Introduction to Splunk Forwarder

Splunk Forwarder is the component that you should use for collecting the logs. Suppose, you want to gather logs from a remote machine, then you can accomplish that by using Splunk’s remote forwarders which are independent of the most Splunk instance. Splunk Architect certification has a thorough understanding of Splunk Deployment Methodology and best practices for planning, data collection, and sizing for a distributed deployment and is able to manage and troubleshoot a standard distributed deployment with indexer and search head clustering.

In fact, you can install several such forwarders in multiple machines, which will forward the log data to a Splunk Indexer for processing and storage. What if you want to do a real-time analysis of the data? Splunk forwarders are used for that purpose too. You can configure the forwarders to send data to Splunk indexers in real-time. You can install them in multiple systems and collect the data simultaneously from different machines in real-time.

To understand how real-time forwarding of data happens, you can read my blog on how Domino’s is using Splunk to achieve operational efficiency.

Compared to other traditional monitoring tools, Splunk Forwarder consumes very less CPU ~1-2%. You can scale them up to tens of thousands of remote systems easily, and collect terabytes of data with minimal impact on performance.

Now, let us understand the different types of Splunk forwarders.

Universal Forwarder – you'll opt for a universal forwarder if you want to forward the information collected at the source. It is a simple component that performs minimal processing on the incoming data streams before forwarding them to an indexer.

Data transfer is a major problem with almost every tool in the market. Since there is minimal processing on the data before it is forwarded, a lot of unnecessary data is also forwarded to the indexer resulting in performance overheads.

Why go through the trouble of transferring all the info to the Indexers and then filter out only the relevant data? Wouldn’t it be better to only send the relevant data to the Indexer and save on bandwidth, time, and money? This can be solved by using Heavy forwarders which I have explained below.

Heavy Forwarder – You can use a heavy forwarder and eliminate half your problems because one level of knowledge processing happens at the source itself before forwarding data to the indexer. Heavy Forwarder typically does parsing and indexing at the source and also intelligently routes the data to the Indexer saving on bandwidth and storage space. So when a significant forwarder parses the data, the indexer only needs to handle the indexing segment.

Intended for more details check out this Youtube Link: https://www.youtube.com/watch?v=ZDK2omJmxuY

Digital Marketing Specialist at igmGuru.