New collectors in Apache Karaf Decanter 2.4.0

Apache Karaf Decanter 2.4.0 will be released soon and include a set of new collectors.

Oshi Collector

The oshi collector harvest a bunch of data about the hardware and the operating system. It’s a scheduled collector, executed periodically (every minute by default).

You can have all details about the machine thanks to this collector: motherboard, cpu, sensors, disks, etc, etc.

By default, the oshi collector retrieves all details, but you can filter what you want to harvest in the etc/org.apache.karaf.decanter.collector.oshi.cfg configuration file.

It means now we have the system collector that allows you to periodically execute scripts and shell commands, and the oshi collector that harvests all details about the system.

ConfigAdmin Collector

The ConfigAdmin collector is a event driven collector. It “listens” for any change on the Karaf configuration and send an event for each change.

Prometheus Collector

Karaf Decanter 2.3.0 introduced the Prometheus appender to expose metrics on a Prometheus servlet exposed by Decanter.

On the other hand, the Prometheus collector is able to “parse” a Prometheus servlet to get the gauges and creates the corresponding event.

Elasticsearch Collector

Like we have the Elasticsearch appender to store collected data in Elasticsearch, the Elasticsearch collector is a scheduled collector that periodically executes a search on Elasticsearch instance. The documents hits are created as event sent into Decanter.

Redis Collector

Again, as we have the Redis appender to store collected data in Redis map, the Redis collector periodically retrieves key-value pairs (eventually using key pattern).

Comments

Post a Comment

Popular posts from this blog

Quarkus and "meta" extension

Quarkus is a great Java framework with a large ecosystem thanks to the extensions . Basically, if using Maven, your pom.xml contains the list of the extensions you want to use, meaning something like this: ... <dependency> <groupId>io.quarkus</groupId> <artifactId>quarkus-grpc</artifactId> </dependency> <dependency> <groupId>io.quarkus</groupId> <artifactId>quarkus-opentelemetry</artifactId> </dependency> ... It can quickly become large, and sometime you can have "conflict" between extensions (for example if you mix in the same dependencies set reactive and non reactive extensions). Quarkus "meta" extension To simplify the dependencies for the extensions you want to use (especially if it's basically the same extensions in all your services), you can create your own "meta" extension. This extension doesn't contain actual code, it "wraps" other exten
Read more

Getting started with Apache Karaf Minho

Image
Apache Karaf Minho Apache Karaf Minho is a new runtime, completely created from scratch. The objectives are: super easy to use and run ligtning fast, including GraalVM support Kubernetes compliant to be easily used on the cloud helping to optimize cloud costs by supporting applications colocation providing turnkey services allowing you to create Minho applications in a minute Minho is composed by: minho-boot is the core of Minho. It contains the runtime launcher and the core services (service registry, lifecycle service, configuration service, ...). minho-* services provided additional functionnalities in the runtime. You can use these services to create your own services, it's also where you will find the services managing sepcific kind of applications, allowing colocation. minho-tooling optionally provide convenient tools to create your own runtime. It includes minho-maven-plugin for instance. In this blog post, we will take a quick tour on A
Read more

Apache Karaf Minho and OpenTelemetry

Image
Telemetry is an important part of a software stack, especially on distributed system (where services call each others). OpenTelemetry is a CNCF project coming from OpenTracing and OpenCensus. It provides API to easily instrument your code and tools to collect and export telemetry data (metrics, logs and traces). Before going into how to use OpenTelemetry with Minho, we have to understand some key concepts: Span is a single building block representing an operation or some unit of work that you want to capture. They are capable of standing on their own, referencing (or following) from other spans, storing metadata, tags, etc. Trace is a visualisation of the life of a request (or series of other operations) that is made up of one or more spans. This collection of spans work together to paint a picture of the overall request, allowing you to easily reconstruct what happened within each span, etc. SpanContext is a wrapper of key values pairs that associates a trace to one or mor
Read more