Dashbird helped us refine the size of our Lambdas, resulting in significantly reduced costs. We have Dashbird alert us in seconds via email when any of our functions behaves abnormally. Their app immediately makes the cause and severity of errors obvious.
Dashbird does not require any instrumentation from you. Instead, we provide a CloudFormation stack that will connect your cloud logs and metrics to our monitoring infrastructure. This is an automated process that takes less than 5 minutes.
Dashbird requires limited read-only access to your AWS account to collect the data. Click here for detailed instructions about our integration process and CloudFormation stack.
Which functions are we importing?
Dashbird polls lambda functions from all regions after a fixed interval of 10 minutes. By default, all functions are imported. It’s possible to create custom importing limits from the client settings.
AWS Lambda Monitoring
Dashbird periodically polls your AWS account for data. All the data we receive is encrypted and stored safely in region us-east-1.
The exact polling interval is determined by the amount of lambda functions and the amount of requests they have. We usually poll data a few times a minute for a single function.
Polling adheres to AWS limits and we track each and every throttle error from the AWS API, doing our best not to overwhelm them. However, if there are other services using the same client APIs, then throttles might still occur.
After importing the first batch of logs, an aggregator starts, going through all imported data and extracting metrics by each invocation. The Aggregator detects the result, duration, memory usage and other meaningful information about the invocation.
Other Cloud Resources
Similarly to AWS Lambda logs, Dashbird also polls AWS on a regular basis to collect metrics from all your cloud components: SQS queues, API Gateways, ECS containers, etc.
You can select the most suitable polling interval for each type of resource:
After collection, the metrics data goes through Dashbird internal algorithms to identify potential faults, policy violations, performance degradation, etc. When applicable, our system will generate proactive alerts.
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