{"id":2232,"date":"2026-04-24T08:12:07","date_gmt":"2026-04-24T08:12:07","guid":{"rendered":"https:\/\/sreschool.com\/blog\/?p=2232"},"modified":"2026-04-24T08:18:20","modified_gmt":"2026-04-24T08:18:20","slug":"grafana-lab-with-graphite-datasource-metrics-exploring","status":"publish","type":"post","link":"https:\/\/sreschool.com\/blog\/grafana-lab-with-graphite-datasource-metrics-exploring\/","title":{"rendered":"Grafana Lab with Graphite Datasource metrics – Exploring"},"content":{"rendered":"\n

Below is Step 1 only<\/strong>: a deep, student-ready lab guide for Exploring Graphite metrics in Grafana 13.x using Explore<\/strong>.<\/p>\n\n\n\n

This assumes:<\/p>\n\n\n\n

Graphite datasource is already added in Grafana\nGraphite URL is working\nTelegraf is sending Linux metrics to Graphite\nMetrics prefix is telegraf\nHostname is linux-demo\n<\/code><\/pre>\n\n\n\n
Grafana Explore is designed for ad-hoc investigation before creating dashboards. Grafana describes Explore as the place to query, analyze, and aggregate data without first creating a dashboard. (Grafana Labs<\/a>)<\/p>\n\n\n\n
\n\n\n\n
Lab 1: Explore Linux Metrics from Graphite in Grafana Explore<\/h1>\n\n\n\n
Objective<\/h2>\n\n\n\n
By the end of this lab, students will be able to:<\/p>\n\n\n\n
1. Open Grafana Explore\n2. Select the Graphite datasource\n3. Browse available Graphite metrics\n4. Query CPU, memory, disk, network, and load metrics\n5. Use Graphite functions in Grafana Explore\n6. Compare multiple metrics\n7. Understand Graphite metric naming\n8. Troubleshoot common Explore\/query issues\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Lab Architecture<\/h1>\n\n\n\n
Your current data flow is:<\/p>\n\n\n\n
Linux Host\n   \u2193\nTelegraf\n   \u2193\nGraphite Carbon :2003\n   \u2193\nWhisper Storage\n   \u2193\nGraphite Web :8080\n   \u2193\nGrafana Graphite Datasource\n   \u2193\nGrafana Explore\n<\/code><\/pre>\n\n\n\n
In this setup, Telegraf collects Linux metrics every 10 seconds and sends them to Graphite. Grafana then reads those metrics from Graphite.<\/p>\n\n\n\n
\n\n\n\n
Important Concepts Before Starting<\/h1>\n\n\n\n
What is Grafana Explore?<\/h2>\n\n\n\n
Explore<\/strong> is Grafana\u2019s investigation workspace. It is used to test queries, inspect metrics, compare values, and experiment before building dashboards or alerts. Grafana\u2019s documentation says Explore lets you query and analyze data without creating a dashboard first. (Grafana Labs<\/a>)<\/p>\n\n\n\n
In simple words:<\/p>\n\n\n\n
Dashboard = saved view for long-term monitoring\nExplore = temporary workspace for investigation and learning\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
What is Graphite Query Format?<\/h2>\n\n\n\n
Graphite metrics are usually stored in a dot-separated path format:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_active\n<\/code><\/pre>\n\n\n\n
Breakdown:<\/p>\n\n\n\n
Part<\/th>Meaning<\/th><\/tr><\/thead>
telegraf<\/code><\/td>Metric prefix configured in Telegraf<\/td><\/tr>
linux-demo<\/code><\/td>Hostname configured in Telegraf<\/td><\/tr>
cpu<\/code><\/td>Measurement type<\/td><\/tr>
cpu-total<\/code><\/td>CPU instance<\/td><\/tr>
usage_active<\/code><\/td>Actual metric field<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Grafana includes a Graphite-specific query editor that helps users navigate metric paths, add functions, and build Graphite queries. (Grafana Labs<\/a>)<\/p>\n\n\n\n
\n\n\n\n
Pre-Lab Validation<\/h1>\n\n\n\n
Before students open Grafana Explore, validate that Graphite has metrics.<\/p>\n\n\n\n
Run this on the Graphite server:<\/p>\n\n\n\n
docker exec -it graphite find \/opt\/graphite\/storage\/whisper\/telegraf -type f | head -20\n<\/code><\/pre>\n\n\n\n
Expected output should look similar to:<\/p>\n\n\n\n
\/opt\/graphite\/storage\/whisper\/telegraf\/linux-demo\/cpu\/cpu-total\/usage_active.wsp\n\/opt\/graphite\/storage\/whisper\/telegraf\/linux-demo\/mem\/used_percent.wsp\n\/opt\/graphite\/storage\/whisper\/telegraf\/linux-demo\/system\/load1.wsp\n<\/code><\/pre>\n\n\n\n
If .wsp<\/code> files are visible, Graphite is storing metrics.<\/p>\n\n\n\n
Also test Graphite API:<\/p>\n\n\n\n
curl \"http:\/\/localhost:8080\/render?target=telegraf.linux-demo.cpu.cpu-total.usage_active&from=-10min&format=json\"\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
JSON output with datapoints\n<\/code><\/pre>\n\n\n\n
If this works, Grafana should also be able to query Graphite.<\/p>\n\n\n\n
\n\n\n\n
Step 1.1: Open Grafana Explore<\/h1>\n\n\n\n
Login to Grafana.<\/p>\n\n\n\n
From the left-side menu, click:<\/p>\n\n\n\n
Explore\n<\/code><\/pre>\n\n\n\n
Depending on your Grafana 13.x UI, this may appear as:<\/p>\n\n\n\n
Explore\n<\/code><\/pre>\n\n\n\n
or under the navigation\/search menu.<\/p>\n\n\n\n
You should see a query workspace with a datasource selector at the top.<\/p>\n\n\n\n
\n\n\n\n
Step 1.2: Select Graphite Datasource<\/h1>\n\n\n\n
At the top of Explore, select your Graphite datasource.<\/p>\n\n\n\n
Example datasource name:<\/p>\n\n\n\n
Graphite\n<\/code><\/pre>\n\n\n\n
or whatever name you used when adding it.<\/p>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Grafana Explore opens the Graphite query editor.\n<\/code><\/pre>\n\n\n\n
If you see another datasource such as Prometheus, Loki, MySQL, or Elasticsearch, change it to Graphite.<\/p>\n\n\n\n
\n\n\n\n
Step 1.3: Set Time Range<\/h1>\n\n\n\n
At the top-right corner, set the time range to:<\/p>\n\n\n\n
Last 15 minutes\n<\/code><\/pre>\n\n\n\n
Why?<\/p>\n\n\n\n
Telegraf is sending fresh Linux metrics every 10 seconds. A short time range makes it easier to confirm live data.<\/p>\n\n\n\n
Recommended time ranges for this lab:<\/p>\n\n\n\n
Time Range<\/th>Use Case<\/th><\/tr><\/thead>
Last 5 minutes<\/td>Live verification<\/td><\/tr>
Last 15 minutes<\/td>Best for initial lab<\/td><\/tr>
Last 1 hour<\/td>Better trend visibility<\/td><\/tr>
Last 6 hours<\/td>Longer infrastructure trend<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
For first-time testing, use:<\/p>\n\n\n\n
Last 15 minutes\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.4: Run First CPU Query<\/h1>\n\n\n\n
In the Graphite query field, enter:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_active\n<\/code><\/pre>\n\n\n\n
Click:<\/p>\n\n\n\n
Run query\n<\/code><\/pre>\n\n\n\n
or press:<\/p>\n\n\n\n
Shift + Enter\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
A time-series graph showing CPU active usage percentage.\n<\/code><\/pre>\n\n\n\n
This metric shows active CPU usage.<\/p>\n\n\n\n
Example meaning:<\/p>\n\n\n\n
If value is 5, CPU active usage is around 5%.\nIf value is 80, CPU active usage is around 80%.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.5: Understand CPU Metrics<\/h1>\n\n\n\n
Try these CPU queries one by one.<\/p>\n\n\n\n
Total CPU Active Usage<\/h2>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_active\n<\/code><\/pre>\n\n\n\n
Meaning:<\/p>\n\n\n\n
Overall active CPU usage across the server.\n<\/code><\/pre>\n\n\n\n
CPU Idle Percentage<\/h2>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_idle\n<\/code><\/pre>\n\n\n\n
Meaning:<\/p>\n\n\n\n
Percentage of time CPU is idle.\n<\/code><\/pre>\n\n\n\n
CPU User Usage<\/h2>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_user\n<\/code><\/pre>\n\n\n\n
Meaning:<\/p>\n\n\n\n
CPU used by user-space processes.\n<\/code><\/pre>\n\n\n\n
CPU System Usage<\/h2>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_system\n<\/code><\/pre>\n\n\n\n
Meaning:<\/p>\n\n\n\n
CPU used by kernel\/system processes.\n<\/code><\/pre>\n\n\n\n
CPU I\/O Wait<\/h2>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_iowait\n<\/code><\/pre>\n\n\n\n
Meaning:<\/p>\n\n\n\n
CPU waiting for disk I\/O operations.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.6: Compare CPU Active and CPU Idle<\/h1>\n\n\n\n
In Explore, add two queries.<\/p>\n\n\n\n
Query A:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_active\n<\/code><\/pre>\n\n\n\n
Query B:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_idle\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Two lines appear in the graph.\n<\/code><\/pre>\n\n\n\n
Observation:<\/p>\n\n\n\n
When usage_active increases, usage_idle normally decreases.\n<\/code><\/pre>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
CPU active and CPU idle are opposite indicators.\nHigh active CPU means the server is busy.\nHigh idle CPU means the server has spare capacity.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.7: Generate CPU Load for Testing<\/h1>\n\n\n\n
On the Linux server, install stress:<\/p>\n\n\n\n
apt update\napt install -y stress\n<\/code><\/pre>\n\n\n\n
Generate CPU load for 2 minutes:<\/p>\n\n\n\n
stress --cpu 2 --timeout 120\n<\/code><\/pre>\n\n\n\n
Now return to Grafana Explore.<\/p>\n\n\n\n
Run:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_active\n<\/code><\/pre>\n\n\n\n
Set time range:<\/p>\n\n\n\n
Last 5 minutes\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
CPU usage should increase during the stress test.\n<\/code><\/pre>\n\n\n\n
This confirms the full monitoring chain is working:<\/p>\n\n\n\n
Linux CPU activity \u2192 Telegraf \u2192 Graphite \u2192 Grafana Explore\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.8: Explore Memory Metrics<\/h1>\n\n\n\n
Now query memory usage.<\/p>\n\n\n\n
Memory Used Percentage<\/h2>\n\n\n\n
telegraf.linux-demo.mem.used_percent\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Graph shows percentage of memory used.\n<\/code><\/pre>\n\n\n\n
Memory Available<\/h2>\n\n\n\n
telegraf.linux-demo.mem.available\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Graph shows available memory in bytes.\n<\/code><\/pre>\n\n\n\n
Memory Used<\/h2>\n\n\n\n
telegraf.linux-demo.mem.used\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Graph shows used memory in bytes.\n<\/code><\/pre>\n\n\n\n
Memory Free<\/h2>\n\n\n\n
telegraf.linux-demo.mem.free\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Graph shows free memory in bytes.\n<\/code><\/pre>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
used_percent is easier for dashboards and alerts.\nused, available, and free are raw byte values.\n<\/code><\/pre>\n\n\n\n
For student learning, use this as primary memory metric:<\/p>\n\n\n\n
telegraf.linux-demo.mem.used_percent\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.9: Explore System Load Metrics<\/h1>\n\n\n\n
Linux load average shows how many processes are waiting or running.<\/p>\n\n\n\n
Query:<\/p>\n\n\n\n
telegraf.linux-demo.system.load1\n<\/code><\/pre>\n\n\n\n
Other useful queries:<\/p>\n\n\n\n
telegraf.linux-demo.system.load5\ntelegraf.linux-demo.system.load15\n<\/code><\/pre>\n\n\n\n
Meaning:<\/p>\n\n\n\n
Metric<\/th>Meaning<\/th><\/tr><\/thead>
load1<\/code><\/td>1-minute load average<\/td><\/tr>
load5<\/code><\/td>5-minute load average<\/td><\/tr>
load15<\/code><\/td>15-minute load average<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Teaching explanation:<\/p>\n\n\n\n
Load average helps identify whether the system is under sustained pressure.\nA short spike in load1 may be normal.\nIf load1, load5, and load15 are all high, the system may be consistently overloaded.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.10: Compare Load Average Metrics<\/h1>\n\n\n\n
Add three queries in Explore.<\/p>\n\n\n\n
Query A:<\/p>\n\n\n\n
telegraf.linux-demo.system.load1\n<\/code><\/pre>\n\n\n\n
Query B:<\/p>\n\n\n\n
telegraf.linux-demo.system.load5\n<\/code><\/pre>\n\n\n\n
Query C:<\/p>\n\n\n\n
telegraf.linux-demo.system.load15\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Three lines appear in the same graph.\n<\/code><\/pre>\n\n\n\n
Interpretation:<\/p>\n\n\n\n
Pattern<\/th>Meaning<\/th><\/tr><\/thead>
load1<\/code> high, load5\/load15<\/code> low<\/td>Recent spike<\/td><\/tr>
All three high<\/td>Sustained load<\/td><\/tr>
load15<\/code> high, load1<\/code> low<\/td>Load was high earlier but is reducing<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\n\n\n\n
Step 1.11: Explore Disk Metrics<\/h1>\n\n\n\n
Use disk used percentage:<\/p>\n\n\n\n
telegraf.linux-demo.disk.*.used_percent\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
One or more disk mount series appear.\n<\/code><\/pre>\n\n\n\n
The *<\/code> wildcard matches available disk paths or devices.<\/p>\n\n\n\n
You can also try:<\/p>\n\n\n\n
telegraf.linux-demo.disk.*.free\ntelegraf.linux-demo.disk.*.used\ntelegraf.linux-demo.disk.*.total\n<\/code><\/pre>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
Disk percentage is better than raw bytes for quick operational monitoring.\n<\/code><\/pre>\n\n\n\n
Recommended dashboard metric later:<\/p>\n\n\n\n
telegraf.linux-demo.disk.*.used_percent\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.12: Explore Network Metrics<\/h1>\n\n\n\n
Use network received bytes:<\/p>\n\n\n\n
telegraf.linux-demo.net.*.bytes_recv\n<\/code><\/pre>\n\n\n\n
Use network sent bytes:<\/p>\n\n\n\n
telegraf.linux-demo.net.*.bytes_sent\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Network interfaces appear as separate series.\n<\/code><\/pre>\n\n\n\n
Common interfaces may include:<\/p>\n\n\n\n
eth0\nlo\nens5\ndocker0\n<\/code><\/pre>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
lo is loopback traffic.\neth0, ens5, or similar is usually the actual network interface.\ndocker0 may appear if Docker networking exists.\n<\/code><\/pre>\n\n\n\n
For useful server network monitoring, focus on real interfaces, not loopback.<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
telegraf.linux-demo.net.eth0.bytes_recv\n<\/code><\/pre>\n\n\n\n
or on AWS EC2, sometimes:<\/p>\n\n\n\n
telegraf.linux-demo.net.ens5.bytes_recv\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.13: Explore Process Metrics<\/h1>\n\n\n\n
Query:<\/p>\n\n\n\n
telegraf.linux-demo.processes.total\n<\/code><\/pre>\n\n\n\n
Other process metrics:<\/p>\n\n\n\n
telegraf.linux-demo.processes.running\ntelegraf.linux-demo.processes.sleeping\ntelegraf.linux-demo.processes.blocked\ntelegraf.linux-demo.processes.zombies\n<\/code><\/pre>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
Process metrics help detect process growth, stuck processes, and zombie processes.\n<\/code><\/pre>\n\n\n\n
Good operational metric:<\/p>\n\n\n\n
telegraf.linux-demo.processes.zombies\n<\/code><\/pre>\n\n\n\n
Expected normal value:<\/p>\n\n\n\n
0\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.14: Use Wildcards in Graphite Queries<\/h1>\n\n\n\n
Graphite supports wildcard-style metric selection.<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.*\n<\/code><\/pre>\n\n\n\n
This returns multiple CPU fields, such as:<\/p>\n\n\n\n
usage_active\nusage_idle\nusage_user\nusage_system\nusage_iowait\n<\/code><\/pre>\n\n\n\n
Another example:<\/p>\n\n\n\n
telegraf.linux-demo.system.*\n<\/code><\/pre>\n\n\n\n
This returns system metrics such as:<\/p>\n\n\n\n
load1\nload5\nload15\nn_users\nuptime\n<\/code><\/pre>\n\n\n\n
Another example:<\/p>\n\n\n\n
telegraf.linux-demo.mem.*\n<\/code><\/pre>\n\n\n\n
This returns memory metrics.<\/p>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
Wildcards are useful for discovery, but too many series can make graphs noisy.\nFor dashboards and alerts, use specific metrics.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.15: Use Graphite Functions in Explore<\/h1>\n\n\n\n
Graphite functions transform, combine, and calculate values from time-series data. Graphite\u2019s official documentation describes functions as tools to transform, combine, and perform computations on series data. (Graphite Documentation<\/a>)<\/p>\n\n\n\n
Grafana\u2019s Graphite query editor supports adding functions directly in the query editor. (Grafana Labs<\/a>)<\/p>\n\n\n\n
\n\n\n\n
Function 1: alias()<\/h2>\n\n\n\n
Use alias()<\/code> to rename a metric in the graph legend.<\/p>\n\n\n\n
Query:<\/p>\n\n\n\n
alias(telegraf.linux-demo.cpu.cpu-total.usage_active, 'CPU Active %')\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Graph legend shows CPU Active % instead of the long metric path.\n<\/code><\/pre>\n\n\n\n
Why useful:<\/p>\n\n\n\n
Long Graphite metric names are hard to read.\nalias() makes Explore and dashboards easier to understand.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Function 2: movingAverage()<\/h2>\n\n\n\n
Use movingAverage()<\/code> to smooth noisy data.<\/p>\n\n\n\n
Query:<\/p>\n\n\n\n
movingAverage(telegraf.linux-demo.cpu.cpu-total.usage_active, 5)\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
CPU graph becomes smoother.\n<\/code><\/pre>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
movingAverage is useful when raw metrics are too spiky.\nBut for alerts, be careful because smoothing can hide short incidents.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Function 3: summarize()<\/h2>\n\n\n\n
Use summarize()<\/code> to aggregate data into larger time buckets.<\/p>\n\n\n\n
Query:<\/p>\n\n\n\n
summarize(telegraf.linux-demo.cpu.cpu-total.usage_active, '1min', 'avg')\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
CPU usage is shown as 1-minute average values.\n<\/code><\/pre>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
summarize helps convert high-resolution data into cleaner trend data.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Function 4: highestCurrent()<\/h2>\n\n\n\n
Use highestCurrent()<\/code> to find the highest current value among multiple series.<\/p>\n\n\n\n
Example for disk:<\/p>\n\n\n\n
highestCurrent(telegraf.linux-demo.disk.*.used_percent, 5)\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Grafana shows the top 5 disk mount points by current used percentage.\n<\/code><\/pre>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
This is helpful when many disks or mount points exist.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Function 5: averageSeries()<\/h2>\n\n\n\n
Use averageSeries()<\/code> to average multiple matching series.<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
averageSeries(telegraf.linux-demo.cpu.cpu*.usage_active)\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
Average CPU active usage across CPU cores.\n<\/code><\/pre>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
averageSeries is useful when there are multiple CPU cores or multiple similar series.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.16: Use Query Builder vs Raw Query<\/h1>\n\n\n\n
Grafana Graphite datasource usually provides two ways to build queries:<\/p>\n\n\n\n
1. Visual\/query builder style\n2. Raw Graphite query text\n<\/code><\/pre>\n\n\n\n
For students, teach both.<\/p>\n\n\n\n
Query Builder Approach<\/h2>\n\n\n\n
Use the metric browser to select:<\/p>\n\n\n\n
telegraf\n \u2192 linux-demo\n   \u2192 cpu\n     \u2192 cpu-total\n       \u2192 usage_active\n<\/code><\/pre>\n\n\n\n
Then run the query.<\/p>\n\n\n\n
Raw Query Approach<\/h2>\n\n\n\n
Directly type:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_active\n<\/code><\/pre>\n\n\n\n
Teaching recommendation:<\/p>\n\n\n\n
Beginners should start with metric browsing.\nIntermediate users should learn raw Graphite query syntax.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.17: Explore Multiple Metrics Together<\/h1>\n\n\n\n
Now create an investigation view.<\/p>\n\n\n\n
Add these queries:<\/p>\n\n\n\n
Query A:<\/p>\n\n\n\n
alias(telegraf.linux-demo.cpu.cpu-total.usage_active, 'CPU Active %')\n<\/code><\/pre>\n\n\n\n
Query B:<\/p>\n\n\n\n
alias(telegraf.linux-demo.mem.used_percent, 'Memory Used %')\n<\/code><\/pre>\n\n\n\n
Query C:<\/p>\n\n\n\n
alias(telegraf.linux-demo.system.load1, 'Load 1m')\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
CPU, memory, and load appear together.\n<\/code><\/pre>\n\n\n\n
Important warning:<\/p>\n\n\n\n
CPU and memory are percentages.\nLoad average is not a percentage.\nSo this combined graph is useful for quick investigation, but not always perfect for dashboard presentation.\n<\/code><\/pre>\n\n\n\n
Teaching point:<\/p>\n\n\n\n
Explore is good for correlation.\nDashboards should be cleaner and better structured.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.18: Use Split View in Explore<\/h1>\n\n\n\n
Grafana Explore supports side-by-side investigation.<\/p>\n\n\n\n
In Explore, use:<\/p>\n\n\n\n
Split\n<\/code><\/pre>\n\n\n\n
or:<\/p>\n\n\n\n
Split view\n<\/code><\/pre>\n\n\n\n
depending on the UI.<\/p>\n\n\n\n
Left side:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_active\n<\/code><\/pre>\n\n\n\n
Right side:<\/p>\n\n\n\n
telegraf.linux-demo.mem.used_percent\n<\/code><\/pre>\n\n\n\n
Expected result:<\/p>\n\n\n\n
CPU and memory can be investigated side by side.\n<\/code><\/pre>\n\n\n\n
Use case:<\/p>\n\n\n\n
When CPU is high, check whether memory also increased.\nWhen load is high, check whether disk or network also changed.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.19: Inspect Query Results<\/h1>\n\n\n\n
In Explore, after running a query, use the query result\/inspect options if available.<\/p>\n\n\n\n
Look for:<\/p>\n\n\n\n
Data\nStats\nQuery\nJSON\n<\/code><\/pre>\n\n\n\n
The exact UI may vary slightly depending on Grafana 13.x build and permissions.<\/p>\n\n\n\n
Students should understand:<\/p>\n\n\n\n
Graph view shows trend.\nData\/table view shows actual datapoints.\nQuery inspector helps troubleshoot query and response.\n<\/code><\/pre>\n\n\n\n
This is especially useful when:<\/p>\n\n\n\n
Graph is empty\nMetric path is wrong\nDatasource is not responding\nTime range is incorrect\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.20: Recommended Metrics for Students to Explore<\/h1>\n\n\n\n
Use this table as the main lab reference.<\/p>\n\n\n\n
Area<\/th>Graphite Query<\/th>Meaning<\/th><\/tr><\/thead>
CPU<\/td>telegraf.linux-demo.cpu.cpu-total.usage_active<\/code><\/td>Active CPU usage %<\/td><\/tr>
CPU<\/td>telegraf.linux-demo.cpu.cpu-total.usage_idle<\/code><\/td>Idle CPU %<\/td><\/tr>
CPU<\/td>telegraf.linux-demo.cpu.cpu-total.usage_iowait<\/code><\/td>CPU waiting on disk I\/O<\/td><\/tr>
Memory<\/td>telegraf.linux-demo.mem.used_percent<\/code><\/td>Memory used %<\/td><\/tr>
Memory<\/td>telegraf.linux-demo.mem.available<\/code><\/td>Available memory bytes<\/td><\/tr>
Load<\/td>telegraf.linux-demo.system.load1<\/code><\/td>1-minute load average<\/td><\/tr>
Load<\/td>telegraf.linux-demo.system.load5<\/code><\/td>5-minute load average<\/td><\/tr>
Load<\/td>telegraf.linux-demo.system.load15<\/code><\/td>15-minute load average<\/td><\/tr>
Disk<\/td>telegraf.linux-demo.disk.*.used_percent<\/code><\/td>Disk usage %<\/td><\/tr>
Network<\/td>telegraf.linux-demo.net.*.bytes_recv<\/code><\/td>Network received bytes<\/td><\/tr>
Network<\/td>telegraf.linux-demo.net.*.bytes_sent<\/code><\/td>Network sent bytes<\/td><\/tr>
Processes<\/td>telegraf.linux-demo.processes.total<\/code><\/td>Total process count<\/td><\/tr>
Processes<\/td>telegraf.linux-demo.processes.zombies<\/code><\/td>Zombie processes<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\n\n\n\n
Step 1.21: Recommended Explore Exercises<\/h1>\n\n\n\n
Exercise 1: Find CPU Usage<\/h2>\n\n\n\n
Task:<\/p>\n\n\n\n
Find current CPU active usage.\n<\/code><\/pre>\n\n\n\n
Query:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_active\n<\/code><\/pre>\n\n\n\n
Expected learning:<\/p>\n\n\n\n
Students understand CPU percentage metric.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Exercise 2: Compare CPU Active and Idle<\/h2>\n\n\n\n
Queries:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_active\ntelegraf.linux-demo.cpu.cpu-total.usage_idle\n<\/code><\/pre>\n\n\n\n
Expected learning:<\/p>\n\n\n\n
Students understand relationship between CPU busy and idle time.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Exercise 3: Find Memory Usage<\/h2>\n\n\n\n
Query:<\/p>\n\n\n\n
telegraf.linux-demo.mem.used_percent\n<\/code><\/pre>\n\n\n\n
Expected learning:<\/p>\n\n\n\n
Students understand memory utilization percentage.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Exercise 4: Find System Load Trend<\/h2>\n\n\n\n
Queries:<\/p>\n\n\n\n
telegraf.linux-demo.system.load1\ntelegraf.linux-demo.system.load5\ntelegraf.linux-demo.system.load15\n<\/code><\/pre>\n\n\n\n
Expected learning:<\/p>\n\n\n\n
Students understand short-term vs long-term load.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Exercise 5: Find Disk Usage<\/h2>\n\n\n\n
Query:<\/p>\n\n\n\n
telegraf.linux-demo.disk.*.used_percent\n<\/code><\/pre>\n\n\n\n
Expected learning:<\/p>\n\n\n\n
Students understand wildcard-based disk exploration.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Exercise 6: Find Network Traffic<\/h2>\n\n\n\n
Queries:<\/p>\n\n\n\n
telegraf.linux-demo.net.*.bytes_recv\ntelegraf.linux-demo.net.*.bytes_sent\n<\/code><\/pre>\n\n\n\n
Expected learning:<\/p>\n\n\n\n
Students understand network receive\/send metrics.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Exercise 7: Use alias()<\/h2>\n\n\n\n
Query:<\/p>\n\n\n\n
alias(telegraf.linux-demo.mem.used_percent, 'Memory Used %')\n<\/code><\/pre>\n\n\n\n
Expected learning:<\/p>\n\n\n\n
Students understand how to make legends readable.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Exercise 8: Use movingAverage()<\/h2>\n\n\n\n
Query:<\/p>\n\n\n\n
movingAverage(telegraf.linux-demo.cpu.cpu-total.usage_active, 5)\n<\/code><\/pre>\n\n\n\n
Expected learning:<\/p>\n\n\n\n
Students understand smoothing.\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.22: Student Troubleshooting Guide<\/h1>\n\n\n\n
Problem 1: No data in Explore<\/h2>\n\n\n\n
Check time range first.<\/p>\n\n\n\n
Use:<\/p>\n\n\n\n
Last 15 minutes\n<\/code><\/pre>\n\n\n\n
Then check Graphite files:<\/p>\n\n\n\n
docker exec -it graphite find \/opt\/graphite\/storage\/whisper\/telegraf -type f | head\n<\/code><\/pre>\n\n\n\n
If no files exist, Telegraf is not sending metrics.<\/p>\n\n\n\n
\n\n\n\n
Problem 2: Query path not found<\/h2>\n\n\n\n
Try wildcard discovery:<\/p>\n\n\n\n
telegraf.*\n<\/code><\/pre>\n\n\n\n
Then:<\/p>\n\n\n\n
telegraf.linux-demo.*\n<\/code><\/pre>\n\n\n\n
Then:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.*\n<\/code><\/pre>\n\n\n\n
This helps discover the actual metric path.<\/p>\n\n\n\n
\n\n\n\n
Problem 3: Wrong hostname<\/h2>\n\n\n\n
Your hostname is configured as:<\/p>\n\n\n\n
linux-demo\n<\/code><\/pre>\n\n\n\n
But if students changed it, metric path may be different.<\/p>\n\n\n\n
Check actual metric directories:<\/p>\n\n\n\n
docker exec -it graphite find \/opt\/graphite\/storage\/whisper\/telegraf -maxdepth 2 -type d\n<\/code><\/pre>\n\n\n\n
Example output:<\/p>\n\n\n\n
\/opt\/graphite\/storage\/whisper\/telegraf\/linux-demo\n<\/code><\/pre>\n\n\n\n
If hostname is different, use that name in Grafana queries.<\/p>\n\n\n\n
\n\n\n\n
Problem 4: Grafana datasource test works, but Explore has no data<\/h2>\n\n\n\n
Check Graphite directly:<\/p>\n\n\n\n
curl \"http:\/\/YOUR_GRAPHITE_SERVER:8080\/render?target=telegraf.linux-demo.mem.used_percent&from=-10min&format=json\"\n<\/code><\/pre>\n\n\n\n
If Graphite returns datapoints, Grafana query path or time range is wrong.<\/p>\n\n\n\n
If Graphite returns empty datapoints, the metric may not exist or no data is available for the selected time.<\/p>\n\n\n\n
\n\n\n\n
Problem 5: Too many lines in graph<\/h2>\n\n\n\n
This happens with wildcard queries like:<\/p>\n\n\n\n
telegraf.linux-demo.*\n<\/code><\/pre>\n\n\n\n
Fix by using a more specific query:<\/p>\n\n\n\n
telegraf.linux-demo.cpu.cpu-total.usage_active\n<\/code><\/pre>\n\n\n\n
or use Graphite functions like:<\/p>\n\n\n\n
highestCurrent(telegraf.linux-demo.disk.*.used_percent, 5)\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Step 1.23: Lab Completion Checklist<\/h1>\n\n\n\n
Students should complete the following before moving to dashboard creation:<\/p>\n\n\n\n
Task<\/th>Completed<\/th><\/tr><\/thead>
Opened Grafana Explore<\/td>\u2610<\/td><\/tr>
Selected Graphite datasource<\/td>\u2610<\/td><\/tr>
Queried CPU active usage<\/td>\u2610<\/td><\/tr>
Queried memory used percentage<\/td>\u2610<\/td><\/tr>
Queried load average<\/td>\u2610<\/td><\/tr>
Queried disk usage using wildcard<\/td>\u2610<\/td><\/tr>
Queried network receive\/send metrics<\/td>\u2610<\/td><\/tr>
Used alias()<\/code> function<\/td>\u2610<\/td><\/tr>
Used movingAverage()<\/code> function<\/td>\u2610<\/td><\/tr>
Used multiple queries together<\/td>\u2610<\/td><\/tr>
Understood metric path structure<\/td>\u2610<\/td><\/tr>
Verified data changes during stress test<\/td>\u2610<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\n\n\n\n
Step 1.24: Final Student Summary<\/h1>\n\n\n\n
At the end of this lab, students should understand this clearly:<\/p>\n\n\n\n
Grafana Explore is used for ad-hoc metric investigation.\nGraphite stores metrics in dot-separated paths.\nTelegraf is collecting Linux metrics and sending them to Graphite.\nGrafana reads Graphite metrics using the Graphite datasource.\nCPU, memory, disk, network, load, and process metrics can be explored directly.\nGraphite functions such as alias(), movingAverage(), summarize(), and highestCurrent() help make queries more useful.\nExplore is the best place to test queries before creating dashboards and alerts.\n<\/code><\/pre>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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