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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.
Migrating Critical Traffic At Scale with No Downtime — Part 2 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Picture yourself enthralled by the latest episode of your beloved Netflix series, delighting in an uninterrupted, high-definition streaming experience.
As Netflix expanded globally and the volume of title launches skyrocketed, the operational challenges of maintaining this manual process became undeniable. Metadata and assets must be correctly configured, data must flow seamlessly, microservices must process titles without error, and algorithms must function as intended.
Accurately Reflecting Production Behavior A key part of our solution is insights into production behavior, which necessitates our requests to the endpoint result in traffic to the real service functions that mimics the same pathways the traffic would take if it came from the usualcallers. We call this capability TimeTravel.
It requires a state-of-the-art system that can track and process these impressions while maintaining a detailed history of each profiles exposure. In this multi-part blog series, we take you behind the scenes of our system that processes billions of impressions daily.
RabbitMQ is designed for flexible routing and message reliability, while Kafka handles high-throughput event streaming and real-time data processing. RabbitMQ follows a message broker model with advanced routing, while Kafkas event streaming architecture uses partitioned logs for distributed processing. What is Apache Kafka?
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.
Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process. The Netflix video processing pipeline went live with the launch of our streaming service in 2007. The Netflix video processing pipeline went live with the launch of our streaming service in 2007.
Event Prioritization Considering the use cases were wide ranging both in terms of their sources and their importance, we built segmentation into the event processing. We thus assigned a priority to each use case and sharded event traffic by routing to priority-specific queues and the corresponding event processing clusters.
With Dynatrace OneAgent you also benefit from support for traffic routing and traffic control. OneAgent implements network zones to create traffic routing rules and limit cross data-center traffic. Stay tuned for upcoming announcements around OpenTracing and OpenTelemetry. What’s next?
Tracking changes to automated processes, including auditing impacts to the system, and reverting to the previous environment states seamlessly. The ultimate goal of each of these reviews is to identify gaps, quantify risk, and develop recommendations for improving the team, processes, and architecture with each of the five pillars.
Your next challenge is ensuring your DevOps processes, pipelines, and tooling meet the intended goal. For example, by measuring deployment frequency daily or weekly, you can determine how efficiently your team is responding to process changes. Lead time for changes helps teams understand how effective their processes are.
You’re half awake and wondering, “Is there really a problem or is this just an alert that needs tuning? Telltale learns what constitutes typical health for an application, no alert tuning required. Regional traffic evacuations. A regional traffic shift means one region ends up with zero traffic while another region has double.
How viewers are able to watch their favorite show on Netflix while the infrastructure self-recovers from a system failure By Manuel Correa , Arthur Gonigberg , and Daniel West Getting stuck in traffic is one of the most frustrating experiences for drivers around the world. Logs and background requests are examples of this type of traffic.
To ensure high standards, it’s essential that your organization establish automated validations in an early phase of the software development process—ideally when code is written. While the first guardian validates the traffic, the second guardian checks the business transactions generated during the observation period.
Web application security is the process of protecting web applications against various types of threats that are designed to exploit vulnerabilities in an application’s code. Web Application Firewall (WAF) helps protect a web application against malicious HTTP traffic. Whether the process is exposed to the Internet.
Dynatrace Synthetic Monitoring helps you quickly verify if your application is delivering the expected end user experience by offering an outside-in view of all your applications and services, independent of real traffic. So stay tuned! Automated SLA/SLO monitoring using the HTTP monitoring API.
For example, to handle traffic spikes and pay only for what they use. Scale automatically based on the demand and traffic patterns. Data analysis : how to process, aggregate and query observability data from serverless functions effectively, accurately, and comprehensively? Such anomalies can be caused by function cold-starts.
Each of these errors is a canceled request resulting in a retry so this reduction further reduces overall service traffic by this rate: Errors rates per second. Operational simplicity Service owners often reach out to us with questions about excessive pause times and for help with tuning.
Stay tuned for an upcoming blog series where we’ll give you a more hands-on walkthrough of how to ingest any kind of data from StatsD, Telegraf, Prometheus, scripting languages, or our integrated REST API. Telegraf is a plugin-based system for collecting, processing, aggregating, and writing metrics. Stay tuned.
Handling Bursty Traffic : Managing significant traffic spikes during high-demand events, such as new content launches or regional failovers. Sharded Infrastructure : Leveraging the Data Gateway Platform , we can deploy single-tenant and/or multi-tenant infrastructure with the necessary access and traffic isolation.
Network measurements with per-interface and per-process resolution. OneAgent for Z/Linux collects a number of network metrics: input and output traffic measured in bytes and packets, retransmissions, and connectivity. Network metrics are also collected for detected processes. Stay tuned for more announcements on this topic.
As software development grows more complex, managing components using an automated onboarding process becomes increasingly important. The validation process is automated based on events that occur, while the objectives’ configuration, which is validated by the Site Reliability Guardian , is stored in a separate file.
To address this, we use a static limit for the initial queries to the backing store, query with this limit, and process the results. To mitigate these issues, we implemented adaptive pagination which dynamically tunes the limits based on observed data. While processing this request, the server retrieves data from the backing store.
We will also explore the evolution of DevOps automation and the significance of data-driven answers in unlocking streamlined, automated DevOps and SRE processes. Business process automation Business process automation is the foundation for improving operational efficiency.
The goal is to turn more data into insights so the whole organization can make data-driven decisions and automate processes. Grail data lakehouse delivers massively parallel processing for answers at scale Modern cloud-native computing is constantly upping the ante on data volume, variety, and velocity.
Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices. The process started with manual pull of member account information that was part of the session.
Nonetheless, we found a number of limitations that could not satisfy our requirements e.g. stalling the processing of log events until a dump is complete, missing ability to trigger dumps on demand, or implementations that block write traffic by using table locks. Some of DBLog’s features are: Processes captured log events in-order.
STM generates traffic that replicates the typical path or behavior of a user on a network to measure performance for example, response times, availability, packet loss, latency, jitter, and other variables). What DEM and business observability mean for the bottom line.
The challenge, then, is to be able to ingest and process these events in a scalable manner, i.e., scaling with the number of devices, which will be the focus of this blog post. As such, we can see that the traffic load on the Device Management Platform’s control plane is very dynamic over time.
Nonetheless, we found a number of limitations that could not satisfy our requirements e.g. stalling the processing of log events until a dump is complete, missing ability to trigger dumps on demand, or implementations that block write traffic by using table locks. Some of DBLog’s features are: Processes captured log events in-order.
We started seeing signs of scale issues, like: Slowness during peak traffic moments like 12 AM UTC, leading to increased operational burden. At Netflix, the peak traffic load can be a few orders of magnitude higher than the average load. Hence, the system has to withstand bursts in traffic while still maintaining the SLO requirements.
Prodicle Distribution Our service is required to be elastic and handle bursty traffic. We are expected to process 1,000 watermarks for a single distribution in a minute, with non-linear latency growth as the number of watermarks increases. Things got hairy. We wanted a scalable service that was near real-time, 2.
Dynatrace automatically detects processes and services and will observe their behaviour. For instance, when there isn’t enough traffic (late at night), the AI will not act to avoid alert spamming. If you want to understand how Dynatrace detects errors, read my other blog on how to fine-tune it ! How does it work?
Application and service monitoring What will be of particular interest to us here is “Process analysis” One of the key features of OneAgent is not only its ability to monitor the host itself and the system metrics but also to gain deep insight into the applications and services the machine is running.
Dynatrace Cloud Automation leverages the AI and automation capabilities of the Dynatrace Software Intelligence Platform to enhance development, DevOps, and SRE teams’ processes with: Automated SLO validation and quality gates , to ensure high-quality code moves smoothly through the delivery pipeline and does not violate error budgets in production.
Network measurements with per-interface and per-process resolution. OneAgent for the ARM platform collects a number of network metrics: input and output traffic measured in bytes and packets, retransmissions, and connectivity. Network metrics are also collected for detected processes. Stay tuned for more details.
Network measurements with per-interface and per-process resolution. OneAgent for Z/Linux collects a number of network metrics: input and output traffic measured in bytes and packets, retransmissions, and connectivity. Network metrics are also collected for detected processes. Stay tuned for more announcements on this topic.
Operational Reporting is a reporting paradigm specialized in covering high-resolution, low-latency data sets, serving detailed day-to-day activities¹ and processes of a business domain. CDC and data source Change data capture or CDC , is a semantic for processing changes in a source for the purpose of replicating those changes to a sink.
For Inter-Process Communication (IPC) between services, we needed the rich feature set that a mid-tier load balancer typically provides. In order for a service to talk to another, it needs to know two things: the name of the destination service, and whether or not the traffic should be secure.
As a result, e xisting application security approaches can’t keep up with this speed and vari ability of modern development processes. . With DevSecOps processes having shifted security testing “left”, will the teams have enough time to manually analyze, assess, and manage risks based on sampled or scheduled scan results?
Dynatrace analyzes the response time of each service running within each process, displaying findings out-of-the-box for further investigation. So please stay tuned for updates. Technical scalability without limits. Highest availability and security out-of-the-box.
And it added to the network traffic in terms of new version distribution. “How does the new process compare to the old one exactly?” Stay tuned for more announcements on other changes and improvements related to OneAgent installer for Windows, coming to this Dynatrace Blog page shortly! “How can I get the *.exe
Scale and automate SRE into your delivery processes with Dynatrace. Now let’s dive deeper into how Dynatrace can be used to support your Site Reliability Engineering process. This can be detected during any canary deployment or blue/green traffic routing to a new version.
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