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

SkyWalking is an APM (application performance monitor) system, especially designed for microservices, cloud native and container-based (Docker, Kubernetes, Mesos) architectures. SkyWalking is an open source APM system, including monitoring, tracing, diagnosing capabilities for distributed system in Cloud Native architecture. It supports to collect telemetry (metrics, traces, and logs) data from multiple sources and multiple formats.
 
Features:
<ul>
<li>Service, service instance, endpoint metrics analysis</li>
<li>Root cause analysis. Profile the code on the runtime</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 metrics. Detect slow database access statements(including SQL statements)</li>
<li>Alarm</li>
<li>Browser performance monitoring</li>
<li>Infrastructure(VM, network, disk etc.) monitoring</li>
<li>Collaboration across metrics, traces, and logs</li>
</ul>

SkyWalking is an APM (application performance monitor) system, especially designed for microservices, cloud native and container-based (Docker, Kubernetes, Mesos) architectures. SkyWalking is an open source APM system, including monitoring, tracing, diagnosing capabilities for distributed system in Cloud Native architecture. It supports to collect telemetry (metrics, traces, and logs) data from multiple sources and multiple formats.

Skywalking - APM, Application Performance Monitoring System

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 )

Resilience4j is a lightweight fault tolerance library inspired by Netflix Hystrix, but designed for Java 8 and functional programming. Resilience4j provides higher-order functions (decorators) to enhance any functional interface, lambda expression or method reference with a Circuit Breaker, Rate Limiter, Retry or Bulkhead. You can stack more than one decorator on any functional interface, lambda expression or method reference. The advantage is that you have the choice to select the decorators you need and nothing else.

Resilience4j - Fault tolerance library designed for Java8 and functional programming

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.

Cube is an open-source system for visualizing time series data. If you send timestamped events (with optional structured data), you can easily build realtime visualizations of aggregate metrics for internal dashboards. For example, you might use Cube to monitor traffic to your website, counting the number of requests in 5-minute intervals.

Cube - Time Series Visualization

The Sigar API provides a portable interface for gathering system information such as System memory, swap, cpu, load average, uptime, logins, Per-process memory, cpu and lot more. This information is available in most operating systems, but each OS has their own ways providing it. SIGAR provides developers with one API to access this information regardless of the underlying platform.


SIGAR - System Information Gatherer And Reporter

Metrics is a Java library which gives you unparalleled insight into what your code does in production. It provides a powerful toolkit of ways to measure the behavior of critical components in your production environment. 

Metrics - Capturing JVM- and application-level metrics

Snap is an open telemetry framework designed to simplify the collection, processing and publishing of system data through a single API. The goals of this project are to Empower systems to expose a consistent set of telemetry data, Simplify telemetry ingestion across ubiquitous storage systems, Provide powerful clustered control of telemetry workflows across small or large clusters and lot more.

Snap -  A powerful open telemetry framework

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

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

Blueflood is a high throughput, low latency, multi-tenant distributed metric processing system behind Rackspace Metrics, which is currently used in production by the Rackspace Monitoring team and Rackspace Public Cloud team to store metrics generated by their systems. Data from Blueflood can be used to construct dashboards, generate reports, graphs or for any other use involving time-series data. It focuses on near-realtime data, with data that is queryable mere milliseconds after ingestion.


Blueflood - A distributed system designed to ingest and process time series data

Atlas was developed by Netflix to manage dimensional time series data for near real-time operational insight. Atlas features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly. Atlas captures operational intelligence. Whereas business intelligence is data gathered for analyzing trends over time, operational intelligence provides a picture of what is currently happening within a system.

Atlas - In-memory dimensional time series database

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