An application metrics facade for the most popular monitoring tools. Instrument your code with dimensional metrics with a vendor neutral interface and decide on the monitoring backend at the last minute. Micrometer is the instrumentation library underpinning Spring Boot 2.0's metrics collection.
 
As an instrumentation facade, Micrometer allows you to instrument your code with dimensional metrics with a vendor-neutral interface and decide on the monitoring system as a last step. Instrumenting your core library code with Micrometer allows the libraries to be included in applications that ship metrics to different backends. It supports out-of-the-box instrumentation of caches, the class loader, garbage collection, processor utilization, thread pools, and more tailored to actionable insight.

 An application metrics facade for the most popular monitoring tools. Instrument your code with dimensional metrics with a vendor neutral interface and decide on the monitoring backend at the last minute. Micrometer is the instrumentation library underpinning Spring Boot 2.0's metrics collection.

Micrometer - An application metrics facade for the most popular monitoring tools

SkyWalking is an APM(application performance monitor) system, especially designed for microservices, cloud native and container-based (Docker, Kubernetes, Mesos) architectures. It provides distributed tracing, service mesh telemetry analysis, metric aggregation and visualization all-in-one solution. Its feature include:
<ul>
<li>Service, service instance, endpoint metrics analysis</li>
<li>Root cause analysis</li>
<li>Service topology map analysis</li>
<li>Service, service instance and endpoint dependency analysis</li>
<li>Slow services and endpoints detected</li>
<li>Performance optimization</li>
<li>Distributed tracing and context propagation</li>
<li>Database access metric. Detect slow database access statements(including SQL statements).</li>
<li>Alarm</li>
</ul>

SkyWalking is an APM(application performance monitor) system, especially designed for microservices, cloud native and container-based (Docker, Kubernetes, Mesos) architectures. It provides distributed tracing, service mesh telemetry analysis, metric aggregation and visualization all-in-one solution.

Apache Skywalking - A distributed tracing system, and APM ( Application Performance Monitoring )

SCOUTER is an open source APM like new relic and appdynamics. (APM means Application Performance Monitoring or Application Performance Management.) SCOUTER shows metrics about Users (Active user, Recently used user, Today visitor), Services ( Active service, TPS, Response time, Application profiles(method profile, sql profile, external call profile etc) and Resources (Cpu, Memory, Network and Heap usage, Connection pools etc).   


    

SCOUTER - APM (Application Performance Management) tool.

Stagemonitor is a Java monitoring agent that tightly integrates with time series databases like Elasticsearch, Graphite and InfluxDB to analyze graphed metrics and Kibana to analyze requests and call stacks. It includes preconfigured Grafana and Kibana dashboards that can be customized. It supports Real Time Metrics, Call tree of the current request including SQL statements to identify the bottlenecks of a request, Execution time and count of database interactions, Analyze the HTTP headers and lot more.

Stagemonitor - Application performance monitoring for java server applications

Cortex provides horizontally scalable, highly available, multi-tenant, long term storage for Prometheus. Cortex can run across multiple machines in a cluster, exceeding the throughput and storage of a single machine. This enables you to send the metrics from multiple Prometheus servers to a single Cortex cluster and run "globally aggregated" queries across all data in a single place. Cortex makes your PromQL queries blazin' fast through aggressive parallelization and caching.
 
When run in a cluster, Cortex can replicate data between machines. This allows you to survive machine failure without gaps in your graphs. Cortex can isolate data and queries from multiple different independent Prometheus sources in a single cluster, allowing untrusted parties to share the same cluster.
 
Cortex supports Amazon DynamoDB, Google Bigtable, Cassandra, S3, GCS and Microsoft Azure for long term storage of metric data. This allows you to durably store data for longer than the lifetime of any single machine, and use this data for long term capacity planning.

Cortex provides horizontally scalable, highly available, multi-tenant, long term storage for Prometheus. Cortex can run across multiple machines in a cluster, exceeding the throughput and storage of a single machine. This enables you to send the metrics from multiple Prometheus servers to a single Cortex cluster and run "globally aggregated" queries across all data in a single place. Cortex makes your PromQL queries blazin' fast through aggressive parallelization and caching.



Cortex - A multitenant, horizontally scalable Prometheus as a Service

Kamon is a reactive-friendly toolkit for monitoring applications that run on top of the JVM. It provides API to manually create metric recording instruments for any application running on the JVM. It provides bytecode instrumentation modules for Scala, Akka, Spray and Play. It supports several reporting backends like StatsD, Datadog and New Relic. It also allows to create custom reports.

Kamon - Tool for monitoring applications running on the JVM

LinDB is an open-source Time Series Database which provides high performance, high availability and horizontal scalability. LinDB takes a lot of best practice of TSDB and implements some optimizations based on the characteristics of time series data. Unlike writing a lot of Continuous-Query for InfluxDB, LinDB supports rollup in specific interval automatically after creating the database. Moreover, LinDB is extremely fast for parallel querying and computing of distributed time series data.
 
LinDB supports Metric + Tags + Fields, and is schema-free; The LinQL is handy for realtime data analytics. Tags based sharding strategy in LinDB solves the hotspots problem, and is truly horizontally expanded available by simply adding new broker and storage nodes.

LinDB is an open-source Time Series Database which provides high performance, high availability and horizontal scalability. LinDB takes a lot of best practice of TSDB and implements some optimizations based on the characteristics of time series data. Unlike writing a lot of Continuous-Query for InfluxDB, LinDB supports rollup in specific interval automatically after creating the database. Moreover, LinDB is extremely fast for parallel querying and computing of distributed time series data.


Lindb - Distributed Time Series Database

Hawkular Metrics is a metrics collection, aggregation, visualization framework. Hawkular is a set of Open Source projects designed to be a generic solution for common monitoring problems. The Hawkular projects provide REST services that can be used for all kinds of monitoring needs. The aim is to provide a generic solution that can be used for common monitoring problems.
 
The main goal of the Hawkular organization is to provide tools to monitor, view, predict, and guide you to make informed decisions about deployed applications and infrastructure. Anyone looking for Java-based components for alerting, metric storage, or distributed tracing should be interested in Hawkular.

Hawkular Metrics is a metrics collection, aggregation, visualization framework. Hawkular is a set of Open Source projects designed to be a generic solution for common monitoring problems. The Hawkular projects provide REST services that can be used for all kinds of monitoring needs. The aim is to provide a generic solution that can be used for common monitoring problems.


Hawkular metrics - Time Series Metrics Engine based on Cassandra

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