Hi. We'll need to add more JMS Messaging Data Input (JMS Modular Input v1.3.7) to connect to different queues from different MQ providers. We'll have probably 7 or 8 providers. The JMS Modular Input is installed on a heavy Forwarder instance that's sending ingested events to the indexers on different servers. Would adding more Data Inputs need more CPU, memory or impact any other resources on this Forwarder instance?
Is your system currently constrained for MEM or CPU? As you add more inputs, they do use more system resources. So if you are trying to add to a system that has resource constraints already then you will most likely have issues.
If your system as enough resources, you can add more inputs.
Our environment is new. We'll have 2 heavy Forwarders with JMS Mod Input installed. Each Forwarder has 4 cores CPU, 4GB RAM and 40GB storage. I'm trying to figure out which resource (CPU, memory) would be impacted as we add more Queues/Providers and be ready to increase those impacted resource as needed.
CPU and memory would be of concern. Whats the current state? Base current utilisation as your metric, e.g., number of inputs, and scale based on that.
If you have 20 JMX inputs, and you're seeing 95% utilisation, adding 7 more providers will most likely cause system resource issues. But if you have 20 inputs and are only at 40% utilisation, you should be able to sale you can scale up.
Since these machines are not 100% cpu / memory dedicated to Splunk (think OS and other programs running,) its hard to scale without knowing the historical utilisation of the boxes.
Presuming you are just using the JMS Modular Input out of the box (and haven't created a custom message handler for pre processing / custom handling) :
1) multiple JMS stanzas will run in the same JVM. So there won't be any change to memory consumption unless you manually increase the JVM heap size in jms_ta/bin/jms.py
2) more JMS stanzas will linearly use more CPU, as more threads are fired up to connect to the different MQ providers. But CPU usage should still be pretty low , there aren't any compute intensive algorithms.
The resource bottleneck , depending on what sort of message throughput volume you are processing is usually the STDIN/STDOUT buffer between the Modular Input process (writes to STDOUT) and the Splunk Heavy Forwarder process(reads form STDIN)
To achieve scale , I generally recommend on scaling out horizontally with multiple JMS Modular Input instances on multiple forwarders (Universal or Heavy).
See slides 20/21 here : http://www.slideshare.net/damiendallimore/splunk-conf-2014-getting-the-message