LogDNA announced significant performance and usability updates that enable developers to more easily query, filter and gain insight from their log data.
The new features help developers identify and resolve issues to reduce downtime and quickly fix performance issues.
"The complexity of developing, deploying and scaling applications is exponentially more complicated today than even just a few months ago, and the amount of data even small teams deal with on a daily basis is becoming untenable," said Peter Cho, VP of Product Management at LogDNA. "We are constantly evolving LogDNA to empower developers to tame the chaos generated by ever-increasing data volumes and quickly surface the insights hidden in their logs."
LogDNA supports today's complex development processes and operation environments and is the log management solution of choice by many of the world's leading enterprises. Innovative LogDNA features such as LiveTail, multi-channel alerting and natural language queries have been designed to make logging easy and applicable to a variety of use cases.
New features include:
- Agent v2: Leverages the Linux kernel to monitor log files and directories for changes, freeing up CPU utilization, improving stability and accuracy, and removing duplicate lines with symbolic linked log files.
- Hourly Archiving: Receive archived logs faster and unzip just a portion of your logs instead of an entire day's worth. The new hourly archiving format additionally enables easier data analysis with new Hive partition folder formats.
- Extract and Aggregate Fields: Allows users to extract, aggregate and export fields from log lines that have already been indexed. Unlike the custom log parser, the extract and aggregate feature allows users to parse out additional fields ad-hoc from historical logs without having to re-ingest them, creating the ability to preview the extracted fields before making bigger changes with custom parsing.
- Custom Webhooks: Alert integration which enables customers to easily integrate LogDNA alerts with additional 3rd-party services of their choice, such as JIRA or Microsoft Teams. Using custom webhooks, users can automatically trigger a task in their ticketing system or automatically send a message on a chat platform.
The Latest
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...