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
This gives fascinating insights into the network topography of our visitors, and how much we might be impacted by high latency regions. Round-trip-time (RTT) is basically a measure of latency—how long did it take to get from one endpoint to another and back again? What is RTT? RTT isn’t a you-thing, it’s a them-thing. Go and sign up.
An AI observability strategy—which monitors IT system performance and costs—may help organizations achieve that balance. AI performs frequent data transfers. They can do so by establishing a solid FinOps strategy. Continuously monitor AI models’ performance. AI requires more compute and storage.
CPU isolation and efficient system management are critical for any application which requires low-latency and high-performance computing. To achieve this level of performance, such systems require dedicated CPU cores that are free from interruptions by other processes, together with wider system tuning.
When serving and storing files on the web, there are a number of different things we need to take into consideration in order to balance ergonomics, performance, and effectiveness. Given that 66% of all websites (and 77% of all requests ) are running HTTP/2, I will not discuss concatenation strategies for HTTP/1.1 in this article.
In this post, we are going to compare the performance and pricing of DigitalOcean PostgreSQL vs. ScaleGrid PostgreSQL to help you determine the best PostgreSQL hosting service on DigitalOcean. Compare Latency. lower latency compared to DigitalOcean for PostgreSQL. PostgreSQL DigitalOcean Performance Test. Compare Pricing.
Mobile applications (apps) are an increasingly important channel for reaching customers, but the distributed nature of mobile app platforms and delivery networks can cause performance problems that leave users frustrated, or worse, turning to competitors. What is mobile app performance?
As an engineer, you probably know that server performance under heavy load is crucial for maintaining the availability and responsiveness of your services. In this post, we'll explore both strategies through a simple simulation in Colab, allowing you to see the impact of changing parameters on system performance.
Replication Strategy. High performance. Does it affect latency? Yes, you can see an increase in latency. So, if you’re hosting your application in AWS or Azure and move your database to DigitalOcean, you will see an increase in latency. Meltdown Performance Impact on MongoDB: AWS, Azure & DigitalOcean.
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. To launch Phase 1 safely, we used AB Testing.
By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.
A lot of companies—even if they are aware that performance is key to their business—are often unsure of how, when, or where performance testing sits within their development lifecycle. To make things worse, they’re also usually unsure whose responsibility performance measuring and monitoring is.
This blog series will examine the tools, techniques, and strategies we have utilized to achieve this goal. The first phase involves validating functional correctness, scalability, and performance concerns and ensuring the new systems’ resilience before the migration. This approach has a handful of benefits.
Buckle up as we delve into the world of Redis monitoring, exploring the most important Redis metrics, discussing essential tools, and even peering into the future of Redis performance management. Key Takeaways Redis monitoring is essential for safeguarding performance, reliability, and security.
By Jose Fernandez , Sebastien Dabdoub , Jason Koch , Artem Tkachuk The Compute and Performance Engineering teams at Netflix regularly investigate performance issues in our multi-tenant environment. Traditional performance analysis tools such as perf can introduce significant overhead, risking further performance degradation.
Telemetry Telemetry involves collecting and analyzing data from distributed sources to provide insights into how a system is performing. Quantitative measurements that track the performance and health of systems over time. Traces are used for performance analysis, latency optimization, and root cause analysis.
Mastering Hybrid Cloud Strategy Are you looking to leverage the best private and public cloud worlds to propel your business forward? A hybrid cloud strategy could be your answer. Understanding Hybrid Cloud Strategy A hybrid cloud merges the capabilities of public and private clouds into a singular, coherent system.
In the realm of production applications, it is customary to address performance bottlenecks such as CPU and memory issues in order to identify and resolve their underlying causes. It is worth noting that this data collection process does not impact the performance of the application.
Firstly, developers struggled to reason about consistency, durability and performance in this complex global deployment across multiple stores. These include challenges with tail latency and idempotency, managing “wide” partitions with many rows, handling single large “fat” columns, and slow response pagination.
Performance is usually a primary concern when using stream processing frameworks. See more about the performance of stream processing frameworks in our published paper. ShuffleBench i s a benchmarking tool for evaluating the performance of modern stream processing frameworks. This significantly increases event latency.
A well-planned multi cloud strategy can seriously upgrade your business’s tech game, making you more agile. Key Takeaways Multi-cloud strategies have become increasingly popular due to the need for flexibility, innovation, and the avoidance of vendor lock-in. They can also bolster uptime and limit latency issues or potential downtimes.
Buckle up as we delve into the world of Redis® monitoring, exploring the most important Redis® metrics, discussing essential tools, and even peering into the future of Redis® performance management. Key Takeaways Redis® monitoring is essential for safeguarding performance, reliability, and security.
Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. CFS is widely used and therefore well tested and Linux machines around the world run with reasonable performance.
Rajiv Shringi Vinay Chella Kaidan Fullerton Oleksii Tkachuk Joey Lynch Introduction As Netflix continues to expand and diversify into various sectors like Video on Demand and Gaming , the ability to ingest and store vast amounts of temporal data — often reaching petabytes — with millisecond access latency has become increasingly vital.
The service should be able to serve real-time, aka UI, applications so CRUD and search operations should be achieved with low latency. Our service will be used by a lot of internal UI applications hence the latency for CRUD and search operations must be low. Search latency for the generic text queries are in milliseconds.
The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability. And how can you verify this performance consistently across a multicloud environment that also uses Microsoft Azure and Google Cloud Platform frameworks?
It is a transparent layer for the data consumer in terms of user usability except to improve performance. Performance is the other reason to use a cache system such as in-memory databases to provide a high-performance solution with low latency, high throughput, and concurrency.
This blog post explores how AI observability enables organizations to predict and control costs, performance, and data reliability. Data dependencies and framework intricacies require observing the lifecycle of an AI-powered application end to end, from infrastructure and model performance to semantic caches and workflow orchestration.
A small percentage of production traffic is redirected to the two new clusters, allowing us to monitor the new version’s performance and compare it against the current version. Canaries also provide an opportunity to measure system performance under different load conditions, allowing us to identify and resolve any performance bottlenecks.
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.
To that end, it’s important that we prevent significant performance regressions from reaching the production app. Any performance regression that makes it into a product release will degrade user experience, so the challenge is to detect and fix such regressions before they ship. What do we mean by Performance?
Real-time stream processing to perform live activity tracking, data cleansing, metrics generation, and more. The function itself performs a small unit of work and Lambda charges subscribers by the millisecond. Optimizing Lambda for performance. AWS continues to improve how it handles latency issues.
The primary goal of ITOps is to provide a high-performing, consistent IT environment. Organizations measure these factors in general terms by assessing the usability, functionality, reliability, and performance of products and services. Performance. What does IT operations do? ITOps vs. DevOps and DevSecOps.
Every new origin we need to visit needs a connection opening, and that can be very costly: DNS resolution, TCP handshakes, and TLS negotiation all add up, and the story gets worse the higher the latency of the connection is. On a slower, higher-latency connection, the story is much, mush worse. All completely avoidable. to just 3.6s.
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. Improved performance and availability. Inconsistent performance.
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.
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 approach ensures that no RabbitMQ node becomes a bottleneck or a single point of failure.
However, not all cloud strategies are the same. Reduced latency. By using cloud providers with multiple server sites, organizations can reduce function latency for end users. Serverless computing frameworks typically rely on software containers to provide on-demand performance and provisioning. Optimizes resources.
But with that speed and agility comes new complications and complexity, all while maintaining performance and reliability with less than 1% down-time per year. Reduced latency. If you haven’t implemented either, a best practice to get started is to develop a strategy that incorporates both DevOps and SRE practices.
In that scenario, the system would need to deal with the data propagation latency directly, for example, by use of timeouts or client-originated update tracking mechanisms. We started seeing increased response latencies and leader servers running at dangerously high utilization.
So, what are the performance implications of all of this? Although this response has a 0B filesize, we will always take the latency hit on every single page view (and this response is basically 100% latency). com , which introduces yet more latency for the connection setup. It is actively harmful for performance.
Historically, NoSQL paid a lot of attention to tradeoffs between consistency, fault-tolerance and performance to serve geographically distributed systems, low-latency or highly available applications. Read/Write latency. Read/Write requests are processes with a minimal latency. Read/Write scalability. Fault-tolerance.
So, for the last several years, I, along with other performance engineers like me, have been recommending that our clients move over from Gzip and to Brotli instead. This simple, elegant strategy manages to balance caution with optimism, and applies to every new TCP connection that your web application makes. Running the Tests.
This methodology aims to improve software system reliability using several key categories such as availability, performance, latency, efficiency, capacity, and incident response. Organizations that are new to both practices will want to adopt a strategy that incorporates both. What are SLOs?
Because cloud services rely on a uniquely distributed and dynamic architecture, observability may also sometimes refer to the specific software tools and practices businesses use to interpret cloud performance data. Observability enables you to understand what is slow or broken and what needs to be done to improve performance.
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