Splunk Frequently Asked Questions

What Is Splunk?

Splunk is a software platform that helps you to collect, analyze, and visualize machine-
generated data. Splunk is used by businesses of all sizes to monitor their IT infrastructure,
security, and compliance.

Splunk is a powerful tool that can be used to:
 Identify and troubleshoot problems quickly.
 Comply with regulations.
 Gain insights into your business.

Splunk works by collecting data from a variety of sources, including:
 Log files.
 Network traffic.
 Security events.
 Application data.

Splunk then analyzes this data and provides you with insights into what is happening in your
environment. You can use these insights to identify and troubleshoot problems, comply with
regulations, and gain insights into your business.

Splunk Use-Cases

The benefits of using Splunk include:
 The ability to collect and analyze large amounts of data.
 The ability to identify and troubleshoot problems quickly.
 The ability to comply with regulations.
 The ability to gain insights into your business.

Splunk's ability to collect and analyze large amounts of data is one of its most powerful
features. This allows you to gain insights quickly from a non-formatted data source into your
Splunk's ability to comply with regulations is also important. Splunk can help you to ensure that
your organization is compliant with a variety of regulations, such as HIPAA, PCI DSS, and SOX.
Splunk's ability to gain insights into your business is one of its most valuable features. This can
help you to make better decisions about your business by providing you with insights into
customer behavior, market trends, and other factors.

You can use Splunk to monitor your IT infrastructure by creating searches that look for
specific events. For example, you could create a search that looks for all events that contain the
string "error". This would allow you to quickly identify and troubleshoot any errors that occur in
your IT infrastructure. As you grow, your Splunk query will become more robust in detecting
errors in IT infrastructure.

Then you can set up an alert in Splunk to send notifications when a specific event occurs. For
example, you could create an alert that is sent when an error occurs on a critical server. This
would allow you to be notified of any critical errors so that you can take action to resolve them,
leading to faster issue resolution in Infrastructure.

You can use Splunk to analyze security data by creating searches that look for specific
events. For example, you could create a search that looks for all events that contain the string
“failed” OR “Failure”. This would allow you to search for possible login failures across all the
different systems available in your environment.
The benefit of Splunk over traditional tools is that you can detect login failures (in the above
example) from all the various systems that you use, instead of going to each one of them
Again, you could create alerts in Splunk to trigger email when certain conditions are met to
inform the security team as soon as it happens in almost real-time.


Some of the most common Splunk commands include:
 Search - This command is used to search for events in Splunk.
o The search command is the most basic Splunk command. It is used to search for
events that match a specific criterion. For example, the following command
would search for all events that contain the string "error":
o search source type="syslog" error
 lookup - This command is used to look up values in a lookup table.
o Lookup tables are a way to store data that is used frequently.
o For example, you could create a lookup table that contains the IP addresses of all
your servers. You could then use the lookup command to look up the IP address
of a specific server.
 | lookup server_ip_addresses ip as local_ip OUTPUT server_name

 eval - This command is used to evaluate expressions.
o For example, the following expression would do cidr match of IP to check if the
given public IP is the one owned by company or not?
 | eval isLocal=if(cidrmatch("", ip), "local", "not local")

 Stats – This command is used to group the data together.
o For example, if you wish to get when a particular category of error occurred last,
use below:
 | stats latest(_time) as last_error_time by error_category

Splunk uses configuration files called "props.conf" and "transforms.conf" to define
data parsing and extraction rules. Props.conf specifies how to identify and segment events in
the raw data, while transforms.conf defines custom field extractions. Splunk provides a range of
built-in extraction methods, including regular expressions, key-value pair extractions, and field
extractions based on fixed-position or delimited data formats. These configurations enable
Splunk to accurately parse and extract relevant fields from raw data, making it searchable and

Splunk lookup tables are reference tables used to enrich or enhance data during the
indexing or search process. Lookup tables contain static or dynamic data that can be used to
augment events with additional information. Splunk allows you to define lookup tables from
various sources such as CSV files or KVstore (database).
Lookup tables can be used to map IP addresses to geographic locations, match user IDs with
employee information, or correlate events with known threat indicators. By utilizing lookup
tables, Splunk enables advanced analysis and correlation of data across multiple sources.

Splunk's event correlation and alerting capabilities are powered by its search and
alerting features. Splunk allows you to define complex search queries using SPL to correlate
events based on specific conditions or patterns.
You can combine search commands, functions, and logical operators to create sophisticated
correlations. Once defined, these searches can be saved as alerts to trigger actions such as
sending notifications, executing scripts, or invoking other third-party tools (available on
Splunkbase, and most are free to use).
Splunk's flexibility in constructing search queries and its robust alerting mechanisms make it
ideal for identifying and responding to critical events in real time.

SPL is Splunk's search language used to construct search queries and perform data
analysis. It allows you to search, filter, transform, and visualize data.
With SPL, you can combine commands, functions, and operators to build complex queries.
For instance, to find the top 10 source IP addresses with the highest event count, you can use
the following query:
 index=network_logs | stats count by src_ip | sort -count | head 10

 This query searches the "network_logs" index, calculates the count of events per source
IP, sorts them in descending order, and selects the top 10.

Splunk's data model is a structured representation of data that provides a unified view
of information across different data sources. It defines relationships between fields, events, and
objects to facilitate efficient data analysis.
The data model allows users to pivot, drill down, and explore data using pre-defined
acceleration structures. It enables advanced analytics, such as trend analysis, anomaly
detection, and statistical computations, without the need for complex search queries. By
leveraging the data model, users can gain deeper insights and perform complex analyses of
their data with ease.
The data-models further can be accelerated to run queries faster on huge amounts of data.

Splunk's powerful time-based analysis capabilities allow you to analyze data over
specific time ranges. You can utilize time-based commands, for example, a timechart to
aggregate and visualize data based on specific time intervals.
For example, to see a time chart of HTTP status codes over the last 24 hours, you can use the
following search query:
 index=web_logs | timechart count by status
 This will generate a time series chart displaying the count of each HTTP status code over

Splunk offers multiple integration options to connect with external systems and tools.
It provides a wide range of add-ons and connectors that enable seamless integration with
popular technologies and platforms.
For example, Splunk can integrate with ticketing systems, collaboration tools, cloud platforms,
and more. Splunk also offers RESTful APIs and software development kits (SDKs) for building

custom integrations. These integration capabilities allow organizations to centralize and
correlate data from diverse sources, enhancing operational efficiency and enabling cross-
platform visibility.

Yes, there are many third-party already created integrations available with Splunk.
Users can download it from Splunkbase – https://splunkbase.splunk.com
Most of the third-party integrations are free to download as well.

Summary indexing in Splunk allows you to pre-aggregate and store summarized data
for faster retrieval and analysis.
It enables you to generate and save summary statistics based on specific search criteria. For
instance, you can create a summary index that calculates the average response time per hour
from a large volume of web server logs. Subsequently, you can search and visualize this
summary index to quickly analyze response time trends over time without the need to
reprocess the raw data.

Splunk provides several tools and features to monitor and troubleshoot the data
ingestion pipeline.
 The Splunk Monitoring Console allows you to monitor the health and performance of
Splunk instances, including indexing performance, data volume, and forwarder status.
 You can also utilize the splunkd.log file and splunkd process metrics to identify issues
related to data ingestion and indexing.
 Additionally, Splunk's internal index called _introspection provides valuable insights into
the status and health of various components involved in the data ingestion pipeline.

Splunk provides tools and techniques to monitor the performance of search queries
and dashboards. Insights Performance dashboard in the Splunk Monitoring Console offers

insights into the performance of search jobs, including execution time, resource utilization, and
search failures.

Splunk supports integration with external authentication systems such as Lightweight
Directory Access Protocol (LDAP), Active Directory (AD), and Security Assertion Markup
Language (SAML) providers.
By configuring Splunk to use these authentication systems, user authentication can be
centralized and managed through existing identity and access management frameworks. This
integration simplifies user onboarding, enforces consistent security policies, and enables single
sign-on (SSO) capabilities for Splunk users.

Monitoring and optimizing the performance of Splunk ensures that the platform
operates efficiently, processes data effectively, and provides timely insights. It helps identify
bottlenecks, optimize resource utilization, and maintain high availability of the system.

Key components include indexing performance, search execution times, CPU and
memory utilization, disk I/O, network latency, and system availability. Monitoring these metrics
helps identify performance issues, optimize resource allocation, and ensure a smooth user

Monitoring indexing performance involves tracking index throughput, analyzing data
ingestion rates, monitoring disk space utilization, and ensuring data integrity. Utilizing
monitoring tools like the Splunk Monitoring Console can help visualize and analyze indexing
performance metrics.

Techniques include tuning indexing settings, optimizing search queries, leveraging
summary indexing and data model acceleration, right-sizing hardware resources, and
implementing data lifecycle management strategies to optimize resource utilization.

Monitoring search performance involves tracking search execution times, identifying
long-running searches, analyzing search concurrency, optimizing search queries, and utilizing
search acceleration techniques like summary indexing and precomputed lookups. One can use
the built-in Monitoring Console App to analyze the performance of searches.

Proper data retention policies help manage the size of indexes, optimize disk space
usage, and improve search performance. Setting appropriate retention periods based on data
analysis requirements ensures efficient storage and retrieval of relevant data.

High availability can be achieved through clustering and distributed deployment
architectures, configuring replication and failover mechanisms, and implementing load

Troubleshooting involves analyzing system logs and error messages, reviewing
performance metrics, identifying bottlenecks, tuning configurations, utilizing debug mode for
query optimization, and leveraging Splunk's extensive documentation and community

Strategies include monitoring resource utilization, forecasting data growth, analyzing
indexing and search concurrency trends, vertical and horizontal scaling, utilizing distributed
search and index clustering, and regularly reviewing and adjusting capacity based on changing


The Splunk REST API is an interface that allows programmatic access to Splunk
functionalities and data. It is important because it enables automation, integration with
external systems, and the development of custom applications that interact with Splunk.

Common use cases include retrieving search results, accessing and manipulating
configuration settings, creating, and managing indexes, performing data ingestion, and
developing custom dashboards and visualizations.

The REST API provides endpoints and methods for programmatically ingesting data
into Splunk. Developers can use these interfaces to push log files, metrics, events, and other
types of data from external systems, making them available for analysis and search within

You could use Splunk HTTP Event Collector, a separate REST Endpoint specifically for
programmatic data ingestion from an external system.

Below is the sample Python code reference that can be used.

import requests.
import json

# Define the Splunk REST API endpoint for data ingestion
url = 'https://your_splunk_instance:8088/services/collector'

# Define the authentication token for accessing the endpoint
token = 'your_splunk_authentication_token'

# Define the event data to be ingested
event_data = {
'event': 'This is a sample event',
'source': 'your_source',
'sourcetype': 'your_sourcetype',
'index': 'your_index'

# Convert the event data to JSON
payload = json.dumps(event_data)

# Set the request headers, including the authentication token
headers = {

'Authorization': f'Splunk {token}',
'Content-Type': 'application/json'

# Send the POST request to ingest the event data
response = requests.post(url, data=payload, headers=headers, verify=False)

# Check the response status code
if response.status_code == 200:
print('Data successfully ingested into Splunk.')
print('Failed to ingest data into Splunk.')
print(f'Response: {response.text}')

Splunk supports various authentication methods, including username/password
authentication, token-based authentication, and OAuth 2.0. The chosen authentication method
depends on the specific requirements and security considerations of your application.
Below is a sample Python code for username-password-based authentication with Python.

import requests

# Splunk REST API endpoint for authentication
url = 'https://your_splunk_instance:8089/services/auth/login'

# Credentials for authentication

username = 'your_username'
password = 'your_password'

# Data payload for authentication request
payload = {
'username': username,
'password': password

# Send a POST request to authenticate
response = requests.post(url, data=payload, verify=False)

# Check the response status code
if response.status_code == 200:
session_key = response.content.decode('utf-8')
print('Authentication successful.')
print(f'Session Key: {session_key}')
print('Authentication failed.')
print(f'Response: {response.text}')

Yes, the REST API provides capabilities to execute search queries against Splunk
indexes, apply filters and aggregations, retrieve search results, and perform real-time analysis
of the data.

Below is a sample python code to execute Splunk search queries programmatically using REST
API. (This uses Splunk Python SDK library)
import splunklib.client as client

# Splunk connection settings
HOST = 'your_splunk_instance'
PORT = 8089
USERNAME = 'your_username'
PASSWORD = 'your_password'

# Create a Splunk service instance
service = client.connect(

# Define the search query
search_query = 'index=your_index | stats count by source'

# Run the search
job = service.jobs.create(search_query)

# Wait for the search job to complete

while not job.is_done():

# Get the search results
results = job.results()

# Print the search results
for result in results:

# Close the search job

The REST API provides endpoints to create, update, and delete indexes in Splunk.
These endpoints allow you to define index settings, adjust retention policies, and manage data
storage for different types of data.
Users could use similar Python code as above, but with REST API Endpoints below.
 List Indexes:
o /services/data/indexes
 Create Index:
o /services/data/indexes
 Update Index:
o /services/data/indexes/{index_name}
 Delete Index:
o /services/data/indexes/{index_name}
 Disable Indexing:
 /services/data/indexes/{index_name}
 Enable Indexing:
o /services/data/indexes/{index_name}

Yes, Splunk imposes certain rate limits to prevent abuse and ensure system stability.
These limits control the number of API requests per minute or per hour that can be made by a
user or an application. It is important to review and adhere to these limits to avoid potential

Yes, the REST API provides capabilities to create custom dashboards and visualizations
using JSON payloads. You can use the endpoints and data models provided by the API to define
panels, charts, tables, and other visual components to display and analyze Splunk data.

Yes, the REST API allows developers to extend Splunk's capabilities by integrating with
external systems, automating administrative tasks, building custom data pipelines, creating new
data inputs, and developing plugins or extensions to enhance the platform.
Read the Full Doc here -

Splunk App Inspect Check is a command-line tool provided by Splunk that allows
developers to analyze Splunk apps for potential security vulnerabilities and best practices.

You can run Splunk App Inspect Check by executing the splunk-appinspect CLI or API.
Check this Reference for full details -

Splunk App Inspect Check performs various checks such as authentication and
authorization, data handling, permissions, secure coding practices, and adherence to Splunk's
App Certification Program requirements.
See this Reference for complete details -

Yes, you can customize the checks by providing a custom configuration file with
additional checks or by disabling specific checks using the --disable flag.

Splunk App Inspect Check generates an HTML report that provides a summary of
passed and failed checks along with detailed information about each check, including
recommendations for remediation.

Yes, you can integrate Splunk App Inspect Check into your CI/CD pipeline by running
the command as part of your build or deployment process and capturing the results for further
analysis or automation.
Here is the already-built solution for GitHub, which uses the API version of App-Inspect check
which is usually more robust than the CLI version of App-Inspect -

Its usage for your Splunk App is as simple as 4 lines, see the below example, which you could
put as part of your GitHub workflow.
- uses: VatsalJagani/splunk-app-action@v1
app_dir: "cyences_app_for_splunk"
app_build_name: "cyences_app_for_splunk"
splunkbase_username: ${{ secrets.SPLUNKBASE_USERNAME }}
splunkbase_password: ${{ secrets.SPLUNKBASE_PASSWORD }}

For more examples and documentation refer - https://github.com/VatsalJagani/splunk-app-action
Check it on GitHub Market Place - https://github.com/marketplace/actions/run-splunk-app-

Yes, you can use the Splunk App Inspect Check Python library to programmatically
invoke the tool and retrieve the results. You can find more information and code examples in
the Splunk App Inspect Check Python Library documentation.

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