Splunk Architecture

Splunk is a fantastic tool for individuals or organizations that are into Big data analysis. This tool will be a perfect fit where there is a lot of machine data should be analyzed. This tool can be used for data visualization, report generation, data analysis, etc. Based on the feedback on the data, the IT team will be able to take the necessary steps to improve their overall efficiency.

So as we understand now what is Splunk and what is the major utilization of Splunk let’s dig deep in and understand the details about Splunk Architecture:

Before we understand the Splunk Architecture in detail, it will be helpful for us to understand the various components used within Splunk. The use of these components will help you understand how this tool works and what are the major components that one should know.

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Different Stages In Data Pipeline

There are three different stages in Data Pipeline that one needs to understand:

• Data Input Stage

• Data Storage Stage

• Data Searching Stage

Related Page: Splunk Universal Forwarder

Data Input Stage:

In this stage, all the data will be accessed from the source and turns it into 64k blocks. The metadata keys include the following :

• Hostname

• Source

• Source type of data

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Data Storage Stage:

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

• Parsing

• Indexing

Parsing

In this phase, the Splunk software examines, analyzes, and transforms the data. This stage is called event processing where all the data sets are broken down into different events. The following activities happen within this parsing phase.

• Stream of data is broken down into individual lines

• Identifies and sets time stamps

• Transforms the metadata and events according to regex standards.

Indexing phase:

In this phase, the Splunk software writes parsed events to the index queue. The main benefit of using this is to make sure the data is easily available for anyone at the time of the search.

Data Searching Stage:

In this stage, how the data is been accessed, used, and viewed is controlled. In the Splunk software, it will store user-defined knowledge objects, like reports, event types, and alerts.

Splunk Components:

In general, they are three components in Splunk.

• Splunk Forwarder: which is used to forward the data

• Splunk Indexer: which is used for Parsing data and Indexing the data

• Search Head: It is User interface where the user will have an option to search, analyze and report data.

Related Page: What Are Splunk Universal Forwarder And Its Benefits

Different types of Splunk forwarders.

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

Splunk Forwarder:

This component will be used to collect all of the log’s data. If you are looking to collect logs from a remote system then you need to use Splunk remote forwarders to do the job.

Splunk Forwarders can be used to gather real-time data so that the users can analyze real-time data. For this to happen one should configure the Splunk Forwarders to send the data to Splunk Indexers in real-time.

It consumes very less processing power when compared to another traditional monitoring tool. The scalability is another important beneficial factor.

We have two different types of Forwarders:

• Splunk Universal Forwarder

• Splunk Heavy Forwarder

In this article, we will not go in detail about these forwarders but will discuss the overall Splunk Architecture.

Splunk Indexer:

This is another component that we can use for indexing and store the data that is fed from the forwarders. The Splunk Indexer tool actually helps the data to be converted into events and indexed so that it is easy for performing search operations efficiently.

If the data is coming through Universal forwarder then Splunk Indexer will first parse the data and then Index it. Parsing the data will eliminate unwanted data.

If the data is coming through Heavy forwarder then Splunk Indexer will only index the data.

As the Splunk Indexer indexes the files then these files will have the following:

Compressed Raw data can be observed

Index files, i.e. tsidx files.

One benefit of using Splunk Indexer is data replication. One doesn’t need to worry about the loss of data because Splunk keeps multiple copies of the indexed data. This process is called Index replication or Indexer Clustering.

Related Page: Splunk Cloud

Splunk Search Head:

This stage actually provides a graphical user interface where the user will be able to perform different operations based on his requirements. By keying the keywords in the search box, the user will be able to get the expected results based on the keyword.

Splunk Search Head can be installed on different servers and only we need to make sure that we enable Splunk Web services on the Splunk server so that the interactions are not stopped.

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

• Search head

• Search Peer

Search Head: It is exactly the user interface where only the data can be retrieved based on the keywords and no indexing happens to it.

Search peer is something that it can accommodate both search results also caters indexing.

Splunk Architecture:

To basically discuss the Splunk Architecture, knowledge about its components are needed.

Look at the image below which gives a consolidated view of the different components that are involved in the process and their functionalities:

1. You can receive data from different sources and this process can be set to be automatic data forwarding by executing a few scripts.

2. All the files that are coming can be monitored and real-time detection of changes can be achieved.

3. The forwarder can play an important role to clone the data, capable of load balancing, and intelligently route the data. All these activities can be done before the data being reached out to the indexer.

4. A deployment server is used to manage the entire configuration, deployment, and policies, etc.

5. Once the data is received, it will reach to the indexer. Once the data is indexed, it will be stored in the form of events. This makes it very simple to perform any search activity

6. Using the Search heads, they can be visualized and analyzed using the graphical user interface.

7. Using the Search peers, one will be able to save the searches and will be able to generate reports and also do analysis by visualization dashboard.

8. Using Knowledge objects, you will be able to enrich the existing unstructured data.

9. The search heads and knowledge objects can be accessed via the Splunk web interface. All the communications happen via REST API connection.

Related Page: Introduction To Splunk Rex

So in this article, we have discussed different components that are available in the Splunk tool and how they are utilized in real-time. The overall Splunk Architecture is explained by explaining each and every individual component.

If you want to add any more topics to the topic and feel that they are vital, please comment in the “Comments” section.