![]() You might need to do some experimentation to get the correct detected events. The event subcommand passes a key-value query to Infrastructure Monitoring. You can use the data from Infrastructure Monitoring without paying twice for that data ingestion.įor more information about the flow subcommand, see flow query syntax in the Splunk Infrastructure Monitoring Add-on manual. It’s important to note that executing this command does NOT bring any data into Splunk indexes, so it doesn’t increase your data ingestion costs. Anything from Infrastructure Monitoring that helps you produce a useful visualization.īecause this data now exists in your core Splunk instance, you can include a variety of other (possibly older) data alongside this visualization, such as your syslog-based monitoring, or your network devices being monitored by Splunk Enterprise. This chart is a simple example, but imagine the possibilities! You could retrieve the instances by AWS account, or by tag, or by region. This query retrieves the per-minute cost of your AWS instances directly from your Infrastructure Monitoring environment, and contains some SPL to produce the following visualization: | sim flow query="data('st', rollup='average').publish()" Here’s an example of using SignalFlow within the Splunk Infrastructure Monitoring Add-on, then manipulating that data with SPL to produce a more meaningful visualization: Consider attending a SignalFlow training to learn some useful components of these queries. You can then manipulate the data using SPL just like any other indexed data in Splunk.įor more information about the SignalFlow query syntax, see SignalFlow Analytics Language in the Infrastructure Monitoring documentation. The flow subcommand lets you pass a SignalFlow query directly into your Infrastructure Monitoring instance and ingest it directly into Splunk without bringing any of it into your Splunk indexes. The next two sections go over the details of each subcommand. After one of those subcommands you pass a query. ![]() Following the pipe are two possible subcommands: flow and event. ![]() Because it’s an operating command, it always begins with a pipe. This sim command within the Splunk Infrastructure Monitoring Add-on has a simple syntax. The add-on also provides an out-of-the-box modular input to help you efficiently fetch data on a regular basis from your Infrastructure Monitoring environment. These commands let you retrieve data on-demand from your Infrastructure Monitoring realm. The add-on includes a command called sim as well as two subcommands that you can use in Splunk Processing Language (SPL). To integrate Splunk Infrastructure Monitoring with Splunk, start by downloading and installing the new Splunk Infrastructure Monitoring Add-on from Splunkbase. Part 1: Splunk Infrastructure Monitoring Add-on In this two-part article we examine how to leverage the integration of these offerings in your environment to save you time in finding the problem, and also uncover problems you didn’t know you had. ITSI also provides dynamic visualizations that appeal to all levels of the organization from the C-level to the system administrator.Ĭombining the capabilities of Splunk Infrastructure Monitoring (formerly known as SignalFx) with the power of ITSI provides a solution that's far greater than the sum of its parts. ITSI provided the ability to create correlation searches and key performance indicators (KPIs), and then use predictive health scoring, event analytics, and complex machine learning algorithms to drastically reduce mean time to resolution and minimize alert fatigue. In 2015, Splunk upped the ante by introducing Splunk IT Service Intelligence (ITSI). Over the years, customers have discovered different ways to leverage the Splunk platform to quickly and easily search, correlate, and reason over huge quantities of data (think petabytes per day!). Splunk was founded in 2003 to help customers build monitoring and troubleshooting capabilities by transforming and searching textual data such as log files, API data, system data, and any type of machine-generated data. ![]() If you haven’t already, take the training sessions offered. This combination of efficiency and standardization is exposed through the SignalFlow language to allow access and manipulation of the vast amount of metric data. The efficient metrics storage technology enables both high cardinality of metrics as well as the no-sample method of gathering every APM trace. S ignalFx was founded in 2013 to enable customers to gather and monitor key information for both their application code and infrastructure.
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