At Lowe’s, we’ve made significant progress in our multiyear technology transformation. To modernize our systems and build new capabilities for our customers and associates, we leverage Google’s SRE framework and Google Cloud, which helps us meet their needs faster and more effectively. With these efforts, we’ve been able to go from one release every two weeks to 20+ releases daily—about 20X more releases per month.
We know that developers or operators troubleshooting applications and systems have a lot of data to sort through while getting to the root cause of issues. Often there are fields like error response codes that are critical for finding answers and resolving those issues. Today, we’re proud to announce log field analytics in Cloud Logging, a new way to search, filter and understand the structure of your logs so you can find answers faster and easier than ever before.
Network traffic analysis is one of the core ways an organization can understand how workloads are performing, optimize network behavior and costs, and conduct troubleshooting—a must when running mission-critical applications in production. VPC Flow Logs is one such enterprise-grade network traffic analysis tool, providing information about TCP and UDP traffic flow to and from VM instances on Google Cloud, including the instances used as Google Kubernetes Engine (GKE) nodes.
A big part of ensuring the availability of your applications is establishing and monitoring service-level metrics—something that our Site Reliability Engineering (SRE) team does every day here at Google Cloud. The end goal of our SRE principles is to improve services and in turn the user experience. The concept of SRE starts with the idea that metrics should be closely tied to business objectives. In addition to business-level SLAs, we also use SLOs and SLIs in SRE planning and practice.
For decades, application development and operations teams have struggled with the best way to generate, collect, and analyze telemetry data from systems and apps. In 2010, we discussed our approach to telemetry and tracing in the Dapper papers, which eventually spawned the open-source OpenCensus project, which merged with OpenTracing to become OpenTelemetry.
Site Reliability Engineering (SRE) and Operations teams responsible for operating virtual machines (VMs) are always looking for ways to provide a more stable, more scalable environment for their development partners. Part of providing that stable experience is having telemetry data (metrics, logs and traces) from systems and applications so you can monitor and troubleshoot effectively.
As applications move from monolithic architectures to microservices-based architectures, DevOps and Site Reliability Engineering (SRE) teams face new operational challenges. Microservices are updated constantly with new features and resource managers/schedulers (like Kubernetes and GKE) can add/remove containers in response to changing workloads. The old way of creating alerts based on learned behaviors of your monolithic applications will not work with microservices applications.
Elite software development teams automate and integrate monitoring observability tools more frequently than lower performing teams, per the Accelerate: State of DevOps report. Organizations that need the highest levels of reliability, security, and scalability for their applications choose Google Kubernetes Engine (GKE). Recently we introduced GKE Autopilot to further simplify Kubernetes operations by automating the management of the cluster infrastructure, control plane, and nodes.
System and application logs provide crucial data for operators and developers to troubleshoot and keep applications healthy. Google Cloud automatically captures log data for its services and makes it available in Cloud Logging and Cloud Monitoring. As you add more services to your fleet, tasks such as determining a budget for storing logs data and performing granular cross-project analysis can become challenging.
Running and troubleshooting production services requires deep visibility into your applications and infrastructure. Virtual machines running on Google Compute Engine (GCE) provide some system logs and metrics without any configuration required, but capturing application and advanced system data has required the installation of both a metrics agent and a logging agent.
