Elastic Observability 8.2 offers granular control over data collection and storage

Elastic announced new features and enhancements in the Elastic Observability solution to support modern cloud-native environments, including smarter queue-based sampling for application performance monitoring (APM) and improved visibility into cloud services AWS.

Eliminate Blind Spots with Elastic Tail Based Sampling

Tail-based sampling can help DevOps and Site Reliability Engineering (SRE) teams eliminate application performance blind spots by providing finer-grained control over trace sampling conditions in multi-tasking systems. high volume with millions of transactions.

While common head-based sampling that applies a fixed-rate methodology can be effective in low-volume application server environments, tail-based sampling is better suited for more complex cloud-native applications. With elastic tail-based sampling, the decision to retain or discard a sample is made after a trace has been made and observed. Therefore, tail-based sampling can help customers maximize visibility and reduce their data storage costs by capturing only the most critical transactions.

“As more organizations adopt cloud-native technologies and microservices-based architectures, troubleshooting applications becomes increasingly complex,” said Alvaro Lobato, vice president, Observability, Elastic. “We designed elastic tail-based sampling to help customers avoid the trade-off between complete application visibility and cost. Therefore, Elastic Observability provides maximum visibility while allowing the kind of granular control needed when working in complex cloud-native environments. ”

Additionally, elastic tail-based sampling allows DevOps and SRE teams to easily adjust sampling rates to better understand application performance by evaluating each trace against a set of rules or policies and the results. transactions. The resulting APM insights can accelerate root cause analysis for faster time to resolution.

Improved visibility and faster troubleshooting on AWS cloud services

Now generally available, the ability to natively collect serverless traces from AWS Lambda functions gives customers detailed end-to-end visibility into distributed transactions to speed up troubleshooting. Development teams can collect serverless application traces from Lambda functions written in Node.js, Python, and Java with a new AWS Lambda APM agent. Elastic also supports native cloud monitoring with the ability to collect Lambda traces via OpenTelemetry (Java and Python only).

“We are excited to start using Elastic’s AWS Lambda APM agent for our cloud-native applications,” said Jose Navarro, software engineer at Accolade, a healthcare company. “Our team at Accolade particularly appreciates the ability to see if a particular Lambda function invocation involved a cold start directly in the trace waterfall graph. The availability of Lambda-specific metrics, such as cold start rate, at the service and transaction group level is also very helpful. »

Additionally, customers can now ingest custom logs from Amazon S3 and CloudWatch into Elasticsearch and optionally configure index patterns, ingest pipelines, and output specifications. And, with Elastic 8.2, Elastic Serverless Forwarder now supports CloudWatch, Kinesis Data Streams, and SQS direct as additional input sources for log ingestion.

These enhancements provide customers with greater flexibility by providing acquisition options that meet their existing operating procedures and architectural preferences.

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