Introduction: Architecture Drives Performance
A reliable, performant, and efficient Splunk environment is not just the result of well-designed searches or efficient dashboards. It begins with not only the underlying compute, network, and storage infrastructure, but also the architecture design, atop which Splunk runs.
Your Splunk deployment infrastructure and architecture directly impacts, not just the performance of Splunk, but also the final cost (even if you run on-prem with an ingestion volume-based license, someone pays for that compute, network, and storage overhead!). Without a well-architected foundation, teams can spend more time troubleshooting and tuning, or waiting for search results, and less time deriving value from the platform.
This blog explores how an optimized Splunk deployment improves performance, lowers maintenance overhead, and positions your environment for future expansion and automation.
What an Optimized Splunk Deployment Looks Like
First, let’s discuss some very general guidelines regarding the definition of an “optimized” Splunk environment, because after all, this depends on the needs of the business, the size of ingestion, etc.
In order to account for a variety of use cases, Splunk has created a series of “Splunk Validated Architectures”, or SVAs, and published the details in a white paper. You can read it HERE.
For the vast majority of use cases, one of the documented SVAs should be used. And, for an optimized Splunk environment that is scalable and highly available, we are talking about a distributed environment.
A distributed Splunk deployment includes these key components working in harmony:
- Indexers: Parse incoming data prior to writing to disk; respond to search requests
- Search Heads: Dispatch search requests and display results in the form of events, tables, and other visualizations
- Forwarders: Collect and send data to the indexers
- Splunk Management Components: License Manager, Cluster Manager, Deployer, Deployment Server
In a distributed environment, Forwarders collect the data and send it to the Indexers, where it is parsed and transformed prior to being written to disk. From there, the Search Heads request data according to user inputs.
Splunk components are separated across multiple hosts to provide improved scalability and performance, as well as higher availability and redundancy. While it may seem more complex, administration and troubleshooting are considerably more efficient due to having fewer endpoints to touch, as opposed to having many single instances of Splunk.
Important Deployment Considerations
Architectural refinement is easier in a distributed environment, as scaling and important configurations can happen more seamlessly. Here are some important factors to consider and even reconsider as time passes…
- How many scheduled searches do I need to run?
- How much data am I ingesting compared to how much I expect to ingest in the next 2 years?
- How much downtime of search availability is acceptable?
- How much downtime of indexing availability is acceptable?
- How much data loss is acceptable?
- How many daily Splunk users are there?
Having a distributed environment allows your business to configure Splunk according to the answers to the above questions.
Key Architectural Best Practices
Even if you start small, you should plan for the future of your Splunk journey. Few environments remain at their starting size, so non-distributed Splunk environments are usually for testing purposes (even where exceptions to this apply, it is usually a distributed production environment with all-in-one Splunk instances reserved for special use cases).
- If you virtualize Splunk, you must reserve the resources
- Use indexer clustering to enable redundancy of data and high availability for searching
- Implement search head clustering for consistent user experience, search load distribution, and high availability
- Use the Deployment Server to manager Forwarders
- Separate roles by having distinct systems for ingestion, search, and management
- Plan capacity based on license volume, data sources and parsing/transformation needs, and number of searches
- Follow one of the SVAs
Deployment Strategies That Improve Efficiency
- Leverage heavy forwarders, Splunk Edge Processor, or Cribl to preprocess and route data
- Support hybrid deployments that span on-prem and cloud for flexibility
- Standardize server configurations across nodes for predictable behavior
- Use deployment servers or configuration automation to maintain consistency
Efficient deployments are easier to scale, support, and secure.
Common Challenges and How to Address Them
Challenge | Solution | ||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Indexer saturation or search lag | Horizontal scaling is easy with indexer/search head clustering! | ||||||||||||||||||||||||||||||||||||||||||||||
Configuration drift across nodes | Use the deployment server or automation tools to enforce consistency of configurations | ||||||||||||||||||||||||||||||||||||||||||||||
Slow search performance | Tune search logic, educate users, distribute search load, and monitor disk, cpu, and memory utilization across indexing and search tiers to isolate the problem | ||||||||||||||||||||||||||||||||||||||||||||||
Unnoticed system failures | Set up proactive monitoring, health alerts, and performance baselines | ||||||||||||||||||||||||||||||||||||||||||||||
Disaster Recovery Scenario | Utilize multi-site clustering | ||||||||||||||||||||||||||||||||||||||||||||||
Protect against data loss | Ensure appropriate sizing of the memory/wait/persistent queues on forwarders, and utilize multi-site indexer clustering | ||||||||||||||||||||||||||||||||||||||||||||||
Hardware Migration | Indexer and Search Head Clustering allow for “plug and play” of new hosts, requiring minimal administrative or engineering overhead | ||||||||||||||||||||||||||||||||||||||||||||||
How Deployment Impacts Future Outcomes
A distributed and optimized Splunk deployment supports more than the current workload. It sets the stage for the later stages of the Splunk Maturity Journey.
- Orchestration: Improved integration: with external pipelines or data routing/management tools (Cribl, ServiceNow, etc)
- Automation: this depends on predictable search and alert execution, and a distributed environment means fewer endpoints that your automation must touch
- Optimization: through clear metrics and usage baselines of the different Splunk components, not to mention improved scalability and troubleshooting efficiency
Deployment optimization enables smarter analytics, faster response, and better cost management down the line. It is a sign of a stage 2 efficient Splunk environment.
Next Steps for Improving Your Splunk Deployment
- Review your current topology and document all components
- Plan search and ingestion capacity needs for the next 12 to 24 months
- Identify your business needs in terms of availability, ingestion, redundancy, and disaster recovery
- Identify architectural bottlenecks or inconsistencies
- Evaluate whether your current environment meets your needs, or if clustering or other configuration or scaling changes are necessary
- Engage expert guidance to align infrastructure with performance goals and business needs
Strengthen Your Splunk Environment with Expert Guidance
Efficient deployment unlocks better performance, smoother operations, and easier scaling. Let Presidio’s Splunk Solutions team help you optimize your architecture for long-term success.
