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Migrating Critical Traffic At Scale with No Downtime — Part 1 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience. This approach has a handful of benefits.
To this end, we developed a Rapid Event Notification System (RENO) to support use cases that require server initiated communication with devices in a scalable and extensible manner. In this blog post, we will give an overview of the Rapid Event Notification System at Netflix and share some of the learnings we gained along the way.
Chances are, youre a seasoned expert who visualizes meticulously identified key metrics across several sophisticated charts. For example, if you’re monitoring network traffic and the average over the past 7 days is 500 Mbps, the threshold will adapt to this baseline.
The first part of this blog post briefly explores the integration of SLO events with AI. Consequently, the AI is founded upon the related events, and due to the detection parameters (threshold, period, analysis interval, frequent detection, etc), an issue arose. By analogy, envision an apple tree where an apple drops.
It should also be possible to analyze data in context to proactively address events, optimize performance, and remediate issues in real time. This enables proactive changes such as resource autoscaling, traffic shifting, or preventative rollbacks of bad code deployment ahead of time.
To extend Dynatrace diagnostic visibility into network traffic, we’ve added out-of-the-box DNS request tracking to our infrastructure monitoring capabilities. While our competitors only provide generic traffic monitoring without artificial intelligence, Dynatrace automatically analyzes DNS-related anomalies.
The Challenge of Title Launch Observability As engineers, were wired to track system metrics like error rates, latencies, and CPU utilizationbut what about metrics that matter to a titlessuccess? Using the source of truth: Logs serve as a reliable source of truth by providing a comprehensive record of system events.
Collecting Raw Impression Events As Netflix members explore our platform, their interactions with the user interface spark a vast array of raw events. These events are promptly relayed from the client side to our servers, entering a centralized event processing queue.
To do this, we devised a novel way to simulate the projected traffic weeks ahead of launch by building upon the traffic migration framework described here. New content or national events may drive brief spikes, but, by and large, traffic is usually smoothly increasing or decreasing.
They need event-driven automation that not only responds to events and triggers but also analyzes and interprets the context to deliver precise and proactive actions. These initial automation endeavors paved the way for greater advancements, leading to the next evolution of event-driven automation.
Scaling RabbitMQ ensures your system can handle growing traffic and maintain high performance. Optimizing RabbitMQ performance through strategies such as keeping queues short, enabling lazy queues, and monitoring health checks is essential for maintaining system efficiency and effectively managing high traffic loads.
So, we relied on higher-level metrics-based testing: AB Testing and Sticky Canaries. The control group’s traffic utilized the legacy Falcor stack, while the experiment population leveraged the new GraphQL client and was directed to the GraphQL Shim. The Replay Tester tool samples raw traffic streams from Mantis.
RabbitMQ is designed for flexible routing and message reliability, while Kafka handles high-throughput event streaming and real-time data processing. Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. What is Apache Kafka?
In my last blog , I’ve provided an example of this happening, whereby the traffic spiked and quadrupled the usual incoming traffic. These are all interesting metrics from marketing point of view, and also highly interesting to you as they allow you to engage with the teams that are driving the traffic against your IT-system.
This opens the door to auto-scalable applications, which effortlessly matches the demands of rapidly growing and varying user traffic. Containers can be replicated or deleted on the fly to meet varying end-user traffic. Event logs for ad-hoc analysis and auditing. In production, containers are easy to replicate. What is Docker?
Each SNMP-enabled device provides access to its state and performance metrics in a simple and robust way that allows Dynatrace to fetch the metrics and run them through Davis®, our AI causation engine. Events and alerts. Some SNMP-enabled devices are designed to report events on their own with so-called SNMP traps.
To emit a run queue latency metric, we leveraged three eBPF hooks: sched_wakeup, sched_wakeup_new, and sched_switch. During this event, we generate a timestamp and store it in an eBPF hash map using the process ID as the key. ' They let us identify when a process is ready to run and is waiting for CPU time.
A quick canary test was free of errors and showed lower latency, which is expected given that our standard canary setup routes an equal amount of traffic to both the baseline running on 4xl and the canary on 12xl. What’s worse, average latency degraded by more than 50%, with both CPU and latency patterns becoming more “choppy.”
Problems API v2 now includes event evidence data as part of the event details. Custom events for alerting using the Build tab and advanced query mode now apply the same metric dimension limits that are applied to Code -tab-based configurations. Events API v2 has been updated. Local self-monitoring metrics.
Furthermore, with this update you can: Get insights into connection pool metrics in context with the applications and services that use them. Get insights into connection pool metrics in context with the applications and services that use them. On the overview page you’ll find the metrics aggregated across all detected pools.
VPC Flow Logs is a feature that gives you the capability to capture more robust IP traffic data that traverses your VPCs. A full list of metrics can be found here and include dimensions such as the following: Packets. Problems have defined lifespans and are updated in real time with all incoming events and findings. Log Events.
It also enhances syslog messages with additional context and optimizes network traffic, improving overall system resilience and security. Logs are immediately available for troubleshooting, security investigations, and auditing, becoming integral to the platform alongside traces and metrics.
In cloud-native environments, there can also be dozens of additional services and functions all generating data from user-driven events. Metrics, logs , and traces make up three vital prongs of modern observability. As a result, logging tools record large event volumes in real time. What else did the initial event affect?
Existing data got updated to be backward compatible without impacting the existing running production traffic. After reading the asset ids using one of the ways, an event is created per asset id to be processed synchronously or asynchronously based on the use case. Generally, this flow is used for small datasets.
Dynatrace is fully committed to the OpenTelemetry community and to the seamless integration of OpenTelemetry data , including ingestion of custom metrics , into the Dynatrace open analytics platform. With Dynatrace OneAgent you also benefit from support for traffic routing and traffic control.
However, understanding the performance of different application types requires an emphasis on different performance metrics, that is, key performance metrics. For many traditional web applications , User action duration is considered the best metric available for web-performance optimization.
VPC Flow Logs is an Amazon service that enables IT pros to capture information about the IP traffic that traverses network interfaces in a virtual private cloud, or VPC. By default, each record captures a network internet protocol (IP), a destination, and the source of the traffic flow that occurs within your environment.
Rexed, Singh, and Stull outline the importance of metrics, traces, logs, events, and the role they play in achieving full–context Kubernetes observability and driving automated responses in hybrid and multi-cloud environments. To ensure everything runs smoothly, they employ the Dynatrace automated monitoring and observability solution.
Organizations that have transitioned to agile software development strategies (including the adoption of a DevOps culture and continuous delivery automation) enforce automated solutions for such decision making—or at the very least, use automation in the gathering of a release-quality metrics. Events ingestion. Kubernetes metadata.
A metric crossed a threshold. Metrics are a key part of understanding application health. But sometimes you can have too many metrics, too many graphs, and too many dashboards. An application is part of an ecosystem that can be subtly influenced by property changes or radically altered by region-wide events. By Andrei U.,
Certain SLOs can help organizations get started on measuring and delivering metrics that matter. With this objective, the app ensures that users experience real-time feedback and immediate updates when logging workouts, recording sets and reps, or tracking performance metrics. The Apdex score of 0.85
For example, to handle traffic spikes and pay only for what they use. Serverless applications are composed of event-driven functions that run on demand in response to triggers from various sources, such as HTTP requests, messages, or timers. Scale automatically based on the demand and traffic patterns.
Open-source metric sources automatically map to our Smartscape model for AI analytics. We’ve just enhanced Dynatrace OneAgent with an open metric API. Here’s a quick overview of what you can achieve now that the Dynatrace Software Intelligence Platform has been extended to ingest third-party metrics. Dynatrace news.
In the Device Management Platform, this is achieved by having device updates be event-sourced through the control plane to the cloud so that NTS will always have the most up-to-date information about the devices available for testing. The RAE is configured to be effectively a router that devices under test (DUTs) are connected to.
Look for timeout events Exploitation attempts for this vulnerability can be identified by many lines of “Timeout before authentication” in the logs. Using the VPC flow log default pattern available in DPL Architect, we can extract the meaningful fields to see only the network traffic targeting the SSH port.
Even worse, if your service logs record critical events such as errors in a non-standard way, those errors might go unnoticed by your observability team. Whether a web server, mobile app, backend service, or other custom application, log data can provide you with deep insights into your software’s operations and events.
RUM gathers information on a variety of performance metrics. Data collected on page load events, for example, can include navigation start (when performance begins to be measured), request start (right before the user makes a request from the server), and speed index metrics (measure page load speed). Tools may be limited.
To effectively address such warning signs, organizations need to focus on putting observability data into context—mapping and visualizing relationships and dependencies within all collected telemetry data—not only traces, metrics, and logs. With Dynatrace OneAgent you also benefit from support for traffic routing and traffic control.
By implementing service-level objectives, teams can avoid collecting and checking a huge amount of metrics for each service. First, it helps to understand that applications and all the services and infrastructure that support them generate telemetry data based on traffic from real users. So how can teams start implementing SLOs?
You also might be required to capture syslog messages from cloud services on AWS, Azure, and Google Cloud related to resource provisioning, scaling, and security events. Without seeing syslog data in the context of your infrastructure, metrics, and transaction traces, you’re slowed down by manual work with siloed data.
IoT is transforming how industries operate and make decisions, from agriculture to mining, energy utilities, and traffic management. Both methods allow you to ingest and process raw data and metrics. They enable real-time tracking and enhanced situational awareness for air traffic control and collision avoidance systems.
Exploratory data analytics is an analysis method that uses visualizations, including graphs and charts, to help IT teams investigate emerging data trends and circumvent issues, such as unexpected traffic spikes or performance degradations. Start by asking yourself what’s there, whether it’s logs, metrics, or traces.
Log data—the most verbose form of observability data, complementing other standardized signals like metrics and traces—is especially critical. Take the example of Amazon Virtual Private Cloud (VPC) flow logs, which provide insights into the IP traffic of your network interfaces. Managing this change is difficult.
Each tenant gets its own e-commerce site deployed on a shared Kubernetes cluster, isolated through separate namespaces and additional traffic isolation. There was not much traffic during the weekend, but as Monday came along, Dynatrace started sending alerts about a high HTTP failure rate across almost every tenant on the backend service.
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