AWS Lambda performance takes a crucial part in your everyday AWS Lambda usage. Monitoring helps you identify any performance issues, and it can also send you alerts and notify you of anything you might need to know. The world is slowly getting to a point where machines and computers will be flawless, but until then, if we let them perform various tasks for us, we could at least monitor their performance.
For this to happen, we need another kind of program which would monitor the activity of our automated work. There are programs (or tools) that can offer significant help with the monitoring of your AWS Lambda performance, and in this article, I will cover the top 3 AWS Lambda monitoring tools and explain how they work and what exactly are they used for.
Dashbird is excellent in providing error alerts and also in monitoring support. Dashbird collects and analyzes CloudWatch logs while zeroing the effects on your AWS Lambda performance. Integration with the Slack account is also possible, and that brings all alerts about early exits, crashes, cold starts, timeouts, runtime errors, etc. to your development chat. Dashbird’s error diagnostics, advanced log searching, and function statistics are only a few of benefits Dashbird offers to its users.
All the needed information is available on a dashboard including an overview of all invocations, top active functions, system health and recent errors. Going down to invocation level data is yet another offering from Dashbird, and there you can analyze all of the functions separately. It’s very user-friendly while it provides all the information you could possibly ask for. Dashbird detailed views for performance tracking, optimization and error handling, tracking and error monitoring, and troubleshooting are what makes Dashbird a tool you always wanted. Providing a quick overview of everything going on with your serverless infrastructure which includes invocation volumes, latency, failures, and overall health.
In case you wish to learn more about the specific technical working principles of each platform and to compare them for pros and cons, or even to see what benefits they have, check out the documentation, and you’ll be able to find much more information.
Datadog provides the unity of metrics, logs, and traces. Aggregating events and metrics from more than 200 technologies such as Amazon Web Services, MongoDB, Slack, Docker, Chef, and many others. Datadog also explores enriched data, does searches and analyze log data while tracing requests across the distributed systems and alerts you on app performance.
Datadog also provides its users with real-time insights allowing you to drag-and-drop dashboards to graphs. It also gives you the opportunity to analyze and to have a correlation between metrics and events. Seamless AWS integration is not science fiction anymore. Datadog allows you to both discover and monitor all of your AWS services like EBS, ELB, EC2, RDS, ElastiCache and many others.
Datadog will notify you if any performance problems arise, regardless of the situation if they have affected a massive cluster or just a single host. You can choose between various channels to be notified from such as Slack, e-mail, PagerDuty and others. Building a complex alert logic using several trigger conditions is also able with Datadog while you can mute all alerts by a single push of a button when the system is upgrading or during the maintenance period.
Logz.io offers ELK service as the best choice for scaling and performance with ease while there’s no need to perform upgrades or capacity management. Logz.io security is enterprise-grade, and it keeps your data private and secure while it’s also compliant with key industry standards. Logz.io goes way beyond the ELK service in order to provide Enterprise-Class log analytics platform consisted of features like integrated alerts and multiple sub-accounts.
Fast issue resolving is happening because of their advanced machine learning setup which locates critical and unnoticed errors and exceptions in real time in combination with actionable and contextual data for faster resolutions. Logz.io also uses AI-Powered analytics system which applies pre-built machine learning across data which is specified by use-case, user behavior and community knowledge which further allows anomaly identification and surface the value which is hidden in the data. Providing a suite of analytics and optimizing tools that help organizations in reducing the overall logging expenses as their data grows is yet another perk offered by Logz.io.
Learning about how to approach the serverless monitoring architecture will for sure make your life (and work) much easier, and with a proper understanding of the AWS infrastructure, you are one step closer to a new skill called “observability” regarding the lambda functions. The price is set, but it’s a small one compared to the lambda function benefits you’ll obtain.
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