Chapter 5. Configuring Satellite for performance Satellite comes with a number of components that communicate with each other. You can tune these components independently of each other to achieve the maximum possible performance for your scenario. 5.1. Applying configurations In following sections we suggest various tunables and how to apply them. Please always test changing these in non production environment first, with valid backup and with proper outage window as in most of the cases Satellite restart is required. It is also a good practice to setup a monitoring before applying any change as it will allow you to evaluate effect of the change. Our testing environment might be too far from what you will see although we are trying hard to mimic real world environment. Changing systemd service files If you have changed some systemd service file, you need to notify systemd daemon to reload the configuration: Restart Satellite services: Changing configuration files If you have changed a configuration file such as /etc/foreman-installer/custom-hiera.yaml , rerun installer to apply your changes: Running installer with additional options If you need to rerun installer with some new options added: Checking basic sanity of the setup Optional: After any change, run this quick Satellite health-check: 5.2. Puma tunings Puma is a ruby application server which is used for serving the Foreman related requests to the clients. For any Satellite configuration that is supposed to handle a large number of clients or frequent operations, it is important for the Puma to be tuned appropriately. 5.2.1. Puma threads Number of Puma threads (per Puma worker) is configured using two values: threads_min and threads_max . Value of threads_min determines how many threads each worker spawns at a worker start. Then, as concurrent requests are coming and more threads is needed, worker will be spawning more and more workers up to threads_max limit. We recommend setting threads_min to same value as threads_max as having fewer Puma threads lead to higher memory usage on your Satellite Server. For example, we have compared these two setups using concurrent registrations test: Satellite VM with 8 CPUs, 40 GiB RAM Satellite VM with 8 CPUs, 40 GiB RAM --foreman-foreman-service-puma-threads-min=0 --foreman-foreman-service-puma-threads-min=16 --foreman-foreman-service-puma-threads-max=16 --foreman-foreman-service-puma-threads-max=16 --foreman-foreman-service-puma-workers=2 --foreman-foreman-service-puma-workers=2 Setting the minimum Puma threads to 16 results in about 12% less memory usage as compared to threads_min=0 . 5.2.2. Puma workers and threads auto-tuning If you do not provide any Puma workers and thread values with satellite-installer or they are not present in your Satellite configuration, the satellite-installer configures a balanced number of workers. It follows this formula: This should be fine for most cases, but with some usage patterns tuning is needed to either limit the amount of resources dedicated to Puma (so other Satellite components can use these) or for any other reason. Each Puma worker consumes around 1 GiB of RAM. View your current Satellite Server settings View the currently active Puma workers 5.2.3. Manually tuning Puma workers and threads count If you decide not to rely on Section 5.2.2, "Puma workers and threads auto-tuning" , you can apply custom numbers for these tunables. In the example below we are using 2 workers, 5 and 5 threads: Apply your changes to Satellite Server. For more information, see Section 5.1, "Applying configurations" . 5.2.4. Puma workers and threads recommendations In order to recommend thread and worker configurations for the different tuning profiles, we conducted Puma tuning testing on Satellite with different tuning profiles. The main test used in this testing was concurrent registration with the following combinations along with different number of workers and threads. Our recommendation is based purely on concurrent registration performance, so it might not reflect your exact use-case. For example, if your setup is very content oriented with lots of publishes and promotes, you might want to limit resources consumed by Puma in favor of Pulp and PostgreSQL. Name Number of hosts RAM Cores Recommended Puma Threads for both min & max Recommended Puma Workers default 0 - 5000 20 GiB 4 16 4 - 6 medium 5000 - 10000 32 GiB 8 16 8 - 12 large 10000 - 20000 64 GiB 16 16 12 - 18 extra-large 20000 - 60000 128 GiB 32 16 16 - 24 extra-extra-large 60000+ 256 GiB+ 48+ 16 20 - 26 Tuning number of workers is the more important aspect here and in some case we have seen up to 52% performance increase. Although installer uses 5 min/max threads by default, we recommend 16 threads with all the tuning profiles in the table above. That is because we have seen up to 23% performance increase with 16 threads (14% for 8 and 10% for 32) when compared to setup with 4 threads. To figure out these recommendations we used concurrent registrations test case which is a very specific use-case. It can be different on your Satellite which might have more balanced use-case (not only registrations). Keeping default 5 min/max threads is a good choice as well. These are some of our measurements that lead us to these recommendations: 4 workers, 4 threads 4 workers, 8 threads 4 workers, 16 threads 4 workers, 32 threads Improvement 0% 14% 23% 10% Use 4 - 6 workers on a default setup (4 CPUs) - we have seen about 25% higher performance with 5 workers when compared to 2 workers, but 8% lower performance with 8 workers when compared to 2 workers - see table below: 2 workers, 16 threads 4 workers, 16 threads 6 workers, 16 threads 8 workers, 16 threads Improvement 0% 26% 22% -8% Use 8 - 12 workers on a medium setup (8 CPUs) - see table below: 2 workers, 16 threads 4 workers, 16 threads 8 workers, 16 threads 12 workers, 16 threads 16 workers, 16 threads Improvement 0% 51% 52% 52% 42% Use 16 - 24 workers on a 32 CPUs setup (this was tested on a 90 GiB RAM machine and memory turned out to be a factor here as system started swapping - proper extra-large should have 128 GiB), higher number of workers was problematic for higher registration concurrency levels we tested, so we cannot recommend it. 4 workers, 16 threads 8 workers, 16 threads 16 workers, 16 threads 24 workers, 16 threads 32 workers, 16 threads 48 workers, 16 threads Improvement 0% 37% 44% 52% too many failures too many failures 5.2.5. Configuring Puma workers If you have enough CPUs, adding more workers adds more performance. For example, we have compared Satellite setups with 8 and 16 CPUs: Table 5.1. satellite-installer options used to test effect of workers count Satellite VM with 8 CPUs, 40 GiB RAM Satellite VM with 16 CPUs, 40 GiB RAM --foreman-foreman-service-puma-threads-min=16 --foreman-foreman-service-puma-threads-min=16 --foreman-foreman-service-puma-threads-max=16 --foreman-foreman-service-puma-threads-max=16 --foreman-foreman-service-puma-workers={2|4|8|16} --foreman-foreman-service-puma-workers={2|4|8|16} In 8 CPUs setup, changing the number of workers from 2 to 16, improved concurrent registration time by 36%. In 16 CPUs setup, the same change caused 55% improvement. Adding more workers can also help with total registration concurrency Satellite can handle. In our measurements, setup with 2 workers were able to handle up to 480 concurrent registrations, but adding more workers improved the situation. 5.2.6. Configuring Puma threads More threads allow for lower time to register hosts in parallel. For example, we have compared these two setups: Satellite VM with 8 CPUs, 40 GiB RAM Satellite VM with 8 CPUs, 40 GiB RAM --foreman-foreman-service-puma-threads-min=16 --foreman-foreman-service-puma-threads-min=8 --foreman-foreman-service-puma-threads-max=16 --foreman-foreman-service-puma-threads-max=8 --foreman-foreman-service-puma-workers=2 --foreman-foreman-service-puma-workers=4 Using more workers and the same total number of threads results in about 11% of speedup in highly concurrent registrations scenario. Moreover, adding more workers did not consume more CPU and RAM but gets more performance. 5.2.7. Configuring Puma DB pool The effective value of USDdb_pool is automatically set to equal USDforeman::foreman_service_puma_threads_max . It is the maximum of USDforeman::db_pool and USDforeman::foreman_service_puma_threads_max but both have default value 5, so any increase to the max threads above 5 automatically increases the database connection pool by the same amount. If you encounter ActiveRecord::ConnectionTimeoutError: could not obtain a connection from the pool within 5.000 seconds (waited 5.006 seconds); all pooled connections were in use error in /var/log/foreman/production.log , you might want to increase this value. View current db_pool setting 5.2.8. Manually tuning db_pool If you decide not to rely on automatically configured value, you can apply custom number like this: Apply your changes to Satellite Server. For more information, see Section 5.1, "Applying configurations" . 5.3. Apache HTTPD performance tuning Apache httpd forms a core part of the Satellite and acts as a web server for handling the requests that are being made through the Satellite web UI or exposed APIs. To increase the concurrency of the operations, httpd forms the first point where tuning can help to boost the performance of your Satellite. 5.3.1. Configuring the open files limit for Apache HTTPD With the tuning in place, Apache httpd can easily open a lot of file descriptors on the server which may exceed the default limit of most of the Linux systems in place. To avoid any kind of issues that may arise as a result of exceeding max open files limit on the system, please create the following file and directory and set the contents of the file as specified in the below given example: Procedure Set the maximum open files limit in /etc/systemd/system/httpd.service.d/limits.conf : Apply your changes to Satellite Server. For more information, see Section 5.1, "Applying configurations" . 5.3.2. Tuning Apache httpd child processes By default, httpd uses event request handling mechanism. When the number of requests to httpd exceeds the maximum number of child processes that can be launched to handle the incoming connections, httpd raises an HTTP 503 Service Unavailable error. Amidst httpd running out of processes to handle, the incoming connections can also result in multiple component failures on your Satellite services side due to the dependency of some components on the availability of httpd processes. You can adapt the configuration of httpd event to handle more concurrent requests based on your expected peak load. Warning Configuring these numbers in custom-hiera.yaml locks them. If you change these numbers using satellite-installer --tuning= My_Tuning_Option , your custom-hiera.yaml will overwrite this setting. Set your numbers only if you have specific need for it. Procedure Modify the number of concurrent requests in /etc/foreman-installer/custom-hiera.yaml by changing or adding the following lines: The example is identical to running satellite-installer --tuning=medium or higher on Satellite Server. Apply your changes to Satellite Server. For more information, see Section 5.1, "Applying configurations" . 5.4. Dynflow tuning Dynflow is the workflow management system and task orchestrator which is a Satellite plugin and is used to execute the different tasks of Satellite in an out-of-order execution manner. Under the conditions when there are a lot of clients checking in on Satellite and running a number of tasks, Dynflow can take some help from an added tuning specifying how many executors can it launch. For more information about the tunings involved related to Dynflow, see https://satellite.example.com/foreman_tasks/sidekiq . Increase number of Sidekiq workers Satellite contains a Dynflow service called dynflow-sidekiq that performs tasks scheduled by Dynflow. Sidekiq workers can be grouped into various queues to ensure lots of tasks of one type will not block execution of tasks of other type. Red Hat recommends to increase the number of sidekiq workers to scale the Foreman tasking system for bulk concurrent tasks, for example for multiple content view publications and promotions, content synchronizations, and synchronizations to Capsule Servers. There are two options available: You can increase the number of threads used by a worker (worker's concurrency). This has limited impact for values larger than five due to Ruby implementation of the concurrency of threads. You can increase the number of workers, which is recommended. Procedure Increase the number of workers from one worker to three while remaining five threads/concurrency of each: Optional: Check if there are three worker services: For more information, see How to add sidekiq workers in Satellite6? . 5.5. Pull-based REX transport tuning Satellite has a pull-based transport mode for remote execution. This transport mode uses MQTT as its messaging protocol and includes an MQTT client running on each host. For more information, see Transport Modes for Remote Execution in Managing hosts . 5.5.1. Increasing host limit for pull-based REX transport You can tune the mosquitto MQTT server and increase the number of hosts connected to it. Procedure Enable pull-based remote execution on your Satellite Server or Capsule Server: Note that your Satellite Server or Capsule Server can only use one transport mode, either SSH or MQTT. Create a config file to increase the default number of hosts accepted by the MQTT service: This example sets the limit to allow the mosquitto service to handle 5000 hosts. Run the following commands to apply your changes: 5.5.2. Decreasing performance impact of the pull-based REX transport When Satellite Server is configured with the pull-based transport mode for remote execution jobs using the Script provider, Capsule Server sends notifications about new jobs to clients through MQTT. This notification does not include the actual workload that the client is supposed to execute. After a client receives a notification about a new remote execution job, it queries Capsule Server for its actual workload. During the job, the client periodically sends outputs of the job to Capsule Server, further increasing the number of requests to Capsule Server. These requests to Capsule Server together with high concurrency allowed by the MQTT protocol can cause exhaustion of available connections on Capsule Server. Some requests might fail, making some child tasks of remote execution jobs unresponsive. This also depends on actual job workload, as some jobs are causing additional load to Satellite Server, making it compete for resources if clients are registered to Satellite Server. To avoid this, configure your Satellite Server and Capsule Server with the following parameters: MQTT Time To Live - Time interval in seconds given to the host to pick up the job before considering the job undelivered MQTT Resend Interval - Time interval in seconds at which the notification should be re-sent to the host until the job is picked up or cancelled MQTT Rate Limit - Number of jobs that are allowed to run at the same time. You can limit the concurrency of remote execution by tuning the rate limit which means you are going to put more load on Satellite. Procedure Tune the MQTT parameters on your Satellite Server: Capsule Server logs are in /var/log/foreman-proxy/proxy.log . Capsule Server uses Webrick HTTP server (no httpd or Puma involved), so there is no simple way to increase its capacity. Note Depending on the workload, number of hosts, available resources, and applied tuning, you might hit the Bug 2244811 , which causes Capsule to consume too much memory and eventually be killed, making the rest of the job fail. At the moment there is no universally applicable workaround. 5.6. PostgreSQL tuning PostgreSQL is the primary SQL based database that is used by Satellite for the storage of persistent context across a wide variety of tasks that Satellite does. The database sees an extensive usage is usually working on to provide the Satellite with the data which it needs for its smooth functioning. This makes PostgreSQL a heavily used process which if tuned can have a number of benefits on the overall operational response of Satellite. The PostgreSQL authors recommend disabling Transparent Hugepage on servers running PostgreSQL. For more information, see Section 4.3, "Disable Transparent Hugepage" . You can apply a set of tunings to PostgreSQL to improve its response times, which will modify the postgresql.conf file. Procedure Append /etc/foreman-installer/custom-hiera.yaml to tune PostgreSQL: postgresql::server::config_entries: max_connections: 1000 shared_buffers: 2GB work_mem: 8MB autovacuum_vacuum_cost_limit: 2000 You can use this to effectively tune down your Satellite instance irrespective of a tuning profile. Apply your changes to Satellite Server. For more information, see Section 5.1, "Applying configurations" . In the above tuning configuration, there are a certain set of keys which we have altered: max_connections : The key defines the maximum number of connections that can be accepted by the PostgreSQL processes that are running. shared_buffers : The shared buffers define the memory used by all the active connections inside PostgreSQL to store the data for the different database operations. An optimal value for this will vary between 2 GiB to a maximum of 25% of your total system memory depending upon the frequency of the operations being conducted on Satellite. work_mem : The work_mem is the memory that is allocated on per process basis for PostgreSQL and is used to store the intermediate results of the operations that are being performed by the process. Setting this value to 8 MB should be more than enough for most of the intensive operations on Satellite. autovacuum_vacuum_cost_limit : The key defines the cost limit value for the vacuuming operation inside the autovacuum process to clean up the dead tuples inside the database relations. The cost limit defines the number of tuples that can be processed in a single run by the process. Red Hat recommends setting the value to 2000 as it is for the medium , large , extra-large , and extra-extra-large profiles, based on the general load that Satellite pushes on the PostgreSQL server process. For more information, see BZ1867311: Upgrade fails when checkpoint_segments postgres parameter configured . 5.6.1. Benchmarking raw DB performance To get a list of the top table sizes in disk space for both Candlepin, Foreman, and Pulp check postgres-size-report script in satellite-support git repository. PGbench utility (note you may need to resize PostgreSQL data directory /var/lib/pgsql directory to 100 GiB or what does benchmark take to run) might be used to measure PostgreSQL performance on your system. Use dnf install postgresql-contrib to install it. For more information, see github.com/RedHatSatellite/satellite-support . Choice of filesystem for PostgreSQL data directory might matter as well. Warning Never do any testing on production system and without valid backup. Before you start testing, see how big the database files are. Testing with a really small database would not produce any meaningful results. For example, if the DB is only 20 GiB and the buffer pool is 32 GiB, it won't show problems with large number of connections because the data will be completely buffered. 5.7. Redis tuning Redis is an in-memory data store. It is used by multiple services in Satellite. The Dynflow and Pulp tasking systems use it to track their tasks. Given the way Satellite uses Redis, its memory consumption should be stable. The Redis authors recommend disabling Transparent Hugepage on servers running Redis. For more about it please see Section 4.3, "Disable Transparent Hugepage" . 5.8. Capsule configuration tuning Capsules are meant to offload part of Satellite load and provide access to different networks related to distributing content to clients but they can also be used to execute remote execution jobs. What they cannot help with is anything which extensively uses Satellite API as host registration or package profile update. 5.8.1. Capsule performance tests We have measured multiple test cases on multiple Capsule configurations: Capsule HW configuration CPUs RAM minimal 4 12 GiB large 8 24 GiB extra large 16 46 GiB Content delivery use case In a download test where we concurrently downloaded a 40MB repo of 2000 packages on 100, 200, .. 1000 hosts, we saw roughly 50% improvement in average download duration every time when we doubled Capsule Server resources. For more precise numbers, see the table below. Concurrent downloading hosts Minimal (4 CPU and 12 GiB RAM) Large (8 CPU and 24 GiB RAM) Large (8 CPU and 24 GiB RAM) Extra Large (16 CPU and 46 GiB RAM) Minimal (4 CPU and 12 GiB RAM) Extra Large (16 CPU and 46 GiB RAM) Average Improvement ~ 50% (e.g. for 700 concurrent downloads in average 9 seconds vs. 4.4 seconds per package) ~ 40% (e.g. for 700 concurrent downloads in average 4.4 seconds vs. 2.5 seconds per package) ~ 70% (e.g. for 700 concurrent downloads in average 9 seconds vs. 2.5 seconds per package) When we compared download performance from Satellite Server vs. from Capsule Server, we have seen only about 5% speedup, but that is expected as Capsule Server's main benefit is in getting content closer to geographically distributed clients (or clients in different networks) and in handling part of the load Satellite Server would have to handle itself. In some smaller hardware configurations (8 CPUs and 24 GiB), Satellite Server was not able to handle downloads from more than 500 concurrent clients, while a Capsule Server with the same hardware configuration was able to service more than 1000 and possibly even more. Concurrent registrations use case For concurrent registrations, a bottleneck is usually CPU speed, but all configs were able to handle even high concurrency without swapping. Hardware resources used for Capsule have only minimal impact on registration performance. For example, Capsule Server with 16 CPUs and 46 GiB RAM have at most a 9% registration speed improvement when compared to a Capsule Server with 4 CPUs and 12 GiB RAM. During periods of very high concurrency, you might experience timeouts in the Capsule Server to Satellite Server communication. You can alleviate this by increasing the default timeout using the following tunable in /etc/foreman-installer/custom-hiera.yaml : Remote execution use case We have tested executing Remote Execution jobs via both SSH and Ansible backend on 500, 2000 and 4000 hosts. All configurations were able to handle all of the tests without errors, except for the smallest configuration (4 CPUs and 12 GiB memory) which failed to finish on all 4000 hosts. Content sync use case In a sync test where we synced Red Hat Enterprise Linux 6, 7, 8 BaseOS and 8 AppStream we have not seen significant differences among Capsule configurations. This will be different for syncing a higher number of content views in parallel.

Commands:
systemctl daemon-reload
satellite-maintain service restart
satellite-installer
satellite-installer new options
satellite-maintain health check
min(CPU_COUNT * 1.5, RAM_IN_GB - 1.5)
cat /etc/systemd/system/foreman.service.d/installer.conf
systemctl status foreman
satellite-installer --foreman-foreman-service-puma-workers=2 --foreman-foreman-service-puma-threads-min=5 --foreman-foreman-service-puma-threads-max=5
grep pool /etc/foreman/database.yml pool: 5
satellite-installer --foreman-db-pool 10
[Service] LimitNOFILE=640000
apache::mod::event::serverlimit: 64 apache::mod::event::maxrequestworkers: 1024 apache::mod::event::maxrequestsperchild: 4000
satellite-installer --foreman-dynflow-worker-instances 3 # optionally, add --foreman-dynflow-worker-concurrency 5
systemctl -a | grep dynflow-sidekiq@worker-[0-9] dynflow-sidekiq@worker-1.service loaded active running Foreman jobs daemon - worker-1 on sidekiq dynflow-sidekiq@worker-2.service loaded active running Foreman jobs daemon - worker-2 on sidekiq dynflow-sidekiq@worker-3.service loaded active running Foreman jobs daemon - worker-3 on sidekiq
satellite-installer --foreman-proxy-plugin-remote-execution-script-mode pull-mqtt
cat >/etc/systemd/system/mosquitto.service.d/limits.conf << EOF [Service] LimitNOFILE=5000 EOF
systemctl daemon-reload systemctl restart mosquitto.service
satellite-installer --foreman-proxy-plugin-remote-execution-script-mqtt-rate-limit My_MQTT_Rate_Limit --foreman-proxy-plugin-remote-execution-script-mqtt-resend-interval My_MQTT_Resend_Interval --foreman-proxy-plugin-remote-execution-script-mqtt-ttl My_MQTT_Time_To_Live
postgresql::server::config_entries: max_connections: 1000 shared_buffers: 2GB work_mem: 8MB autovacuum_vacuum_cost_limit: 2000
apache::mod::proxy::proxy_timeout: 600