Sifting through all your logs to find what you need can be challenging—especially during an outage, when time is critical and you’re flooded with WARN and ERROR messages. To help you immediately surface useful information from large volumes of logs, we developed Log Patterns.
This recipe is similar to the previous rsyslog + Redis + Logstash one, except that we’ll use Kafka as a central buffer and connecting point instead of Redis. You’ll have more of the same advantages.
Starting from Elasticsearch 5.0, you’re able to define pipelines within it that process your data, in the same way you’d normally do it with something like Logstash. We decided to take it for a spin and see how this new functionality (called Ingest) compares with Logstash filters in both performance and functionality. Is it worth sending data directly to Elasticsearch or should we keep Logstash?
Since its first release in 2007, Splunk quickly became one of the leading log management solutions. Its focus on enterprise grade log analysis and security incident and event management (SIEM) made it the de facto choice for organizations generating large volumes of log files and machine data. But over the past decade, the log management landscape has changed drastically.
Most software and systems generate audit logs. They are a means to examine what activities have occurred on the system and are typically used for diagnostic performance and error correction. System Administrators, network engineers, developers, and help desk personnel all use this data to aid them in their jobs and maintain system stability. Audit logs have also taken on new importance for cybersecurity and are often the basis of forensic analysis, security analysis, and criminal prosecution.
Here at Sematext we use Java and rely on Logsene, our hosted ELK logging SaaS, a lot. We like them so much that we regularly share our logging experience with everyone and help others with logging, especially, ELK stack. Centralized logging plays nice with Java (and anything else that can write pretty logs). However, there is one tricky thing that can be hard to get right: properly capturing exception stack traces.
Tag-based metrics are typically used by IT operations and DevOps teams to make it easier to design and scale their systems. Tags help you to make sense of metrics by allowing you to filter on things like host, cluster, services, etc. However, knowing which tags to use, and when, can be confusing. For instance, have you ever wondered about the difference between intrinsic tags (or dimensions) and meta tags with respect to custom application metrics? If so, you’re not alone.
Welcome to autumn! Here in New England we anxiously await the transition to winter as our leaves start to change color and the summer heat begins to temper. It’s been a busy few seasons for us – we’ve introduced new features and launched our new community efforts. Want to learn more? Read on!
This year, at Sumo Logic’s third annual user conference, Illuminate 2018, we presented Sumo Logic Notebooks as a way to do data science in Sumo Logic. Sumo Logic Notebooks are an experimental feature that integrate Sumo Logic, notebooks and common machine learning frameworks. They are a bold attempt to go beyond what the current Sumo Logic product has to offer and enable a data science workflow leveraging our core platform.
