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  1. Stackups
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  3. Monitoring
  4. Monitoring Tools
  5. Dropwizard Metrics vs OpenTelemetry

Dropwizard Metrics vs OpenTelemetry

OverviewComparisonAlternatives

Overview

Dropwizard Metrics
Dropwizard Metrics
Stacks29
Followers18
Votes0
GitHub Stars7.9K
Forks1.8K
OpenTelemetry
OpenTelemetry
Stacks203
Followers148
Votes4

Dropwizard Metrics vs OpenTelemetry: What are the differences?

Comparison between Dropwizard Metrics and OpenTelemetry

Dropwizard Metrics and OpenTelemetry are both popular libraries used for collecting, exporting, and visualizing application metrics in software systems. However, there are several key differences between these two frameworks:

  1. Data Collection Approach: Dropwizard Metrics follows a push-based approach for data collection, where the application explicitly pushes metrics to the metrics registry. On the other hand, OpenTelemetry follows a pull-based approach, where the metrics are extracted automatically from instrumented components or frameworks using agents or interceptors.

  2. Instrumentation Libraries: Dropwizard Metrics provides a set of built-in instrumentation libraries for collecting metrics from various components like JVM, HTTP clients, and databases. In contrast, OpenTelemetry offers a wide range of instrumentation libraries for different programming languages and frameworks, enabling automatic metric collection without explicit code modifications.

  3. Supported Protocols: Dropwizard Metrics primarily supports exporting metrics to Ganglia, JMX, Graphite, and other popular monitoring systems. In contrast, OpenTelemetry supports a broader range of export protocols and formats, including popular observability platforms like Prometheus, Jaeger, and Elasticsearch, allowing for more flexibility in integrating with existing monitoring solutions.

  4. Context Propagation: OpenTelemetry provides built-in support for distributed tracing and context propagation, allowing metrics to be correlated with tracing data to gain insights into the performance of individual operations within a distributed system. Dropwizard Metrics, on the other hand, focuses solely on collecting and exporting metrics without built-in tracing capabilities.

  5. Development Community and Support: Dropwizard Metrics has been around for a longer time and has an established community, with a wealth of resources, documentation, and community support available. OpenTelemetry, being a relatively newer project, is rapidly gaining popularity and has an active and growing community, resulting in continuous development and improvements to the library.

  6. Standardization and Interoperability: OpenTelemetry aims to establish a standard for observability instrumentation and provides compatibility across different observability tools and vendors. This focus on standardization and interoperability makes it easier to switch between monitoring platforms or integrate multiple tools seamlessly. Dropwizard Metrics, although widely used, is more focused on providing a complete solution for metrics within the Dropwizard ecosystem, which may limit its flexibility outside of that ecosystem.

In summary, Dropwizard Metrics and OpenTelemetry differ in their data collection approach, supported instrumentation libraries, export protocols, context propagation capabilities, development community, and focuses on standardization and interoperability. While Dropwizard Metrics is more established and provides a comprehensive solution for metrics within the Dropwizard ecosystem, OpenTelemetry offers a broader range of features, standardization, and compatibility with different observability tools and systems.

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Detailed Comparison

Dropwizard Metrics
Dropwizard Metrics
OpenTelemetry
OpenTelemetry

It is a Java library which gives you 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. It provides you with full-stack visibility.

It 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.

Full-stack visibility; application-level metrics
-
Statistics
GitHub Stars
7.9K
GitHub Stars
-
GitHub Forks
1.8K
GitHub Forks
-
Stacks
29
Stacks
203
Followers
18
Followers
148
Votes
0
Votes
4
Pros & Cons
No community feedback yet
Pros
  • 4
    OSS
Integrations
Graphite
Graphite
Logback
Logback
Log4j
Log4j
Jetty
Jetty
No integrations available

What are some alternatives to Dropwizard Metrics, OpenTelemetry?

Grafana

Grafana

Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.

Kibana

Kibana

Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.

Prometheus

Prometheus

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Nagios

Nagios

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

Netdata

Netdata

Netdata collects metrics per second & presents them in low-latency dashboards. It's designed to run on all of your physical & virtual servers, cloud deployments, Kubernetes clusters & edge/IoT devices, to monitor systems, containers & apps

Zabbix

Zabbix

Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics.

Sensu

Sensu

Sensu is the future-proof solution for multi-cloud monitoring at scale. The Sensu monitoring event pipeline empowers businesses to automate their monitoring workflows and gain deep visibility into their multi-cloud environments.

Graphite

Graphite

Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand

Lumigo

Lumigo

Lumigo is an observability platform built for developers, unifying distributed tracing with payload data, log management, and real-time metrics to help you deeply understand and troubleshoot your systems.

StatsD

StatsD

It is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP or TCP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

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