This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
What’s the problem with Black Friday traffic? But that’s difficult when Black Friday traffic brings overwhelming and unpredictable peak loads to retailer websites and exposes the weakest points in a company’s infrastructure, threatening application performance and user experience. These kinds of problems are unacceptable.
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.
Digital transformation strategies are fundamentally changing how organizations operate and deliver value to customers. They help organizations streamline and automate complex and time-consuming procedures and improve overall performance. Previously, they had 12 tools with different traffic thresholds. Competitive advantage.
Although this indexing strategy worked smoothly for a while, interesting challenges started coming up and we started to notice performance issues over time. We tried both, and in many cases it helps, but sometimes it is a short term fix and the performance problems come back after a while; and it did for us.
This blog post will share broadly-applicable techniques (beyond GraphQL) we used to perform this migration. 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 Replay Tester tool samples raw traffic streams from Mantis.
For cloud operations teams, network performance monitoring is central in ensuring application and infrastructure performance. Network traffic growth is the main reason for increasing spending, largely because of the adoption of hybrid and multi-cloud architectures.
In this article, we’ll dive deep into the concept of database sharding, a critical technique for scaling databases to handle large volumes of data and high levels of traffic. We’ll start by defining what sharding is and why it’s essential for modern, high-performance databases. Here’s what you can expect to learn: What is Sharding?:
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.
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.
It’s also critical to have a strategy in place to address these outages, including both documented remediation processes and an observability platform to help you proactively identify and resolve issues to minimize customer and business impact. Outages can disrupt services, cause financial losses, and damage brand reputations.
A cloud migration strategy, however, provides technical optimization that’s also firmly rooted in the business value chain. Migrating to the cloud is a strategy many organizations pursue to streamline and consolidate their security efforts. Likewise, you can scale down when your application experiences decreased traffic.
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?
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. These next-generation cloud monitoring tools present reports — including metrics, performance, and incident detection — visually via dashboards.
Over the last 15+ years, Ive worked on designing APIs that are not only functional but also resilient able to adapt to unexpected failures and maintain performance under pressure. In this article, Ill share practical strategies for designing APIs that scale, handle errors effectively, and remain secure over time.
In my last blog , I’ve provided an example of this happening, whereby the traffic spiked and quadrupled the usual incoming traffic. Below is a step-by-step guide on how to do so, but if you’d prefer to watch the steps check out my Performance Clinic here. Step #3 Get an overview of the campaign traffic.
Ensuring smooth operations is no small feat, whether you’re in charge of application performance, IT infrastructure, or business processes. 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.
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.
For a more proactive approach and to gain further visibility, other SLOs focusing on performance can be implemented. 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. What characterizes a weak SLO?
The implications of software performance issues and outages have a significantly broader impact than in the past—with the potential to negatively impact revenue, customer experiences, patient outcomes, and, of course, brand reputation. With global e-commerce spending projected to reach $6.3
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.
Find and prevent application performance risks A major challenge for DevOps and security teams is responding to outages or poor application performance fast enough to maintain normal service. It should also be possible to analyze data in context to proactively address events, optimize performance, and remediate issues in real time.
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.
We’re happy to announce that with Dynatrace version 1.189, you can give your baselining routines more time to evaluate short-lived performance conditions. 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).
As an application architect, Smith noted it was challenging to ensure software quality and performance when making large-scale changes, including a cloud infrastructure migration and front-end modernization to their unemployment insurance application. The stakes were high. At times, this process extended into weeks and months.
At Dynatrace, we’re constantly striving to come up with solutions that can help modernize your performance and user experience monitoring strategies. Cost and traffic control. The following settings can be applied: Cost and traffic control : 100%. The sections that follow discuss each use case in detail.
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.
Synthetic testing is an IT process that uses software to discover and diagnose performance issues with user journeys by simulating real-user activity. Types of synthetic testing There are three broad types of synthetic testing: availability, web performance, and transaction. Browser clickpaths. HTTP monitors.
The good news: even for latecomers to the compliance party, compliance is perfectly doable within the timeframe given the right tools and strategies. Unified observability is the ability to know how systems and infrastructure are performing based on the data they generate, such as logs, metrics, and traces.
The crisis has emphasized the importance of having a strategy for maintaining stability and performance. Real-time monitoring with out-of-the-box features Real-time data and monitoring are crucial for maintaining situational awareness of IT environment stability and performance, especially during a crisis.
These development and testing practices ensure the performance of critical applications and resources to deliver loyalty-building user experiences. Real user monitoring (RUM) is a performance monitoring process that collects detailed data about users’ interactions with an application. What is real user monitoring?
As Netflix scaled, we faced the mounting challenge of providing accurate, timely answers to increasingly complex queries about title performance and discoverability. By logging all titles as they are displayed, we can process these logs to identify anomalies and gain insights into system performance.
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.
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.
264/AVC Main profile family still represents a substantial portion of the members viewing hours and an even larger portion of the traffic. Performance results In this section, we present an overview of the performance of our new encodes compared to our existing H.264 Yet, given its wide support, our H.264/AVC
He joined us at Perform 2021 to share his experience, highlighting why automatic and intelligent observability and AIOps are crucial. You can see the traffic reaching each milestone of an online shopping journey. SAP makes observability a first-class citizen. Automatic and intelligent observability insights extend to the business.
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. Effortlessly optimize Azure database performance.
Production assets operations are performed in parallel with older data reprocessing without any service downtime. Existing data got updated to be backward compatible without impacting the existing running production traffic. Instead we use Elasticsearch to search those assets which are more performant.
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. This can result in significant cost savings for high traffic applications. ScaleGrid BYOC Pricing: $232/month.
With Dynatrace, we follow a combination of agent and agent-less approach where the “secret sauce” lies in our Dynatrace OneAgent (watch my Performance Clinic YouTube tutorial with our Chief Software Architect Helmut Spiegl ). Resource consumption & traffic analysis. It covers these key areas: Technology & Dependency Analysis.
A quick configuration change may do the trick in improving the performance of your AWS RDS for MySQL instance. Here, we will discuss a notable new feature in Amazon RDS, the Dedicated Log Volume (DLV), that has been introduced to boost database performance. 2xlarge c5.2xlarge MySQL 8.0.31
Let’s delve deeper into how these capabilities can transform your observability strategy, starting with our new syslog support. Customers can also proactively address issues using Davis AI’s predictive analytics capabilities by analyzing network log content, such as retries or anomalies in performance response times.
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.
In their case, this is specifically about the pensions element of their platform which had seen 6-7x as much traffic during the pandemic. The first thing the team did was make sure system performance and responsiveness were front of mind and visible to all stakeholders.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content