Splunk architecture| Know from the basics [ OverView ]

• In this article you will get
• Different stages in Data Pipeline
• Splunk Components
• Splunk Architecture
• Conclusion

Different stages in Data Pipeline

The different stages in Data Pipeline:

• Data Input Stage
• Data Storage Stage
• Data Searching Stage

Data Input Stage:

At this point, we retrieve the original data source and transform it into 64k blocks. Included in the list of metadata keys are:

• Hostname.
• Source.

Source type of data:

Data Storage Stage:

This stage is carried out in two different phases, i.e

• Parsing
• Indexing

Parsing:During this process, the data is being analysed, manipulated, then analysed again by the Splunk software. When all the data sets are separated into their constituent events, we have reached the event processing phase. This parsing step includes the following actions.

• Lines are extracted from a continuous stream of data.
• Detects and records time stamps.
• Metadata and events are modified using a regular expression.

Indexing:At this point, Splunk software adds processed events to the index queue. The biggest advantage of utilising this is that the information will be readily visible to anybody conducting a search at that same moment.

Data Searching Stage:At this point, restrictions are put in place on who may see, use, and access the information. Knowledge objects, including as reports, event kinds, and alerts, may be created and saved by users inside the Splunk programme.

Splunk Components

Three components in Splunk are:

Splunk Forwarder:That is used to transmit information.

Splunk Indexer:It serves to both parse and index information.

Search Head:The user interface is the point at which one may do a search, perform an analysis, and generate a report.

Splunk Forwarder:

This component will be used to capture all of the log’s data. If you are trying to gather logs from a distant system then you need to utilise Splunk remote forwarders to perform the task.Splunk Forwarders may be used to capture real-time data so that the users can evaluate real-time data. For this to happen one need setup the Splunk Forwarders to deliver the data to Splunk Indexers in real-time.It requires extremely minimal processing power as compared to another typical monitoring tool. The scalability is another major favourable element.

We have two different types of forwarders:

• Splunk universal forwarder
• Splunk heavy forwarder

Splunk Indexer:This is another component that we may utilise for indexing and storing the data that is provided by the forwarders. The Splunk Indexer tool really allows the data to be turned into events and indexed so that it is easier for executing search operations quickly.If the data is flowing via Universal forwarder then Splunk Indexer will first parse the data and then Index it. Parsing the data will remove unnecessary info.If the data is flowing via Heavy forwarder then Splunk Indexer will just index the data.

As the Splunk indexer indexes the files then these files will contain the following:

• Compressed Raw data may be noticed.
• Index files, i.e. tsidx files.

One advantage of utilising Splunk Indexer is data replication. One doesn’t need to worry about the loss of data since Splunk stores several copies of the indexed data. This procedure is called Index replication or Indexer Clustering.

Splunk Search Head:

This stage really offers a graphical user interface where the user will be able to do various activities depending on his needs. By keying the keywords in the search box, the user will be able to retrieve the desired results depending on the term.Splunk Search Head may be installed on several servers and only we need to make sure that we activate Splunk Web services on the Splunk server so that the interactions are not interrupted.

We have two forms of Search heads, i.e.

• Search head
• Search peer

Search Head:This is the precise user interface where data can only be obtained based on keywords and is not indexed.

Search peer:One thing that can support both search results and indexing is search peer.

Splunk Architecture

Take a look at the illustration below for a condensed perspective of the many parts involved in the process and their functions:

• By running a few scripts, you may get data from many sources and set up this procedure to transfer data automatically.
• It is possible to keep track of all incoming files and identify modifications in real time.
• The forwarder can play a crucial role in data cloning, load balancing, and intelligent data routing. Before the data is sent to the indexer, all of these tasks can be completed.
• The complete setup, deployment, and policies, among other things, are managed by a deployment server.
• The data will eventually go to the indexer after being received. The information will be kept as events after it has been indexed. This makes carrying out any search activity very straightforward.
• They may be seen and examined using the graphical user interface when utilising the Search heads.
• With the help of the Search peers, one may produce reports, store searches, and do analysis using a dashboard view.
• You will be able to improve the current unstructured data using knowledge objects.
• Through the Splunk web interface, users may access the search heads and knowledge items. REST API connections are used for all conversations.

Conclusion

If you or your company is interested in Big Data analysis, Splunk is an excellent tool. If you need to evaluate a large amount of machine data, this is the tool for you. Data visualisation, report creation, data analysis, etc. are just some of the uses for this handy application. The IT staff will be able to make adjustments to their workflow based on the data and the comments provided.

To fully grasp the Splunk Architecture, it’s important to first get familiar with its individual parts. You may learn more about the function of this instrument and the key parts one should know by using these parts.