Cache-Control for Civilians

CSS Wizardry

To this end, having a solid caching strategy can make all the difference for your visitors. ?? How is your knowledge of caching and Cache-Control headers? That being said, more and more often in my work I see lots of opportunities being left on the table through unconsidered or even completely overlooked caching practices. Cache-Control. One of the most common and effective ways to manage the caching of your assets is via the Cache-Control HTTP header.

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How to Boost Redis With Local Caching in Java

DZone

You may also like: Java-Distributed Caching in Redis. Local caching is one of the most useful tactics for improving the performance of databases such as Redis. Redis developers who want to do local caching in Java will find that Redis doesn’t support this functionality out of the box. In this article, we’ll discuss how you can take advantage of local caching in Java and Redis with a third-party Redis Java client such as Redisson. Boost Redis.

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Design Of A Modern Cache—Part Deux

High Scalability

The previous article described the caching algorithms used by Caffeine , in particular the eviction and concurrency models. This allows for quickly discarding new arrivals that are unlikely to be used again, guarding the main region from cache pollution. cacheThis is a guest post by Benjamin Manes , who did engineery things for Google and is now doing engineery things as CTO of Vector.

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From Caching to CDN: How To Decide Which Way to Go

DZone

There are two effective methods to improve the load time — data caching and using a content delivery network (CDN). performance caching cdn website speed data cachingIn an attempt to speed up their websites, owners are ready to take various measures.

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How to Create a Simple and Efficient PHP Cache

DZone

Caching is extremely useful in order to speed up PHP webpages. In this article, I’ll show you how to make a simple PHP caching system for your web pages. tutorial performance cache page cachingWhen working on PHP websites made from scratch and without a framework, speed can often be an issue.

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The Best Way to Completely Purge a Varnish Cache

DZone

performance cache varnish vclIt's cargo-cult fighting time! Today, we are going to look at a ban expression that you probably have used, and maybe even have recommended (gasp!) to your fellow Varnish users: req.url ~ /.

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Comparing Apache Ignite In-Memory Cache Performance With Hazelcast In-Memory Cache and Java Native Hashmap

DZone

java big data performance apache ignite in-memory data grid in-memory caching distributed cacheOverview. This article compares different options for the in-memory maps and their performances in order for an application to move away from traditional RDBMS tables for frequently accessed data. In this case, for the sake of demonstration, I have taken 2 million dummy physician records that reside in the database table and migrated them to in-memory maps.

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Using Cache in Spring Boot

DZone

A solution to that problem could be using a cache, but how do you implement it? In that article, I explain how to use a basic cache in Spring Boot. java performance spring boot cacheLet's imagine a web application, where for each request received, it must read some configuration data of a database. That data doesn't change usually, but the application, in each request, must connect, execute the correct instructions to read the data, pick it up from the network, etc.

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WebP Caching has Landed!

KeyCDN

We’re happy to announce that WebP Caching has landed! How Does WebP Caching Work? Enable the Feature for your Zones Cache Key WebP can be enabled for all Pull Zones. Once enabled, a Zone will cache each image separately as WebP and the other image format (e.g. In the beginning, this can impact your cache hit percentage depending on the number of images you’re delivering since each image is now cached twice.

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Use Distributed Caching to Accelerate Online Web Sites

ScaleOut Software

The Solution: Distributed Caching. A widely used technology called distributed caching meets this need by storing frequently accessed data in memory on a server farm instead of within a database. Also called in-memory data grids , distributed caches, such as ScaleOut StateServer® , use server farms to both scale storage capacity and accelerate access throughput, thereby maintaining fast data access at all times. Distributed Caches Can Help Now.

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Use Distributed Caching to Accelerate Online Web Sites

ScaleOut Software

The Solution: Distributed Caching. A widely used technology called distributed caching meets this need by storing frequently accessed data in memory on a server farm instead of within a database. Also called in-memory data grids , distributed caches, such as ScaleOut StateServer® , use server farms to both scale storage capacity and accelerate access throughput, thereby maintaining fast data access at all times. Distributed Caches Can Help Now.

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Exploring a back/forward cache for Chrome

Addy Osmani

bfcache creates a cache allowing for instant navigations to previously-visited pages

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Static Analysis of Java Enterprise Applications: Frameworks and Caches, the Elephants in the Room

The Morning Paper

Static analysis of Java enterprise applications: frameworks and caches, the elephants in the room , Antoniadis et al., PLDI’20. Static analysis is a key component of many quality and security analysis tools. Being static , it has the advantage that analysis results can be produced solely from source code without the need to execute the program.

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Design Patterns: Cache-Aside Pattern

cdemi

Applications that rely heavily on a data-store usually can benefit greatly from using the Cache-Aside Pattern. If used correctly, this pattern can improve performance and help maintain consistency between the cache and the underlying data store. Using the Cache-Aside Pattern dictates that when you want to retrieve an item from the Data Store, first you check in your cache. If the item exists in the cache, you can use that. Lifetime of Cached Data.

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AppFabric Caching: Retry Later

ScaleOut Software

Given all this, we thought it would be a good opportunity to see how we are doing relative to the competition, and in particular, relative to Microsoft’s AppFabric caching for Windows on-premise servers. During load-balancing, the client gets the following exception when accessing the cache: ErrorCode<ERRCA0017>:SubStatus<ES0006>:There is a temporary failure. One or more specified cache servers are unavailable, which could be caused by busy network or servers. …).

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Intro to Redis Cluster Sharding – Advantages, Limitations, Deploying & Client Connections

High Scalability

Redis Cluster is the native sharding implementation available within Redis that allows you to automatically distribute your data across multiple nodes without having to rely on external tools and utilities. At ScaleGrid, we recently added support for Redis Clusters on our platform through our fully managed Redis hosting plans.

Compress objects, not cache lines: an object-based compressed memory hierarchy

The Morning Paper

Compress objects, not cache lines: an object-based compressed memory hierarchy Tsai & Sanchez, ASPLOS’19. Existing cache and main memory compression techniques compress data in small fixed-size blocks, typically cache lines. The first one is that object-based applications perform memory accesses within objects and follow pointers to other objects: Therefore objects, not cache lines, are the right compression unit.

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How to Make Your Website Load Faster

DZone

website load speed website speed wordpress website design and development website speed test in drupal caching with vanish drupal caching wordpress cachingFaster loading? Sign me up! If you implement testing your landing page you will get to know that you are wasting your money by just throwing them inside pits. The reason behind this is that your pages are simply not loading as expected as fast enough.

Improving Spark Memory Resource With Off-Heap In-Memory Storage

DZone

performance storage spark caching in-memory spark caching spark memoryImprove your Spark memory. In the previous tutorial , we demonstrated how to get started with Spark and Alluxio. To share more thoughts and experiments on how Alluxio enhances Spark workloads, this article focuses on how Alluxio helps to optimize the memory utilization of Spark applications.

Top Redis Use Cases by Core Data Structure Types

Scalegrid

Depending on how it is configured, Redis can act like a database, a cache or a message broker. Session Cache: Many websites leverage Redis Strings to create a session cache to speed up their website experience by caching HTML fragments or pages. Since data is stored temporarily in the RAM, this attribute makes Redis a perfect choice as a session cache. Learn how to build your own Twitter feed in our Caching tweets using Node.js, Redis and Socket.io

PostgreSQL Connection Pooling: Part 2 – PgBouncer

Scalegrid

Once the authentication succeeds: PgBouncer checks for a cached connection, with the same username+database combination. If a cached connection is found, it returns the connection to the client. If a cached connection is not found, it creates a new connection, provided creating a new connection does not: Increase the number of connections to > pool_size. When it comes to connection pooling in the PostgreSQL world, PgBouncer is probably the most popular option.

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How It Works (It Just Runs Faster): Non-Volatile Memory SQL Server Tail Of Log Caching on NVDIMM

SQL Server According to Bob

Note: We received feedback that there was some confusion on us calling this functionality “tail of the log caching” because our documentation and prior history has referred to the tail of the log as the portion of the hardened log that has not been backed up. This feature is actually officially called Persisted Log Buffer and is an acceleration of the hardening of the log cache as blogged by Kevin Farlee at [link]. Tail Of Log Caching.

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Expanding the Cloud: More memory, more caching and more performance for your data

All Things Distributed

Amazon ElastiCache is a fully managed, in-memory caching service for customers to optimize the latency, performance and cost of their read workloads. You can augment your database tier with a caching layer using Amazon ElastiCache to lower read costs and reduce read latency using Memcached and now Redis, especially if you need those advanced data structures that are not typically provided by your database tier.

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Re-Architecting the Video Gatekeeper

The Netflix TechBlog

The Tech Hollow , an OSS technology we released a few years ago, has been best described as a total high-density near cache : Total : The entire dataset is cached on each node?—?there there is no eviction policy, and there are no cache misses. Near : the cache exists in RAM on any instance which requires access to the dataset. with the same amount of memory Hollow may be able to cache 100% of your dataset and achieve a 100% hit rate.

Application Scalability — How To Do Efficient Scaling

DZone

performance performance testing performance and monitoring performance tuning performance engineering application scaling performance caching scalability and performanceWhen you build a great product or application, sooner or later, it will be drawing attention more and more users who will expect a flawless, perfect application as the demand grows in the time it handles more and more requests per minute.

Self-Host Your Static Assets

CSS Wizardry

Users might already have the file cached. If website-a.com links to [link] , and a user goes from there to website-b.com who also links to [link] , then the user will already have that file in their cache. Penalty: Caching. This makes it very safe and sensible to enforce a reasonably aggressive cache policy. That said, this isn’t always the case, and by self-hosting your assets you can design much more bespoke caching strategies. Myth: Cross-Domain Caching.

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The Power of Integrated Analytics Within an IMDG

ScaleOut Software

For more than fifteen years, ScaleOut StateServer® has demonstrated technology leadership as an in-memory data grid (IMDG) and distributed cache. ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . In-Memory Data Grids for Fast-Changing Data.

The Power of Integrated Analytics Within an IMDG

ScaleOut Software

For more than fifteen years, ScaleOut StateServer® has demonstrated technology leadership as an in-memory data grid (IMDG) and distributed cache. ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . In-Memory Data Grids for Fast-Changing Data.

The Amazing Evolution of In-Memory Computing

ScaleOut Software

From Distributed Caches to Real-Time Digital Twins. Emerging in the early 2000s, the first such platforms provided distributed caching on clustered servers with straightforward APIs for storing and retrieving in-memory objects. For example, ecommerce applications adopted distributed caching to store session-state, shopping carts, product descriptions, and other data that shoppers need to be able to access quickly.

The Amazing Evolution of In-Memory Computing

ScaleOut Software

From Distributed Caches to Real-Time Digital Twins. Emerging in the early 2000s, the first such platforms provided distributed caching on clustered servers with straightforward APIs for storing and retrieving in-memory objects. For example, ecommerce applications adopted distributed caching to store session-state, shopping carts, product descriptions, and other data that shoppers need to be able to access quickly.

Top Redis Use Cases by Core Data Structure Types

High Scalability

Depending on how it is configured, Redis can act like a database, a cache or a message broker. C C++ Caching Clustering Database DevOps Message Broker Redis Redis Cluster Redis Hashes Redis Lists Redis Queues Redis Sets Redis Sorted Sets Redis Strings Use Cases administrator cache data management database databases in-memory key-value store nosql open source queue redis

Working with fio “distribution /pereto” parameter

n0derunner

We would expect a similar shape for any sort of caching mechanism. Nutanix X-Ray caching fio modelingThe fio Pareto parameter allows us to create a workload, which references a very large dataset, but specify a hotspot for the access pattern. Here’s an example using the same setup as the ILM experiment, but using a Pareto value of 0:8. My fio file looks like this. global]. ioengine=libaio. direct=1. time_based. norandommap. random_distribution=pareto:0.8.

Redis vs Memcached

Software Architecture

nosql caching memcached database redisMemcached is in-memory key value store whereas Redis is in-memory data structures store. Memcached supports only string data type which is ideal for storing read-only data. Redis supports almost all types of data. Redis can also be used as a messaging system such as pubsub. Memcached cannot be used as a pubsub system. Memcached is more memory efficient than Redis. Redis is more memory efficient only after you use hashes.

Redis Cloud Gets Easier with Fully Managed Hosting on Azure

High Scalability

The demand for Redis is skyrocketing across dozens of use cases, particularly for cache, queues, geospatial data, and high speed transactions. ScaleGrid, a rapidly growing leader in the Database-as-a-Service (DBaaS) space, has just launched their new fully managed Redis on Azure service.

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Memory Latency on the Intel Xeon Phi x200 “Knights Landing” processor

John McCalpin

The modes that are important are: “Flat” vs “Cache” In “Flat” mode, MCDRAM memory is used as directly accessible memory, occupying the upper 16 GiB of physical address space. In “Cache” mode, MCDRAM memory is used as an L3 cache for the main DDR4 memory. I will discuss the performance characteristics of Cache mode at a later date.

Use Parallel Analysis – Not Parallel Query – for Fast Data Access and Scalable Computing Power

ScaleOut Software

Looking beyond distributed caching, it’s their ability to perform data-parallel analysis that gives IMDGs such exciting capabilities. Application developers often deploy IMDGs as a distributed cache that sits between an application and its database; the IMDG offloads ephemeral data from the database. For more than a decade, in-memory data grids (IMDGs) have proven their usefulness for storing fast-changing data in enterprise applications.

Use Parallel Analysis – Not Parallel Query – for Fast Data Access and Scalable Computing Power

ScaleOut Software

Looking beyond distributed caching, it’s their ability to perform data-parallel analysis that gives IMDGs such exciting capabilities. Application developers often deploy IMDGs as a distributed cache that sits between an application and its database; the IMDG offloads ephemeral data from the database. For more than a decade, in-memory data grids (IMDGs) have proven their usefulness for storing fast-changing data in enterprise applications.

Invited Talk at SuperComputing 2016!

John McCalpin

Computer Architecture Computer Hardware Performance cache DRAM high performance computing memory bandwidth memory latency STREAM benchmark“Memory Bandwidth and System Balance in HPC Systems” If you are planning to attend the SuperComputing 2016 conference in Salt Lake City next month, be sure to reserve a spot on your calendar for my talk on Wednesday afternoon (4:15pm-5:00pm).

PostgreSQL Connection Pooling: Part 2 – PgBouncer

High Scalability

Client Connections Connection Pooler Connection Pooling Database DevOps Performance PgBouncer Pooling Modes PostgreSQL Server Postgres administration cache connection-pool data management database database scalability deployment devops haproxy high performance load balancer open source postgresql server server load sql tutorialWhen it comes to connection pooling in the PostgreSQL world, PgBouncer is probably the most popular option.

Why Do We Need the Volatile Keyword?

DZone

Even if my application runs in the cloud on the JVM, despite all of those software layers abstracting away the underlying hardware, the volatile keyword is still needed due to the cache of the processor that my software runs on. The Volatile Keyword and the Cache of Modern Processors. Modern processors, like the Intel Xeon or the AMD Ryzen, cache the values from the main memory in per-core caches to improve the memory access performance.

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Memory Bandwidth Requirements of the HPL benchmark

John McCalpin

The algorithms used by HPL have lots of data re-use (both in registers and from the caches), but the data still has to go to and from memory, so the bandwidth requirement is not zero, which means that at some point in scaling the number of cores or frequency or FP operations per cycle, we are going to run out of the available memory bandwidth. Algorithms Performance arithmetic cache high performance computing memory bandwidth

Expanding the Cloud - Introducing Amazon ElastiCache - All Things.

All Things Distributed

Today AWS has launched Amazon ElastiCache , a new service that makes it easy to add distributed in-memory caching to any application. Amazon ElastiCache handles the complexity of creating, scaling and managing an in-memory cache to free up brainpower for more differentiating activities. Systems that make extensive use of caching almost all report a significant reduction in the cost of their database tier. All Things Distributed.

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PostgreSQL Connection Pooling: Part 3 – Pgpool-II

Scalegrid

Once authentication succeeds, it checks if it already has a cached connection for this database+user combination. Once the client disconnects, Pgpool-II has to decide whether to cache the connection: If it has an empty slot, it caches it. If it doesn’t have an empty slot (that is, caching this connection would exceed the max_pool_size allowed), it will decide based on an internal algorithm.

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