Jaeger vs StatsD

Overview

StatsD

Stacks373

Followers293

Votes31

Jaeger

Stacks340

Followers464

Votes25

GitHub Stars22.0K

Forks2.7K

Jaeger vs StatsD: What are the differences?

## Key Differences Between Jaeger and StatsD

Jaeger and StatsD are both tools used for monitoring and tracing in distributed systems, but they have significant differences that set them apart.

Data Collection Method: Jaeger is a distributed tracing system that collects timing data from various parts of your system, providing a detailed view of how requests propagate through your application. On the other hand, StatsD is a simple network daemon that receives and logs custom application metrics from many sources.
Data Granularity: Jaeger focuses on providing detailed traces of individual requests, capturing the full path a request takes across various services. In contrast, StatsD captures aggregated metrics over time, giving a broader overview of system performance and behavior.
Usage: Jaeger is commonly used for identifying performance bottlenecks and understanding the flow of requests in complex systems, making it ideal for debugging and optimization. StatsD, on the other hand, is often used for monitoring system health, tracking key performance indicators, and triggering alerts based on predefined thresholds.
Instrumentation: Jaeger requires instrumenting your code to generate trace data, usually through the use of dedicated libraries or SDKs provided for popular programming languages. StatsD, on the other hand, typically involves adding a few lines of code to your application to send custom metrics to the StatsD server.
Visualization: Jaeger comes with a user-friendly web interface that allows you to visualize and analyze trace data, making it easier to pinpoint issues and understand the behavior of your system. StatsD, on the other hand, relies on integrating with other monitoring tools or dashboards for visualization and analysis of the collected metrics.

Summary: In summary, Jaeger provides detailed tracing of individual requests for debugging and optimization, while StatsD focuses on aggregated metrics for monitoring system health and performance.

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CLI (Node.js)

Manual

Detailed Comparison

StatsD	Jaeger
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, a Distributed Tracing System
Network daemon; Runs on the Node.js platform; Sends aggregates to one or more pluggable backend services	-
Statistics
GitHub Stars -	GitHub Stars 22.0K
GitHub Forks -	GitHub Forks 2.7K
Stacks 373	Stacks 340
Followers 293	Followers 464
Votes 31	Votes 25
Pros & Cons
Pros 9 Open source 7 Single responsibility 5 Efficient wire format 3 Handles aggregation 3 Loads of integrations Cons 1 No authentication; cannot be used over Internet	Pros 7 Easy to install 7 Open Source 6 Feature Rich UI 5 CNCF Project
Integrations
Node.js Docker Graphite	Golang Elasticsearch Cassandra

What are some alternatives to StatsD, Jaeger?

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.

Telegraf

It is an agent for collecting, processing, aggregating, and writing metrics. Design goals are to have a minimal memory footprint with a plugin system so that developers in the community can easily add support for collecting metrics.

Related Comparisons

Jaeger vs StatsD: What are the differences?

## Key Differences Between Jaeger and StatsD

Jaeger and StatsD are both tools used for monitoring and tracing in distributed systems, but they have significant differences that set them apart.

Data Collection Method: Jaeger is a distributed tracing system that collects timing data from various parts of your system, providing a detailed view of how requests propagate through your application. On the other hand, StatsD is a simple network daemon that receives and logs custom application metrics from many sources.
Data Granularity: Jaeger focuses on providing detailed traces of individual requests, capturing the full path a request takes across various services. In contrast, StatsD captures aggregated metrics over time, giving a broader overview of system performance and behavior.
Usage: Jaeger is commonly used for identifying performance bottlenecks and understanding the flow of requests in complex systems, making it ideal for debugging and optimization. StatsD, on the other hand, is often used for monitoring system health, tracking key performance indicators, and triggering alerts based on predefined thresholds.
Instrumentation: Jaeger requires instrumenting your code to generate trace data, usually through the use of dedicated libraries or SDKs provided for popular programming languages. StatsD, on the other hand, typically involves adding a few lines of code to your application to send custom metrics to the StatsD server.
Visualization: Jaeger comes with a user-friendly web interface that allows you to visualize and analyze trace data, making it easier to pinpoint issues and understand the behavior of your system. StatsD, on the other hand, relies on integrating with other monitoring tools or dashboards for visualization and analysis of the collected metrics.

Jaeger vs StatsD

Overview