Think of yourself as a data analyst examining a massive array of data points. It is when Splunk TimeChart turns into your reliable companion. Envision it as a multi-purpose tool that neatly sorts your data over time on the X-axis, giving you space to experiment with various measures on the Y-axis. It uses its skills to gather statistical data from chosen fields, transforming complex information into easy-to-understand visual stories in lines, areas, or bar charts.
When you first encounter Splunk TimeChart, it may seem complex, but it's akin to learning a new, user-friendly language. Consider the command structure a recipe:
timechart [sep=] [format=] [partial=] [cont=] [limit=] [agg=] [... ] ( ( [BY ] ) | () BY )
Imagine it as a toolbox where every tool has a unique function. Be it the eval-expression or the single-agg, these parameters are vital to discovering various aspects of TimeChart.
It has stuff like literals, fields, operators, and functions. It would help if you used the suitable value types, or it won't work.
Syntax: | | | |
This one goes after a sole field. No wildcard support, though. You have to tell it which field, except with 'count'.
Syntax: | ( )
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So, this splits fields automatically, especially for the number fields. It also puts in the number of columns.
Syntax: ( ) … [ ]
TimeChart has many options, but this summary will highlight the essential parts to simplify it.
Through clear examples, let's see how we can use Splunk TimeChart in real-life situations.
We'll inspect Splunk's log data. Our goal is to create a visual of the standard indexing speed over time, split by processor:
index=_internal "group=thruput" | timechart avg(instantaneous_eps) by processor
We're making a chart. It shows standard CPU & MEM interactions. Each host is computed for every ten mins:
...|timechart span=10m eval(avg(CPU) * avg(MEM)) BY host
We check the standard usage of CPU seconds. The processor is doing the work. All results are rounded for accuracy:
... | timechart eval(round(avg(cpu_seconds),4)) BY processor
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We're looking at average CPU usage. It's calculated per minute for each host:
... | timechart span=1m avg(CPU) BY host
We demonstrate average throughput. It is for all hosts. It's calculated over a period:
| timechart avg(cpu_seconds) BY host | outlier action=tf
Here's how to see average host throughput over time:
| timechart span=10m avg(thruput) BY host
Want to chart occasion counts per source IP with a set count threshold? Use this:
sshd failed OR failure | timechart span=10m count(eventtype) BY source_ip usenull=f WHERE count>25
We've looked at Splunk's TimeChart, features, and essential uses. Also, we have covered various insights on it with specific illustrations. We hope this learning helped you well in understanding Splunk TimeChart in detail. Stay tuned for more information on time charts and Splunk in this space.
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As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.
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