Metrics

Open-Source-Lösung zur Metrikanalyse

Ob Sensordaten von Drohnen oder Informationen zur CPU-Auslastung, Elasticsearch hat die leistungsstarken Funktionen, die du an der Textsuche so schätzt, von Anfang an auch auf Metriken angewendet. Und es wird immer besser.

Entdecke Metrikanalysen mit Elastic. Probier es aus

Analysiere deine Zahlen flexibel und nach deinen Wünschen

Experimentiere mit Dimensionen, Tags, Kardinalität und Feldern. Elastic schreibt dir nicht vor, wie du deine Daten durchsuchen musst, und es gibt auch keine Einschränkungen. Im Gegenteil: Du kannst laufend und schnell nach Attributen suchen (z. B. Hostname, IP-Adresse, Implementierung oder Farbe) – und das in großem Umfang, nach deinen Anforderungen, in beliebiger Reihenfolge und in der Visualisierung, die dir gefällt.

Du wusstest nicht, dass eine Suchmaschine so gut mit Zahlen umgehen kann? Kein Problem, du weißt es jetzt.
Hier kannst du direkt loslegen.

Geschwindigkeit und Skalierbarkeit, die sich bemerkbar machen

Damit das gelingt, sind wir über die Indizierung mittels invertierter Dateien hinausgegangen. Wir haben neue Datentypen entwickelt, BKD-Bäume implementiert und ein Spaltenlayout für die Speicherung hinzufügt. Das alles resultiert in einer effizienteren Datenstrukturierung für eine schnellere Suche sowie weniger Speichernutzung und eine geringere Datenträgerauslastung. Oder anders formuliert: Du kannst in bemerkenswerter Geschwindigkeit auf Felder und Werte aus einem Petabyte an Daten zugreifen.

Jetzt ausprobieren

Mit einer Neuinstallation kannst du direkt loslegen.
In Elasticsearch install directory:
Once Elasticsearch starts, in Elasticsearch install directory (separate window):

Note the password for elastic user as <es_pw>

Note the password for kibana user as <kibana_pw>

In Kibana install directory:

Modify config/kibana.yml to set credentials for Elasticsearch

elasticsearch.username: "kibana"     
elasticsearch.password: "<kibana_pw>"
			
In Metricbeat install directory:

Modify metricbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<es_pw>"
			

To modify defaults, edit modules.d/system.yml.

Open browser @
http://localhost:5601 (login: elastic/<es_pw>)
Open dashboard:
"[Metricbeat System] Overview" and click on your host.
What just happened?

Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can start viewing CPU and memory utilization, and process-level statistics.

Didn't work for you?

Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.

In Elasticsearch install directory:
Once Elasticsearch starts, in Elasticsearch install directory (separate window):

Note the password for elastic user as <es_pw>

Note the password for kibana user as <kibana_pw>

In Kibana install directory:

Modify config/kibana.yml to set credentials for Elasticsearch

elasticsearch.username: "kibana"     
elasticsearch.password: "<kibana_pw>"
			
In Metricbeat install directory:

Modify metricbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<es_pw>"
			

To modify defaults, edit modules.d/apache.yml.

Open browser @
http://localhost:5601 (login: elastic/<es_pw>)
Open dashboard:
What just happened?

Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can start viewing connection statistics and HTTP worker details.

Didn't work for you?

Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.

In Elasticsearch install directory:
Once Elasticsearch starts, in Elasticsearch install directory (separate window):

Note the password for elastic user as <es_pw>

Note the password for kibana user as <kibana_pw>

In Kibana install directory:

Modify config/kibana.yml to set credentials for Elasticsearch

elasticsearch.username: "kibana"     
elasticsearch.password: "<kibana_pw>"
			
In Metricbeat install directory:

Modify metricbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<es_pw>"
			

To modify defaults, edit modules.d/mongodb.yml.

Open browser @
http://localhost:5601 (login: elastic/<es_pw>)
Open dashboard:
What just happened?

Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can start viewing data statistics, health and status information about your MongoDB deployment.

Didn't work for you?

Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.

In Elasticsearch install directory:
Once Elasticsearch starts, in Elasticsearch install directory (separate window):

Note the password for elastic user as <es_pw>

Note the password for kibana user as <kibana_pw>

In Kibana install directory:

Modify config/kibana.yml to set credentials for Elasticsearch

elasticsearch.username: "kibana"
elasticsearch.password: "<kibana_pw>"
			
In Metricbeat install directory:

Modify metricbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<es_pw>"
			

To modify defaults, edit modules.d/docker.yml.

Open browser @
http://localhost:5601 (login: elastic/<es_pw>)
Open dashboard:
What just happened?

Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can start viewing data statistics, health and status information about your Docker deployment.

Didn't work for you?

Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.

In Elasticsearch install directory:
Once Elasticsearch starts, in Elasticsearch install directory (separate window):

Note the password for elastic user as <es_pw>

Note the password for kibana user as <kibana_pw>

In Kibana install directory:

Modify config/kibana.yml to set credentials for Elasticsearch

elasticsearch.username: "kibana"
elasticsearch.password: "<kibana_pw>"
            
In Metricbeat install directory:

Modify metricbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<es_pw>"
From your machine or wherever you run kubectl:
env:
  - name: ELASTICSEARCH_USERNAME
    value: elastic
  - name: ELASTICSEARCH_PASSWORD
    value: changeme
Open browser @
http://localhost:5601 (login: elastic/<es_pw>)
What just happened?

Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can monitor your Kubernetes cluster.

Didn't work for you?

Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.

In Elasticsearch install directory:
In Kibana install directory:

Note the password for elastic user as <es_pw>

Note the password for kibana user as <kibana_pw>

In Heartbeat install directory:

Modify heartbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<es_pw>"
            
Open browser @
http://localhost:5601 (login: elastic/<es_pw>)
Open dashboard:
What just happened?

Heartbeat is designed to do distributed uptime checks from each of your hosts to ensure that they can each reach every endpoint they are supposed to. This is amazing for service oriented architectures. In this case, you’ve asked Heartbeat to check the uptime for the two local ports corresponding to the Elasticsearch and Kibana defaults. Heartbeat then sends this data to Elasticsearch and you can see the data in the Kibana dashboard.

Didn't work for you?

Heartbeat was set to use the default ports for Elasticsearch and Kibana in this example. See the documentation for configuration options.

  • Register, if you do not already have an account
  • Log into the Elastic Cloud console
To create a cluster, in Elastic Cloud console:
  • Select Create Cluster, leave size slider at 4 GB RAM, and click Create
  • Note the Cloud ID as <cloud.id>
  • Note the cluster Password as <password>
  • In Overview >> Endpoints section note Kibana URL as <kibana_url>
  • Wait until cluster plan completes

Download and unpack Metricbeat

In Metricbeat install directory:

Modify metricbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<password>"
				

To modify defaults, edit modules.d/system.yml.

Open browser @
http://<kibana_url>:5601 (login: elastic/<password>)
Open dashboard:
"[Metricbeat System] Overview"
What just happened?

Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can start viewing CPU and memory utilization, and process-level statistics.

Didn't work for you?

Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.

  • Register, if you do not already have an account
  • Log into the Elastic Cloud console
To create a cluster, in Elastic Cloud console:
  • Select Create Cluster, leave size slider at 4 GB RAM, and click Create
  • Note the Cloud ID as <cloud.id>
  • Note the cluster Password as <password>
  • In Overview >> Endpoints section note Kibana URL as <kibana_url>
  • Wait until cluster plan completes

Download and unpack Metricbeat

In Metricbeat install directory:

Modify metricbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<password>"
				

To modify defaults, edit modules.d/apache.yml.

Open browser @
http://<kibana_url>:5601 (login: elastic/<password>)
Open dashboard:
"[Metricbeat Apache] Overview"
What just happened?

Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can start viewing connection statistics and HTTP worker details.

Didn't work for you?

Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.

  • Register, if you do not already have an account
  • Log into the Elastic Cloud console
To create a cluster, in Elastic Cloud console:
  • Select Create Cluster, leave size slider at 4 GB RAM, and click Create
  • Note the Cloud ID as <cloud.id>
  • Note the cluster Password as <password>
  • In Overview >> Endpoints section note Kibana URL as <kibana_url>
  • Wait until cluster plan completes

Download and unpack Metricbeat

In Metricbeat install directory:

Modify metricbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<password>"
				

To modify defaults, edit modules.d/mongodb.yml.

Open browser @
http://<kibana_url>:5601 (login: elastic/<password>)
Open dashboard:
"[Metricbeat MongoDB] Overview"
What just happened?

Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can start viewing data statistics, health and status information about your MongoDB deployment.

Didn't work for you?

Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.

  • Register, if you do not already have an account
  • Log into the Elastic Cloud console
To create a cluster, in Elastic Cloud console:
  • Select Create Cluster, leave size slider at 4 GB RAM, and click Create
  • Note the Cloud ID as <cloud.id>
  • Note the cluster Password as <password>
  • In Overview >> Endpoints section note Kibana URL as <kibana_url>
  • Wait until cluster plan completes

Download and unpack Metricbeat

In Metricbeat install directory:

Modify metricbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<password>"
				
Open browser @
http://<kibana_url>:5601 (login: elastic/<password>)
Open dashboard:
"[Metricbeat Docker] Overview"
What just happened?

Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can start viewing data statistics, health and status information about your Docker deployment.

Didn't work for you?

Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.

  • Register, if you do not already have an account
  • Log into the Elastic Cloud console
To create a cluster, in Elastic Cloud console:
  • Select Create Cluster, leave size slider at 4 GB RAM, and click Create
  • Note the Cloud ID as <cloud.id>
  • Note the cluster Password as <password>
  • In Overview >> Endpoints section note Kibana URL as <kibana_url>
  • Wait until cluster plan completes

Download and unpack Metricbeat on your local machine

In Metricbeat install directory:

Modify metricbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<password>"
				

From your machine or wherever you run kubectl:

env:
  - name: ELASTICSEARCH_USERNAME
    value: elastic
  - name: ELASTICSEARCH_PASSWORD
    value: changeme
				
Open browser @
http://<kibana_url>:5601 (login: elastic/<password>)
Open dashboard:
"[Metricbeat Kubernetes] Overview"
What just happened?

Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can monitor your Kubernetes cluster.

Didn't work for you?

Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.

  • Register, if you do not already have an account
  • Log into the Elastic Cloud console
To create a cluster, in Elastic Cloud console:
  • Select Create Cluster, leave size slider at 4 GB RAM, and click Create
  • Note the Cloud ID as <cloud.id>
  • Note the cluster Password as <password>
  • In Overview >> Endpoints section note Kibana URL as <kibana_url>
  • Wait until cluster plan completes

Download and unpack Heartbeat (Beta)

In Heartbeat install directory:

Modify heartbeat.yml to set credentials for Elasticsearch output

output.elasticsearch:
  username: "elastic"
  password: "<password>"
				
Open browser @
http://<kibana_url>:5601 (login: elastic/<password>)
Open dashboard:
"[Heartbeat] HTTP Monitoring"
What just happened?

Heartbeat is designed to do distributed uptime checks from each of your hosts to ensure that they can each reach every endpoint they are supposed to. This is amazing for service oriented architectures. In this case, you’ve asked Heartbeat to check the uptime for the two local ports corresponding to the Elasticsearch and Kibana defaults. Heartbeat then sends this data to Elasticsearch and you can see the data in the Kibana dashboard.

Didn't work for you?

Heartbeat was set to use the default ports for Elasticsearch and Kibana in this example. See the documentation for configuration options.

Mit Machine-Learning-Jobs Auffälligkeiten entdecken

Wenn Daten skaliert werden, kann es leicht passieren, dass problematische Datenpunkte unter der Vielzahl an Durchschnittswerten, Messungen und Gesamtwerten übersehen werden. Und es ist unpraktisch, alle Visualisierungen permanent zu analysieren. (Schließlich sind wir alle nur Menschen.)

Die Machine-Learning-Funktionen im Elastic Stack automatisieren die Erkennung von Auffälligkeiten in großem Umfang. Das System lernt, was bei deinen Daten normal ist, und erkennt auch Anomalitäten, um dich anschließend zu benachrichtigen.

Sogar Supercomputer nutzen Elastic

1,2 Milliarden Dokumente, 160 GB – diese Datenmenge erfasst das National Energy Research Scientific Computing Center (NERSC) Tag für Tag. Dabei werden die unterschiedlichsten Arten von Metriken indiziert und für wissenschaftliche Untersuchungen genutzt: KPIs zum Stromverbrauch in Umspannwerken, Luft- und Wassertemperatur in Gebäuden, Auslastung von Festplatten, Netzwerkein-/ausgabe und Systemlast.

Das sind nicht die einzigen Unternehmen, die mit Elastic ihre Zahlen durchsuchen. Hier findest du weitere Kundenbeispiele.

Metriken sind nur eine Möglichkeit

Hast du auch Netzwerkdaten? Infrastruktur-Logs? Dokumente mit einer Unmenge an Text? Zentralisiere alle diese Daten im Elastic Stack zusammen mit deinen Kennzahlen und erhalte umfassendere Analysen, optimiere deine Workflows und vereinfache deine Architektur.

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Suche nach Dokumenten, Geodaten usw.

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