Splunk Knowledge Management
What is Splunk knowledge?
Splunk software offers a powerful search and analysis engine to help we see the specifics as well as the broader trends in our IT knowledge. If we're using Splunk tools, we're doing more than looking at individual entries in our log files; we 're using the collective knowledge they carry to find out more about our IT setting.
Splunk software extracts from our IT data different kinds of knowledge (events, fields, timestamps, etc.) to help we harness that information in a better, smarter, more focused way. Some of this information is extracted at index time, because our IT data is indexed by Splunk software. But, at search time, the bulk of this information is created by both Splunk software and its users.
Unlike databases or schema-based analytical tools that decide what information to pull out or analyze in advance, Splunk software allows us to extract knowledge dynamically from raw data as needed.
As our organization uses Splunk software, additional Splunk software knowledge object categories are created including types of events, tags, searches, field extractions, workflow actions, and saved searches.
We can think of Splunk knowledge of software as a multitool that we use to discover and analyze various aspects of our IT results. Event types allow us to quickly and easily classify and similar group events together. We can then use them to perform analytical searches on precisely defined event subgroups.
The Knowledge Manager manually shows how to keep sets of knowledge objects for the organization through Splunk Web and configuration files. It also shows the way to use Splunk knowledge to solve the real-world problems for the organization.
Knowledge of Splunk software is grouped into five categories:
Data interpretation: Fields and field extractions
Fields and field extractions constitute the first order of knowledge of the Splunk software. The fields that Splunk software extracts from our IT data automatically help bring meaning to our raw data, clarifying what may seem incomprehensible at first glance. The fields, we manually remove extend and build upon this sense layer.
Data classification: Event types and transactions
We use event types and transactions to pool interesting sets of similar events together. Event types bring together sets of events found through searches, while transactions are collections of time-spanning, conceptually linked events.
Data enrichment: Lookups and workflow actions
Lookups and workflow actions are categories of objects of knowledge which extend our data 's usefulness in various ways. Field lookups allow us to add fields from external data sources, such as static tables or commands based on Python, to our data. Workflow actions allow interactions between data fields and other applications or web resources, such as a WHOIS search on a field containing an IP address.
Data normalization: Tags and aliases
Tags and aliases are used for administering and normalizing field information sets. Tags and aliases can be used to group sets of related field values together and to give tags of extracted fields that reflect different aspects of their identity. For instance, we can group events from a collection of hosts together in a similar location (such as a building or city)-just give each host the same tag. Or perhaps we have two different sources that use different field names to refer to the same data - we can normalize our data using aliases (for example, by aliasing clientip to ipaddress).
Data models are representations of one or more datasets and drive the Pivot tool, allowing Pivot users to quickly generate useful tables, complex visualizations and robust reports without interacting with the Splunk search language of the software. Data models are developed by the information managers who completely understand their indexed data format and semantics. A typical data model makes use of certain types of information objects discussed in this manual, including lookups, transactions, extractions of search-time fields and measured fields.
The Manual of Knowledge Managers contains information on the following topic:
Summary-based report and data model acceleration
Use the Splunk software to speed things up when searches and pivots are slow to complete. This chapter addresses acceleration of reports (for searches), acceleration of data models (for pivots) and indexing of summaries (for special case searches).
Knowledge managers should have a fundamental understanding of the concepts of data input setup, event processing and indexing.
Why manage Splunk knowledge?
When we have to maintain a relatively large number of objects of knowledge during our Splunk deployment, we know it is necessary to manage the information. It is especially true in companies with a large number in Splunk apps, and more so if we have several user teams operating with Splunk software. This is simply because a greater proliferation of users results in a greater proliferation of additional knowledge of Splunk.
If we leave a situation like this unchecked, our users may find themselves searching through vast sets of objects with confusing or contradictory names, trying to locate and use objects that have applied unevenly assignments and permissions, and wasting valuable time generating objects like reports and field extractions that already exist elsewhere in the system.
Splunk information managers have centralized control of information about Splunk applications. The benefits which managers of knowledge can offer include:
Prerequisites for knowledge management
Most tasks in knowledge management are cantered around manipulation of search time events. In other words, a typical knowledge manager does not usually focus on work before events are indexed, such as setting up data inputs, adjusting event processing activities, correcting default field extraction problems, creating and maintaining indexes, setting up forwarding and receiving and so on. We do recommend, though, that all knowledge managers understand these concepts well. Strong grounding in these topics allows knowledge managers to better plan their approach to knowledge object management for their deployment ... and it helps them solve problems that will inevitably arise over time.
Here are some topics that should be familiar to knowledge managers, with links to get we started: