For DevOps specialists, having a consolidated view of IT infrastructure is crucial. At the time of an incident, these specialists must be able to correlate metrics from various sources to get to the root of the problem. With Postman’s just-released New Relic integration, you can now configure Postman monitors to send metrics to New Relic to help solve problems and resolve incidents more quickly. This new integration will help you get a consolidated view of your API metrics and any existing infrastructure metrics—including CPU usage, memory consumption, and database query patterns—directly in New Relic.
Sending metrics from Postman monitors to New Relic
Metrics in the image above are sent to New Relic and accessed from the Data Explorer in New Relic.
Postman sends both run-level and request-level metrics. Run-level metrics comprise metrics valid for the entire run—the number of errors, for example—while request-level metrics will send you the data for each request within the collection. Here are further details about some of the metrics that are pushed from Postman to New Relic:
postman.monitor.run.failedtests will give you the number of failed tests across all requests written in the Postman test script.
postman.monitor.run.totallatency will give you the total latency after executing all the requests in the collection.
postman.monitor.run.errors will give you the total number of errors—such as DNS resolution errors—that occurred while executing the requests.
postman.monitor.run.totalbytes will give you total bytes exchanged while running the monitor.
postman.monitor.run.2XX, 4XX, 5XX will give you the total count of these status codes of your APIs.
postman.monitor.request.failedtests and postman.monitor.request.totallatency will give you metrics at the request level. You can filter to each specific request using the dimensions passed along with the metric as shown below.
Pivot your data across dimensions
Postman allows you to monitor your API across regions, which is helpful for APIs that are consumed in different global geographies. In New Relic, you can then pivot the data received with the “region” dimension which will give you a clear picture of the latency of your service across regions, as seen here:
Postman also sends other attributes that you can use to slice and dice the data. These include the names and identifiers for your monitor, collection, and request. Here’s an example:
Latency data pivoted with the “request name” dimension
External API monitoring
By comparing your API’s performance (e.g., latency metrics) and functionality (e.g., response correctness) with your IT infrastructure metrics such as CPU load, RAM usage, and network stats, you can more quickly and precisely discover the cause of issues in your API.
Related: What is API Monitoring?
Several factors could cause degradation of your API, and usable dashboards are the first requirement to debug rapidly.
A sample architecture with New Relic collecting metrics from the microservices and Postman monitors testing user-facing API latency and responses
Externally monitoring your API for latency is key to getting the most accurate depiction of API performance, because any monitoring agent that sits on your server will not be able to consider factors that an actual client will experience, like DNS resolution, SSL handshakes, and caching.
Consider complex tasks or workflows where multiple API calls are aggregated into a view for the user. These can be easily modeled inside Postman, where you can use variables and environments to manage data in each API call. You can reference variables across requests, collections, and environments to effectively create workflows.
Here’s an example: A payment transaction can involve multiple steps from the client involving passing data from one request to another. Specifically, the payment transaction might involve creating a session ID and using it in subsequent calls. A simplified flow diagram is seen here:
Workflow demonstrating steps involved in completing a payment
With Postman, you can re-create the flow in a collection with requests and variables and then set up a monitor to track the latency for the complete, end-to-end transaction (see sample below). You can even add time delays between requests to mock actual user behavior.
A sample showing test cases covering user response latencies and data validation across the whole end-to-end flow
Alerting in New Relic
The new Postman-New Relic integration is also useful if you want to capitalize on the various intelligent alerting capabilities that New Relic provides, such as self-adjusting thresholds, anomaly detection, and outlier detection. You can define static warnings and critical thresholds, and any breach on your defined metrics would create an alert in New Relic. You want to define more flexible “baseline” thresholds that dynamically adjust to how your data behaves.
Static threshold
Baseline threshold
Dashboards in New Relic
You can easily create a dashboard to effectively view all of the Postman monitor data in a single view. Metrics can be shown on the dashboard using the UI or using New Relic Query Langauge (NRQL).
To get started, visit the New Relic integration page in the Postman Learning Center.
Postman provides a wide range of functions and features to assist with API development, testing, and collaboration. Here are some commonly used functions in Postman:
Creating and Managing Requests: Postman allows you to create API requests by specifying the request method, URL, headers, parameters, and body. You can manage and organize requests within collections, including creating folders, adding descriptions, and reordering requests.
Request and Response Visualization: Postman provides a user-friendly interface to view and analyze request and response data. It supports syntax highlighting for various data formats such as JSON, XML, and HTML, making it easier to understand and validate the data.
Environment and Variables: Postman allows you to define variables and environments. Variables enable you to store and reuse dynamic values across requests, making them flexible and easy to maintain. Environments provide sets of variables specific to different environments (e.g., development, staging, production).
Tests and Assertions: Postman supports writing test scripts using JavaScript for automated API testing. You can write assertions to validate response status codes, headers, response bodies, and more. Postman's testing framework allows you to assert and validate different aspects of API responses.
Pre-request Scripts: Postman enables you to execute scripts before sending API requests using pre-request scripts. These scripts can be used to dynamically generate values, manipulate data, or set variables based on specific conditions.
Collection Runner: The Collection Runner allows you to execute a series of requests in a collection. It enables you to perform data-driven testing by iterating over multiple sets of data or environments. You can configure iterations, delays, and data sources for more comprehensive testing.
Mock Servers: Postman allows you to create mock servers for simulating API responses without a live backend. Mock servers are useful during development, allowing frontend developers to work independently by providing simulated API responses.
Documentation Generation: Postman can automatically generate documentation for your APIs based on your requests and collections. It provides a simple way to share API specifications and details with stakeholders.
Collaboration and Teamwork: Postman offers collaboration features such as sharing collections, collaborating on requests, and commenting on specific requests or collections. It also supports version control integration to manage changes and updates effectively.
Integration and Automation: Postman integrates with various tools and services, including version control systems (e.g., Git), CI/CD platforms (e.g., Jenkins), and API management solutions. It provides options for integrating with these tools to automate API testing and deployment processes.
