Elastic has announced new features and enhancements to the Elastic Observability solution to support today’s cloud environments, including smarter tail-based sampling for application performance monitoring (APM) and improved visibility in AWS cloud services.
Eliminate blind spots with Elastic tail-based sampling
Tail-based sampling can help DevOps and Site Engineering Reliability (SRE) teams eliminate application performance blind spots by providing finer control over trace sampling conditions in high-volume systems with millions of transactions.
While conventional head-based sampling, which applies a fixed-rate methodology, can be effective in a low-volume application server environment, tail-based sampling is better suited for more complex cloud-based applications. When sampling based on Elastic tail, the decision to retain or reject the sample is made after the trace has been completed and observed. As a result, tail-based sampling can help customers maximize visibility and reduce storage costs by capturing only the most important transactions.
“As more organizations adopt cloud technologies and microservice-based architectures, troubleshooting applications is becoming more challenging,” said Alvaro Labata, vice president of surveillance, Elastic. “We built the sample based on Elastic tail to help customers avoid compromises between full app visibility and cost. As a result, Elastic Observability provides maximum visibility while providing the type of fine-grained control needed when working in complex cloud environments. ”
In addition, Elastic tail-based sampling allows DevOps and SRE teams to easily adjust the sampling rate to gain a better understanding of application performance by evaluating each trace according to a set of rules or policies and transaction results. The resulting APM information can speed up the analysis of root causes for a faster solution.
Improve visibility and speed up troubleshooting in AWS cloud services
Now there is a public opportunity to collect serverless traces AWS Lambda features provide customers with detailed end-to-end visibility of distributed transactions to accelerate troubleshooting. Developer teams can compile server-free application traces from Lambda features written in Node.js, Python and Java, using the new AWS Lambda APM agent. Elastic additionally supports native cloud monitoring with the ability to collect lambda traces via OpenTelemetry (Java and Python only).
“We are excited to begin using the Elastic AWS Lambda APM agent for our cloud applications,” said Jose Navarro, software engineer at Accolade, a medical company. “Our Accolade team especially likes the fact that you can see if a specific lambda cold start function call is related directly to the waterfall trace diagram. Having lambda-specific metrics, such as cold start speed, at the service level and transaction group is also very helpful. ”
In addition, customers can now receive custom logs from Amazon S3 and CloudWatch in Elasticsearch and, if desired, customize index templates, reception pipelines, and output specifications. And with Elastic 8.2 Elastic Serverless Forwarder now supports CloudWatch, Kinesis Data Streams and direct SQS as additional input sources for receiving logs.
These improvements give customers additional flexibility by providing admission options that match their existing operating procedures and architectural preferences.