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Optimizing systems and knowledge objects

 

To achieve optimal system performance and knowledge object efficiency, you'll need customized configurations, consistent tuning, streamlined objects, effective summary indexing, acceleration, lookup optimization, and simplified data models. This comprehensive approach ensures Splunk operates at its peak, and the strategies provided in this pathway will help you accomplish this goal. You can work through them sequentially or in any order that suits your current level of progress in system and object optimization.

This article is part of the Improve Performance Outcome. For additional pathways to help you succeed with this outcome, click here to see the Improve Performance overview.

Cleaning up knowledge objects 

The capability of the Splunk platform to transform machine data into meaningful insights is expansive, and one of the cornerstones of this capability lies in its use of knowledge objects. These entities allow users to harness the full potential of the Splunk platform, ensuring data is not only interpretable but actionable. As Splunk environments grow and evolve, it's common for them to accumulate a variety of knowledge objects, some of which might become outdated or redundant over time. That is why you need to manage these objects in order to maintain operational efficiency and ensure that your users get the most value from the platform.

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In this section, you will learn about knowledge object management through the following steps:

  1. Understanding the importance of maintaining an organized Splunk environment
  2. Understanding knowledge objects
  3. Managing knowledge objects
  4. Cleaning up knowledge objects
  5. Following best practices for ongoing knowledge object management

Understanding the importance of maintaining an organized Splunk environment

Maintaining an organized Splunk environment is important for several reasons:

  • Performance Efficiency: Just like a well-organized library facilitates quicker book retrieval, an organized Splunk setup ensures efficient and speedy data search and retrieval. Redundant or obsolete elements can slow down search performance, hinder process automation, and increase system resource consumption.
  • Operational Clarity: A cluttered Splunk environment can make system navigation cumbersome. Cleaning up and organizing knowledge objects can help in streamlining operations and improving user experience.
  • Cost Efficiency: Splunk licensing often revolves around data ingestion rates. Keeping the environment free from obsolete objects can help in optimizing data indexing, potentially leading to cost savings.
  • Enhanced Security: Regularly reviewing and maintaining the Splunk environment can also help in identifying and rectifying potential security vulnerabilities, ensuring that the data and the system remain secure.

Understanding knowledge objects

In the Splunk platform, the term knowledge objects refers to entities that help users add structure to incoming data, enhance the data after it's indexed, and share the enriched data with others. Knowledge objects can be anything from field extractions, tags, and event types to saved searches, reports, dashboards, and alerts. These objects help in interpreting and visualizing raw data, turning it into meaningful insights.

For a detailed explanation of  Splunk  knowledge, see the Knowledge Manager Manual.

Significance of knowledge objects in the Splunk platform

Knowledge objects in the Splunk platform play a critical role in:

  • Data Enhancement: They allow users to add layers of context and meaning to the incoming data.
  • Operational Efficiency: With knowledge objects, repeated tasks can be automated, and insights can be rendered in easily digestible formats.
  • Collaboration: Knowledge objects can be shared, promoting collaborative efforts among users to derive insights from data.

Overview of different types of knowledge objects

Knowledge objects in the Splunk platform come in various types, each serving a distinct purpose:

  • Saved Searches: These are specific search parameters saved for reuse. Saved searches can automate the process of running regularly used search queries, making data retrieval efficient.
  • Reports: A report in the Splunk platform is essentially a saved search that comes with added visualization. It allows users to represent data in charts, graphs, and tables, offering a visual interpretation of the underlying data.
  • Alerts: Alerts are automated reactions to specific conditions or patterns in data. For instance, if a certain event occurs or a threshold is breached, the Splunk platform can send notifications, ensuring real-time awareness of critical events.
  • Dashboards: A collection of visualizations, reports, and other elements, dashboards provide an at-a-glance view of specific data metrics, trends, or patterns.
  • Event Types and Tags: These knowledge objects help in categorizing events based on certain criteria, making them easily identifiable.

These are just a few examples. The types of knowledge objects in the Splunk platform is expansive, with each type catering to specific user needs and data challenges.

The impact of accumulated unused knowledge objects on Splunk platform performance

While knowledge objects enhance the functionality of the Splunk platform, an accumulation of unused or obsolete ones can have repercussions:

  • Reduced Search Efficiency: Unnecessary saved searches or reports can clog the search pipeline, leading to slower search results.
  • Increased Resource Consumption: Obsolete knowledge objects can consume valuable system resources, impacting the overall efficiency of the Splunk instance.
  • Operational Complexity: A cluttered environment with redundant or outdated knowledge objects can make navigation and operation cumbersome, reducing user efficiency.
  • Potential Security Concerns: Unused objects, especially if they're not updated or monitored, can become security vulnerabilities over time.

Regular management and cleanup of knowledge objects are therefore essential not just for maintaining an organized environment but also for ensuring optimal performance and security. These processes are discussed in the following two sections.

Managing knowledge objects

In the modern world, the tools and configurations you rely upon might eventually become redundant or less relevant. In the Splunk platform, this is particularly true for knowledge objects. Maintaining an efficient environment requires not only creating these objects but also routinely identifying and removing those that have outlived their utility.

Tracking knowledge object usage with tools and methods

Splunk provides a suite of internal tools and methods tailored for tracking the usage of various knowledge objects.

  • Monitor and Organize Knowledge Objects: Splunk documentation provides some suggestions around how to achieve an organized deployment.
  • Knowledge Endpoints: The RESTful API for the Splunk platform offers metadata endpoints that can be queried to retrieve information about when knowledge objects were last accessed or modified.
  • Internal Indexes: The Splunk platform maintains internal indexes that log various system and user activities. Queries against these indexes can reveal insights about the frequency and recency of knowledge object usage.

Recognizing patterns that suggest an object Is no longer relevant or useful

Certain patterns and signs might hint at a knowledge object's reduced relevance.

  • Infrequent Access: If a saved search, report, or dashboard hasn't been accessed for an extended period, it might indicate its declining relevance to current operations.
  • Obsolete Data Sources: Knowledge objects tied to data sources that are no longer active or have changed in structure might be candidates for removal.
  • Redundancy: Over time, users might create multiple similar knowledge objects that serve the same purpose. Identifying and consolidating these redundancies can streamline the environment.
  • Deprecated Features: If a knowledge object relies on features or syntax that are deprecated in newer versions of the Splunk platform, it's a sign that the object needs updating or removal.

Identifying unused knowledge objects with audits

Regular audits play a pivotal role in the management of knowledge objects.

  • Scheduled Reviews: Periodically reviewing the suite of knowledge objects can help in identifying those that are rarely used or have become obsolete.
  • Audit Dashboards: Leveraging the capabilities of the Splunk platform, users can create dashboards specifically designed to audit knowledge object usage, providing a visual representation of object activity and relevance.
  • Documentation Checks: Ensuring that every knowledge object is well-documented can aid in audits. When an object lacks clear documentation or its purpose is no longer clear, it might be an indication that it's time for a review or removal.

Incorporating regular audits into Splunk management practices ensures the environment remains streamlined, efficient, and devoid of unnecessary clutter.

Cleaning up knowledge objects

Maintaining an optimized Splunk environment involves more than just identifying unused or obsolete knowledge objects, it requires a systematic approach to safely and efficiently purge them. This section outlines a guide to help ensure that your knowledge object cleanup process is both thorough and safe.

Pre-cleanup preparations: Backing up your Splunk instance

Before making any significant changes to your Splunk environment, you should first take precautionary backup measures.

  • Full Backup: Consider performing a complete backup of your Splunk instance, which includes configuration files, indexed data, knowledge objects, and user profiles. This ensures you can revert to the original state if any unexpected issues arise.
  • Backup Specific Objects: In addition to a full backup, extract and store a copy of specific knowledge objects you're planning to delete. If you do not have access to the configuration files directly, you can leverage the Splunk API to extract the knowledge objects and use the export feature to save off a copy prior to deletion.
  • Storage: Ensure backups are securely stored in a location that is both accessible to authorized personnel and safeguarded against data breaches or loss.

Review: confirming the list of knowledge objects marked for deletion

After you've safeguarded your data, you'll need to scrutinize the objects slated for removal.

  • Validation: Before deletion, cross-check the list of identified unused or obsolete knowledge objects with team members or stakeholders to ensure no critical objects are inadvertently removed.
  • Dependencies: Determine whether other objects or configurations rely on the knowledge objects marked for deletion. Removing an object that is key to another process will lead to disruptions.
  • Documentation Review: Cross-reference the objects with any associated documentation. This might offer insights into the object's past relevance or utility, aiding in the decision-making process.

The cleanup process: Safely removing unused or obsolete objects

Having taken the preparatory steps, you should now be equipped to start the cleanup:

  • Splunk's User Interface: Utilize the built-in user interface to delete specific knowledge objects. The interface offers a visual approach, making the process more intuitive.
  • CLI Commands: For users comfortable with the command-line interface, the Splunk platform provides commands tailored for deleting knowledge objects. Ensure you're familiar with the exact syntax to avoid inadvertent deletions.
  • Post-Deletion Audit: After the cleanup, perform an audit to ensure that only the intended objects were deleted. Monitor performance and functionalities in the Splunk platform to verify that no disruptions have occurred due to the removals.
  • Update Documentation: Reflect the changes made during the cleanup in any associated documentation, ensuring that it remains updated and accurate.

By following these steps, Splunk administrators and knowledge managers can ensure their environment remains organized, optimized, and free of unnecessary clutter, all while minimizing potential disruptions.

Following best practices for ongoing knowledge object management

The efficient operation of a Splunk environment is contingent on the removal of obsolete knowledge objects and also on proactive and ongoing management. This section will go into some suggested best practices that ensure sustained system organization and optimum performance.

Setting up regular audits to identify unused objects

  • Scheduled Reviews: Configure regular system checks, at least quarterly, or more frequently depending on the volume of knowledge objects created. This ensures that unused or obsolete objects are promptly identified.
  • Automated Tools: Consider leveraging native tools in the Splunk platform or third-party plugins that can automatically detect unused knowledge objects. These tools can flag objects based on the last accessed date, making the review process more efficient.
  • Audit Logs: Scrutinize audit logs, which provide insights into object usage patterns, aiding in the identification of redundant objects.

Creating a naming convention or documentation process for new knowledge objects

  • Standardized Naming: Establish a clear and consistent naming convention for all knowledge objects. This facilitates easy identification and understanding of an object's purpose, especially when multiple team members are involved.
  • Documentation: Suggest utilizing comments for newly created knowledge objects. This should encompass its purpose, creator, creation date, and any other pertinent metadata. Such a practice can significantly simplify future audits.
  • Templates: Consider creating templates for specific knowledge objects, ensuring consistency and adherence to best practices from the outset.

Educating Splunk users on the importance of removing obsolete knowledge objects and keeping the system organized

  • Training Sessions: Periodically conduct training sessions for Splunk users, emphasizing the importance of a clutter-free environment and the impact of obsolete objects on system performance.
  • Clear Deletion Policies: Establish and communicate clear guidelines about when and how to remove knowledge objects. For instance, if a saved search hasn't been accessed for a year, it might be a candidate for deletion.
  • Feedback Mechanism: Create a feedback loop where users can report obsolete or redundant objects they come across during their interactions with the Splunk platform. This collective vigilance can significantly augment the cleanup process.

By adhering to these best practices, you can foster a culture of continuous optimization, ensuring that your Splunk deployment remains streamlined, efficient, and aligned with your objectives.

Helpful resources

Splunk OnDemand Services: Use these credit-based services for direct access to Splunk technical consultants with a variety of technical services from a pre-defined catalog. Most customers have OnDemand Services per their license support plan. Engage the ODS team at OnDemand-Inquires@splunk.com if you require assistance.

Using summary indexing 

The Splunk platform offers a variety of techniques to streamline your data analysis processes. One such technique is summary indexing, a valuable strategy that can significantly enhance the performance of your searches, especially when dealing with large datasets. Summary indexing involves pre-calculating and storing aggregated or summarized data from your raw event data. Instead of running searches on the original data every time you need insights, summary indexing allows you to create and maintain smaller, pre-processed datasets that capture key metrics, counts, or calculations based on specific fields or events. These summary indexes are designed to answer common queries quickly and efficiently.

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In this section, you will learn:

  1. Understanding the benefits of summary indexing
  2. Choosing common use cases
  3. Implementing summary indexing

Understanding the benefits of summary indexing

Summary indexing offers a range of benefits:

  • Faster Search Performance: One of the most compelling advantages of summary indexing is the remarkable enhancement in search speed. When dealing with large datasets, real-time searches can be time-consuming and resource-intensive. With summary indexes, you're querying pre-processed, summarized data that can deliver results in significantly reduced time..
  • Optimized Resource Utilization: Real-time searches can consume substantial system resources, especially when dealing with massive datasets. Summary indexing reduces this strain on system resources by offloading the intensive processing to scheduled searches that populate the summary indexes. This optimized resource usage improves the overall performance of your Splunk environment.
  • Improved User Experience: Faster search responses translate to a better user experience. Whether you're an analyst seeking insights or a decision-maker relying on data-driven choices, summary indexing empowers you to access critical information swiftly, enabling timely and informed decisions.
  • Complex Query Handling: Summary indexes are particularly effective for handling complex queries that involve aggregations, calculations, or frequent repetitive analyses. These queries can be resource-intensive when executed in real-time. Summary indexes provide pre-calculated results, streamlining the analysis process.
  • Customizable Summaries: With summary indexing, you have the flexibility to define what data you want to summarize and how often you want to update these summaries. This allows you to tailor the summary indexes to your specific business needs and analytical requirements. Summary indexing permits the exposure of information and statistics about data that the user might not have explicit permission to view.
  • Enhanced Scalability: As your data volume grows, the efficiency provided by summary indexing becomes even more pronounced. It enables your Splunk environment to scale more effectively by reducing the burden of processing vast amounts of data in real-time.

Choosing common use cases

  • Website Analytics: Companies analyzing web traffic can use summary indexing to track page views, unique visitors, and other key metrics over time.
  • Security Analysis: In cybersecurity, summary indexing can aggregate information about attack patterns, identifying trends and anomalies in network traffic more efficiently.
  • Infrastructure Monitoring: For IT operations, summary indexing can provide insights into server performance metrics, enabling proactive issue detection and resolution.
  • Business Intelligence: Summary indexes can be leveraged to create dashboards and reports that provide a high-level overview of business KPIs, allowing stakeholders to quickly assess performance.

Implementing summary indexing

  1. Choose the Right Data: Identify the datasets that would benefit the most from summary indexing. These could be frequently used queries or reports involving large datasets.
  2. Define Summary Indexes: Create summary indexes that store aggregated or calculated data based on specific fields or events. Define how often these summaries should be updated.
  3. Configure Searches: Set up scheduled searches that populate your summary indexes. These searches aggregate and calculate the required data, populating the summary index.
  4. Query the Summary Index: When running queries, refer to the summary index instead of the raw data. Search Processing Language (SPL) allows you to seamlessly integrate summary index data into your analyses.
    For a demonstration of summary indexing, see  Using summary indexing to accelerate searches.

Best practices

  • Choose summary fields judiciously, focusing on those most relevant to your analysis needs.
  • Optimize the frequency of summary updates based on data volatility and search requirements.
  • Regularly review and fine-tune your summary indexes to align with changing business needs.

Summary indexing is a powerful technique that enhances the speed, efficiency, and scalability of data analysis in the Splunk platform. By pre-calculating and storing summarized data, you can accelerate your search processes, optimize resource usage, and provide a more seamless user experience. This technique is particularly valuable for handling complex queries and facilitating efficient decision-making across various business domains.

Helpful resources

Splunk OnDemand Services: Use these credit-based services for direct access to Splunk technical consultants with a variety of technical services from a pre-defined catalog. Most customers have OnDemand Services per their license support plan. Engage the ODS team at OnDemand-Inquires@splunk.com if you require assistance.

Optimizing searches and dashboards

Efficient search and reporting capabilities help you derive valuable insights from your data while minimizing costs. Optimizing searches and dashboards in the Splunk platform not only improves performance but also contributes to reducing the total cost of ownership (TCO) by optimizing resource utilization and maximizing productivity.

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In this section, you will explore how the following practices can lead to significant TCO savings:

  1. Refactoring search queries
  2. Leveraging search commands
  3. Optimizing search jobs
  4. Optimizing dashboards

Refactoring search queries 

Optimizing search queries directly impacts TCO by reducing resource consumption and enhancing performance.

  • Reduced Data Processing: By assigning specific time ranges through role permissions, you can control and limit the scope of data users can access and search. As a result, costs associated with processing and storage are reduced, and users are provided with a more focused and relevant dataset based on their roles.
  • Positive Matching: Using positive matching to filter data reduces the amount of data retrieved from the indexers. While it is possible to filter data using negative arguments (for example, NOT), results are only filtered when data is collected from the indexers which results in inefficient searching. This can also affect search results.
  • Efficient Resource Utilization: Filtering data early in the search pipeline minimizes the amount of data processed by subsequent search commands. This reduces the strain on system resources, leading to improved performance and lower infrastructure costs.
  • Streamlined Data Analysis:
    • Aggregation commands such as stats and chart help consolidate and summarize data, reducing the volume of data processed. This optimization results in faster search execution, reducing the need for extensive computational resources and lowering infrastructure costs.
    • MapReduce allows for the efficient segregation of large volumes of data across various nodes (the 'map' phase), followed by the aggregation and synthesis of this data to derive meaningful insights (the 'reduce' phase). By distributing the computational load, MapReduce ensures that the Splunk platform can sift through logs and datasets effectively.

For specific guidance on refactoring search queries, see Optimizing search.

Leveraging search commands

Utilizing Splunk's search commands efficiently enhances search performance and contributes to TCO reduction.

  • Optimized Field Manipulation: Leveraging eval commands for field calculations and formatting improves data analysis efficiency. By preparing data for analysis during the search phase, subsequent processing steps can be simplified, reducing overall resource consumption and lowering TCO.
  • Streamlined Data Analysis: Aggregation commands such as stats and chart help consolidate and summarize data, reducing the volume of data processed. This optimization results in faster search execution, reducing the need for extensive computational resources and lowering infrastructure costs.

Optimizing search jobs

Efficient search job management minimizes resource waste and contributes to TCO reduction.

  • Controlled Result Sizes: Limiting the number of search results using commands like head or top reduces memory consumption and improves search performance. By managing result sizes, you can optimize infrastructure costs while still obtaining the necessary insights from the data.
  • Workload Management: Workload Management can be used to ensure the most important jobs are not resource constrained. For more information, see  Workload management in the Reducing your infrastructure footprint pathway.
  • Identifying Unnecessary Scheduled Saved Searches: Identifying and cleaning up unnecessary scheduled saved searches, especially out-of-the-box (OOTB) scheduled saved searches that aren't required, streamlines the system and trims infrastructure costs. This includes removing redundant or unused saved searches in applications like Splunk Enterprise Security (ES) or Splunk ITSI (ITSI). 
  • Resource Cleanup: Properly managing search job lifecycles, including canceling or terminating unnecessary or long-running jobs, prevents resource waste and optimizes system performance. This optimization reduces infrastructure costs by eliminating unnecessary resource usage.
    • Monitoring Console or the Cloud Monitoring Console can aid in this review process by providing at-a-glance insights into system health and performance. Regularly optimize, update, or remove searches based on changing needs.
    • Search Job Monitoring: Regularly monitor the ongoing search jobs in your Splunk environment. You can do this through the Splunk Search Job Inspector, which provides insights into active and historical search jobs.
    • Scheduled Searches Review: Examine your scheduled searches and reports. Determine whether all scheduled searches are still relevant and producing valuable insights. If there are reports that are rarely accessed or no longer provide significant value, consider discontinuing or optimizing them.
    • Stuck or Abandoned Jobs: Keep an eye out for search jobs that are stuck, running indefinitely, or have been abandoned. Canceling or terminating these jobs can free up resources.
    • Audit Search Usage: Review the usage and popularity of saved searches and reports. If certain searches are hardly ever used by users or teams, they might be candidates for optimization or removal.
    • Regular Review: Conduct periodic reviews of your search jobs to ensure they align with your organization's goals and requirements. Regularly optimize, update, or remove searches based on changing needs.

For specific guidance on optimizing search jobs, see Optimizing search.

The optimizations discussed here result in lower infrastructure costs, reduced hardware requirements, improved operational efficiency, and increased productivity. By embracing these best practices, you can extract maximum value from your Splunk deployment while minimizing total cost of ownership, ultimately leading to a more cost-effective and efficient data analysis environment. 

Optimizing dashboards

Well-optimized dashboards not only improve user experience but also contribute to TCO reduction.

  • Resource Optimization: Consolidating multiple panels into a single panel reduces resource consumption and enhances dashboard loading times. This optimization translates to lower hardware requirements, reducing infrastructure costs.
  • Base and Chain Searches: Base and chain searches encapsulate common search logic and filters, which can be reused across multiple searches and dashboards. This approach minimizes redundant code, improves consistency, and simplifies maintenance by executing a single search and reusing the returned data through other sub-searches. By leveraging base and chain searches, you reduce development time and effort, optimize resource utilization, and enhance performance.
  • Accelerated Data Models: Utilizing data model acceleration or reducing the data set searched improves search and dashboard performance. Faster rendering and reduced computational demands result in lower resource consumption, leading to cost savings in terms of infrastructure and operational efficiency.
  • Efficient Data Access: By leveraging summary indexing for frequently used searches, dashboards can load faster and require fewer computational resources. This optimization minimizes the need for extensive data retrieval, reducing infrastructure costs and improving overall dashboard performance.

For specific guidance on optimizing dashboards, see Following best practices for working with dashboards.

Helpful resources

Splunk OnDemand Services: Use these credit-based services for direct access to Splunk technical consultants with a variety of technical services from a pre-defined catalog. Most customers have OnDemand Services per their license support plan. Engage the ODS team at OnDemand-Inquires@splunk.com if you require assistance.