Azure Log Analytics CPU Usage Query

Azure Log Analytics CPU Usage Query is a powerful tool that allows businesses to analyze and optimize their CPU utilization in real-time. By leveraging the capabilities of Azure Log Analytics, organizations can gain deep insights into their CPU performance, helping them make informed decisions to enhance system efficiency and reduce costs.

With Azure Log Analytics CPU Usage Query, businesses can track and monitor their CPU utilization patterns, identify bottlenecks, and detect any performance issues that may hinder their operations. This query provides an in-depth view of CPU usage across various servers, enabling IT teams to proactively address potential problems and optimize resource allocation for better performance.

Understanding Azure Log Analytics CPU Usage Query

Azure Log Analytics provides a powerful tool for monitoring and analyzing various aspects of your Azure infrastructure. One important metric to monitor is CPU usage, as it can help identify performance bottlenecks and resource utilization. In this article, we will explore different aspects of Azure Log Analytics CPU usage queries and how they can be utilized to gain insights into your resources' CPU utilization patterns.

1. Retrieving CPU Usage Data

The first step in analyzing CPU usage is retrieving the relevant data from Azure Log Analytics. The CPU usage data can be obtained from the Azure Monitor Logs, which collects and stores logs and metrics from various Azure resources. To retrieve CPU usage data, you can use the 'Perf' table, which contains performance counter data for the monitored resources.

The 'Perf' table contains a 'CounterName' column that specifies the performance counter, and a 'CounterValue' column that holds the actual value of the counter. To filter the data for CPU usage only, you can use the 'CounterName' column and specify the counter name as 'Processor Time'.

Here's an example Log Analytics query to retrieve CPU usage data:

Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time"

This query filters the 'Perf' table to include only the rows where the 'ObjectName' is "Processor" and the 'CounterName' is "% Processor Time", which gives the CPU usage percentage.

1.1 Time Range and Granularity

When retrieving CPU usage data, it's essential to consider the time range and granularity of the data. Azure Log Analytics allows you to specify the time range for the query using the 'timestamp' column. You can use the 'ago' operator to specify a relative time range, such as 'ago(1h)' for the past one hour or 'ago(1d)' for the past one day.

Additionally, you can control the granularity of the data by using the 'bin' function. The 'bin' function groups the data into specified time intervals, allowing you to aggregate the CPU usage data at a desired granularity. For example, if you want to aggregate the data into one-minute intervals, you can use the 'bin' function as follows:

Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time"
| summarize CPUUsage = avg(CounterValue) by bin(timestamp, 1m)

This query uses the 'summarize' operator to calculate the average CPU usage ('CPUUsage') per one-minute interval by using the 'avg' function on the 'CounterValue'. The 'bin' function groups the data based on the 'timestamp' column with a one-minute granularity.

1.2 Filtering by Resource

In large-scale Azure environments, it's often necessary to filter the CPU usage data by specific resources. You can achieve this by adding additional filters to the Log Analytics query. One way to filter by resource is by using the 'ObjectName' column, which specifies the resource name.

For example, if you want to retrieve CPU usage data only for a specific virtual machine named 'MyVM', you can add a filter to the query as follows:

Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time"
| where Computer == "MyVM"

This query adds an additional 'where' clause to filter the data by the 'Computer' column, which represents the name of the resource. Adjust the 'Computer' value to match the desired resource.

2. Analyzing CPU Usage Data

Once you have retrieved the CPU usage data, you can perform various analysis and visualization tasks to gain insights into your resource's CPU utilization patterns. Azure Log Analytics provides powerful tools for analyzing and visualizing data, such as the 'timechart' and 'render' operators.

The 'timechart' operator can be used to create a time series chart that shows the CPU usage over a specific time range. It allows you to visualize the CPU utilization trends and identify any spikes or anomalies. Here's an example query that uses the 'timechart' operator:

Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time"
| timechart avg(CounterValue) by bin(timestamp, 1h)
| render timechart

This query calculates the average CPU usage per hour interval using the 'avg' function and groups the data based on the 'timestamp' column with a one-hour granularity. The 'timechart' operator visualizes the data as a time series chart, and the 'render' operator renders the chart.

2.1 Detecting Anomalies

Identifying anomalies in CPU usage can be crucial for addressing performance issues. Azure Log Analytics offers capabilities to detect anomalies in the data using the 'series_decompose_anomalies' operator. This operator breaks down the CPU usage data into trend, seasonality, and anomalies components, allowing you to focus on any unexpected or irregular patterns.

Here's an example query that uses the 'series_decompose_anomalies' operator to detect anomalies in CPU usage:

Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time"
| timechart avg(CounterValue) by bin(timestamp, 1h)
| series_decompose_anomalies(3, 0.01)
| render linechart

This query applies the 'series_decompose_anomalies' operator with a sensitivity of 3 and a maximum anomaly contribution of 0.01. The operator decomposes the CPU usage data into trend, seasonality, and anomalies components and visualizes it as a line chart using the 'render' operator.

2.2 Aggregating CPU Usage Data

Aggregation is another important aspect of analyzing CPU usage data. Azure Log Analytics provides various aggregation functions, such as 'summarize', 'avg', 'min', 'max', 'count', etc., which can be used to calculate different metrics from the CPU usage data.

• The 'summarize' function can be used to calculate the total CPU usage over a specific time range.
• The 'avg' function can be used to calculate the average CPU usage.
• The 'min' function can be used to find the minimum CPU usage.
• The 'max' function can be used to find the maximum CPU usage.
• The 'count' function can be used to count the number of CPU usage measurements.

By utilizing these aggregation functions, you can gain a comprehensive understanding of your resource's CPU utilization patterns and make informed decisions based on the analyzed data.

3. Creating Alerts for High CPU Usage

To proactively monitor and address high CPU usage issues, you can create alerts in Azure Log Analytics. Alerts can notify you or trigger automated actions when certain conditions are met, such as the CPU usage exceeding a specified threshold.

Here's an example query that creates an alert for high CPU usage exceeding 90%:

Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time"
| summarize LastCpuUsage = avg(CounterValue) by bin(timestamp, 5m)
| where LastCpuUsage > 90
| project timestamp, LastCpuUsage

This query calculates the average CPU usage per 5-minute interval and filters the results to include only the rows where the 'LastCpuUsage' exceeds 90%. The 'project' operator selects the 'timestamp' and 'LastCpuUsage' columns for the alert notification.

By creating alerts based on CPU usage, you can promptly take remedial actions or investigate any underlying issues before they impact your system's performance.

Exploring Azure Log Analytics CPU Usage Query: Advanced Techniques

Going beyond the basics, let's explore some advanced techniques for CPU usage querying in Azure Log Analytics. These techniques allow for even more granular analysis and insights into your resources' CPU utilization.

1. Cross-Resource CPU Usage Comparison

Comparing CPU usage across multiple resources can provide valuable insights into resource allocation and efficiency. To perform cross-resource CPU usage comparison, you can leverage the 'join' operator in Log Analytics.

Here's an example query that compares the CPU usage between two virtual machines, 'VM1' and 'VM2':

Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time"
| where Computer == "VM1" or Computer == "VM2"
| summarize avg(CounterValue) by bin(timestamp, 1h), Computer
| render timechart

This query retrieves CPU usage data for virtual machines 'VM1' and 'VM2' and groups the data based on the 'timestamp' and 'Computer' columns. The 'render' operator visualizes the data as a time series chart, allowing easy comparison between the CPU usage patterns of the two virtual machines.

1.1 Calculating the Percentage Difference

To further enhance the cross-resource CPU usage comparison, you can calculate the percentage difference between the CPU usage of different resources. This allows you to identify the variance in CPU utilization and optimize resource allocation.

Here's an example query that calculates the percentage difference in CPU usage between 'VM1' and 'VM2':

let vm1CpuUsage = Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time" and Computer == "VM1";
let vm2CpuUsage = Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time" and Computer == "VM2";
union (vm1CpuUsage, vm2CpuUsage)
| summarize avg(CounterValue) by bin(timestamp, 1h), Computer
| extend PercentageDifference = ((avgif(CounterValue, Computer == "VM1") - avgif(CounterValue, Computer == "VM2")) / avgif(CounterValue, Computer == "VM2")) * 100
| render timechart

In this query, two subqueries are created, 'vm1CpuUsage' and 'vm2CpuUsage', which retrieve the CPU usage data for 'VM1' and 'VM2' respectively. The 'union' operator combines the two datasets, and then the 'summarize' operator groups the data based on the 'timestamp' and 'Computer' columns.

The 'extend' operator calculates the percentage difference by subtracting the average CPU usage of 'VM2' from the average CPU usage of 'VM1', dividing the result by the average CPU usage of 'VM2', and multiplying by 100. The 'render' operator visualizes the data, including the percentage difference, in a time series chart.

2. Correlating CPU Usage with Other Metrics

Correlating CPU usage with other metrics can provide deeper insights into resource utilization and help identify potential dependencies. Azure Log Analytics allows you to combine queries to correlate CPU usage with metrics like memory usage or disk I/O.

Here's an example query that correlates CPU usage with memory usage:

let cpuUsage = Perf
| where ObjectName == "Processor" and CounterName == "% Processor Time";
let memUsage = Perf
| where ObjectName == "Memory" and CounterName == "Available MBytes";
union (cpuUsage, memUsage)
| summarize avg(CounterValue) by bin(timestamp, 1h), ObjectName, CounterName
| render timechart

This query defines two subqueries, 'cpuUsage' and 'memUsage', which retrieve the CPU usage and memory usage data respectively. The 'union' operator combines the two datasets, and then the 'summarize' operator groups the data based on the 'timestamp', 'ObjectName', and 'CounterName' columns.

The 'render' operator visualizes the data in a time series chart, providing an overview of the CPU usage and memory usage trends. By correlating these metrics, you can identify potential patterns and bottlenecks in resource utilization.

2.1 Calculation of Correlation Coefficient

Querying CPU Usage in Azure Log Analytics

Azure Log Analytics provides a powerful tool for monitoring and analyzing the performance of your Azure resources. One essential aspect of monitoring is tracking the CPU usage of your virtual machines (VMs). By querying CPU usage data in Log Analytics, you can gain valuable insights into the resource utilization of your VMs, identify performance bottlenecks, and optimize your infrastructure.

There are several steps you can follow to query the CPU usage in Azure Log Analytics:

• First, navigate to the Log Analytics workspace in the Azure portal.
• Open the Log Analytics Query editor and select the appropriate workspace.
• Write a query to retrieve the CPU usage data, specifying the desired time range and VMs of interest.
• Execute the query and analyze the results. You can visualize the data using charts or export it for further analysis.

By querying CPU usage in Azure Log Analytics, you can proactively monitor the health and performance of your VMs, optimize resource allocation, and ensure the smooth operation of your Azure infrastructure.

Frequently Asked Questions

Here are some frequently asked questions about Azure Log Analytics CPU Usage Query:

1. How can I retrieve CPU usage logs using Azure Log Analytics?

To retrieve CPU usage logs using Azure Log Analytics, you can run the following query:

Perf | where ObjectName == "Processor" and CounterName == "% Processor Time" | summarize AvgCPUUsage = avg(CounterValue) by Computer, bin(TimeGenerated, 1d) | project TimeGenerated, Computer, AvgCPUUsage

This query retrieves the average CPU usage by computer for each day. It filters the log data for the "Processor" object and the "% Processor Time" counter, and then calculates the average CPU usage value. The results are grouped by computer and displayed with the corresponding time and average CPU usage.

2. Can I customize the time range for the CPU usage query?

Yes, you can customize the time range for the CPU usage query in Azure Log Analytics. By modifying the bin(TimeGenerated, 1d) part of the query, you can adjust the time granularity to your desired range. For example, you can change it to bin(TimeGenerated, 1h) for hourly data or bin(TimeGenerated, 1w) for weekly data.

Additionally, you can use the | where TimeGenerated >= datetime("2021-01-01") and TimeGenerated <= datetime("2021-12-31") filter to specify a specific time range. Simply replace the dates with your desired start and end dates in the format "YYYY-MM-DD".

3. How can I filter CPU usage logs by specific computers?

To filter CPU usage logs by specific computers, you can add the following clause to the query:

| where Computer in ("Computer1", "Computer2", "Computer3")

Replace "Computer1", "Computer2", and "Computer3" with the names of the computers you want to filter. This clause will only include CPU usage logs from the specified computers in the query results.

4. How can I sort the CPU usage query results?

You can sort the CPU usage query results in Azure Log Analytics by adding the following clause at the end of the query:

| order by AvgCPUUsage desc

This clause will arrange the query results in descending order based on the average CPU usage. If you want to sort in ascending order, replace "desc" with "asc".

5. Is it possible to visualize CPU usage query results?

Yes, you can visualize CPU usage query results in Azure Log Analytics. You can utilize the built-in features of Log Analytics, such as charts and dashboards, to create visual representations of the data.

By selecting the appropriate visualization options, you can present the CPU usage trends over time or compare the CPU usage across different computers. This visualization can aid in understanding and analyzing the CPU usage patterns in your environment.

In summary, the Azure Log Analytics CPU Usage Query is a powerful tool for monitoring and analyzing CPU usage in your Azure environment. By using this query, you can gather valuable insights into the performance and efficiency of your virtual machines, helping you identify any issues and make informed decisions.

With the Azure Log Analytics CPU Usage Query, you can easily track CPU usage over time, analyze trends, and identify patterns. This query provides you with important data that can help you optimize your infrastructure, allocate resources effectively, and ensure optimal performance of your applications and services.