update docu

src/site/markdown/engine/metrics.md

@@ -84,40 +84,208 @@ The default metric api endpoint for Payara Server is:
 
 A Prometheus configuration to scrape the metrics from Imixs-Workflow can look like in the following example:
 
-
-	global:
-	  scrape_interval:     15s # By default, scrape targets every 15 seconds.
-	  # Attach these labels to any time series or alerts when communicating with
-	  # external systems (federation, remote storage, Alertmanager).
-	  external_labels:
-	    monitor: 'imixs-monitor'
-	
-	scrape_configs:
-	  # Prometheus itself
-	  - job_name: 'prometheus'
-	    scrape_interval: 5s
-	    static_configs:
-	      - targets: ['localhost:9090']
-	     
-	  # Imixs-Office Job
-	  - job_name: 'imixs'
-	    scrape_interval: 5s
-	    metrics_path: /metrics
-	    static_configs:
-	      - targets: ['app:8080']        
-
+```
+global:
+	scrape_interval:     15s # By default, scrape targets every 15 seconds.
+	# Attach these labels to any time series or alerts when communicating with
+	# external systems (federation, remote storage, Alertmanager).
+	external_labels:
+	monitor: 'imixs-monitor'
+
+scrape_configs:
+	# Prometheus itself
+	- job_name: 'prometheus'
+	scrape_interval: 5s
+	static_configs:
+		- targets: ['localhost:9090']
+		
+	# Imixs-Office Job
+	- job_name: 'imixs'
+	scrape_interval: 5s
+	metrics_path: /metrics
+	static_configs:
+		- targets: ['app:8080']        
+ ```
 
 
 ### Prometheus Dashboard
       
+To setup Prometheus with docker-compose you can add the container like shown in the following example:
+
+```yaml      
+  prometheus:
+    image: prom/prometheus:latest
+    ports:
+      - "9090:9090"
+    volumes:
+      - ./prometheus/prometheus.yml:/etc/prometheus/prometheus.yml 
+      - prometheusdata:/prometheus/
+
+volumes:
+  prometheusdata:
+```
+
+
 From the Prometheus Dashboard you can test the data within your web browser:
 
 <img src="../images/engine/imixs-metrics-prometheus-768x448.png" />
 
 ### Monitoring Metrics with Grafana
 
+  
+  grafana:
+    image: grafana/grafana:7.1.0
+    ports: 
+      - "3000:3000" 
+
 To monitor you workflow you can easily connect your Prometheus server with a Grafana Instance. This allows you to visualize your metrics in an individual and more detailed dashboard. 
 
 <img src="../images/engine/imixs-metrics-grafana-768x376.png" />
 
-There are a huge amount of functions available in Grafana to analyze and monitor data. You can also activate individual alerts to notify your process owner about the load of your business processes. 
+There are a huge amount of functions available in Grafana to analyze and monitor data. You can also activate individual alerts to notify your process owner about the load of your business processes. 
+
+
+## Custom Metric Service
+
+You can also implement your own custom metric service by just observing the #
+
+
+```java
+
+@ApplicationScoped
+public class MyCustomMetricService {
+
+	@Inject
+	@ConfigProperty(name = "metrics.enabled", defaultValue = "false")
+	private boolean metricsEnabled;
+
+	@Inject
+	@RegistryScope(scope = MetricRegistry.APPLICATION_SCOPE)
+	MetricRegistry metricRegistry;
+
+	/**
+	 * ProcessingEvent listener to generate a metric.
+	 */
+	public void onProcessingEvent(@Observes ProcessingEvent processingEvent) throws AccessDeniedException {
+
+		if (!metricsEnabled) {
+			return;
+		}
+		try {
+			Counter counter = buildMetric(processingEvent);
+			counter.inc();
+		} catch (IncompatibleClassChangeError | ObserverException oe) {
+			mpMetricNoSupport = true;
+			logger.warning("...Microprofile Metrics not supported!");
+		}
+	}
+
+
+	/**
+	 * This method builds a custom Microprofile Metric 
+	 */
+	private Counter buildMetric(DocumentEvent event) {
+
+		
+		// create a metadata object....
+		Metadata metadata = Metadata.builder().withName(METRIC_DOCUMENTS)
+				.withDescription("My custom metric").build();
+		....
+		........
+		// export custom metrics....
+		Tag[] tags = { new Tag("my_metric", method) };
+		Counter counter = metricRegistry.counter(metadata, tags);
+
+		return counter;
+	}
+}
+```
+
+### Metric Types
+
+The Eclipse MicroProfile Metrics Framework supports the different metric types:
+
+
+**Counter:**
+
+
+A simple incrementing counter that can only be increased
+Useful for counting events like API calls or errors
+
+Example:
+
+```java
+@Counted(name = "requestCount", absolute = true)
+public void doRequest() {
+    // Method implementation
+}
+```
+
+**Gauge**
+
+A gauge represents a single numerical value that can increase and decrease.
+Perfect for instantaneous values like CPU usage or active threads
+
+Example:
+
+```java
+@Gauge(unit = MetricUnits.NONE)
+public int getQueueSize() {
+    return queue.size();
+}
+```
+
+**Meter**
+
+The Meter metric measures the rate at which events occur over time
+Calculates mean rate, one-minute, five-minute, and fifteen-minute rates
+
+Example:
+
+```java
+@Metered(name = "requestMeter")
+public void handleRequest() {
+    // Request processing
+}
+```
+
+**Timer**
+
+A Timer combines Meter and Histogram for time measurements
+Measures both frequency and duration of events
+
+Example:
+
+```java
+@Timed(name = "processingTime")
+public void processData() {
+    // Time-intensive operation
+}
+```
+
+
+**Histogram**
+
+A histogram tracks the distribution of values over time
+Calculates statistical values like min, max, mean, and percentiles
+
+Example:
+
+```java
+@Metric(name = "histogram")
+private Histogram histogram;
+```
+
+**ConcurrentGauge**
+
+Specialized gauge for concurrent access
+Tracks current, maximum, and minimum concurrent invocations
+
+Example:
+
+```java
+@ConcurrentGauge(name = "activeRequests")
+public void handleRequest() {
+    // Concurrent request handling
+}
+```