What are datapoints in LogicMonitor-step by step

 In LogicMonitor (LM), datapoints are the individual metrics collected and monitored for a resource or device. Each datapoint represents a specific measurement or piece of data that is gathered from a resource. These datapoints are part of a DataSource, which is a collection of related metrics for a particular type of resource. Here’s a detailed explanation of datapoints in LogicMonitor:

Datapoints Overview

Definition

• Datapoints: Individual metrics or values collected from a monitored resource. Examples include CPU usage, memory usage, disk I/O, network latency, etc.

Relationship to DataSources

• DataSources: Collections of datapoints that define what data to collect from a particular type of resource. Each DataSource can have multiple datapoints.

Types of Datapoints

1. Counter Datapoints

   • Measure the rate of change over time, such as the number of bytes transmitted per second.

2. Gauge Datapoints

   • Represent a specific value at a point in time, such as CPU usage percentage or memory utilization.

3. Derived Datapoints

   • Calculated from other datapoints using mathematical functions, such as calculating the average CPU usage from multiple CPU cores.

4. Complex Datapoints

   • Generated by combining multiple metrics or applying advanced calculations and logic to derive meaningful insights.

Creating and Managing Datapoints

Adding Datapoints to DataSources

1. Navigate to DataSources:

   • In the LogicMonitor portal, go to Settings > DataSources.

2. Select or Create a DataSource:

   • Choose an existing DataSource or create a new one by clicking Add.

3. Define Datapoints:

   • Within the DataSource, define the datapoints by specifying:
     • Name: The name of the datapoint.
     • Type: Counter, gauge, or derived.
     • OID/Path: For SNMP, specify the Object Identifier (OID). For WMI, specify the WMI query path.
     • Collection Interval: How often the data should be collected.

4. Add Calculations (if needed):

   • For derived datapoints, define the calculation formula using existing datapoints.

Configuring Alerts on Datapoints

1. Thresholds:

   • Set thresholds for datapoints to define when alerts should be triggered. For example, setting a high threshold for CPU usage to trigger an alert when usage exceeds 90%.

2. Alert Rules:

   • Create alert rules to specify how alerts should be handled, such as notifying specific users or triggering automated responses.

Viewing and Analyzing Datapoints

1. Graphs and Dashboards:

   • Datapoints can be visualized in graphs and dashboards within LogicMonitor. This helps in monitoring trends and identifying issues.
   • Go to the Resources section, select a device, and view the graphs for the relevant datapoints.

2. Reports:

   • Generate reports to analyze historical data for datapoints, providing insights into performance trends and capacity planning.

Use Cases for Datapoints

1. Performance Monitoring:

   • Track critical metrics like CPU usage, memory usage, and disk I/O to ensure optimal performance of resources.

2. Capacity Planning:

   • Use historical data to predict future resource needs and plan for capacity expansion.

3. Incident Management:

   • Set up alerts to detect anomalies or performance degradation, enabling quick response to incidents.

4. Compliance and Auditing:

   • Maintain logs and reports of key metrics for compliance with industry standards and internal auditing.

Example of Datapoints in a DataSource

For a server DataSource, the following datapoints might be included:

• CPU Usage (%): Gauge datapoint measuring the percentage of CPU used.
• Memory Usage (MB): Gauge datapoint measuring the amount of memory used.
• Disk Read Throughput (MB/s): Counter datapoint measuring the rate of data read from the disk.
• Network Latency (ms): Gauge datapoint measuring the network latency.
• Average CPU Load: Derived datapoint calculating the average load from multiple CPU cores.

Conclusion

Datapoints in LogicMonitor are fundamental metrics that provide detailed insights into the performance and health of monitored resources. By defining, collecting, and analyzing these datapoints, organizations can ensure their IT infrastructure operates efficiently and meets business needs.