Remove Latency Remove Metrics Remove Servers
article thumbnail

Optimising for High Latency Environments

CSS Wizardry

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? RTT data should be seen as an insight and not a metric.

Latency 234
article thumbnail

Next-level interaction and customization of data visualizations in Dynatrace Dashboards and Notebooks

Dynatrace

You can use it to visualize CPU utilization across your hosts, disk space used, server-side response time, web request/service failure rates, or any other area where you need to spot outliers immediately. That way, you can compare multiple charts more easily, regardless of the metric or time span.

Latency 246
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Time To First Byte: Beyond Server Response Time

Smashing Magazine

Time To First Byte: Beyond Server Response Time Time To First Byte: Beyond Server Response Time Matt Zeunert 2025-02-12T17:00:00+00:00 2025-02-13T01:34:15+00:00 This article is sponsored by DebugBear Loading your website HTML quickly has a big impact on visitor experience. But actually, theres a lot more to optimizing this metric.

Servers 78
article thumbnail

Migrating Netflix to GraphQL Safely

The Netflix TechBlog

Before GraphQL: Monolithic Falcor API implemented and maintained by the API Team Before moving to GraphQL, our API layer consisted of a monolithic server built with Falcor. A single API team maintained both the Java implementation of the Falcor framework and the API Server. To launch Phase 1 safely, we used AB Testing.

Traffic 357
article thumbnail

Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

The second phase involves migrating the traffic over to the new systems in a manner that mitigates the risk of incidents while continually monitoring and confirming that we are meeting crucial metrics tracked at multiple levels. It provides a good read on the availability and latency ranges under different production conditions.

Traffic 347
article thumbnail

Introducing Impressions at Netflix

The Netflix TechBlog

These events are promptly relayed from the client side to our servers, entering a centralized event processing queue. We accomplish this by gathering detailed column-level metrics that offer insights into the state and quality of each impression. This queue ensures we are consistently capturing raw events from our global userbase.

Tuning 166
article thumbnail

RabbitMQ vs. Kafka: Key Differences

Scalegrid

Its partitioned log architecture supports both queuing and publish-subscribe models, allowing it to handle large-scale event processing with minimal latency. Kafka clusters can be deployed in Kubernetes using Helm charts to simplify scaling and management across multiple servers.

Latency 147