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OpenTracing vs Prometheus: What are the differences?
Introduction
Prometheus and OpenTracing are two popular tools used for monitoring and observability in software systems. While they both serve the purpose of gathering data, there are several key differences between them. In this article, we will discuss the main differences between Prometheus and OpenTracing.
Data Collection Approach: One of the key differences between Prometheus and OpenTracing is their data collection approach. Prometheus collects data through a pull model, where it periodically scrapes metrics from targets. On the other hand, OpenTracing collects data through a push model, where the instrumentation libraries send span data to the tracing collector. This difference in data collection approach can impact the scalability and real-time nature of the data.
Data Scope: Another difference between Prometheus and OpenTracing is the scope of the data they collect. Prometheus is focused on collecting and storing time-series data for monitoring and alerting purposes. It enables metric-based analysis and alerting based on thresholds and rules. On the other hand, OpenTracing focuses on distributed tracing, providing insights into the latency and dependencies of requests as they traverse a distributed system. It helps identify bottlenecks and performance issues.
Data Model: Prometheus and OpenTracing have different data models. Prometheus uses a metric-based data model, where metrics are defined by metric names and labels. It provides a flexible way to define and query metrics. OpenTracing, on the other hand, uses a trace-based data model, where a trace represents a single request as it traverses a distributed system. It captures spans, which represent individual units of work or events within a trace.
Querying and Analysis: Prometheus and OpenTracing also differ in their querying and analysis capabilities. Prometheus provides a powerful query language called PromQL, which allows users to query and analyze time-series data. It supports functions, aggregations, and mathematical operations. OpenTracing, on the other hand, focuses more on visualization and analysis of traces. It provides tools to visualize trace data, identify bottlenecks, and understand the flow of requests.
Ecosystem and Integrations: Prometheus and OpenTracing have different ecosystems and integrations. Prometheus has a rich ecosystem with support for various exporters, alerting tools, and visualization platforms. It integrates well with popular monitoring and observability tools. OpenTracing, on the other hand, has a growing ecosystem with support for different tracing libraries and integrations with distributed systems. It is often used in combination with other observability tools for comprehensive insights.
Adoption and Community Support: Another difference between Prometheus and OpenTracing lies in their adoption and community support. Prometheus has gained wide adoption and has a large and active community. It is used by many organizations and has extensive documentation and community support. OpenTracing, although growing in popularity, is relatively newer and may have a smaller community and fewer resources available.
In summary, Prometheus and OpenTracing differ in their data collection approach, data scope, data model, querying and analysis capabilities, ecosystem and integrations, as well as adoption and community support. Understanding these differences can help in choosing the right tool for monitoring and observability needs in software systems.
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.
You can look out for Prometheus Instrumentation (https://prometheus.io/docs/practices/instrumentation/) Client Library available in various languages https://prometheus.io/docs/instrumenting/clientlibs/ to create the custom metric you need for AS4000 and then Grafana can query the newly instrumented metric to show on the dashboard.
Hi, We have a situation, where we are using Prometheus to get system metrics from PCF (Pivotal Cloud Foundry) platform. We send that as time-series data to Cortex via a Prometheus server and built a dashboard using Grafana. There is another pipeline where we need to read metrics from a Linux server using Metricbeat, CPU, memory, and Disk. That will be sent to Elasticsearch and Grafana will pull and show the data in a dashboard.
Is it OK to use Metricbeat for Linux server or can we use Prometheus?
What is the difference in system metrics sent by Metricbeat and Prometheus node exporters?
Regards, Sunil.
If you're already using Prometheus for your system metrics, then it seems like standing up Elasticsearch just for Linux host monitoring is excessive. The node_exporter is probably sufficient if you'e looking for standard system metrics.
Another thing to consider is that Metricbeat / ELK use a push model for metrics delivery, whereas Prometheus pulls metrics from each node it is monitoring. Depending on how you manage your network security, opting for one solution over two may make things simpler.
Hi Sunil! Unfortunately, I don´t have much experience with Metricbeat so I can´t advise on the diffs with Prometheus...for Linux server, I encourage you to use Prometheus node exporter and for PCF, I would recommend using the instana tile (https://www.instana.com/supported-technologies/pivotal-cloud-foundry/). Let me know if you have further questions! Regards Jose
We're looking for a Monitoring and Logging tool. It has to support AWS (mostly 100% serverless, Lambdas, SNS, SQS, API GW, CloudFront, Autora, etc.), as well as Azure and GCP (for now mostly used as pure IaaS, with a lot of cognitive services, and mostly managed DB). Hopefully, something not as expensive as Datadog or New relic, as our SRE team could support the tool inhouse. At the moment, we primarily use CloudWatch for AWS and Pandora for most on-prem.
this is quite affordable and provides what you seem to be looking for. you can see a whole thing about the APM space here https://www.apmexperts.com/observability/ranking-the-observability-offerings/
I worked with Datadog at least one year and my position is that commercial tools like Datadog are the best option to consolidate and analyze your metrics. Obviously, if you can't pay the tool, the best free options are the mix of Prometheus with their Alert Manager and Grafana to visualize (that are complementary not substitutable). But I think that no use a good tool it's finally more expensive that use a not really good implementation of free tools and you will pay also to maintain its.
The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.
Pros of OpenTracing
Pros of Prometheus
- Powerful easy to use monitoring47
- Flexible query language38
- Dimensional data model32
- Alerts27
- Active and responsive community23
- Extensive integrations22
- Easy to setup19
- Beautiful Model and Query language12
- Easy to extend7
- Nice6
- Written in Go3
- Good for experimentation2
- Easy for monitoring1
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Cons of OpenTracing
Cons of Prometheus
- Just for metrics12
- Bad UI6
- Needs monitoring to access metrics endpoints6
- Not easy to configure and use4
- Supports only active agents3
- Written in Go2
- TLS is quite difficult to understand2
- Requires multiple applications and tools2
- Single point of failure1
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