Breser stands for Business Rules & Expression Syntax for Easy Retrieval. It is a powerful and flexible query language designed for efficient log processing and structured data filtering.
Save 90% of time searching and browsing logs with Logdy, a tool that provides a powerful and secure UI for log management, supporting any format and offering a low-code TypeScript log parser.
Elasticsearch’s latest innovation in log management cuts the storage footprint of log data by up to 65%, enabling observability and security teams to expand visibility without exceeding their budget while keeping all data accessible and searchable.
Key features of LogsDB Index Mode include:
- Improved indexing speed and efficiency for log data, which is typically unstructured and high volume.
- Enhanced storage efficiency by utilizing a columnar storage format, which is better suited for log data analysis.
- Simplified configuration and tuning, making it easier to manage and optimize log data workflows.
Key features of LogsDB Index Mode include:
- Improved indexing speed and efficiency for log data, which is typically unstructured and high volume.
- Enhanced storage efficiency by utilizing a columnar storage format, which is better suited for log data analysis.
- Simplified configuration and tuning, making it easier to manage and optimize log data workflows.
This article discusses how traditional machine learning methods, particularly outlier detection, can be used to improve the precision and efficiency of Retrieval-Augmented Generation (RAG) systems by filtering out irrelevant queries before document retrieval.
klogg is an open-source multi-platform GUI application for searching through text log files using regular expressions. It offers various features like handling large files, fast searching, and color-coded results.
OpenTelemetry is not just an observability platform, it's a set of best practices and standards that can be integrated into platform engineering or DevOps.
OpenLogParser, an unsupervised log parsing approach using open-source LLMs, improves accuracy, privacy, and cost-efficiency in large-scale data processing.
Approach:
- Log grouping: Clusters logs based on shared syntactic features.
- Unsupervised LLM-based parsing: Uses retrieval-augmented approach to separate static and dynamic components.
- Log template memory: Stores parsed templates for future use, minimizing LLM queries.
Results:
- Processes logs 2.7 times faster than other LLM-based parsers.
- Improves average parsing accuracy by 25% over existing parsers.
- Handles over 50 million logs from the LogHub-2.0 dataset.
- Achieves high grouping accuracy (87.2%) and parsing accuracy (85.4%).
- Outperforms other state-of-the-art parsers like LILAC and LLMParserT5Base in processing speed and accuracy.
Approach:
- Log grouping: Clusters logs based on shared syntactic features.
- Unsupervised LLM-based parsing: Uses retrieval-augmented approach to separate static and dynamic components.
- Log template memory: Stores parsed templates for future use, minimizing LLM queries.
Results:
- Processes logs 2.7 times faster than other LLM-based parsers.
- Improves average parsing accuracy by 25% over existing parsers.
- Handles over 50 million logs from the LogHub-2.0 dataset.
- Achieves high grouping accuracy (87.2%) and parsing accuracy (85.4%).
- Outperforms other state-of-the-art parsers like LILAC and LLMParserT5Base in processing speed and accuracy.
Linux log management can be a tricky process. This article guides you through best practices for managing logs on Linux systems.
Lnav is a log file viewer for large plain text files. It can handle files of any size and offers features like search, filter, and regex highlighting. It's built with C and supports Linux, macOS, and Unix systems.
Hydrolix is a streaming data lake platform designed to handle large amounts of immutable log data at a lower cost than traditional solutions. The platform is particularly well-suited for observability data and offers real-time query performance on terabyte-scale data. Hydrolix uses an ANSI-compliant SQL interface, is schema-based and fully indexed, and is designed for high-cardinality data. It is purpose-built for log data and focuses on data that comes in once and never changes. Hydrolix is currently used by companies in industries like media, gaming, ad tech, and telecom security that require long-term retention of data. The company recently announced a $35 million Series B round, and its technology serves as the basis for Akamai's observability product TrafficPeak. The platform is designed to save costs for companies dealing with billions of transactions a day and terabytes of data, as it can store data for longer periods than traditional solutions like Splunk or Datadog, thereby reducing costs or increasing retention.
