance degradation and should be used with caution.

As a last resort, you can tune ASF for MinMemoryUsage through OptimizationMode global config property. Read carefully its purpose, as this is serious performance degradation for nearly no memory benefits. This is typically the last thing you want to do, long after you go through runtime tuning to ensure that you're forced to do this.

Recommended optimization

Start from simple ASF setup tricks, perhaps you're just using your ASF in a wrong way such as starting the process several times for all of your bots, or keeping all of them active if you need just one or two to autostart.
If it's still not enough, enable all configuration knobs listed above by setting appropriate COMPlus_ environment variables. Especially GCLatencyLevel offers significant runtime improvements for little cost on performance.
If even that didn't help, as a last resort enable MinMemoryUsage OptimizationMode. This forces ASF to execute almost everything in synchronous matter, making it work much slower but also not relying on thread pool to balance things out when it comes to parallel execution.

It's physically impossible to decrease memory even further, your ASF is already heavily degraded in terms of performance and you depleted all your possibilities, both code-wise and runtime-wise. Consider adding some extra memory for ASF to use, even 128 MB would make a great difference.