Opentelemetry vs Prometheus
Prometheus monitors, stores, and visualises metrics but does not keep logs or support traces for root cause analysis. The application cases of Prometheus are more limited than OpenTelemetry.
Programming language-agnostic integrations allow OpenTelemetry to track more complicated metrics than Prometheus. Automated instrumentation models make OTel more scalable and extensible than the Prometheus. OpenTelemetry requires a back-end infrastructure and no storage solution, unlike Prometheus.
Quick summary
- Prometheus calculates cumulative measurements as a total, whereas OpenTelemetry uses deltas.
- Prometheus stores short-term data and metrics, whereas OTel may be used with a storage solution.
- OpenTelemetry uses a consolidated API to send or pull metrics, logs, and traces and transform them into a single language, unlike Prometheus. Prometheus pulls data from hosts to collect and store time-series metrics.
- OTel can translate measurements and is language agonistic, providing developers additional options. Data and metrics are aggregated by Prometheus using PromQL.
- Web-visualized metrics and customisable alarms are provided by Prometheus. Integration with visualisation tools is required for OpenTelemetry.
- OTel represents metric values as integers instead of floating-point numbers, which are more precise and understandable. Prometheus cannot use integer metrics.
Your organization’s demands will determine which option is best. OpenTelemetry may be better for complex contexts with dispersed systems, data holistic comprehension, and flexibility. This also applies to log and trace monitoring.
Prometheus may be suited for monitoring specific systems or processes using alerting, storage, and visualisation models.
Prometheus and OpenTelemetry
Application performance monitoring and optimisation are crucial for software developers and companies. Enterprises have more data to collect and analyse as they deploy more applications. Without the correct tools for monitoring, optimising, storing, and contextualising data, it’s useless.
Monitoring and observability solutions can improve application health by discovering issues before they happen, highlighting bottlenecks, dispersing network traffic, and more. These capabilities reduce application downtime, improve performance, and enhance user experience.
App monitoring tools OpenTelemetry and the Prometheus are open-source Cloud Native Computing Foundation (CNCF) initiatives. An organization’s goals and application specifications determine which data and functions need which solutions. Before using OpenTelemetry or Prometheus, you should know their main distinctions and what they offer.
Java Opentelemetry
OTel exports these three forms of telemetry data to Prometheus and other back ends. This lets developers chose their analysis tools and avoids vendor or back-end lock-in. OpenTelemetry integrates with many platforms, including Prometheus, to increase observability. Its flexibility increases because OTel supports Java, Python, JavaScript, and Go. Developers and IT staff may monitor performance from any browser or location.
Its ability to gather and export data across multiple applications and standardise the collecting procedure make OpenTelemetry strong. OTel enhances distributed system and the microservice observability.
For application monitoring, OpenTelemetry and Prometheus integrate and operate well together. The DevOps and IT teams can use OpenTelemetry and Prometheus to collect and transform information for performance insights.
Opentelemetry-demo
OpenTelemetry (OTel) helps generate, collect, export, and manage telemetry data including logs, metrics, and traces in one place. OTel was founded by OpenCensus and OpenTracing to standardise data gathering through APIs, SDKs, frameworks, and integrations. OTel lets you build monitoring outputs into your code to ease data processing and export data to the right back end.
Telemetry data helps determine system health and performance. Optimised observability speeds up troubleshooting, improves system reliability, reduces latency, and reduces application downtime.
Opentelemetry architecture
APIs
OpenTelemetry APIs uniformly translate programming languages. This lets APIs capture telemetry data. These APIs help standardise OpenTelemetry measurements.
SDKs
Software development tools. Frameworks, code libraries, and debuggers are software development building elements. OTel SDKs implement OpenTelemetry APIs and provide telemetry data generation and collection tools.
OpenTelemetry collector
The OpenTelemetry collector accepts, processes, and exports telemetry data. Set OTel collectors to filter specified data types to the back end.
Instrumentation library
OTel offers cross-platform instrumentation. The instrumentation libraries let OTel integrate with any programming language.
Opentelemetry collector contrib
Telemetry data including metrics, logs, and traces can be collected without modifying code or metadata using the OpenTelemetry protocol (OTLP).
Metrics
A high-level overview of system performance and health is provided via metrics. Developers, IT, and business management teams decide what metrics to track to fulfil business goals for application performance. A team may measure network traffic, latency, and CPU storage. You may also track application performance trends with metrics.
Logs
Logs record programme or application events. DevOps teams can monitor component properties with logs. Historical data might demonstrate performance, thresholds exceeded, and errors. Logs track application ecosystem health.
Traces
Traces provide a broader picture of application performance than logs and aid optimisation. They track a request through the application stack and are more focused than logs. Traces let developers pinpoint when mistakes or bottlenecks occur, how long they remain, and how they effect the user journey. This data improves microservice management and application performance.
What’s Prometheus?
Application metrics are collected and organised using Prometheus, a monitoring and alerting toolkit. SoundCloud created the Prometheus server before making it open-source.
End-to-end time-series data monitoring is possible using Prometheus. Time-series metrics capture regular data, such as monthly sales or daily application traffic. Visibility into this data reveals patterns, trends, and business planning projections. Prometheus collects application metrics for dedicated functions that DevOps teams want to monitor after integration with a host.
Using PromQL, Prometheus metrics offer data points with the metric name, label, timestamp, and value. For better visualisation, PromQL lets developers and IT departments aggregate data metrics into histograms, graphs, and dashboards. Enterprise databases and exporters are accessible to Prometheus. Application exporters pull metrics from apps and endpoints.
Prometheus tracks four metrics
Counters
Counters measure increasing numerical values. Counters count completed tasks, faults, and processes or microservices.
Gauges
Gauges measure numerical data that fluctuate due to external variables. They can monitor CPU, memory, temperature, and queue size.
Histograms
Events like request duration and answer size are measured via histograms. They split the range of these measurements into buckets and count how many fall into each bucket.
Summaries
Summaries assess request durations and response size like histograms, but they also count and total all observed values.
Prometheus’ data-driven dashboards and graphs are also useful.
Benefits of Prometheus
Prometheus provides real-time application monitoring for accurate insights and fast troubleshooting. It also permits function-specific thresholds. When certain thresholds are hit or exceeded, warnings might speed up problem resolution. Prometheus stores and provides analytics teams with massive amounts of metrics data. It stores data for instant examination, not long-term storage. Prometheus typically stores data for two to fifteen days.
Prometheus works perfectly with Kubernetes, an open-source container orchestration technology for scheduling, managing, and scaling containerised workloads. Kubernetes lets companies create hybrid and multicloud systems with many services and microservices. These complicated systems gain full-stack observability and oversight with Prometheus and Kubernetes.
Grafana Opentelemetry
Grafana, a powerful visualisation tool, works with Prometheus to create dashboards, charts, graphs, and alerts. Grafana can visualise metrics with Prometheus. The compatibility between these platforms makes complex data easier to share between teams.
Integration of OpenTelemetry with Prometheus
No need to choose OpenTelemetry and Prometheus are compatible. Prometheus data models support OpenTelemetry metrics and OTel SDKs may gather them. Together, these systems provide the best of both worlds and enhanced monitoring. As an example:
- When combined, OTel and Prometheus monitor complex systems and deliver real-time application insights.
- OTel’s tracing and monitoring technologies work with Prometheus’ alerting.
- Prometheus handles big data. This capability and OTel’s ability to combine metrics, traces, and logs into one interface improve system and application scalability.
- PromQL can generate visualisation models using OpenTelemetry data.
To provide additional monitoring tools, OpenTelemetry and Prometheus interface with IBM Instana and Turbonomic. Instana’s connection map, upstream/downstream service connection, and full-stack visibility let OTel monitor all services. They give the same wonderful experience with OTel data as with all other data sources, providing you the context you need to swiftly detect and address application problems. Turbonomic automates real-time data-driven resourcing choices using Prometheus’ data monitoring capabilities. These optimised integrations boost application ecosystem health and performance.
