October 2021 Summaries

Security vulnerabilities are a growing concern for organizations, as thousands of new ones are discovered each year, prompting the need for regular updates and patches across IT environments, including cloud infrastructures. Logz.io emphasizes the importance of proactive vulnerability management, highlighting recent vulnerabilities such as the Confluence Server vulnerability CVE-2021-26084, which allows unauthenticated code execution, and an issue in Microsoft Azure Cosmos DB that could lead to unauthorized access. The company underscores the necessity for security teams to stay informed about potential threats and offers a cloud SIEM solution to help manage these risks effectively. Logz.io aims to provide ongoing updates about emerging vulnerabilities to assist organizations in protecting their digital assets and invites feedback and engagement from users.

Engineering teams require efficient access to logs, metrics, and traces from their cloud infrastructure and applications to achieve full-stack observability and quickly resolve issues impacting customer experiences. Logz.io provides a centralized platform that unifies these data types, allowing engineers to swiftly identify problem root causes. The platform's recent updates, including the new user interface called Logz.io's New Lookz and the introduction of the Logz.io Search Bar, enhance user experience by facilitating quick navigation to data visualizations, alerts, and other platform features. The Search Bar remembers recent searches, further streamlining access to frequently used assets. Originally focused on log management and analytics, Logz.io has expanded into a comprehensive observability platform, continuously improving its capabilities to simplify data exploration and navigation across multiple products and data types.

OpenTelemetry is a recommended approach for instrumenting applications with distributed tracing in a standardized and vendor-neutral manner, offering a stable tracing specification and encompassing metrics and logs as well. The article provides a comprehensive guide to implementing OpenTelemetry tracing in an ASP.NET Core 3.1 application using the OpenTelemetry .NET SDK, detailing steps such as adding necessary packages, configuring the trace provider, setting up an OpenTelemetry Collector, and exporting traces to a backend like Logz.io’s managed Jaeger service. The example involves two ASP.NET Core Web APIs, Service A and Service B, which communicate via HTTP requests, with spans emitted and collected by the OpenTelemetry Collector before being visualized in the Jaeger UI. Both automatic and manual instrumentation options are discussed, including the addition of user-defined context with baggage, allowing for the tracking and tagging of trace information across service boundaries. The guide emphasizes the ease of integration and the ability to achieve finer-grain tracing by manually adding spans, making it a valuable resource for developers seeking to enhance observability in their .NET applications.

The tutorial provides a comprehensive guide on using OpenTelemetry to auto-instrument a basic Python application with distributed tracing capabilities. It uses a simple "Hello World" application built with Flask, demonstrating step-by-step installation and configuration of necessary OpenTelemetry components, including the exporter and collector. The trace data generated by the application is sent to an OpenTelemetry Collector, which then exports the trace to a chosen distributed tracing analytics tool such as Jaeger. The process showcases the ease of setting up OpenTelemetry in Python applications without altering existing code, with optional advanced steps for adding tracing context to Python logs. Additionally, the tutorial highlights the simplicity of integrating OpenTelemetry for trace visualization and analysis, specifically using Logz.io’s Distributed Tracing service.

Logz.io has introduced an upgraded self-service parsing tool designed to simplify the process of cataloging, organizing, and analyzing log data by enabling users to create and edit custom parsing rules independently. This new feature, powered by Sawmill, an open-source JSON transformation tool, allows users to input sample logs, write parsing rules, and push these rules to their accounts with ease. The tool's interface includes an autocomplete feature to assist users in writing rules, and it supports the testing and validation of these rules to ensure accuracy in log formatting. Users can select existing log types or add new ones, tailoring rule sets accordingly, while also having the option to collaborate with the Support team for additional assistance. Logz.io offers a 14-day free trial for users to experience the enhanced flexibility and efficiency of the new parsing tool.