Proper normalization and tagging of data, either at collection time (through metadata tags and Field Extraction Rules) or at search time usings Sumo Logic’s query language, can greatly improve the SecOps teams ability to correlate and alert on security events of interest. Mean-time-to-response (MTTR) and Mean-time-to-detection (MTTD) of security incidents can be significantly reduced when leveraging solutions that provide threat detection and incident response for modern IT environments such as hybrid, multi-cloud, and microservices. Its ideal use cases include compliance, security, and configuration for modern cloud architectures. Proper data tagging, such as defining which stage in the application development systems are, such as dev, QA, prod or test, help developers and operations teams track changes easily.Ī security team can use Sumo Logic as a cloud security intelligence platform to improve the organization's security posture, risk management and threat hunting capabilities. Back-end developers can monitor the latency, volume and overall performance of requests to the application's back-end, ensuring that code is optimized to deliver the best user experience. With Sumo Logic, front-end developers can access real-time business analytics to assess the impact of GUI changes on user behavior. With efficient data collection and data tagging, searching and correlation across these diverse datasets can reduce outages from days to hours or minutes.Ī development team may leverage application and infrastructure logs while designing, building, testing and deploying new features that will be delivered to customers or web-based applications hosted in the cloud. CPU, memory usage, system errors, and network traffic can all be cross-correlated for quick root cause analysis without having to log into individual systems or tools. Outages, monitor end-to-end service levels and detect system anomalies. As such, different teams can use real-time search, visualizations and alerting to address different challenges.Īn operations team may use event and metric data to monitor and troubleshoot issues and Once the data has been ingested into the platform, it can be leveraged for a wide variety of use cases. This data can be structured or unstructured, come in the form of event logs and messages or as time-series metrics. Sumo Logic is an analytics platform that can ingest almost any type of machine data. The frustrated queries are everything not captured by these two counters, so count as total_logs gives us everything else we need, assuming our log source only contains access logs.Overview Introduction Data Classification and Metadata taggingĭata classification can be broadly defined as the process of organizing and tagging data by categories so that collected data may be used and protected in the most efficient way possible. This simply creates a counter for satisfied and tolerating using nested if functions with the matches operator. We use structured logging, so our logs are JSON formatted, but you could do this just as easily via a regex capture on apache style access logs to extract the status code and response time. | ((satisfied +tolerating / 2) /total_logs) as apdex | count as total_logs, sum(satisfied_counter) as satisfied, sum(tolerating_counter) as tolerating by _timeslice | if(statusCode matches "2*", if(responseTime, 1, 0), 0) as tolerating_counter So how can we build this measure in SumoLogic? Let's take a look | json auto field =raw_log 2xx or 3xx status codes.Ī tolerating request is successful in more than T, and less than 4T.įrustrated requests exceed 4T or fail, e.g. It divides all served requests into three categories: satisfied, tolerating, and frustrated.Ī user's request is said to be satisfied when it occurs within some T value, such as 400ms, and is successful, e.g. Application Performance Index (Apdex) is a standardised method for calculating the perceived satisfaction of a user accessing your service.
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