Note: This article provides details for 4th and 5th generation AWS instances. Stack release stable-v5-3.0.58 introduced a small worker count tweak for 3rd and 4th generation instances. For worker counts for 3rd and 4th generation instances on older v5 and v4 stacks, as well as older instance types, please see this article.
Update April 23rd 2020: As of the stable-v6-1.0.33 release we now support the setting of the worker configuration via Environmental Variables. Customers with environments running on the stable-v6 stack should upgrade to this version in order to be able to set configuration, whilst customers on older major stack versions should continue to contact Support in order to request the setting of the worker configuration in their metadata.
Worker definition
Workers are the processes that allow your application to respond to incoming web requests. Regardless of the application server stack you select, it provides one or more workers per instance to run your application.
Standard worker allocations based on instance ECUs and memory configuration
For Passenger or Unicorn, worker processes are allocated using an algorithm that takes into consideration ECUs and memory available to the instance to determine the number of workers to allocate, as shown in the table below.
| Instance Type | Memory (GiB) | Swap (MB) | ECU | Pool Size |
|---|---|---|---|---|
| General Purpose (T2) Micro | 1 | 8192 | 3.25* | 6 |
| General Purpose (T2) Small | 2 | 8192 | 3.25* | 7 |
| General Purpose (T2) Medium | 4 | 8192 | 6.5* | 14 |
| General Purpose (T2) Large | 8 | 8192 | 6.5* | 14 |
| General Purpose (T2) Extra Large | 16 | 8192 | 13* | 28 |
| General Purpose (T2) 2x Extra Large | 32 | 8192 | 26* | 56 |
| General Purpose (T3) Micro | 1 | 8192 | 8* | 6 |
| General Purpose (T3) Small | 2 | 8192 | 8* | 10 |
| General Purpose (T3) Medium | 4 | 8192 | 8* | 16 |
| General Purpose (T3) Large | 8 | 8192 | 8* | 16 |
| General Purpose (T3) Extra Large | 16 | 8192 | 16* | 32 |
| General Purpose (T3) 2x Extra Large | 32 | 8192 | 32* | 64 |
| General Purpose (M4) Large | 8 | 8192 | 6.5 | 13 |
| General Purpose (M4) Extra Large | 16 | 8192 | 13 | 26 |
| General Purpose (M4) 2x Extra Large | 32 | 8192 | 26 | 52 |
| General Purpose (M4) 4x Extra Large | 64 | 8192 | 53.5 | 100 |
| General Purpose (M4) 10x Extra Large | 160 | 8192 | 124.5 | 100 |
| General Purpose (M4) 16x Extra Large | 256 | 8192 | 188 | 100 |
| General Purpose (M5) Large | 8 | 8192 | 8 | 16 |
| General Purpose (M5) Extra Large | 16 | 8192 | 16 | 32 |
| General Purpose (M5) 2x Extra Large | 32 | 8192 | 31 | 62 |
| General Purpose (M5) 4x Extra Large | 64 | 8192 | 60 |
100 |
| General Purpose (M5) 8x Extra Large | 32 | 8192 | 128 |
100 |
| General Purpose (M5) 12x Extra Large | 192 | 8192 | 173 | 100 |
| General Purpose (M5) 24x Extra Large | 384 | 8192 | 345 | 100 |
| Compute Optimized (C4) Large | 3.75 | 8192 | 8 | 15 |
| Compute Optimized (C4) Extra Large | 7.5 | 8192 | 16 | 29 |
| Compute Optimized (C4) 2x Extra Large | 15 | 8192 | 31 | 59 |
| Compute Optimized (C4) 4x Extra Large | 30 | 8192 | 62 | 100 |
| Compute Optimized (C4) 8x Extra Large | 60 | 8192 | 132 | 100 |
| Compute Optimized (C5) Large | 4 | 8192 | 9 | 15 |
| Compute Optimized (C5) Extra Large | 8 | 8192 | 17 | 30 |
| Compute Optimized (C5) 2x Extra Large | 16 | 8192 | 34 | 61 |
| Compute Optimized (C5) 4x Extra Large | 32 | 8192 | 68 | 100 |
| Compute Optimized (C5) 9x Extra Large | 72 | 8192 | 141 | 100 |
| Compute Optimized (C5) 18x Extra Large | 144 | 8192 | 281 | 100 |
| Memory Optimized (R4) Large | 15.25 | 8192 | 7 | 14 |
| Memory Optimized (R4) Extra Large | 30.5 | 8192 | 13.5 | 27 |
| Memory Optimized (R4) 2x Extra Large | 61 | 8192 | 27 | 54 |
| Memory Optimized (R4) 4x Extra Large | 122 | 8192 | 53 | 100 |
| Memory Optimized (R4) 8x Extra Large | 244 | 8192 | 99 | 100 |
| Memory Optimized (R4) 16x Extra Large | 488 | 8192 | 195 | 100 |
| Memory Optimized (R5) Large | 16 | 8192 | 10 | 20 |
| Memory Optimized (R5) Extra Large | 32 | 8192 | 19 | 38 |
| Memory Optimized (R5) 2x Extra Large | 64 | 8192 | 38 | 76 |
| Memory Optimized (R5) 4x Extra Large | 128 | 8192 | 71 | 100 |
| Memory Optimized (R5) 8x Extra Large | 256 | 8192 | 128 | 100 |
| Memory Optimized (R5) 12x Extra Large | 384 | 8192 | 173 | 100 |
| Memory Optimized (R5) 16x Extra Large | 512 | 8192 | 256 | 100 |
| Memory Optimized (R5) 24x Extra Large | 768 | 8192 | 347 | 100 |
| High I/O (I3) Large | 15.25 | 8192 | 7 | 14 |
| High I/O (I3) Extra Large | 30.5 | 8192 | 13 | 26 |
| High I/O (I3) 2x Extra Large | 61 | 8192 | 27 | 54 |
| High I/O (I3) 4x Extra Large | 122 | 8192 | 53 | 100 |
| High I/O (I3) 8x Extra Large | 244 | 8192 | 99 | 100 |
| High I/O (I3) 16x Extra Large | 488 | 8192 | 200 | 100 |
About ECUs and vCPUs
We use "vCPUs" as a generic label for virtual CPU cores (and in some cases hyperthreads) available to your instance. Amazon (IaaS provider for Engine Yard Cloud) uses the term ECU, or Elastic Compute Units, to convey the relative compute power available on each instance type. This value is different than the virtual CPUs reported by the operating system, such as in top or /proc/cpuinfo, but better allows you to determine the amount of effective compute resources you need. According to Amazon, the definition of ECU is:
"One EC2 Compute Unit provides the equivalent CPU capacity of a 1.0-1.2 GHz 2007 Opteron or 2007 Xeon processor."
* Note: Due to their burstable nature, there are no official ECU figures for T2 and T3 instances. Therefore, for the purposes of the worker count calculation we make an assumption as to the value, based on the vCPU count for the instance size. If your workers utilise high levels of CPU it may be required to lower the worker count to keep CPU usage under the baseline and avoid bursting under standard usage.
Tune for your application's needs
The values in the table (above) are based on generic parameters of an idealized application. However, if your app falls outside these parameters, you should consider tuning your environment, to either:
- Reduce the worker counts to improve responsiveness because your app requires more resources.
- Increase available workers for a lightweight app with high requests per minute.
Tune your app using parameters
You can tune your app by setting parameters on your environment. The sections below explain the various parameters that can be tuned. Once you know what values you want tuned, you need to open a support ticket to have them put in effect.
pool_size
This is the most direct way of modifying your pool size. By specifying pool_size, you can set it regardless of resource availability. This is recommended only if you are fully aware of your resource needs and don't plan on changing your instance size in the future.
min_pool_size
Slightly less forceful, the min_pool_size parameter allows you to ensure your application always has the minimum number of workers available. Useful if using the smaller instance types and you don't mind overloading the resources — be careful though, overloaded instances may result in underperforming or even hung instances, potentially triggering a takeover.
If you find yourself using this and having poor responsiveness or takeovers, please consider upgrading to a larger instance size.
Default: 3
max_pool_size
Likewise, the max_pool_size parameter allows you to ensure you don't have more workers than you need, regardless of resource availability. You may want to do this if your workers connect to external entities that cannot handle too many simultaneous connections.
Default: 100
worker_memory_size
This is the best and most accurate way of tuning your app based on memory usage. The standardized values above are based on an app in which each worker uses 250MB of memory, which is close the upper limit of usage we've seen across thousands of apps. If your app is significantly different than this, it is best to tune this value to get accurate resource usage calculations.
Default: 250 (MB)
workers_per_ecu
Some apps are compute heavy, where others barely use any of the processor capacity. The standard configuration provides 2 workers per ECU; tuning this to your app's needs can improve its responsiveness.
Default: 2
swap_usage_percent
While it is not a good idea to go in to swap in most cases, it is a balancing act to provide the optimal number of workers. We have chosen a modest 25% usage of swap by the workers in order to provide a sufficient worker count without excessive paging. If you find that your app is spending a great deal of time in swap, you may want to change this value.
Note: It is usually due to CPU constraints rather than memory constraints that determines the workers available, but depending on how many workers you allow per CPU, this may not be the case. Under default settings, this value most significantly impacts the Compute Optimized instances.
Default: 20 (%)
reserved_memory
If you are running background workers or other processes that eat into your memory resources, you may want to specify a chunk of memory to reserve. By default, this is set to a generous value that accommodates the OS and support processes.
Default: 1500 (MB)
reserved_memory_solo
Similar to reserved_memory but also takes into consideration the database that shared the instance in a single-instance (solo) environment.
Default: 2000 (MB)
db_vcpu_max and db_workers_per_vcpu
These settings are only applicable to single instance environments. By default, no consideration is taken to reserve ECU resources for the database, but if you have a database-heavy application, it would be beneficial to allocate some of the worker resources to the database instead. The number of workers represented by the db_workers_per_ecu parameter is removed from each ECU, up to the db_vcpu_max value.
Default: db_vcpu_max = 0; db_workers_per_ecu = 0.5
Planning Tool
Use the following form to determine how many workers your instances will have, and how much free memory and swap usage will be expected.
| Settings | Calculated value | |||||||||||||||||||||||||
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Edit the fields below. Leave blank for default value, which is shown in grey italics when not editing the field.
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