High Scalability

JANUARY 11, 2022

from a client it performs two parallel operations: i) persisting the action in the data store ii) publish the action in a streaming data store for a pub-sub model. User Feed Service, Media Counter Service) read the actions from the streaming data store and performs their specific tasks. Data Models. Graph Data Models.

Design 334

Design Media Storage Logistics 334

Smashing Magazine

SEPTEMBER 28, 2022

Five Data-Loading Patterns To Improve Frontend Performance. Five Data-Loading Patterns To Improve Frontend Performance. Data loading patterns are an essential part of your application as they will determine which parts of your application are directly usable by visitors. But isn’t waiting for the data the point?

Cache 126

Cache Performance Servers Social Media 126

The Morning Paper

MARCH 8, 2020

Building an elastic query engine on disaggregated storage , Vuppalapati, NSDI’20. This paper describes the design decisions behind the Snowflake cloud-based data warehouse. An increasingly large fraction of data in modern workloads comes from less predictable and highly variable sources. joins) during query processing.

Storage 112

Storage Engineering Cache Serverless 112

Dynatrace

DECEMBER 13, 2023

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. Here are some ways observability data is important to mobile app performance monitoring.

Best Practices 203

Best Practices Mobile Metrics Performance 203

Dynatrace

AUGUST 1, 2022

Kubernetes was initially designed with a strong focus on stateless workloads, meaning these workloads do not need to store any persistent data. Interestingly, our partner RedHat reported in 2021 that around 80% of deployed workloads are databases or data caches, storing data in persistent volume claims (PVCs).

Storage 193

Storage Database Network Metrics 193

The Netflix TechBlog

SEPTEMBER 24, 2021

As described by the white paper Apple ProRes ( link ), the target data rate of the Apple ProRes HQ for 1920x1080 at 29.97 From chunk encoding to assembly and packaging, the result of each previous processing step must be uploaded to cloud storage and then downloaded by the next processing step. is 220 Mbps.

Cloud 237

Cloud Media Storage Cache 237

Scalegrid

JANUARY 25, 2024

Effective management of memory stores with policies like LRU/LFU proactive monitoring of the replication process and advanced metrics such as cache hit ratio and persistence indicators are crucial for ensuring data integrity and optimizing Redis’s performance. All these contribute significantly towards ensuring smooth functioning.

Metrics 130

Metrics Monitoring Latency Cache 130

ScaleOut Software

APRIL 22, 2020

In this time of extremely high online usage, web sites and services have quickly become overloaded, clogged trying to manage high volumes of fast-changing data. Maintaining rapidly changing data in back-end databases creates bottlenecks that impact responsiveness. The Solution: Distributed Caching.

Cache 52

Cache Storage Servers Database 52

ScaleOut Software

APRIL 22, 2020

In this time of extremely high online usage, web sites and services have quickly become overloaded, clogged trying to manage high volumes of fast-changing data. Maintaining rapidly changing data in back-end databases creates bottlenecks that impact responsiveness. The Solution: Distributed Caching.

Cache 52

Cache Storage Servers Database 52

Scalegrid

DECEMBER 21, 2023

In today’s data-driven world, the ability to effectively monitor and manage data is of paramount importance. Redis®, a powerful in-memory data store, is no exception. To monitor Redis® instances effectively, collect Redis metrics focusing on cache hit ratio, memory allocated, and latency threshold.

Strategy 130

Strategy Monitoring Latency DevOps 130

Percona

APRIL 24, 2023

The more indexes, the more the requirement of memory for effective caching. Indexes need more cache than tables Due to random writes and reads, indexes need more pages to be in the cache. Cache requirements for indexes are generally much higher than associated tables.

Tuning 124

Tuning Cache Storage Database 124

Percona

APRIL 17, 2023

We’ll also go over some best practices for MongoDB security as well as MongoDB data modeling. You can set this by adding the following lines to the /etc/sysctl.conf : vm.dirty_ratio = 15 vm.dirty_background_ratio = 5 Filesystems mount options MongoDB recommends the XFS filesystem for on-disk database data.

Best Practices 52

Best Practices Design Tuning Database 52

Dynatrace

SEPTEMBER 13, 2023

These workflows also utilize Davis® , the Dynatrace causal AI engine, and all your observability and security data across all platforms, in context, at scale, and in real-time. Storing frequently accessed data in faster storage, usually in-memory caching, improves data retrieval speed and overall system performance. Beyond

AWS 252

AWS Efficiency Azure Cloud 252

Scalegrid

NOVEMBER 23, 2023

Configure the PostgreSQL hostname by editing configuration files and restarting the server, with secure storage of connection details to enhance security. PostgreSQL uses the label “hostname” to denote a host connected to a computer network, enabling recognition of this particular entity in all forms of electronic communication.

Database 130

Database Servers Storage Network 130

HammerDB

APRIL 19, 2023

As database performance is heavily influenced by the performance of storage, network, memory, and processors, we must understand the upper limit of these key components. For storage, FIO is generally used. Storage: The system has a SATA drive for the operating system and one NVMe (Intel SSD D7-P5510 (3.84 4.22 %usr 38.40

Performance 54

Performance Benchmarking Cache Storage 54

The Morning Paper

SEPTEMBER 10, 2019

Procella: unifying serving and analytical data at YouTube Chattopadhyay et al., Anchored in the primary use case of supporting Google’s YouTube business, what we’re looking at here could well be the future of data processing at Google. Because they had too many data processing systems! ;). VLDB’19. are divided.

Analytics 75

Analytics Latency Cache Google 75

All Things Distributed

OCTOBER 12, 2016

Fast Data is an emerging industry term for information that is arriving at high volume and incredible rates, faster than traditional databases can manage. Three years ago, as part of our AWS Fast Data journey we introduced Amazon ElastiCache for Redis , a fully managed in-memory data store that operates at sub-millisecond latency.

Scalability 130

Scalability Analytics Cache AWS 130

Percona

JUNE 22, 2023

Database uptime and availability Monitoring database uptime and availability is crucial as it directly impacts the availability of critical data and the performance of applications or websites that rely on the MySQL database. Disk space usage Monitor the disk space usage of MySQL data files, log files, and temporary files.

Performance 102

Performance Monitoring Traffic Database 102

Smashing Magazine

DECEMBER 23, 2020

However, let’s take a step further and learn how to deploy modern qualities to PWAs, such as offline functionality, network-based optimizing, cross-device user experience, SEO capabilities, and non-intrusive notifications and requests. The service workers also retrieve the latest data once the server connection is restored.

Cache 124

Cache Internet Internet Google 124

Smashing Magazine

MAY 25, 2022

Properties of a data structure are considered reachable while the data structure is in memory, and they are usually kept in memory. According to Mozilla Developer Network, the Map object holds key-value pairs and remembers the original insertion order of the keys. Additional Data. smashing = null; // To access the object.

Cache 117

Cache Programming Storage Code 117

The Morning Paper

FEBRUARY 6, 2020

Last week we looked at a function shipping solution to the problem; Cloudburst uses the more common data shipping to bring data to caches next to function runtimes (though you could also make a case that the scheduling algorithm placing function execution in locations where the data is cached a flavour of function-shipping too).

Lambda 98

Lambda Serverless Cache Latency 98

ScaleOut Software

MAY 15, 2014

We have spent a great deal of time at ScaleOut Software re-architecting our in-memory data grid (IMDG)’s code base to make best use of many cores and large memory. For example, the IMDG must be able to efficiently create millions of objects in each server to make use of its huge storage capacity. Please retry later.

Cache 40

Cache Servers Network Design 40

IO River

NOVEMBER 2, 2023

This is where a well-architected Content Delivery Network (CDN) shines. The goal is to boost the pitfalls of network disruptions and vendor dependencies, all while pocketing cost savings. Given its unchanging nature, static content is ideal for caching. It doesn’t change very often and is generally not affected by user sessions.

Architecture 52

Architecture Performance Internet Internet 52

ScaleOut Software

JULY 27, 2018

For more than a decade, in-memory data grids (IMDGs) have proven their usefulness for storing fast-changing data in enterprise applications. Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads.

Scalability 40

Scalability Cache Airlines Healthcare 40

ScaleOut Software

JULY 27, 2018

For more than a decade, in-memory data grids (IMDGs) have proven their usefulness for storing fast-changing data in enterprise applications. Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads.

Scalability 40

Scalability Cache Airlines Healthcare 40

The Morning Paper

NOVEMBER 19, 2019

Local-first software: you own your data, in spite of the cloud Kleppmann et al., The primary issue with cloud apps (the SaaS model) is ownership of the data. Although they let you access your data anywhere, all data access must go via the server, and you can only do the things that the server will let you do.

Cloud 83

Cloud Software Software Internet 83

Particular Software

MAY 22, 2023

1 But inevitably, starting a new project involves lots of meetings with business stakeholders to hash out initial requirements and canonical data models. So we’re now combining data and behavior together…this is essentially an aggregate which means now we’re doing domain-driven design! ? We all love building greenfield projects.

Systems 98

Systems Cache Strategy Database 98

ScaleOut Software

JUNE 22, 2020

From Distributed Caches to Real-Time Digital Twins. The pace of these changes has made it challenging for server-based infrastructures to manage fast-growing populations of users and data sources while maintaining fast response times.

Ecommerce 52

Ecommerce Cache Scalability IoT 52

ScaleOut Software

JUNE 22, 2020

From Distributed Caches to Real-Time Digital Twins. The pace of these changes has made it challenging for server-based infrastructures to manage fast-growing populations of users and data sources while maintaining fast response times.

Ecommerce 52

Ecommerce Cache Scalability IoT 52

The Morning Paper

AUGUST 29, 2019

There’s a broad set of choices including where you store the data, whether you run your own DBMS nodes or use a service, the kinds of instance types to go for if you do run your own, and so forth. We focused on OLAP-oriented parallel data warehouse products available for AWS and restricted our attention to commercially available systems.

Architecture 76

Architecture Cloud Storage Serverless 76

Abhishek Tiwari

DECEMBER 27, 2017

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. There are several emerging data trends that will define the future of ETL in 2018. A common theme across all these trends is to remove the complexity by simplifying data management as a whole.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

IO River

NOVEMBER 2, 2023

Add-on Services - Special services can include capabilities like security features (WAF, Origin Shield), resource capping (rate limiting), image/video optimization, and more.CDN Pricing Examples1) Amazon Cloudfront’s data routing prices are determined according to amount of traffic and region.

Infrastructure 52

Infrastructure Traffic Cache Strategy 52

The Morning Paper

JUNE 23, 2019

Coupled with stateless application servers to execute business logic and a database-like system to provide persistent storage, they form a core component of popular data center service archictectures. If you want to store time-expiring data that should be shared across application processes, used Memcached or Redis.

Cache 79

Cache Latency Google Lambda 79

Brendan Gregg

FEBRUARY 28, 2023

Titus, the Netflix container management platform, is now open source,” [link] Apr 2018 - [Cutress 19] Dr. DDR6: Here's What to Expect in RAM Modules,” [link] Nov 2020 - [Salter 20] Jim Salter, “Western Digital releases new 18TB, 20TB EAMR drives,” [link] Jul 2020 - [Spier 20] Martin Spier, Brendan Gregg, et al.,

Performance 110

Performance Latency Cache Virtualization 110

Smashing Magazine

JANUARY 11, 2021

Build Optimizations JavaScript modules, module/nomodule pattern, tree-shaking, code-splitting, scope-hoisting, Webpack, differential serving, web worker, WebAssembly, JavaScript bundles, React, SPA, partial hydration, import on interaction, 3rd-parties, cache. It will help you build up a company-tailored case study with real data.

Performance 145

Performance Cache Media Metrics 145

IO River

NOVEMBER 2, 2023

This is where a well-architected Content Delivery Network (CDN) shines.  The goal is to boost the pitfalls of network disruptions and vendor dependencies, all while pocketing cost savings. ‍Static Part The major bulk of data transfer in video streaming is static content. Only overflow will await you.Â

Architecture 52

Architecture Performance Internet Internet 52

Highly Scalable

AUGUST 20, 2013

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. This system has been designed to supplement and succeed the existing Hadoop-based system that had too high latency of data processing and too high maintenance costs.

Big Data 154

Big Data Processing Lambda Database 154