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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. This is where large-scale system migrations come into play.
Activate Davis AI to analyze charts within seconds Davis AI can help you expand your dashboards and dive deeper into your available data to extract additional information. 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.
Part 3: System Strategies and Architecture By: VarunKhaitan With special thanks to my stunning colleagues: Mallika Rao , Esmir Mesic , HugoMarques This blog post is a continuation of Part 2 , where we cleared the ambiguity around title launch observability at Netflix. We call this capability TimeTravel.
Scaling RabbitMQ ensures your system can handle growing traffic and maintain high performance. Youll also learn strategies for maintaining data safety and managing node failures so your RabbitMQ setup is always up to the task. This decoupling is crucial in modern architectures where scalability and fault tolerance are paramount.
Managing High Availability (HA) in your PostgreSQL hosting is very important to ensuring your database deployment clusters maintain exceptional uptime and strong operational performance so your data is always available to your application. Automatic failover is a critical strategy to achieve this.
The three strategies we will discuss today are AB Testing , Replay Testing, and Sticky Canaries. Let’s discuss the three testing strategies in further detail. 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.
As an engineer, you probably know that server performance under heavy load is crucial for maintaining the availability and responsiveness of your services. But what happens when traffic bursts overwhelm your system? Queueing requests is a common solution, but what's the best approach: FIFO or LIFO?
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.
We can experiment with different content placements or promotional strategies to boost visibility and engagement. Analyzing impression history, for example, might help determine how well a specific row on the home page is functioning or assess the effectiveness of a merchandising strategy.
While most government agencies and commercial enterprises have digital services in place, the current volume of usage — including traffic to critical employment, health and retail/eCommerce services — has reached levels that many organizations have never seen before or tested against. There are proven strategies for handling this.
Cloud migration is the process of transferring some or all your data, software, and operations to a cloud-based computing environment that offers unlimited scale and high availability. A cloud migration strategy, however, provides technical optimization that’s also firmly rooted in the business value chain.
Even when the staging environment closely mirrors the production environment, achieving a complete replication of all potential scenarios, such as simulating extremely high traffic volumes to assess software performance, remains challenging. This can lead to a lack of insight into how the code will behave when exposed to heavy traffic.
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.
Let’s consider the business challenges of an online shop that is powered by a microservice architecture where several instances of each microservice run, including the shopping cart service, to ensure the highest possible availability. With Dynatrace OneAgent you also benefit from support for traffic routing and traffic control.
With more organizations taking the multicloud plunge, monitoring cloud infrastructure is critical to ensure all components of the cloud computing stack are available, high-performing, and secure. Website monitoring examines a cloud-hosted website’s processes, traffic, availability, and resource use. Database monitoring.
Read Also: Best PostgreSQL GUI Incremental Backups PostgreSQL 17 introduces incremental backups , a game-changer for large and high-traffic databases. Get automated backups, high availability, and seamless scalingso you can focus on your applications, not database maintenance. Start your free trial today!
com and the strategies we use to keep it up and running with high availability. The number of services that compose our product in order to scale our organization and handle the increases in traffic went from under 10 to over 30 services. A lot has changed since then in Auth0.
Network traffic growth is the main reason for increasing spending, largely because of the adoption of hybrid and multi-cloud architectures. Unifying data, unifying observability Merging siloed data streams in a unified observ ability strategy requires a different approach.
Confused about multi-cloud vs hybrid cloud and which is the right strategy for your organization? Real-world examples like Spotify’s multi-cloud strategy for cost reduction and performance, and Netflix’s hybrid cloud setup for efficient content streaming and creation, illustrate the practical applications of each model.
Enhanced customer confidence through excellent service availability. The good news: even for latecomers to the compliance party, compliance is perfectly doable within the timeframe given the right tools and strategies. Organisations typically waste valuable time discussing and deciding the right strategy for hunting down the problem.
To make data count and to ensure cloud computing is unabated, companies and organizations must have highly available databases. This guide provides an overview of what high availability means, the components involved, how to measure high availability, and how to achieve it. How does high availability work?
Its design prioritizes high availability and efficient data transfer with minimal overhead, making it a practical choice for handling real-time data pipelines and distributed event processing. It follows a push-based approach, ensuring messages are distributed to consumers as soon as they become available.
This means that Dynatrace alerts more quickly when an error spike occurs in a high-traffic service (compared to a low-traffic service where statistical confidence is lower). The configuration is available at the global level as well as the service level. Close issues sooner with shorter event timeouts.
In this post, we compare ScaleGrid’s Bring Your Own Cloud (BYOC) plan vs. the standard Dedicated Hosting model to help you determine the best strategy for your MySQL, PostgreSQL, Redis™ and MongoDB® database deployment. Both AWS EC2 instances and Azure VM instances are available as Reserved Instances, and can be used through the BYOC plan.
Also called continuous monitoring or synthetic monitoring , synthetic testing mimics actual users’ behaviors to help companies identify and remediate potential availability and performance issues. Types of synthetic testing There are three broad types of synthetic testing: availability, web performance, and transaction.
Streamline development and delivery processes Nowadays, digital transformation strategies are executed by almost every organization across all industries. SREs use Service-Level Indicators (SLI) to see the complete picture of service availability, latency, performance, and capacity across various systems, especially revenue-critical systems.
This means that our microservices constantly evolve and change, but what doesn’t change is our responsibility to provide a highly available service that delivers 100+ million hours of daily streaming to our subscribers. So, if we evacuate South American traffic to North America, demand for CE and Android DRM won’t grow uniformly.
When the SLO status converges to an optimal value of 100%, and there’s substantial traffic (calls/min), BurnRate becomes more relevant for anomaly detection. Let’s assume we created a service-availability SLO, monitoring the request failure count against the overall request counts. What characterizes a weak SLO?
Observability becomes mandatory for any serious sustainability strategy in IT. During the holiday season, an e-commerce platform anticipating a traffic surge could use preventive observability to predict slowdowns or overloads, proactively scale resources, optimize performance, and balance cloud costs.
Auer oversees the initiative to maintain the performance and availability of SAP Commerce Cloud , which underpins more than 3,500 e-commerce sites in more than 200 countries, processing transactions worth $500 billion annually. You can see the traffic reaching each milestone of an online shopping journey.
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. How Release Analysis works. Kubernetes metadata.
The crisis has emphasized the importance of having a strategy for maintaining stability and performance. By simulating user interactions and running tests from various locations worldwide, synthetic monitoring provides a comprehensive view of application performance and availability. Before a crisis. During a crisis.
As adoption rates for Microsoft Azure continue to skyrocket, Dynatrace is developing a deeper integration with the platform to provide even more value to organizations that run their businesses on Azure or use it as a part of their multi-cloud strategy. Azure Traffic Manager. Azure Batch. Azure DB for MariaDB. Azure DB for MySQL.
Resource consumption & traffic analysis. If you want to read up on migration strategies check out my blog on 6-R Migration Strategies. What is the network traffic going to be between services we migrate and those that have to stay in the current data center? All available in Dynatrace in the UI or through the API!
In this post, we’ll walk you through the best way to host MongoDB on DigitalOcean, including the best instance types to use, disk types, replication strategy, and managed service providers. MongoDB Replication Strategies. DigitalOcean Advantages for MongoDB. minutes of downtime in one year.
However, storing and querying such data presents a unique set of challenges: High Throughput : Managing up to 10 million writes per second while maintaining high availability. Handling Bursty Traffic : Managing significant traffic spikes during high-demand events, such as new content launches or regional failovers.
Let’s delve deeper into how these capabilities can transform your observability strategy, starting with our new syslog support. It also enhances syslog messages with additional context and optimizes network traffic, improving overall system resilience and security.
Existing data got updated to be backward compatible without impacting the existing running production traffic. Data Sharding strategy in elasticsearch is updated to provide low search latency (as described in blog post) Design of new Cassandra reverse indices to support different sets of queries.
RUM, however, has some limitations, including the following: RUM requires traffic to be useful. Because RUM relies on user-generated traffic, it’s hard to indicate persistent issues across the board. This includes development, user acceptance testing, beta testing, and general availability. RUM generates a lot of data.
For example, an organization might use security analytics tools to monitor user behavior and network traffic. Additionally, with the Dynatrace Query Language, data is available in real time. Data retention Data loaded into Grail is automatically retained for up to three years and is fully flexible based on business needs.
Optimizing placements through combinatorial optimization What the OS task scheduler is doing is essentially solving a resource allocation problem: I have X threads to run but only Y CPUs available, how do I allocate the threads to the CPUs to give the illusion of concurrency? It has 8 physical hyperthreaded cores, split on 2 NUMA sockets.
In their case, this is specifically about the pensions element of their platform which had seen 6-7x as much traffic during the pandemic. Due to the uncertainty created during the pandemic, customers wanted instant, self-service access to know the value of their account,” the Lead for Service Reliability explained.
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. Designed with High Availability in mind. Writing events to any output.
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