Perfume is a JavaScript library for measuring Short and Long Script, First Contentful Paint (FCP), Time to Interactive (TTI), Component First Paint (CFM), annotating them to the DevTools timeline and reporting the results to Google Analytics.When a user navigates to a web page, they're typically looking for visual feedback to reassure them that everything is going to work as expected. Page Speed is a feature, and to deliver it we need to understand the many factors and fundamental limitations that are at play. If we can measure it, we can improve it. 

Perfume.js - Web performance library for measuring all User-centric performance metrics 

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

SigNoz is an opensource observability platform. SigNoz uses distributed tracing to gain visibility into your systems and powers data using Kafka (to handle high ingestion rate and backpressure) and Apache Druid (Apache Druid is a high performance real-time analytics database), both proven in the industry to handle scale.
 
Features: 
<ul>
<li>Application overview metrics like RPS, 50th/90th/99th Percentile latencies, and Error Rate</li>
<li>Slowest endpoints in your application</li>
<li>See exact request trace to figure out issues in downstream services, slow DB queries, call to 3rd party services like payment gateways, etc</li>
<li>Filter traces by service name, operation, latency, error, tags/annotations.</li>
<li>Aggregate metrics on filtered traces. Eg, you can get error rate and 99th percentile latency of <code>customer_type: gold</code> or <code>deployment_version: v2</code> or <code>external_call: paypal</code></li>
<li>Unified UI for metrics and traces. No need to switch from Prometheus to Jaeger to debug issues.</li>
<li>In-built workflows to reduce your efforts in detecting common issues like new deployment failures, 3rd party slow APIs, etc (Coming Soon)</li>
<li>Anomaly Detection Framework (Coming Soon)</li>
</ul>

SigNoz is an opensource observability platform. SigNoz uses distributed tracing to gain visibility into your systems and powers data using Kafka (to handle high ingestion rate and backpressure) and Apache Druid (Apache Druid is a high performance real-time analytics database), both proven in the industry to handle scale.

Signoz - Open-source Observability platform and an alternative to DataDog, NewRelic

DalmatinerDB is a metric database written in pure Erlang. It takes advantage of some special properties of metrics to make some tradeoffs. Its goal is to make a store for metric data (time, value of a metric) that is fast, has a low overhead, and is easy to query and manage. DalmatinerDB allows for metric input in second or even sub-second precision. It will interpolate the missing values to the best of it’s abilities. This is usually acceptable for aggregated data.
 
DalmatinerDB is designed to run on ZFS file system and it relies heavily on taking advantage of ZFS facilities such as the ARC, ZIL, checksums and volume compression. The frontend servers give users access to a rich SQL-like query language that's easy to use for both dev and ops. It integrates nicely with Grafana making it easy to build beautiful dashboards.

DalmatinerDB is a metric database written in pure Erlang. It takes advantage of some special properties of metrics to make some tradeoffs. Its goal is to make a store for metric data (time, value of a metric) that is fast, has a low overhead, and is easy to query and manage. DalmatinerDB allows for metric input in second or even sub-second precision. It will interpolate the missing values to the best of it’s abilities. This is usually acceptable for aggregated data.

DalmatinerDB - Fast distributed metrics database in Erlang

This library provides an API that is powerful and java idiomatic for configuring and publishing application metrics. A reference implementation using Stackdriver Monitoring API v3 is included, but other monitoring backend implementations can also be used. Most of other monitoring libraries available are low-level and are tied directly to the backend. This library provides type safety, retry logic and an backend-agnostic approach to Java metrics instrumentation.
  

Java Monitoring Client Library - A java idiomatic monitoring client for collecting metrics 

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

OpenTelemetry provides a single set of APIs, libraries, agents, and collector services to capture distributed traces and metrics from your application. You can analyze them using Prometheus, Jaeger, and other observability tools. 
 
The OpenTelemetry Collector offers a vendor-agnostic implementation on how to receive, process, and export telemetry data in a seamless way. In addition, it removes the need to run, operate, and maintain multiple agents/collectors in order to support open-source observability data formats (e.g. Jaeger, Prometheus, etc.) sending to multiple open-source or commercial back-ends.

OpenTelemetry provides a single set of APIs, libraries, agents, and collector services to capture distributed traces and metrics from your application. You can analyze them using Prometheus, Jaeger, and other observability tools. 



OpenTelemetry makes robust, portable telemetry a built-in feature of cloud-native software

Service to extract and provide metrics on your PostgreSQL database.This tool is a CLI (command line) tool that can be called to extract statistics and create metrics regarding your PostgreSQL database cluster. CLI is runnable in long running process mode, which will periodically send the gathered metrics forward.
 
It helps to collect database and cluster specific metrics like 
<ul><li>Disk usage</li><li>Rate of transactions executed per second</li><li>Time passed in seconds since the last vacuum was run per table in your database</li><li>Long running transactions</li><li>Index hit rate</li><li>Table bloat metric</li><li>Number of client connections</li><li>Lock statistics</li><li>Heap hit ratio</li><li>Replication delay</li></ul>

Service to extract and provide metrics on your PostgreSQL database. This tool is a CLI (command line) tool that can be called to extract statistics and create metrics regarding your PostgreSQL database cluster. CLI is runnable in long running process mode, which will periodically send the gathered metrics forward.

PostgreSQL Metrics - Tool that extracts and provides metrics on your PostgreSQL database

A fully automated database benchmark suite that prints tabular and graphic results for multiple tested disciplines, given JDBC database connections. Some free open source database engines are included.

PolePosition -  the open source database benchmark

Vor is a set of services for gathering metrics by polling systems and delivering them to graphite. Currently there is support for polling metrics from Elasticsearch and Redis.

Vor -  Set of services for gathering metrics by polling systems and delivering them to graphite

cppdep performs dependency analysis among components/packages/package groups of a large C/C++ project. This is a rewrite of dep_utils(adep/cdep/ldep), which is provided by John Lakos' book "Large-Scale C++ Software Design", Addison Wesley (1996).

cppdep - C/C++ Dependency Analyzer

Akumuli is a time-series database for modern hardware. It can be used to capture, store and process time-series data in real-time. The word "akumuli" can be translated from Esperanto as "accumulate".
<ul>
<li>True column-oriented format (not PAX).</li>
<li>Based on novel <a href="http://akumuli.org/akumuli/2017/04/29/nbplustree/" rel="nofollow">LSM and B+tree hybrid datastructure</a> with multiversion concurrency control (no concurrency bugs, parallel writes, optimized for SSD and NVMe).</li>
<li>Crash safety and recovery.</li>
<li>Fast aggregation without pre-configured rollups or materialized views.</li>
<li>Queries can be executed without decompressing the data.</li>
<li>Fast compression algorithm (dictionary + entropy) with small memory overhead (about 2.5 bytes per element on appropriate data).</li>
<li>Compressed in-memory storage for recent data.</li>
<li>Can be used as a server application or an embedded library.</li>
<li>Simple query language based on JSON and HTTP.</li>
<li>Fast range scans and joins, read speed doesn't depend on database cardinality.</li>
<li>Fast data ingestion over the network:
<ul>
<li>4.5M data points per second on 8-core Intel Xeon E5-2670 v2 (m3.2xlarge EC2 instance).</li>
<li>16.1M data points per second on 32-core Intel Xeon E5-2680 v2 (c3.8xlarge EC2 instance).</li>
</ul>
</li>
<li>Query results are streamed to client using the chunked transfer encoding of the HTTP protocol.</li>
<li>Decompression algorithm and input parsers were fuzz-tested.</li>
<li>Grafana <a href="https://github.com/akumuli/akumuli-datasource">datasource</a> plugin.</li>
<li>Fast and compact inverted index for series lookup.</li>
</ul>

Akumuli is a time-series database for modern hardware. It can be used to capture, store and process time-series data in real-time. The word "akumuli" can be translated from Esperanto as "accumulate".

Akumuli - Time-series database

Go port of <a href="/product/dropwizard-metrics">Coda Hale's Metrics library</a>.

go-metrics - Go port of Coda Hale's Metrics library

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