Graphite vs M3

Overview

Graphite

Stacks383

Followers419

Votes42

GitHub Stars6.0K

Forks1.3K

Stacks12

Followers61

Votes0

GitHub Stars4.9K

Forks465

Graphite vs M3: What are the differences?

Introduction

In this article, we will explore the key differences between Graphite and M3, two popular metrics and monitoring systems. Both Graphite and M3 are commonly used for collecting, storing, and visualizing time-series data in a distributed environment. However, there are several distinct features that set them apart.

Scalability: Graphite is primarily designed to work in a single-server environment and struggles to handle large-scale deployments. On the other hand, M3 is built to scale horizontally, allowing it to handle immense amounts of data across a distributed cluster. This scalability makes M3 more suitable for handling enterprise-level workloads.
Storage Engine: Graphite utilizes the Whisper storage engine, which is based on round-robin databases (RRD) and works well for short-term storage and graph rendering. In contrast, M3 employs the M3DB storage engine, which is an open-source distributed timeseries database built specifically for scalability, fault-tolerance, and long-term storage of metrics data.
High Availability: Graphite lacks built-in high availability capabilities. It does not provide automatic replication or data redundancy, making it prone to single points of failure. M3, on the other hand, offers high availability through its distributed nature. With M3, data is automatically replicated across multiple nodes, ensuring fault tolerance and minimized downtime.
Querying and Analytics: Graphite primarily relies on its query language, Graphite Query Language (GQL), for data retrieval and basic analysis. Although powerful, GQL has limitations when dealing with highly complex queries or executing aggregations at scale. M3, on the other hand, provides a more advanced querying language, the M3 Query Language (M3QL), which supports complex analytics, aggregations, filtering, and joins, making it more flexible for in-depth data analysis.
Storage Tiering and Retention: Graphite lacks an out-of-the-box capability for efficient storage tiering and retention. Although possible with custom configurations and additional tools, it requires manual effort and maintenance. In contrast, M3 offers built-in support for storage tiering and retention policies. This allows users to define different storage layers based on data access patterns and enforce automatic retention policies for data lifecycle management.
Integration Ecosystem: Graphite has been around for a long time and has a mature ecosystem with numerous third-party integrations and tools. It has a wide variety of plugins, extensions, and dashboards available. M3, being a relatively newer system, has a smaller but growing ecosystem. While it may not have the same level of maturity or breadth of integrations as Graphite, it offers seamless integration with other modern monitoring tools and frameworks like Prometheus and Grafana.

In Summary, Graphite is a single-server, non-distributed monitoring system suitable for smaller deployments, while M3 is a horizontally scalable, fault-tolerant metrics platform designed for large-scale, high-availability environments. M3 offers better scalability, distributed architecture, advanced querying capabilities, built-in high availability, storage tiering, and retention policies compared to Graphite, but Graphite has a more mature integration ecosystem.

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Advice on Graphite, M3

Susmita

Senior SRE at African Bank

Jul 28, 2020

Needs adviceon

Grafana

Looking for a tool which can be used for mainly dashboard purposes, but here are the main requirements:

Must be able to get custom data from AS400,
Able to display automation test results,
System monitoring / Nginx API,
Able to get data from 3rd parties DB.

Grafana is almost solving all the problems, except AS400 and no database to get automation test results.

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Comments

Detailed Comparison

Graphite	M3
Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand	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.
carbon - a Twisted daemon that listens for time-series data;whisper - a simple database library for storing time-series data (similar in design to RRD);graphite webapp - A Django webapp that renders graphs on-demand using Cairo	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 6.0K	GitHub Stars 4.9K
GitHub Forks 1.3K	GitHub Forks 465
Stacks 383	Stacks 12
Followers 419	Followers 61
Votes 42	Votes 0
Pros & Cons
Pros 16 Render any graph 9 Great functions to apply on timeseries 8 Well supported integrations 6 Includes event tracking 3 Rolling aggregation makes storage managable	No community feedback yet
Integrations
Sensu Nagios Logstash Windows Server Netdata Riemann Diamond Telegraf collectd Ganglia	Grafana Prometheus

What are some alternatives to Graphite, M3?

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.

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).

Jaeger

Jaeger, a Distributed Tracing System

Related Comparisons

Graphite vs M3: What are the differences?

Introduction

Scalability: Graphite is primarily designed to work in a single-server environment and struggles to handle large-scale deployments. On the other hand, M3 is built to scale horizontally, allowing it to handle immense amounts of data across a distributed cluster. This scalability makes M3 more suitable for handling enterprise-level workloads.
Storage Engine: Graphite utilizes the Whisper storage engine, which is based on round-robin databases (RRD) and works well for short-term storage and graph rendering. In contrast, M3 employs the M3DB storage engine, which is an open-source distributed timeseries database built specifically for scalability, fault-tolerance, and long-term storage of metrics data.
High Availability: Graphite lacks built-in high availability capabilities. It does not provide automatic replication or data redundancy, making it prone to single points of failure. M3, on the other hand, offers high availability through its distributed nature. With M3, data is automatically replicated across multiple nodes, ensuring fault tolerance and minimized downtime.
Querying and Analytics: Graphite primarily relies on its query language, Graphite Query Language (GQL), for data retrieval and basic analysis. Although powerful, GQL has limitations when dealing with highly complex queries or executing aggregations at scale. M3, on the other hand, provides a more advanced querying language, the M3 Query Language (M3QL), which supports complex analytics, aggregations, filtering, and joins, making it more flexible for in-depth data analysis.
Storage Tiering and Retention: Graphite lacks an out-of-the-box capability for efficient storage tiering and retention. Although possible with custom configurations and additional tools, it requires manual effort and maintenance. In contrast, M3 offers built-in support for storage tiering and retention policies. This allows users to define different storage layers based on data access patterns and enforce automatic retention policies for data lifecycle management.
Integration Ecosystem: Graphite has been around for a long time and has a mature ecosystem with numerous third-party integrations and tools. It has a wide variety of plugins, extensions, and dashboards available. M3, being a relatively newer system, has a smaller but growing ecosystem. While it may not have the same level of maturity or breadth of integrations as Graphite, it offers seamless integration with other modern monitoring tools and frameworks like Prometheus and Grafana.

Graphite vs M3

Overview

Graphite vs M3: What are the differences?