![]() Neoload now manages these intermittent popups with a simple Fork action in your design. Citrix improvement – Random popup, asynchronous notifications or potential warnings can interrupt your Citrix scenario.This version also brings support for Tosca 13.1 SP3. With automated userpath update, when you make a change to your Tosca testcase, Neoload merges the new user path with the updated one, keeping all the changes you made to your initial NL userpath such as variables, loops, SLA and so on. Support for Tricentis Tosca web tests has been enhanced with the automatic creation of a Neoload Transaction for each Tosca step in the converted testcase. ![]() In this case, you need to use auto-reservation, since planned reservations are not supported for dynamic infrastructure.ĭynamic zones settings are now more flexible and powerful allowing to switch from one cluster to another. Neotys adds Google GKE to the list of supported dynamic infrastructure providers for NeoLoad Web: OpenShift, Kubernetes, Microsoft AKS and Amazon EKS.Īlso, NeoLoad Web now can launch tests using dynamic infrastructure even when the reservation feature is enabled on the account. The following image illustrates an example of NeoLoad load testing data exported to an analytics platform: Dynamic Infrastructure Improvements See the tutorial on exporting contextualized raw data. As an example, you will not only see which specific transaction was slow but you can also see which data was used (example ContractID for each “Edit Contract” transaction). When exporting raw data for transactions from the NeoLoad Controller, you now can add custom fields to tell in which context the execution of the transaction occurred. ![]() When analyzing results in NeoLoad Web, you can filter Transactions, Pages and Requests by Zones, Populations and User Paths, to focus your analysis for greater accuracy. Preconfigured popular CI pipeline examples (such as Jenkins, Azure Devops or GitLab) are also provided to get started even faster: Filtering Result Values The CLI can be combined with NeoLoad’s performance testing as-code capabilities in any of your automation environments.Ī comprehensive documentation is available on the Neotys GitHub repository: Connect to your Neoload Platform (in the example, the default is our SaaS platform).Using the Python CLI for NeoLoad is a simple and speedy approach for automating performance testing in CI pipelines. The Python CLI is a client to the runtime API of NeoLoad Web (SaaS or on-premise) that allows you to define and control a test from anywhere a Python command line is accessible. In this example, we are creating an alert that notifies the appropriate teams when no EC2 hosts with the role of worker are reporting as OK.What’s New in NeoLoad 7.4? Command Line Interface You can also programmatically create alerts that feed messages to the newly configured integrations, as shown below. Slack, PagerDuty, and the custom webhook are all listening for mentions within Datadog alerts to trigger activity on their respective channels. Metrics are flowing into prebuilt Datadog dashboards for all AWS resources supported by CloudWatch. Configuring Datadog alertsĪt this point, we have used Datadog’s integration API to configure integrations with AWS, Slack, PagerDuty, and a custom webhook. See the Datadog API documentation for more information about Datadog’s webhooks integration. # The account must use an email address not associated with any other AWS organizations create-account -email $&run_check=true" The following code calls the AWS Organizations API to create an AWS account for a new team, and saves its ID in AWS_ACCOUNT_ID: To do this efficiently and to ensure consistency, you can use a script to first create a new AWS account, and then to automatically integrate it with Datadog. It’s common for an organization to provision multiple AWS accounts, for example spinning up a new account at the start of a new project or for a new team to use. Integrating AWS with Datadog automatically In this post, we’ll walk through how the Datadog integration API works by presenting an example API call for each of the supported integrations, and finally tying them all together in a Datadog alert. This API-driven approach to configuring integrations can provide efficiency and consistency to organizations with complex, multifaceted environments. ![]() APIs are now available for our AWS, Slack, PagerDuty, and webhooks integrations, with more coming soon. You can now configure integrations programmatically using our API, making your monitoring practices as scalable and repeatable as possible. We’re pleased to introduce a brand-new way to set up your Datadog integrations.
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