M3 vs OpenTelemetry

Overview

Stacks12

Followers61

Votes0

GitHub Stars4.9K

Forks465

OpenTelemetry

Stacks206

Followers148

Votes4

M3 vs OpenTelemetry: What are the differences?

Integration with APM Tools: M3 integrates natively with various application performance monitoring (APM) tools, providing out-of-the-box compatibility for metrics, traces, and logs. On the other hand, OpenTelemetry is an open-source project that aims to provide a vendor-agnostic approach to observability, allowing users to choose their preferred APM tools for instrumentation.
Storage Backends: M3 comes with its own storage backend that is highly optimized for time series data processing and can handle massive scale. In contrast, OpenTelemetry does not provide a specific storage backend but offers exporters to various data stores, giving users the flexibility to choose the most suitable storage solution for their use case.
Language Support: M3 provides support for multiple programming languages, including Go, Java, and Python, with client libraries for easy instrumentation. OpenTelemetry also offers multi-language support but has a broader language range, covering more languages such as JavaScript, Ruby, and PHP.
Community Support: While M3 has a strong community backing with contributions from companies like Uber, OpenTelemetry is a CNCF (Cloud Native Computing Foundation) project that enjoys the support of a broader community, including major tech players like Google, Microsoft, and Red Hat.
Purpose: M3 is primarily focused on monitoring and observability for large-scale distributed systems and is optimized for high performance and scalability. OpenTelemetry, on the other hand, aims to standardize observability instrumentation across different platforms and is more focused on providing a uniform approach to telemetry data collection.
Deployment Flexibility: M3 is often deployed in self-managed environments or as a hosted service, offering users control over their infrastructure. OpenTelemetry can be deployed in various environments, including cloud platforms, on-premises setups, and hybrid architectures, making it adaptable to different deployment scenarios.

In Summary, M3 and OpenTelemetry differ in terms of APM tool integration, storage backends, language support, community backing, focus on observability, and deployment flexibility.

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CLI (Node.js)

Manual

Detailed Comparison

M3	OpenTelemetry
A Prometheus and Graphite compatible metrics platform which includes a native distributed time series database, a highly dynamic and performant aggregation service, query engine and other supporting infrastructure.	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.
Prometheus Integration; Graphite Integration; Scalable Clusters (up to billions of metrics); Reliably Replicated; Highly Compressed; Highly Performant (hundreds of millions of writes per second); Arbitrary Time Precision; Out of order writes; Fully open source	-
Statistics
GitHub Stars 4.9K	GitHub Stars -
GitHub Forks 465	GitHub Forks -
Stacks 12	Stacks 206
Followers 61	Followers 148
Votes 0	Votes 4
Pros & Cons
No community feedback yet	Pros 4 OSS
Integrations
Grafana Prometheus Graphite	No integrations available

What are some alternatives to M3, OpenTelemetry?

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

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

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

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

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).

Related Comparisons

M3 vs OpenTelemetry: What are the differences?

Integration with APM Tools: M3 integrates natively with various application performance monitoring (APM) tools, providing out-of-the-box compatibility for metrics, traces, and logs. On the other hand, OpenTelemetry is an open-source project that aims to provide a vendor-agnostic approach to observability, allowing users to choose their preferred APM tools for instrumentation.
Storage Backends: M3 comes with its own storage backend that is highly optimized for time series data processing and can handle massive scale. In contrast, OpenTelemetry does not provide a specific storage backend but offers exporters to various data stores, giving users the flexibility to choose the most suitable storage solution for their use case.
Language Support: M3 provides support for multiple programming languages, including Go, Java, and Python, with client libraries for easy instrumentation. OpenTelemetry also offers multi-language support but has a broader language range, covering more languages such as JavaScript, Ruby, and PHP.
Community Support: While M3 has a strong community backing with contributions from companies like Uber, OpenTelemetry is a CNCF (Cloud Native Computing Foundation) project that enjoys the support of a broader community, including major tech players like Google, Microsoft, and Red Hat.
Purpose: M3 is primarily focused on monitoring and observability for large-scale distributed systems and is optimized for high performance and scalability. OpenTelemetry, on the other hand, aims to standardize observability instrumentation across different platforms and is more focused on providing a uniform approach to telemetry data collection.
Deployment Flexibility: M3 is often deployed in self-managed environments or as a hosted service, offering users control over their infrastructure. OpenTelemetry can be deployed in various environments, including cloud platforms, on-premises setups, and hybrid architectures, making it adaptable to different deployment scenarios.

In Summary, M3 and OpenTelemetry differ in terms of APM tool integration, storage backends, language support, community backing, focus on observability, and deployment flexibility.

M3 vs OpenTelemetry

Overview