Blog, Cache, Latency and Tuning - Technology Performance Pulse

Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

JANUARY 25, 2024

This blog post lists the important database metrics to monitor. Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities.

Metrics

Metrics Monitoring Latency Cache

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

The Netflix TechBlog

MAY 4, 2023

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. Logging is selective to cases where the old and new responses do not match.

Traffic

Traffic Latency Tuning Systems

The Most Important MySQL Setting

Percona

APRIL 7, 2023

If we were to select the most important MySQL setting, if we were given a freshly installed MySQL or Percona Server for MySQL and could only tune a single MySQL variable, which one would it be? To be fair, that is also true with PostgreSQL; it hasn’t been tuned either, and it, too, can also perform much better.

Tuning

Tuning Cache Servers Benchmarking

The evolution of single-core bandwidth in multicore processors

John McCalpin

APRIL 25, 2023

For most high-end processors these values have remained in the range of 75% to 85% of the peak DRAM bandwidth of the system over the past 15-20 years — an amazing accomplishment given the increase in core count (with its associated cache coherence issues), number of DRAM channels, and ever-increasing pipelining of the DRAMs themselves.

Benchmarking

Benchmarking Cache Latency Tuning

InnoDB Performance Optimization Basics

Percona

MARCH 23, 2023

This blog is in reference to our previous ones for ‘Innodb Performance Optimizations Basics’ 2007 and 2013. Although there have been many blogs about adjusting MySQL variables for better performance since then, I think this topic deserves a blog update since the last update was a decade ago, and MySQL 5.7

Performance

Performance Hardware Tuning Storage

Tuning Autovacuum in PostgreSQL and Autovacuum Internals

Percona

AUGUST 10, 2018

We provided an introduction to VACUUM and bloat in an earlier blog post. To know more about dead tuples and bloat, please read our previous blog post. This may help tune your table level autovacuum settings appropriately. Tuning Autovacuum in PostgreSQL. Setting this parameter to 0 logs every autovacuum to the log file.

Tuning

Tuning Cache Database Storage

MongoDB Best Practices: Security, Data Modeling, & Schema Design

Percona

APRIL 17, 2023

In this blog post, we will discuss the best practices on the MongoDB ecosystem applied at the Operating System (OS) and MongoDB levels. The main objective of this post is to share my experience over the past years tuning MongoDB and centralize the diverse sources that I crossed in this journey in a unique place.

Best Practices

Best Practices Design Tuning Database

Migrating Netflix to GraphQL Safely

The Netflix TechBlog

JUNE 14, 2023

This blog post will share broadly-applicable techniques (beyond GraphQL) we used to perform this migration. And we definitely couldn’t replay test non-functional requirements like caching and logging user interaction. To determine customer impact, we could compare various metrics such as error rates, latencies, and time to render.

Traffic

Traffic Latency Cache Metrics

Netflix Cloud Packaging in the Terabyte Era

The Netflix TechBlog

SEPTEMBER 24, 2021

Our previous tech blog Packaging award-winning shows with award-winning technology detailed our packaging technology deployed on the streaming side. Uploading and downloading data always come with a penalty, namely latency. The requirements and challenges for supporting write operations are different from those for read operations.

Cloud

Cloud Media Storage Cache

Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

The Netflix TechBlog

JULY 21, 2022

Since memory management is not something one usually associates with classification problems, this blog focuses on formulating the problem as an ML problem and the data engineering that goes along with it. all OOM kills with the tradeoff of lowering the performance/clearing the cache an extra 1.1% of the time (False Positives).

Big Data

Big Data Cache Engineering Data Engineering

DBLog: A Generic Change-Data-Capture Framework

The Netflix TechBlog

DECEMBER 17, 2019

Requirements In a previous blog post, we discussed Delta , a data enrichment and synchronization platform. In our Delta blog post , we also described use cases beyond data synchronization, such as event processing. Supports any kind of output , so that the output can be a stream, datastore, or even an API. Figure 4— Delta Connector.

Database

Database Traffic Transportation Open Source

DBLog: A Generic Change-Data-Capture Framework

The Netflix TechBlog

DECEMBER 17, 2019

Requirements In a previous blog post, we discussed Delta , a data enrichment and synchronization platform. In our Delta blog post , we also described use cases beyond data synchronization, such as event processing. Supports any kind of output , so that the output can be a stream, datastore, or even an API. Figure 4— Delta Connector.

Database

Database Traffic Transportation Open Source

HTTP/3: Performance Improvements (Part 2)

Smashing Magazine

AUGUST 22, 2021

Because we are dealing with network protocols here, we will mainly look at network aspects, of which two are most important: latency and bandwidth. Latency can be roughly defined as the time it takes to send a packet from point A (say, the client) to point B (the server). Two-way latency is often called round-trip time (RTT).

Performance

Performance Network Latency Servers

HTTP/3: Practical Deployment Options (Part 3)

Smashing Magazine

SEPTEMBER 6, 2021

This approach was touted to be better for fine-grained caching because each subresource could be cached individually and the full bundle didn’t need to be redownloaded if one of them changed. Finally, not inlining resources has an added latency cost because the file needs to be requested. What Does It All Mean? Large preview ).

Network

Network Servers Cache Traffic

HTTP/3 From A To Z: Core Concepts (Part 1)

Smashing Magazine

AUGUST 9, 2021

You may have read some blog posts or heard conference talks on this topic and think you know the answers. However, many other devices are sitting between the client and the server that also have their own TCP code on board (examples include firewalls, load balancers, routers, caching servers, proxies, etc.). Stay tuned! (vf,

Transportation

Transportation Internet Internet Network

Technology Performance Pulse

Crucial Redis Monitoring Metrics You Must Watch

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

Trending Sources

The Most Important MySQL Setting

The evolution of single-core bandwidth in multicore processors

InnoDB Performance Optimization Basics

Tuning Autovacuum in PostgreSQL and Autovacuum Internals

MongoDB Best Practices: Security, Data Modeling, & Schema Design

Migrating Netflix to GraphQL Safely

Netflix Cloud Packaging in the Terabyte Era

Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

DBLog: A Generic Change-Data-Capture Framework

DBLog: A Generic Change-Data-Capture Framework

HTTP/3: Performance Improvements (Part 2)

HTTP/3: Practical Deployment Options (Part 3)

HTTP/3 From A To Z: Core Concepts (Part 1)

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