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Seth Hobson
2026-01-19 17:07:03 -05:00
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84
plugins/observability-monitoring/skills/distributed-tracing/SKILL.md
View File

@@ -22,6 +22,7 @@ Track requests across distributed systems to understand latency, dependencies, a
## Distributed Tracing Concepts
### Trace Structure
```
Trace (Request ID: abc123)
↓
@@ -34,6 +35,7 @@ Span (api-gateway) [80ms]
```
### Key Components
- **Trace** - End-to-end request journey
- **Span** - Single operation within a trace
- **Context** - Metadata propagated between services
@@ -71,7 +73,7 @@ EOF
### Docker Compose
```yaml
version: '3.8'
version: "3.8"
services:
jaeger:
image: jaegertracing/all-in-one:latest
@@ -80,10 +82,10 @@ services:
- "6831:6831/udp"
- "6832:6832/udp"
- "5778:5778"
- "16686:16686" # UI
- "14268:14268" # Collector
- "14250:14250" # gRPC
- "9411:9411" # Zipkin
- "16686:16686" # UI
- "14268:14268" # Collector
- "14250:14250" # gRPC
- "9411:9411" # Zipkin
environment:
- COLLECTOR_ZIPKIN_HOST_PORT=:9411
```
@@ -95,6 +97,7 @@ services:
### OpenTelemetry (Recommended)
#### Python (Flask)
```python
from opentelemetry import trace
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
@@ -139,21 +142,24 @@ def fetch_users_from_db():
```
#### Node.js (Express)
```javascript
const { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node');
const { JaegerExporter } = require('@opentelemetry/exporter-jaeger');
const { BatchSpanProcessor } = require('@opentelemetry/sdk-trace-base');
const { registerInstrumentations } = require('@opentelemetry/instrumentation');
const { HttpInstrumentation } = require('@opentelemetry/instrumentation-http');
const { ExpressInstrumentation } = require('@opentelemetry/instrumentation-express');
const { NodeTracerProvider } = require("@opentelemetry/sdk-trace-node");
const { JaegerExporter } = require("@opentelemetry/exporter-jaeger");
const { BatchSpanProcessor } = require("@opentelemetry/sdk-trace-base");
const { registerInstrumentations } = require("@opentelemetry/instrumentation");
const { HttpInstrumentation } = require("@opentelemetry/instrumentation-http");
const {
ExpressInstrumentation,
} = require("@opentelemetry/instrumentation-express");
// Initialize tracer
const provider = new NodeTracerProvider({
resource: { attributes: { 'service.name': 'my-service' } }
resource: { attributes: { "service.name": "my-service" } },
});
const exporter = new JaegerExporter({
endpoint: 'http://jaeger:14268/api/traces'
endpoint: "http://jaeger:14268/api/traces",
});
provider.addSpanProcessor(new BatchSpanProcessor(exporter));
@@ -161,22 +167,19 @@ provider.register();
// Instrument libraries
registerInstrumentations({
instrumentations: [
new HttpInstrumentation(),
new ExpressInstrumentation(),
],
instrumentations: [new HttpInstrumentation(), new ExpressInstrumentation()],
});
const express = require('express');
const express = require("express");
const app = express();
app.get('/api/users', async (req, res) => {
const tracer = trace.getTracer('my-service');
const span = tracer.startSpan('get_users');
app.get("/api/users", async (req, res) => {
const tracer = trace.getTracer("my-service");
const span = tracer.startSpan("get_users");
try {
const users = await fetchUsers();
span.setAttributes({ 'user.count': users.length });
span.setAttributes({ "user.count": users.length });
res.json({ users });
} finally {
span.end();
@@ -185,6 +188,7 @@ app.get('/api/users', async (req, res) => {
```
#### Go
```go
package main
@@ -240,6 +244,7 @@ func getUsers(ctx context.Context) ([]User, error) {
## Context Propagation
### HTTP Headers
```
traceparent: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01
tracestate: congo=t61rcWkgMzE
@@ -248,6 +253,7 @@ tracestate: congo=t61rcWkgMzE
### Propagation in HTTP Requests
#### Python
```python
from opentelemetry.propagate import inject
@@ -258,13 +264,14 @@ response = requests.get('http://downstream-service/api', headers=headers)
```
#### Node.js
```javascript
const { propagation } = require('@opentelemetry/api');
const { propagation } = require("@opentelemetry/api");
const headers = {};
propagation.inject(context.active(), headers);
axios.get('http://downstream-service/api', { headers });
axios.get("http://downstream-service/api", { headers });
```
## Tempo Setup (Grafana)
@@ -312,17 +319,17 @@ spec:
template:
spec:
containers:
- name: tempo
image: grafana/tempo:latest
args:
- -config.file=/etc/tempo/tempo.yaml
volumeMounts:
- name: config
mountPath: /etc/tempo
- name: tempo
image: grafana/tempo:latest
args:
- -config.file=/etc/tempo/tempo.yaml
volumeMounts:
- name: config
mountPath: /etc/tempo
volumes:
- name: config
configMap:
name: tempo-config
- name: config
configMap:
name: tempo-config
```
**Reference:** See `assets/jaeger-config.yaml.template`
@@ -330,6 +337,7 @@ spec:
## Sampling Strategies
### Probabilistic Sampling
```yaml
# Sample 1% of traces
sampler:
@@ -338,6 +346,7 @@ sampler:
```
### Rate Limiting Sampling
```yaml
# Sample max 100 traces per second
sampler:
@@ -346,6 +355,7 @@ sampler:
```
### Adaptive Sampling
```python
from opentelemetry.sdk.trace.sampling import ParentBased, TraceIdRatioBased
@@ -358,6 +368,7 @@ sampler = ParentBased(root=TraceIdRatioBased(0.01))
### Finding Slow Requests
**Jaeger Query:**
```
service=my-service
duration > 1s
@@ -366,6 +377,7 @@ duration > 1s
### Finding Errors
**Jaeger Query:**
```
service=my-service
error=true
@@ -375,6 +387,7 @@ tags.http.status_code >= 500
### Service Dependency Graph
Jaeger automatically generates service dependency graphs showing:
- Service relationships
- Request rates
- Error rates
@@ -396,6 +409,7 @@ Jaeger automatically generates service dependency graphs showing:
## Integration with Logging
### Correlated Logs
```python
import logging
from opentelemetry import trace
@@ -415,12 +429,14 @@ def process_request():
## Troubleshooting
**No traces appearing:**
- Check collector endpoint
- Verify network connectivity
- Check sampling configuration
- Review application logs
**High latency overhead:**
- Reduce sampling rate
- Use batch span processor
- Check exporter configuration

87
plugins/observability-monitoring/skills/grafana-dashboards/SKILL.md
View File

@@ -22,6 +22,7 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
## Dashboard Design Principles
### 1. Hierarchy of Information
```
┌─────────────────────────────────────┐
│ Critical Metrics (Big Numbers) │
@@ -33,11 +34,13 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
```
### 2. RED Method (Services)
- **Rate** - Requests per second
- **Errors** - Error rate
- **Duration** - Latency/response time
### 3. USE Method (Resources)
- **Utilization** - % time resource is busy
- **Saturation** - Queue length/wait time
- **Errors** - Error count
@@ -63,7 +66,7 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
"legendFormat": "{{service}}"
}
],
"gridPos": {"x": 0, "y": 0, "w": 12, "h": 8}
"gridPos": { "x": 0, "y": 0, "w": 12, "h": 8 }
},
{
"title": "Error Rate %",
@@ -77,14 +80,14 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
"alert": {
"conditions": [
{
"evaluator": {"params": [5], "type": "gt"},
"operator": {"type": "and"},
"query": {"params": ["A", "5m", "now"]},
"evaluator": { "params": [5], "type": "gt" },
"operator": { "type": "and" },
"query": { "params": ["A", "5m", "now"] },
"type": "query"
}
]
},
"gridPos": {"x": 12, "y": 0, "w": 12, "h": 8}
"gridPos": { "x": 12, "y": 0, "w": 12, "h": 8 }
},
{
"title": "P95 Latency",
@@ -95,7 +98,7 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
"legendFormat": "{{service}}"
}
],
"gridPos": {"x": 0, "y": 8, "w": 24, "h": 8}
"gridPos": { "x": 0, "y": 8, "w": 24, "h": 8 }
}
]
}
@@ -107,13 +110,16 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
## Panel Types
### 1. Stat Panel (Single Value)
```json
{
"type": "stat",
"title": "Total Requests",
"targets": [{
"expr": "sum(http_requests_total)"
}],
"targets": [
{
"expr": "sum(http_requests_total)"
}
],
"options": {
"reduceOptions": {
"values": false,
@@ -128,9 +134,9 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
"thresholds": {
"mode": "absolute",
"steps": [
{"value": 0, "color": "green"},
{"value": 80, "color": "yellow"},
{"value": 90, "color": "red"}
{ "value": 0, "color": "green" },
{ "value": 80, "color": "yellow" },
{ "value": 90, "color": "red" }
]
}
}
@@ -139,35 +145,41 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
```
### 2. Time Series Graph
```json
{
"type": "graph",
"title": "CPU Usage",
"targets": [{
"expr": "100 - (avg by (instance) (rate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)"
}],
"targets": [
{
"expr": "100 - (avg by (instance) (rate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)"
}
],
"yaxes": [
{"format": "percent", "max": 100, "min": 0},
{"format": "short"}
{ "format": "percent", "max": 100, "min": 0 },
{ "format": "short" }
]
}
```
### 3. Table Panel
```json
{
"type": "table",
"title": "Service Status",
"targets": [{
"expr": "up",
"format": "table",
"instant": true
}],
"targets": [
{
"expr": "up",
"format": "table",
"instant": true
}
],
"transformations": [
{
"id": "organize",
"options": {
"excludeByName": {"Time": true},
"excludeByName": { "Time": true },
"indexByName": {},
"renameByName": {
"instance": "Instance",
@@ -181,14 +193,17 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
```
### 4. Heatmap
```json
{
"type": "heatmap",
"title": "Latency Heatmap",
"targets": [{
"expr": "sum(rate(http_request_duration_seconds_bucket[5m])) by (le)",
"format": "heatmap"
}],
"targets": [
{
"expr": "sum(rate(http_request_duration_seconds_bucket[5m])) by (le)",
"format": "heatmap"
}
],
"dataFormat": "tsbuckets",
"yAxis": {
"format": "s"
@@ -199,6 +214,7 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
## Variables
### Query Variables
```json
{
"templating": {
@@ -225,6 +241,7 @@ Design effective Grafana dashboards for monitoring applications, infrastructure,
```
### Use Variables in Queries
```
sum(rate(http_requests_total{namespace="$namespace", service=~"$service"}[5m]))
```
@@ -241,11 +258,11 @@ sum(rate(http_requests_total{namespace="$namespace", service=~"$service"}[5m]))
"params": [5],
"type": "gt"
},
"operator": {"type": "and"},
"operator": { "type": "and" },
"query": {
"params": ["A", "5m", "now"]
},
"reducer": {"type": "avg"},
"reducer": { "type": "avg" },
"type": "query"
}
],
@@ -254,9 +271,7 @@ sum(rate(http_requests_total{namespace="$namespace", service=~"$service"}[5m]))
"frequency": "1m",
"message": "Error rate is above 5%",
"noDataState": "no_data",
"notifications": [
{"uid": "slack-channel"}
]
"notifications": [{ "uid": "slack-channel" }]
}
}
```
@@ -264,13 +279,14 @@ sum(rate(http_requests_total{namespace="$namespace", service=~"$service"}[5m]))
## Dashboard Provisioning
**dashboards.yml:**
```yaml
apiVersion: 1
providers:
- name: 'default'
- name: "default"
orgId: 1
folder: 'General'
folder: "General"
type: file
disableDeletion: false
updateIntervalSeconds: 10
@@ -284,6 +300,7 @@ providers:
### Infrastructure Dashboard
**Key Panels:**
- CPU utilization per node
- Memory usage per node
- Disk I/O
@@ -296,6 +313,7 @@ providers:
### Database Dashboard
**Key Panels:**
- Queries per second
- Connection pool usage
- Query latency (P50, P95, P99)
@@ -309,6 +327,7 @@ providers:
### Application Dashboard
**Key Panels:**
- Request rate
- Error rate
- Response time (percentiles)

72
plugins/observability-monitoring/skills/prometheus-configuration/SKILL.md
View File

@@ -55,7 +55,7 @@ helm install prometheus prometheus-community/kube-prometheus-stack \
### Docker Compose
```yaml
version: '3.8'
version: "3.8"
services:
prometheus:
image: prom/prometheus:latest
@@ -65,9 +65,9 @@ services:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
- prometheus-data:/prometheus
command:
- '--config.file=/etc/prometheus/prometheus.yml'
- '--storage.tsdb.path=/prometheus'
- '--storage.tsdb.retention.time=30d'
- "--config.file=/etc/prometheus/prometheus.yml"
- "--storage.tsdb.path=/prometheus"
- "--storage.tsdb.retention.time=30d"
volumes:
prometheus-data:
@@ -76,20 +76,21 @@ volumes:
## Configuration File
**prometheus.yml:**
```yaml
global:
scrape_interval: 15s
evaluation_interval: 15s
external_labels:
cluster: 'production'
region: 'us-west-2'
cluster: "production"
region: "us-west-2"
# Alertmanager configuration
alerting:
alertmanagers:
- static_configs:
- targets:
- alertmanager:9093
- alertmanager:9093
# Load rules files
rule_files:
@@ -98,25 +99,25 @@ rule_files:
# Scrape configurations
scrape_configs:
# Prometheus itself
- job_name: 'prometheus'
- job_name: "prometheus"
static_configs:
- targets: ['localhost:9090']
- targets: ["localhost:9090"]
# Node exporters
- job_name: 'node-exporter'
- job_name: "node-exporter"
static_configs:
- targets:
- 'node1:9100'
- 'node2:9100'
- 'node3:9100'
- "node1:9100"
- "node2:9100"
- "node3:9100"
relabel_configs:
- source_labels: [__address__]
target_label: instance
regex: '([^:]+)(:[0-9]+)?'
replacement: '${1}'
regex: "([^:]+)(:[0-9]+)?"
replacement: "${1}"
# Kubernetes pods with annotations
- job_name: 'kubernetes-pods'
- job_name: "kubernetes-pods"
kubernetes_sd_configs:
- role: pod
relabel_configs:
@@ -127,7 +128,8 @@ scrape_configs:
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
- source_labels:
[__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
action: replace
regex: ([^:]+)(?::\d+)?;(\d+)
replacement: $1:$2
@@ -140,13 +142,13 @@ scrape_configs:
target_label: pod
# Application metrics
- job_name: 'my-app'
- job_name: "my-app"
static_configs:
- targets:
- 'app1.example.com:9090'
- 'app2.example.com:9090'
metrics_path: '/metrics'
scheme: 'https'
- "app1.example.com:9090"
- "app2.example.com:9090"
metrics_path: "/metrics"
scheme: "https"
tls_config:
ca_file: /etc/prometheus/ca.crt
cert_file: /etc/prometheus/client.crt
@@ -161,27 +163,28 @@ scrape_configs:
```yaml
scrape_configs:
- job_name: 'static-targets'
- job_name: "static-targets"
static_configs:
- targets: ['host1:9100', 'host2:9100']
- targets: ["host1:9100", "host2:9100"]
labels:
env: 'production'
region: 'us-west-2'
env: "production"
region: "us-west-2"
```
### File-based Service Discovery
```yaml
scrape_configs:
- job_name: 'file-sd'
- job_name: "file-sd"
file_sd_configs:
- files:
- /etc/prometheus/targets/*.json
- /etc/prometheus/targets/*.yml
- /etc/prometheus/targets/*.json
- /etc/prometheus/targets/*.yml
refresh_interval: 5m
```
**targets/production.json:**
```json
[
{
@@ -198,14 +201,16 @@ scrape_configs:
```yaml
scrape_configs:
- job_name: 'kubernetes-services'
- job_name: "kubernetes-services"
kubernetes_sd_configs:
- role: service
relabel_configs:
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
- source_labels:
[__meta_kubernetes_service_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
- source_labels:
[__meta_kubernetes_service_annotation_prometheus_io_scheme]
action: replace
target_label: __scheme__
regex: (https?)
@@ -364,16 +369,19 @@ promtool query instant http://localhost:9090 'up'
## Troubleshooting
**Check scrape targets:**
```bash
curl http://localhost:9090/api/v1/targets
```
**Check configuration:**
```bash
curl http://localhost:9090/api/v1/status/config
```
**Test query:**
```bash
curl 'http://localhost:9090/api/v1/query?query=up'
```

21
plugins/observability-monitoring/skills/slo-implementation/SKILL.md
View File

@@ -35,6 +35,7 @@ SLI (Service Level Indicator)
### Common SLI Types
#### 1. Availability SLI
```promql
# Successful requests / Total requests
sum(rate(http_requests_total{status!~"5.."}[28d]))
@@ -43,6 +44,7 @@ sum(rate(http_requests_total[28d]))
```
#### 2. Latency SLI
```promql
# Requests below latency threshold / Total requests
sum(rate(http_request_duration_seconds_bucket{le="0.5"}[28d]))
@@ -51,6 +53,7 @@ sum(rate(http_request_duration_seconds_count[28d]))
```
#### 3. Durability SLI
```
# Successful writes / Total writes
sum(storage_writes_successful_total)
@@ -64,16 +67,17 @@ sum(storage_writes_total)
### Availability SLO Examples
| SLO % | Downtime/Month | Downtime/Year |
|-------|----------------|---------------|
| 99% | 7.2 hours | 3.65 days |
| 99.9% | 43.2 minutes | 8.76 hours |
| 99.95%| 21.6 minutes | 4.38 hours |
| 99.99%| 4.32 minutes | 52.56 minutes |
| SLO % | Downtime/Month | Downtime/Year |
| ------ | -------------- | ------------- |
| 99% | 7.2 hours | 3.65 days |
| 99.9% | 43.2 minutes | 8.76 hours |
| 99.95% | 21.6 minutes | 4.38 hours |
| 99.99% | 4.32 minutes | 52.56 minutes |
### Choose Appropriate SLOs
**Consider:**
- User expectations
- Business requirements
- Current performance
@@ -81,6 +85,7 @@ sum(storage_writes_total)
- Competitor benchmarks
**Example SLOs:**
```yaml
slos:
- name: api_availability
@@ -109,6 +114,7 @@ Error Budget = 1 - SLO Target
```
**Example:**
- SLO: 99.9% availability
- Error Budget: 0.1% = 43.2 minutes/month
- Current Error: 0.05% = 21.6 minutes/month
@@ -287,18 +293,21 @@ rules:
## SLO Review Process
### Weekly Review
- Current SLO compliance
- Error budget status
- Trend analysis
- Incident impact
### Monthly Review
- SLO achievement
- Error budget usage
- Incident postmortems
- SLO adjustments
### Quarterly Review
- SLO relevance
- Target adjustments
- Process improvements