InfoQ Homepage Logging Content on InfoQ
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Three Pillars with Zero Answers: Rethinking Observability with Ben Sigelman
At KubeCon NA, held in Seattle, USA, in December 2018, Ben Sigelman presented “Three Pillars, Zero Answers: We Need to Rethink Observability” and argued that many organisations may need to rethink their approach to metrics, logging and distributed tracing.
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Evolution of Metrics Collection and Log Aggregation at Coinbase
Luke Demi, software engineer at Coinbase, writes about the changes in monitoring and logging that have taken place at Coinbase since mid-2018. Coinbase moved from a self-managed Elasticsearch cluster that served the dual purpose of log analysis and metrics visualization, to Datadog for metrics collection and managed Elasticsearch on AWS for log aggregation.
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Testing Complex Distributed Systems at FT.com: Sarah Wells Shares Lessons Learned
The complexity in complex distributed systems isn’t in the code, it’s between the services or functions. Testing implies balancing finding problems versus delivering value, said Sarah Wells at the European Testing Conference. Testers often have the best understanding of what the system does; they have a good hypothesis about what went wrong, and are able to validate it pretty quickly.
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Grafana Adds Log Data Correlation to Time Series Metrics
The Grafana team announced an alpha version of Loki, their logging platform that ties in with other Grafana features like metrics query and visualization. Loki adds a new client agent promtail and serverside components for log metadata indexing and storage.
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Facebook Open Sources LogDevice - a Distributed Data Store for Log Storage
Facebook open sourced their internal distributed log storage project called LogDevice. It offers high write availability using replication, durable log storage and recovery from failure.
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Understanding Production with DevOps Archeology
Lee Fox spoke at Continuous Lifecycle London about tools and methods to help make sense of today’s complex systems and infrastructure; he calls it DevOps archeology.
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Building Observable Distributed Systems
Today's systems are more and more complex; microservices distributed over the network and scaling dynamically, resulting in many more ways of failure, ways we can't always predict. Investing in observability gives us the ability to ask questions to systems, things we never thought about before. Some of the tools that can be used for this are metrics, tracing, structured and correlated logging.
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Microsoft Adds Application Insights Support for Azure Functions
Microsoft recently announced an initial preview of Application Insights support for Azure Functions. As a result of this integration between the two services, developers now get built-in instrumentation for their code and a portal to view trends in their code’s performance. Developers are also able to set monitoring thresholds which can be used to create alerts or a callout to external webhooks.
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Logz.io Offers Machine Learning Based Log Analysis
Logz.io offers a hosted service which performs intelligent log analysis by using machine learning to derive insights from human interactions with log data that includes discussions on tech forums and public code repositories.
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Five Ways to Not Mess Up Microservices in Production
Alex Zhitnitsky of Takipi has written about five ways to try to improve the chances of successful deployed of microservices into production. As we will see, they share many similarities with other independent efforts, perhaps leading us to agreement on top areas of concern, if not ways of solving these problems.
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How to Effectively Debug Software
InfoQ interviewed Diomidis Spinellis, author of the books Code Reading and Code Quality, about finding and fixing errors in software, principles for debugging software and how to improve the effectiveness of debugging, how to write code that requires less debugging, and what managers can do to support error prevention and handling.
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Log4j 2.6 Goes Garbage-Free
Log4j, the popular logging library for Java, will include a number of configuration options that allows it to run in a completely garbage-free manner. The release follows previous attempts to improve the performance of logging libraries, and has been positively received by the industry. Further changes to increase the number of scenarios in which log4j can run garbage-free are expected.
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DistributedLog at Twitter for High Performance Logging
Twitter is using replicated logs for high performance data collection and analysis of its systems. DistributedLog is the system developed at Twitter for this purpose. Twitter has developed a distributed key-value database, Manhattan. Manhattan can trade consistency for latency in reads following the eventually consistent data model. We examine Twitter's design and tradeoffs for DistributedLog.
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The Transition to a New Log4j: a Q&A with Log4j’s Project Management Committee
As recently published in InfoQ, the Apache Software Foundation announced the end of life of version 1 of Log4j, encouraging users to upgrade to version 2 of the popular logging framework. InfoQ reached out to the members of the Apache Logging Services Team to find out more about the transition to the new version of Log4j and about its future.