The Netflix TechBlog

NOVEMBER 12, 2024

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.

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The Netflix TechBlog

SEPTEMBER 3, 2021

Remote calls are never free; they impose extra latency, increase probability of an error, and consume network bandwidth. How can we achieve a similar functionality when designing our gRPC APIs? This (alongside some other techniques like ZigZag encoding for signed types) makes protobuf messages space-efficient.

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The Netflix TechBlog

SEPTEMBER 10, 2024

With these clear benefits, we continued to build out this functionality for more devices, enabling the same efficiency wins. To support this growth, we’ve revisited Pushy’s past assumptions and design decisions with an eye towards both Pushy’s future role and future stability. It served Pushy’s needs well for many years.

Latency 228

Latency Cache Tuning Efficiency 228

Scalegrid

JANUARY 25, 2024

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. These essential data points heavily influence both stability and efficiency within the system.

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Dynatrace

MAY 23, 2024

Such frameworks support software engineers in building highly scalable and efficient applications that process continuous data streams of massive volume. Stream processing systems, designed for continuous, low-latency processing, demand swift recovery mechanisms to tolerate and mitigate failures effectively.

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Scalegrid

AUGUST 27, 2024

Learn how RabbitMQ can boost your system’s efficiency and reliability in these practical scenarios. Understanding RabbitMQ as a Message Broker RabbitMQ is a powerful message broker that enables applications to communicate by efficiently directing messages from producers to their intended consumers.

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Dynatrace

SEPTEMBER 13, 2023

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.

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The Netflix TechBlog

NOVEMBER 17, 2022

While conventional video codecs remain prevalent, NN-based video encoding tools are flourishing and closing the performance gap in terms of compression efficiency. We employed an adaptive network design that is applicable to the wide variety of resolutions we use for encoding. How do we apply neural networks at scale efficiently?

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The Netflix TechBlog

JANUARY 10, 2024

This architecture shift greatly reduced the processing latency and increased system resiliency. We expanded pipeline support to serve our studio/content-development use cases, which had different latency and resiliency requirements as compared to the traditional streaming use case. divide the input video into small chunks 2.

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The Netflix TechBlog

SEPTEMBER 24, 2021

Figure 1: A Simplified Video Processing Pipeline With this architecture, chunk encoding is very efficient and processed in distributed cloud computing instances. Uploading and downloading data always come with a penalty, namely latency.

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Dynatrace

JANUARY 31, 2024

Model observability provides visibility into resource consumption and operation costs, aiding in optimization and ensuring the most efficient use of available resources. Observing AI models Running AI models at scale can be resource-intensive. However, organizations must consider which use cases will bring them the biggest ROI.

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Cache Azure Infrastructure Monitoring 214

Dynatrace

JUNE 7, 2023

A typical design pattern is the use of a semantic search over a domain-specific knowledge base, like internal documentation, to provide the required context in the prompt. With these latency, reliability, and cost measurements in place, your operations team can now define their own OpenAI dashboards and SLOs.

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Monitoring Latency Metrics Azure 172

Dynatrace

JANUARY 17, 2024

As organizations turn to artificial intelligence for operational efficiency and product innovation in multicloud environments, they have to balance the benefits with skyrocketing costs associated with AI. The good news is AI-augmented applications can make organizations massively more productive and efficient. Use containerization.

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Strategy Artificial Intelligence Storage Cloud 232

Scalegrid

MARCH 28, 2024

Redis Data Types and Structures The design of Redis’s data structures emphasizes versatility. Snapshots provide point-in-time captures of the dataset, which are efficient for recovery on startup. It is designed to cache plain text values, offering fast read and write access to frequently accessed data.

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The Netflix TechBlog

NOVEMBER 20, 2023

We will show how we are building a clean and efficient incremental processing solution (IPS) by using Netflix Maestro and Apache Iceberg. As our business scales globally, the demand for data is growing and the needs for scalable low latency incremental processing begin to emerge. past 3 hours or 10 days).

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Processing Big Data Efficiency Engineering 215

Scalegrid

JANUARY 8, 2024

This article delves into the specifics of how AI optimizes cloud efficiency, ensures scalability, and reinforces security, providing a glimpse at its transformative role without giving away extensive details. Using AI for Enhanced Cloud Operations The integration of AI in cloud computing is enhancing operational efficiency in several ways.

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The Netflix TechBlog

DECEMBER 21, 2020

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture.

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Storage Latency Efficiency Data Engineering 209

Scalegrid

NOVEMBER 4, 2024

The results will help database administrators and decision-makers choose the right platform for their performance, scalability, and cost-efficiency needs. Network Latency : We ran both machines in the same region and conducted the tests from within the same box in that region. Key metrics include TPS and QPS.

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Benchmarking AWS Tuning Metrics 130

The Netflix TechBlog

MARCH 4, 2024

We have deployed Auto Remediation in production for handling memory configuration errors and unclassified errors of Spark jobs and observed its efficiency and effectiveness (e.g., For efficient error handling, Netflix developed an error classification service, called Pensive, which leverages a rule-based classifier for error classification.

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Tuning Efficiency Big Data Engineering 214

Scalegrid

FEBRUARY 8, 2024

Their design emphasizes increasing availability by spreading out files among different nodes or servers — this approach significantly reduces risks associated with losing or corrupting data due to node failure. Variations within these storage systems are called distributed file systems.

Storage 130

Storage Systems Big Data Azure 130

Dynatrace

JULY 24, 2023

In the world of DevOps and SRE, DevOps automation answers the undeniable need for efficiency and scalability. This evolution in automation, referred to as answer-driven automation, empowers teams to address complex issues in real time, optimize workflows, and enhance overall operational efficiency.

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The Netflix TechBlog

OCTOBER 27, 2020

The data warehouse is not designed to serve point requests from microservices with low latency. Therefore, we must efficiently move data from the data warehouse to a global, low-latency and highly-reliable key-value store. As most key-value storage engines support efficiently deleting a namespace (e.g.

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The Netflix TechBlog

APRIL 26, 2022

We will share how its design has evolved over the years and the lessons learned while building it. To understand Axion’s design, we need to know the various components that interact with it. The motivation has not changed since then; the design has. Design evolution Axion fact store has four components?—?fact

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Storage Design Scalability Latency 192

Dynatrace

OCTOBER 4, 2022

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. Data lakehouses deliver the query response with minimal latency. Data warehouses.

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Artificial Intelligence Storage Analytics Government 219

Adrian Cockcroft

NOVEMBER 18, 2024

Paul Reed, Clean Energy & Sustainability, AWS Solutions, Amazon Web Services SUS101 | Advancing sustainable AWS infrastructure to power AI solutions In this session, learn how AWS is committed to innovating with data center efficiency and lowering its carbon footprint to build a more sustainable business.

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AWS Energy Lambda Government 98

Percona

APRIL 17, 2023

The CFQ works well for many general use cases but lacks latency guarantees. The deadline excels at latency-sensitive use cases ( like databases ), and noop is closer to no schedule at all. On the other hand, MongoDB schema design takes a document-oriented approach. Two other schedulers are deadline and noop.

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The Netflix TechBlog

MARCH 1, 2021

It supports both high throughput services that consume hundreds of thousands of CPUs at a time, and latency-sensitive workloads where humans are waiting for the results of a computation. The subsystems all communicate with each other asynchronously via Timestone, a high-scale, low-latency priority queuing system. Warm capacity.

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VoltDB

AUGUST 8, 2024

Real-time data platform defined A real-time data platform is designed to ingest, process, analyze, and act upon data instantaneously — right when it’s generated or received. Improved operational efficiency Real-time data platforms enhance operational efficiency by providing timely insights and automating processes.

IoT 52

IoT Latency Traffic Logistics 52

Percona

MARCH 20, 2023

When designing an architecture, many components need to be considered before deciding on the best solution. In this scenario, it is also crucial to be efficient in resource utilization and scaling with frugality. Let us take a look also the latency: Here the situation starts to be a little bit more complicated.

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The Netflix TechBlog

MARCH 7, 2024

Since its inception , Metaflow has been designed to provide a human-friendly API for building data and ML (and today AI) applications and deploying them in our production infrastructure frictionlessly. In other cases, it is more convenient to share the results via a low-latency API.

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Systems Media Cache Open Source 232

The Netflix TechBlog

OCTOBER 26, 2021

False negatives are closely related to the statistical concept of power , which gives the probability of a true positive given the experimental design and a true effect of a specific size. As a result, if the test treatment results in a small reduction in the latency metric, it’s hard to successfully identify?

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Testing Latency Metrics Design 208

IO River

NOVEMBER 2, 2023

A content delivery network (CDN) is a distributed network of servers strategically located across multiple geographical locations to deliver web content to end users more efficiently. The Four Pillars of CDN Design‍CDN architecture can be broken down into several building blocks, known as the Four Pillars of CDN Design.

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Architecture Cache Performance Latency 52

The Netflix TechBlog

JUNE 13, 2023

By collecting and analyzing key performance metrics of the service over time, we can assess the impact of the new changes and determine if they meet the availability, latency, and performance requirements. One can perform this comparison live on the request path or offline based on the latency requirements of the particular use case.

Traffic 282

Traffic Metrics Systems Strategy 282

Scalegrid

JANUARY 12, 2024

This article analyzes cloud workloads, delving into their forms, functions, and how they influence the cost and efficiency of your cloud infrastructure. Simply put, it’s the set of computational tasks that cloud systems perform, such as hosting databases, enabling collaboration tools, or running compute-intensive algorithms.

Cloud 130

Cloud Virtualization Storage Efficiency 130

Dynatrace

APRIL 5, 2021

The 2014 launch of AWS Lambda marked a milestone in how organizations use cloud services to deliver their applications more efficiently, by running functions at the edge of the cloud without the cost and operational overhead of on-premises servers. AWS continues to improve how it handles latency issues. Dynatrace news.

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Lambda AWS Serverless Hardware 187

ACM Sigarch

MAY 31, 2023

Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications. even lowered the latency by introducing a multi-headed device that collapses switches and memory controllers. The recently announced CXL3.0

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Latency Hardware Cache Architecture 52

The Netflix TechBlog

SEPTEMBER 8, 2020

For each route we migrated, we wanted to make sure we were not introducing any regressions: either in the form of missing (or worse, wrong) data, or by increasing the latency of each endpoint. Being able to canary a new route let us verify latency and error rates were within acceptable limits. This meant that data that was static (e.g.

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Latency Cache Java Traffic 238