By caching this type of data on the client side, the API can reduce the amount of data that needs to be sent over the network and improve the performance of the website or application.

Client-side caching can be particularly useful for mobile users, as it can help to reduce the amount of data that needs to be downloaded and improve performance on slow or congested networks. When deciding whether to use client-side caching, API developers should consider the nature of the data being stored and how frequently it is likely to change.

If the data is unlikely to change frequently, client-side caching can be a useful strategy to improve the performance and scalability of the API. However, if the data is likely to change frequently, client-side caching may not be the best approach, as it could result in outdated data being displayed to the user.

There are pros and cons of client-side caching. API developers should consider these factors when deciding whether to use client-side caching and how to implement it in their API. Server-side caching is a technique used to cache data on the server to reduce the amount of data that needs to be transferred over the network.

This can improve the performance of an API by reducing the time required to serve a request, and can also help to reduce the load on the API server. Database caching involves caching the results of database queries on the server, so that subsequent requests for the same data can be served quickly without having to re-run the query.

This can improve the performance of an API by reducing the time required to fetch data from a database. In-memory caching: This technique involves storing data in the server's RAM, so that when a request for that data is made, it can be quickly retrieved from memory. Since data retrieval from memory is faster than from disk, this can significantly improve the performance of the API.

File system caching involves caching data on the file system, so that subsequent requests for the same data can be served quickly without having to fetch the data from disk. Reverse proxy caching: Reverse proxy caching involves having an intermediary server, known as a reverse proxy, cache API responses.

When a request is made, the reverse proxy checks if it has a cached version of the response, and if so, it returns it to the client. If not, the reverse proxy forwards the request to the API server, caches the response, and then returns the response to the client.

This helps to reduce the load on the API server and improve the overall performance of the API. Content delivery network CDN caching involves using a CDN to cache data from the API server, so that subsequent requests for the same data can be served quickly from the CDN instead of from the API server.

There are pros and cons of server-side caching. API developers should consider these factors when deciding whether to use server-side caching and how to implement it in their API. Determining the best caching strategy for a particular API requires considering several factors, including the requirements, performance goals, the resources you have available to implement your strategy, and architectural considerations.

The requirements of the API, such as the types of data being served, the frequency of updates, and the expected traffic patterns, will help to determine the most appropriate caching strategy. The performance goals of the API, such as the desired response time and the acceptable level of stale data, will also help to determine the most appropriate caching strategy.

The available resources, such as hardware, software, and network infrastructure, will help to determine the most appropriate caching strategy, as some caching strategies may require more resources than others.

The architecture of the data being served by the API, such as the location and format of the data, will also help to determine the most appropriate caching strategy, as some caching strategies may be more appropriate for certain types of data architectures than others.

Finally, the requirements of the API clients, such as the types of devices being used to access the API, the network conditions, and the available storage capacity, will also help to determine the most appropriate caching strategy, as some caching strategies may be more appropriate for certain types of clients than others.

Different caching strategies can have different suitabilities for different use cases, based on factors such as data freshness, data size, data access patterns, performance goals, cost, complexity, and scalability. Client-side caching, using techniques such as HTTP cache headers and local storage, can be a good option for use cases where data freshness is less critical and the size of the data being served is small.

Client-side caching can also be a good option for use cases where the client has limited storage capacity or network bandwidth is limited.

Server-side database caching, which involves caching data in a database that is separate from the primary database, can be a good option for use cases where data freshness is critical and the size of the data being served is large. Server-side database caching can also be a good option for use cases where data access patterns are complex and data needs to be served to multiple clients concurrently.

Server-side in-memory caching, which involves caching data in memory on the server, can be a good option for use cases where data freshness is critical and the size of the data being served is small.

Server-side in-memory caching can also be a good option for use cases where data access patterns are simple and data needs to be served to multiple clients concurrently. Hybrid caching, which involves combining client-side caching and server-side caching, can be a good option for use cases where data freshness is critical and the size of the data being served is large.

Hybrid caching can also be a good option for use cases where data access patterns are complex and data needs to be served to multiple clients concurrently.

These are just a few examples of the suitability of different caching strategies for different use cases. The suitability of a particular caching strategy will depend on the specific requirements of the API and the data being served.

By considering these factors, API developers can choose a caching strategy that provides the desired performance benefits for their API. Use these tips to help you implement caching in an API in a practical and effective manner.

By following these tips, you can improve the performance of your API and provide a better experience for your users. Testing and debugging caching in an API effectively and ensure that it is working as intended.

Regular testing and debugging can help you identify and resolve any issues with your cache and improve the performance of your API. Follow these best practices for testing and debugging your API cache:.

By avoiding these common pitfalls, you can effectively implement caching in your API and ensure that it provides improved performance and a better user experience.

Caching is an effective way to improve API performance by reducing server load and improving response times. There are two main types of caching: client-side caching and server-side caching, each with its own advantages and disadvantages. To determine the best caching strategy for a particular API, it is important to consider factors such as data freshness, data size, and response time.

There are several techniques available for implementing caching, including HTTP cache headers and local storage for client-side caching, and database caching and in-memory caching for server-side caching. When implementing caching, it is important to consider best practices, such as testing and debugging cache behavior, avoiding common pitfalls like overcaching or caching sensitive data, and monitoring cache performance regularly.

By using caching appropriately, APIs can provide faster and more reliable responses, leading to a better user experience. Learn why APIs, service discovery, and registry are essential for your microservices architecture.

By carefully considering these key components and integrating them according to your application's needs, you can design an effective database caching system that boosts your app's performance while ensuring data integrity. Remember, the goal is to strike the right balance between speed, resource consumption, and data accuracy.

The first step in implementing an effective caching strategy is to identify which data benefits most from being cached. Not all data is equally suitable for caching; typically, data that doesn't change frequently but is read often is an ideal candidate. This often includes:. When identifying cachable data, consider the read-to-write ratio.

High read-to-write ratios indicate that the data is accessed frequently but not often updated, making it a prime candidate for caching.

To ensure that your caching strategy delivers the intended benefits, it's crucial to measure and monitor its performance. Implement metrics to track hits and misses in your cache. A "hit" occurs when the data requested is found in the cache, while a "miss" means the data must be fetched from the primary database, indicating a potential area for optimization.

Tools like Redis or Memcached often come with their performance monitoring utilities, but don't forget to integrate these metrics into your application monitoring tools e. Security is paramount when implementing caching, as sensitive data stored in cache can become a vulnerability. Here are some security best practices:.

As your application grows, so too will the demands on your caching layer. Scaling your cache infrastructure efficiently requires planning and foresight. Some strategies include:. Distributed Caching: Instead of a single cache server, use a cluster of cache servers.

This approach distributes the cache load and helps in achieving high availability. Cache Sharding: This involves partitioning your cache data across multiple shards based on some criteria, such as user ID or geographic location, to improve performance and scalability. Auto-scaling: Utilize cloud services that offer auto-scaling capabilities for your cache infrastructure, allowing it to automatically adjust based on load.

Remember, the goal of caching is to reduce the load on your primary database and improve your application's response times. However, it's also important to regularly review and adjust your caching strategy as your application evolves.

Caching technology has come a long way from simple key-value stores. As applications grow in complexity and scale, developers are constantly seeking innovative ways to reduce latency and improve performance. Here are a few trends shaping the future of database caching:.

Tiered Caching Systems : Applications now leverage a multi-layered caching strategy to optimize efficiency. By storing data across different tiers e. Distributed Caching Solutions : With the rise of cloud computing and microservices architectures, distributed caching has become increasingly popular.

These solutions allow data to be cached across multiple nodes, ensuring high availability and scalability. Tools like Redis and Memcached are leading the way, offering robust features for managing distributed caches.

Automated Caching : Machine learning algorithms are beginning to play a role in automating cache management. By analyzing access patterns and predicting future needs, these systems can dynamically adjust caching strategies to optimize performance without manual intervention.

Predictive Caching : By analyzing user behavior and access patterns, ML models can predict which data will be requested next and preemptively cache it, significantly reducing latency.

Smart Eviction Policies : Traditional caching systems often use simple algorithms like Least Recently Used LRU for evicting old data. AI can enhance this by determining which data is least likely to be accessed in the future, making space for more relevant data.

Self-Tuning Caches : AI can monitor cache performance in real-time, adjusting parameters such as cache size and eviction policies on-the-fly to maintain optimal performance under varying loads.

Edge computing and the Internet of Things IoT are pushing data processing closer to the source, necessitating innovative caching strategies to handle the deluge of information efficiently.

In these scenarios, caching plays a pivotal role:. Reducing Latency : By caching data at the edge, closer to where it's being generated or consumed, applications can drastically reduce latency, improving user experience and enabling real-time processing for critical applications like autonomous vehicles and smart cities.

Bandwidth Optimization : Transmitting large volumes of data from IoT devices to centralized data centers can strain network resources. Caching relevant data locally reduces the need for constant data transmission, conserving bandwidth. Enhanced Reliability : Edge devices often operate in challenging environments with intermittent connectivity.

Local caching ensures that these devices can continue functioning and providing essential services even when disconnected from the main network. The future of database caching is bright, driven by advancements in technology and the growing demands of modern applications.

As developers, understanding these trends and preparing to incorporate them into our projects will be key to building fast, efficient, and scalable applications in and beyond.

Subscribe to receive a monthly newsletter with new content, product announcements, events info, and more! Dragonfly is fully compatible with the Redis ecosystem and requires no code changes to implement.

Pricing Docs Community Github Discord Events. Company About us Careers. Install stars. About us Careers. stars Install. Roman Gershman. February 9, Understanding Database Caching What Is Database Caching Database caching is a technique used to improve the speed of web applications by temporarily storing copies of data or result sets.

How Database Caching Works When a request for data is made, the system first checks if the requested data is in the cache. Query Caching Query caching stores the result set of a query. Distributed Caching Distributed caching spreads the cache across multiple machines or nodes, allowing for greater scale and resilience.

Benefits of Database Caching Performance Improvement : The most significant advantage is the reduction in response time for data retrieval. Scalability : Caching helps manage increased load without proportionally increasing database load. Cost Efficiency : Reduces the need for additional database resources and infrastructure.

Challenges in Implementing Database Caching Cache Invalidation : Determining when and how to invalidate or refresh cached data is complex but crucial for maintaining data consistency. Memory Management : Efficiently managing cache memory to avoid running out of space while maximizing cache hits is a delicate balance.

Complexity : Implementing and maintaining caching logic adds complexity to the application architecture. Common Use Cases of Database Caching Read-heavy Applications : Applications like news websites where the content changes infrequently but is read frequently benefit immensely from caching.

Session Storage : Storing session information in a cache can significantly reduce database load for websites with many concurrent users. E-Commerce Platforms : Caching product information, prices, and availability can improve responsiveness for online shopping experiences.

Key Components of Effective Database Caching Cache Invalidation Strategies One of the trickiest aspects of caching is determining when an item in the cache no longer reflects the data in the database — in other words, knowing when to invalidate or update the cache. Time-Based Eviction Time-based eviction is straightforward: data is removed from the cache after a specified duration.

Redis Set a Key with an Expiration Time TTL of 10 Minutes Seconds r. invalidate event. Lazy Loading Lazy loading involves filling the cache only when necessary, i. fetch key cache.

Furthermore, an efficient retrieval algorithm based on exponential skip Bloom filter has been proposed for medical data management in blockchain, ensuring both efficiency and privacy protection.

What is the most efficient kitchen workflow and design? One common method is the golden triangle rule, which arranges the sink, refrigerator, and stove in the shape of a triangle to minimize distance and improve flow.

Mathematical models like linear programming and simulation can optimize kitchen layouts by finding the optimal configuration that meets constraints. User testing and expert evaluations provide valuable feedback and insights into the latest trends and best practices in kitchen design.

Additionally, the integration of intelligent technologies, such as smart home systems and particle swarm intelligence algorithms, can enhance the efficiency of kitchen design and optimization.

By considering practical considerations, user needs, and the post-war context, a functional and efficient kitchen space can be created. What are the different techniques for cache prefetching? Spatial cache prefetching involves bringing data blocks into the cache hierarchy ahead of demand accesses to mitigate the bottleneck caused by frequent main memory accesses.

This technique can be enhanced by exploiting the high usage of large pages in modern systems, which allows prefetching beyond the 4KB physical page boundaries typically used by spatial cache prefetchers. Data item prefetching involves selecting and adding candidate data items to the cache based on their scores, which are determined by their likelihood of being accessed in the future.

These techniques can be combined with machine learning approaches to learn policies for prefetching, admission, and eviction processes, using past misses and future frequency and recency as indicators.

Additionally, prefetching can be performed in mass storage systems by fetching a certain data unit and prefetching additional data units that have similar activity signatures. How can we better design adaptive learning systems to ensure that they are effective and efficient?

One approach is to predict individual learning parameters based on response data, which can help determine the difficulty of the material for each learner and improve model estimates.

Another approach is to formulate the adaptive learning problem as a Markov decision process and use deep reinforcement learning algorithms to find optimal learning policies based on continuous latent traits.

Additionally, combining architecture-based adaptation and control-based adaptation approaches, supported by machine learning, can lead to better adaptive systems. Furthermore, analyzing course features such as alignment, difficulty, and amount of practice can provide guidance for creating effective content in adaptive courseware.

By incorporating these strategies, adaptive learning systems can be designed to maximize learning gains and mitigate the negative effects of a cold start, ultimately improving learning outcomes.

How can we design a task scheduler that is efficient and effective? One approach is to integrate task scheduling with MPI and provide flexibility in job launch configurations. Another strategy is to use LSTM and attention algorithms to extract features from historical data and improve existing scheduling strategies.

Additionally, a task scheduling method based on edge computing can optimize scheduling by classifying task characteristics and matching them with edge nodes. Furthermore, an efficient job scheduler for big data processing systems can be designed using multiple level priority queues and demotion of jobs based on service consumed.

Finally, a real-time task scheduling model based on reinforcement learning can minimize queuing time and improve system load balancing and CPU utilization.

These approaches provide different perspectives on designing efficient and effective task schedulers. How can game theory be used to design caching policies in small cell networks? Another approach is to use game theory to model the caching system as a Stackelberg game, where small-cell base stations SBS are treated as resources.

This allows for the establishment of profit models for network service providers NSPs and video retailers VR , and the optimization of pricing and resource allocation.

Additionally, game theory can be used to model load balancing in small cell heterogeneous networks, leading to the development of hybrid load balancing algorithms that improve network throughput and reduce congestion. Overall, game theory provides a framework for optimizing caching policies and resource allocation in small cell networks.

See what other people are reading How engagement reward offer by social media contribute to dissemination of misinformation? The paper discusses the design of an efficient content caching system by considering content recommendation and information freshness.

It presents an algorithm for optimal cache updates and evaluates its effectiveness through simulations. Fast, efficient, and exactly what you should aim for. The cache miss is the flip side of the coin. In these cases, the server reverts to retrieving the resource from the original source.

It employs a variety of protocols and technologies, like:. Both server-side and client-side caching have their own unique characteristics and strengths. Using both server-side and client-side caching can make your website consistently fast, offering a great experience to users. Cache coherency refers to the consistency of data stored in different caches that are supposed to contain the same information.

When multiple processors or cores are accessing and modifying the same data, ensuring cache coherency becomes difficult. Serving stale content occurs when outdated cached content is displayed to the users instead of the most recent information from the origin server.

This can negatively affect the accuracy and timeliness of the information presented to users. Dynamic content changes frequently based on user interactions or real-time data. It also includes user-specific data, such as personalized recommendations or user account details.

This makes standard caching mechanisms, which treat all requests equally, not effective for personalized content. Finding the optimal caching strategy that maintains this balance is often complex.

NGINX, renowned for its high performance and stability, handles web requests efficiently. It acts as a reverse proxy, directing traffic and managing requests in a way that maximizes speed and minimizes server load.

PHP-FPM FastCGI Process Manager complements this by efficiently rendering dynamic PHP content. This combination ensures that every aspect of your website, from static images to dynamic user-driven pages, is delivered quickly and reliably. It uses the principles of edge computing, which means data is stored and delivered from the nearest server in the network.

This proximity ensures lightning-fast delivery times, as data travels a shorter distance to reach the user. With data being handled by a network of edge servers, your main server is freed up to perform other critical tasks, ensuring overall efficiency and stability.

While both are effective, the edge cache offers a significant advantage in terms of site speed and efficiency. It represents a leap forward in caching technology, providing an unparalleled experience for an array of websites and their visitors:.

Server-side caching is an indispensable tool in ensuring your website runs well. Smooth, efficient, and user-friendly — these are the hallmarks of a website that uses the power of advanced caching solutions.

The result? Her entrepreneurial spirit and freelance background further honed her ability to think creatively and deliver results.

This deep understanding, coupled with her technical skills and customer-centric approach, makes her a great part of the Pressable Team. Her genuine desire to help others and her knack for finding solutions make her a customer success rockstar.

Table of Contents Preface So, What Exactly Is a Content Delivery Network? How Does It Work? CDN Architecture — Storage Nodes — Origin Nodes — Control Nodes — Delivery Nodes What Other Benefits Can It…. Table of Contents Preface — How Fast Should a Website Load? What Are Some Non-Technical Ways to Speed Up a Slow WordPress Site?

We then compare different caching strategies, discussing their pros and cons, and providing guidance on when to use each strategy.

The post also includes a detailed comparison between in-memory caching and distributed caching, two popular caching methods. Furthermore, we highlight the importance of monitoring caching performance, discussing the tools that can be used, how to measure caching performance, and how to interpret caching performance metrics.

Finally, we discuss common pitfalls in caching and provide strategies to avoid them, along with best practices for caching. This blog post is a must-read for anyone looking to optimize software performance using effective caching strategies.

Caching is a fundamental concept in software engineering that plays a vital role in enhancing the performance and scalability of applications.

In computing, caching is a technique used to store and retrieve data quickly and efficiently. Caching works by storing frequently accessed data in a high-speed storage layer, such as RAM, so that future requests for that data can be served faster. Here is a general overview of how caching works:.

When a request is made for data, the caching system checks if the data is already stored in the cache. If the data is found in the cache, it is returned quickly without the need to access the slower primary storage.

If the data is not found in the cache, it is retrieved from the primary storage and stored in the cache for future requests. The caching system may use various strategies to determine which data to evict from the cache when it reaches its capacity. Caching systems often employ techniques such as expiration times or cache invalidation mechanisms to ensure that the data in the cache remains up to date and accurate.

By storing frequently accessed data in a cache, applications can reduce the latency and load on backend systems, resulting in improved performance and scalability. Improved Performance : By serving data from a faster cache, we can significantly reduce the need to access slower data storage systems such as databases or disk-based storage.

This leads to faster response times and improved overall application performance. Scalability : Caching can help handle increased traffic and load on an application.

By serving frequently accessed data from cache, we can minimize the load on databases and servers, allowing the application to scale more effectively. Cost Efficiency : By providing faster data access, caching reduces the need for expensive hardware or infrastructure resources.

This can result in cost savings by reducing the need for additional database instances or disk-based storage. Reducing Latency : Caching allows for faster retrieval of data, reducing the latency associated with accessing slower storage systems.

This is particularly important for applications with real-time requirements, such as gaming or financial systems. Managing Spikes in Demand : Caching can help mitigate the impact of spikes in demand by serving cached data, reducing the load on backend systems and improving overall application stability.

Database Caching : This involves caching frequently accessed data from a database in a high-speed cache, reducing the need to query the database for every request. Content Delivery Network CDN Caching : CDNs use caching to store and serve static content, such as images, videos, and webpages, from edge locations closer to the end users, reducing latency and improving performance.

Web Caching : Web caching involves caching web content, such as HTML, JavaScript, and image files, to reduce the load on web servers and improve response times for users.

Session Caching : Session caching involves caching user session data to provide a consistent user experience across multiple requests or sessions. API Caching : API caching involves caching API responses to improve performance and reduce load on backend systems. In-memory Caching : In-memory caching stores data in fast access hardware, such as RAM, for quick retrieval and improved performance.

This type of caching is often used for high-traffic applications or computationally intensive workloads. Data Request : When a request is made for data, the caching system checks if the data is already stored in the cache. Cache Hit : If the data is found in the cache, it is returned quickly without the need to access the slower primary storage.

This is known as a cache hit. Cache Miss : If the data is not found in the cache, it is retrieved from the primary storage and stored in the cache for future requests. This is known as a cache miss. Cache Eviction : The caching system may use various strategies to determine which data to evict from the cache when it reaches its capacity.

Cache Invalidation : Caching systems often employ techniques such as expiration times or cache invalidation mechanisms to ensure that the data in the cache remains up to date and accurate.

Understanding these basics of caching is crucial as it sets the foundation for the following sections where we will explore different caching strategies, compare in-memory caching and distributed caching, discuss how to monitor and measure caching performance, and explore some common pitfalls in caching and how to avoid them.

Caching strategies are the methods and techniques used to manage how data is stored and retrieved from a cache. Different caching strategies can be used depending on the specific requirements and characteristics of the system. In this section, we will look at several common caching strategies, their pros and cons, and when to use each strategy.

When a request for data is made, the system first checks the cache. If the data is found in the cache a cache hit , it is returned immediately. If the data is not found in the cache a cache miss , the data is retrieved from the primary storage, stored in the cache, and then returned. The Read-Through Cache strategy involves using a cache as the main point of data access.

When a request for data is made, the cache is checked first. If the data is not found in the cache, the cache itself is responsible for retrieving the data from the primary storage and storing it in the cache before returning it. The Write-Through Cache strategy involves writing data to the cache and the primary storage location at the same time.

When a request to write data is made, the data is written to the cache and the primary storage. This ensures that the cache always contains the most up-to-date data. The Write-Around Cache strategy involves writing data directly to the primary storage, bypassing the cache.

This strategy is beneficial for write-once, read-less-frequently scenarios, as it prevents the cache from being filled with write data that may not be read. The Write-Back Cache strategy involves writing data to the cache and marking the cache entry as dirty.

The data is then written to the primary storage at a later time. This strategy improves write performance by reducing the number of write operations to the primary storage. The choice of caching strategy depends on the specific requirements and access patterns of the system:.

By understanding the different caching strategies, their pros and cons, and when to use each strategy, you can make informed decisions to optimize your caching system and improve the performance of your applications. In-memory caching and distributed caching are two commonly used caching strategies that can significantly improve the performance of an application.

While both strategies aim to reduce the latency of data retrieval, they differ in their implementation and use cases. In this section, we will delve into a detailed comparison between in-memory caching and distributed caching, their differences, and their respective advantages and disadvantages.

Since RAM is much faster than disk-based storage, in-memory caching can significantly improve the performance of an application. Distributed caching involves storing cached data across multiple nodes or servers in a network.

This strategy improves the scalability and availability of the cache, as it can handle more data and requests than a single in-memory cache.

In-memory caching and distributed caching each have their own advantages and disadvantages. The choice between the two will depend on the specific requirements of your application.

If your application requires fast data access and you have a limited amount of data, in-memory caching may be the best choice. However, if your application needs to handle large amounts of data and requires high availability and fault tolerance, distributed caching may be a better option.

In the next section, we will discuss how to monitor and measure the performance of different caching strategies.

You also gain from faster response times and deliver a more performant API. Amazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. In a hybrid cloud environment, you may have applications that live in the cloud and require frequent access to an on-premises database.

There are many network topologies that can by employed to create connectivity between your cloud and on-premises environment including VPN and Direct Connect. And while latency from the VPC to your on-premises data center may be low, it may be optimal to cache your on-premises data in your cloud environment to speed up overall data retrieval performance.

When delivering web content to your viewers, much of the latency involved with retrieving web assets such as images, html documents, video, etc. can be greatly reduced by caching those artifacts and eliminating disk reads and server load. Various web caching techniques can be employed both on the server and on the client side.

Server side web caching typically involves utilizing a web proxy which retains web responses from the web servers it sits in front of, effectively reducing their load and latency. Client side web caching can include browser based caching which retains a cached version of the previously visited web content.

For more information on Web Caching, click here. Accessing data from memory is orders of magnitude faster than accessing data from disk or SSD, so leveraging data in cache has a lot of advantages.

For many use-cases that do not require transactional data support or disk based durability, using an in-memory key-value store as a standalone database is a great way to build highly performant applications.

In addition to speed, application benefits from high throughput at a cost-effective price point. Referenceable data such product groupings, category listings, profile information, and so on are great use cases for a general cache. For more information on general cache, click here. An integrated cache is an in-memory layer that automatically caches frequently accessed data from the origin database.

Most commonly, the underlying database will utilize the cache to serve the response to the inbound database request given the data is resident in the cache. This dramatically increases the performance of the database by lowering the request latency and reducing CPU and memory utilization on the database engine.

An important characteristic of an integrated cache is that the data cached is consistent with the data stored on disk by the database engine.

Mobile applications are an incredibly fast growing market segment given the rapid consumer device adoption and the decline in use of traditional computer equipment. Whether it be for games, commercial applications, health applications, and so on, virtually every market segment today has a mobile friendly application.

From an application development perspective, building mobile apps is very similar to building any other form of application. You have the same areas of concern, your presentation tier, business tier and data tier. While your screen real estate and development tools are different, delivering a great user experience is a shared goal across all applications.

With effective caching strategies, your mobile applications can deliver the performance your users expect, scale massively, and reduce your overall cost. The AWS Mobile Hub is a console that provides an integrated experience for discovering, configuring, and accessing AWS cloud services for building, testing, and monitoring usage of mobile apps.

The Internet of Things is a concept behind gathering and delivering information from a device and the physical world via device sensors to the internet or application consuming the data.

The value of IoT is being able to understand the captured data at near real time intervals which ultimately allows the consuming system and applications the ability to respond rapidly to that data.

Take for example, a device that transmits its GPS coordinates. Your IoT application could respond by suggesting points of interest relative to the proximity of those coordinates. Furthermore, if you had stored preferences related to the user of the device, you could fine tune those recommendations tailored to that individual.

In this particular example, the speed at which the application can respond to the coordinates is critical to achieving a great user experience. From an application development perspective, you can essentially code your IoT application to respond to any event given there is a programmatic means to do so.

Important considerations to be made when building an IoT architecture include the response time involved with analyzing the ingested data, architecting a solution that can scale N number of devices and delivering an architecture that is cost-effective.

AWS IoT is a managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices. Further Reading: Managing IoT and Time Series Data with Amazon ElastiCache for Redis. Modern Ad Tech applications are particularly demanding in terms of performance.

An example of a significant area of growth in AdTech is real-time bidding RTB , which is the auction-based approach for transacting digital display ads in real time, at the most granular impression level. RTB was the dominant transaction method in , accounting for When building a real-time bidding app, a millisecond can be the difference between submitting the bid on time and it becoming irrelevant.

This means that getting the bidding information from the database must be extremely fast. Database caching , which can access bidding details in sub milliseconds, is a great solution for achieving that high performance.

Interactivity is a cornerstone requirement for almost any modern game. Nothing frustrates players more than a slow or unresponsive game, and those rarely become successful. The requirement on performance is even more demanding for mobile multiplayer games, where an action that any one player takes needs to be shared with others in real time.

Caching plays a crucial role in keeping the game smooth by providing sub-millisecond query response for frequently accessed data. An example is a video streaming service such as Netflix or Amazon Video, which streams a large amount of video content to the viewers.

This is a perfect fit for a Content Delivery Network , where data is stored on a globally distributed set of caching servers. Another aspect of media applications is that load tends to be spikey and unpredictable. Imagine a blog on a website that a celebrity just tweeted about, or the website of a Football team during the Super Bowl.

Such a large spike of demand to a small subset of content is a challenge to most databases since they are limited in their per-key throughput.

Since memory has a much higher throughput than disk, a database cache would resolve the issue by redirecting the reads to the in memory cache. The connection that this method creates is designed to be used throughout the lifetime of the client application, and the same connection can be used by multiple concurrent threads.

Don't reconnect and disconnect each time you perform a Redis operation because this can degrade performance. You can specify the connection parameters, such as the address of the Redis host and the password. If you use Azure Cache for Redis, the password is either the primary or secondary key that is generated for Azure Cache for Redis by using the Azure portal.

After you have connected to the Redis server, you can obtain a handle on the Redis database that acts as the cache. The Redis connection provides the GetDatabase method to do this.

You can then retrieve items from the cache and store data in the cache by using the StringGet and StringSet methods. These methods expect a key as a parameter, and return the item either in the cache that has a matching value StringGet or add the item to the cache with this key StringSet.

Depending on the location of the Redis server, many operations might incur some latency while a request is transmitted to the server and a response is returned to the client. The StackExchange library provides asynchronous versions of many of the methods that it exposes to help client applications remain responsive.

These methods support the Task-based Asynchronous pattern in the. NET Framework. The following code snippet shows a method named RetrieveItem.

It illustrates an implementation of the cache-aside pattern based on Redis and the StackExchange library. The method takes a string key value and attempts to retrieve the corresponding item from the Redis cache by calling the StringGetAsync method the asynchronous version of StringGet.

If the item isn't found, it's fetched from the underlying data source using the GetItemFromDataSourceAsync method which is a local method and not part of the StackExchange library. It's then added to the cache by using the StringSetAsync method so it can be retrieved more quickly next time.

The StringGet and StringSet methods aren't restricted to retrieving or storing string values. They can take any item that is serialized as an array of bytes. If you need to save a. NET object, you can serialize it as a byte stream and use the StringSet method to write it to the cache.

Similarly, you can read an object from the cache by using the StringGet method and deserializing it as a. NET object. The following code shows a set of extension methods for the IDatabase interface the GetDatabase method of a Redis connection returns an IDatabase object , and some sample code that uses these methods to read and write a BlogPost object to the cache:.

The following code illustrates a method named RetrieveBlogPost that uses these extension methods to read and write a serializable BlogPost object to the cache following the cache-aside pattern:. Redis supports command pipelining if a client application sends multiple asynchronous requests.

Redis can multiplex the requests using the same connection rather than receiving and responding to commands in a strict sequence. This approach helps to reduce latency by making more efficient use of the network.

The following code snippet shows an example that retrieves the details of two customers concurrently. The code submits two requests and then performs some other processing not shown before waiting to receive the results.

The Wait method of the cache object is similar to the. NET Framework Task. Wait method:. For additional information on writing client applications that can use the Azure Cache for Redis, see the Azure Cache for Redis documentation.

More information is also available at StackExchange. The page Pipelines and multiplexers on the same website provides more information about asynchronous operations and pipelining with Redis and the StackExchange library.

The simplest use of Redis for caching concerns is key-value pairs where the value is an uninterpreted string of arbitrary length that can contain any binary data. It's essentially an array of bytes that can be treated as a string.

This scenario was illustrated in the section Implement Redis Cache client applications earlier in this article.

Note that keys also contain uninterpreted data, so you can use any binary information as the key. The longer the key is, however, the more space it will take to store, and the longer it will take to perform lookup operations.

For usability and ease of maintenance, design your keyspace carefully and use meaningful but not verbose keys. For example, use structured keys such as "customer" to represent the key for the customer with ID rather than simply "".

This scheme enables you to easily distinguish between values that store different data types. For example, you could also use the key "orders" to represent the key for the order with ID Apart from one-dimensional binary strings, a value in a Redis key-value pair can also hold more structured information, including lists, sets sorted and unsorted , and hashes.

Redis provides a comprehensive command set that can manipulate these types, and many of these commands are available to.

NET Framework applications through a client library such as StackExchange. The page An introduction to Redis data types and abstractions on the Redis website provides a more detailed overview of these types and the commands that you can use to manipulate them.

Redis supports a series of atomic get-and-set operations on string values. These operations remove the possible race hazards that might occur when using separate GET and SET commands.

The operations that are available include:. INCR , INCRBY , DECR , and DECRBY , which perform atomic increment and decrement operations on integer numeric data values. The StackExchange library provides overloaded versions of the IDatabase. StringIncrementAsync and IDatabase.

StringDecrementAsync methods to perform these operations and return the resulting value that is stored in the cache. The following code snippet illustrates how to use these methods:.

GETSET , which retrieves the value that's associated with a key and changes it to a new value. The StackExchange library makes this operation available through the IDatabase. StringGetSetAsync method. The code snippet below shows an example of this method.

This code returns the current value that's associated with the key "data:counter" from the previous example. Then it resets the value for this key back to zero, all as part of the same operation:. MGET and MSET , which can return or change a set of string values as a single operation.

The IDatabase. StringGetAsync and IDatabase. StringSetAsync methods are overloaded to support this functionality, as shown in the following example:.

You can also combine multiple operations into a single Redis transaction as described in the Redis transactions and batches section earlier in this article. The StackExchange library provides support for transactions through the ITransaction interface.

You create an ITransaction object by using the IDatabase. CreateTransaction method. You invoke commands to the transaction by using the methods provided by the ITransaction object. The ITransaction interface provides access to a set of methods that's similar to those accessed by the IDatabase interface, except that all the methods are asynchronous.

This means that they're only performed when the ITransaction. Execute method is invoked. The value that's returned by the ITransaction. Execute method indicates whether the transaction was created successfully true or if it failed false.

The following code snippet shows an example that increments and decrements two counters as part of the same transaction:. Remember that Redis transactions are unlike transactions in relational databases. The Execute method simply queues all the commands that comprise the transaction to be run, and if any of them is malformed then the transaction is stopped.

If all the commands have been queued successfully, each command runs asynchronously. If any command fails, the others still continue processing. If you need to verify that a command has completed successfully, you must fetch the results of the command by using the Result property of the corresponding task, as shown in the example above.

Reading the Result property will block the calling thread until the task has completed. For more information, see Transactions in Redis. When performing batch operations, you can use the IBatch interface of the StackExchange library. This interface provides access to a set of methods similar to those accessed by the IDatabase interface, except that all the methods are asynchronous.

You create an IBatch object by using the IDatabase. CreateBatch method, and then run the batch by using the IBatch. Execute method, as shown in the following example. This code simply sets a string value, increments and decrements the same counters used in the previous example, and displays the results:.

It's important to understand that unlike a transaction, if a command in a batch fails because it's malformed, the other commands might still run.

The IBatch. Execute method doesn't return any indication of success or failure. Redis supports fire and forget operations by using command flags. In this situation, the client simply initiates an operation but has no interest in the result and doesn't wait for the command to be completed.

The example below shows how to perform the INCR command as a fire and forget operation:. When you store an item in a Redis cache, you can specify a timeout after which the item will be automatically removed from the cache. You can also query how much more time a key has before it expires by using the TTL command.

This command is available to StackExchange applications by using the IDatabase. KeyTimeToLive method. The following code snippet shows how to set an expiration time of 20 seconds on a key, and query the remaining lifetime of the key:.

You can also set the expiration time to a specific date and time by using the EXPIRE command, which is available in the StackExchange library as the KeyExpireAsync method:. You can manually remove an item from the cache by using the DEL command, which is available through the StackExchange library as the IDatabase.

KeyDeleteAsync method. A Redis set is a collection of multiple items that share a single key. You can create a set by using the SADD command.

You can retrieve the items in a set by using the SMEMBERS command. The StackExchange library implements the SADD command with the IDatabase.

SetAddAsync method, and the SMEMBERS command with the IDatabase. SetMembersAsync method. You can also combine existing sets to create new sets by using the SDIFF set difference , SINTER set intersection , and SUNION set union commands.

The StackExchange library unifies these operations in the IDatabase. SetCombineAsync method. The first parameter to this method specifies the set operation to perform. The following code snippets show how sets can be useful for quickly storing and retrieving collections of related items.

This code uses the BlogPost type that was described in the section Implement Redis Cache Client Applications earlier in this article.

A BlogPost object contains four fields—an ID, a title, a ranking score, and a collection of tags. The first code snippet below shows the sample data that's used for populating a C list of BlogPost objects:.

You can store the tags for each BlogPost object as a set in a Redis cache and associate each set with the ID of the BlogPost. This enables an application to quickly find all the tags that belong to a specific blog post.

To enable searching in the opposite direction and find all blog posts that share a specific tag, you can create another set that holds the blog posts referencing the tag ID in the key:.

These structures enable you to perform many common queries very efficiently. For example, you can find and display all of the tags for blog post 1 like this:. You can find all tags that are common to blog post 1 and blog post 2 by performing a set intersection operation, as follows:.

A common task required of many applications is to find the most recently accessed items. For example, a blogging site might want to display information about the most recently read blog posts.

You can implement this functionality by using a Redis list. A Redis list contains multiple items that share the same key. The list acts as a double-ended queue.

You can push items to either end of the list by using the LPUSH left push and RPUSH right push commands. You can retrieve items from either end of the list by using the LPOP and RPOP commands. You can also return a set of elements by using the LRANGE and RRANGE commands. The code snippets below show how you can perform these operations by using the StackExchange library.

This code uses the BlogPost type from the previous examples. As a blog post is read by a user, the IDatabase. As more blog posts are read, their titles are pushed onto the same list.

The list is ordered by the sequence in which the titles have been added. The most recently read blog posts are toward the left end of the list. If the same blog post is read more than once, it will have multiple entries in the list. You can display the titles of the most recently read posts by using the IDatabase.

ListRange method. This method takes the key that contains the list, a starting point, and an ending point. The following code retrieves the titles of the 10 blog posts items from 0 to 9 at the left-most end of the list:. Note that the ListRangeAsync method doesn't remove items from the list. To do this, you can use the IDatabase.

ListLeftPopAsync and IDatabase. ListRightPopAsync methods. To prevent the list from growing indefinitely, you can periodically cull items by trimming the list. The code snippet below shows you how to remove all but the five left-most items from the list:.

By default, the items in a set aren't held in any specific order. You can create an ordered set by using the ZADD command the IDatabase. SortedSetAdd method in the StackExchange library. The items are ordered by using a numeric value called a score, which is provided as a parameter to the command.

The following code snippet adds the title of a blog post to an ordered list. In this example, each blog post also has a score field that contains the ranking of the blog post. You can retrieve the blog post titles and scores in ascending score order by using the IDatabase.

SortedSetRangeByRankWithScores method:. The StackExchange library also provides the IDatabase. SortedSetRangeByRankAsync method, which returns the data in score order, but it doesn't return the scores. You can also retrieve items in descending order of scores, and limit the number of items that are returned by providing additional parameters to the IDatabase.

SortedSetRangeByRankWithScoresAsync method. The next example displays the titles and scores of the top 10 ranked blog posts:. The next example uses the IDatabase. SortedSetRangeByScoreWithScoresAsync method, which you can use to limit the items that are returned to those that fall within a given score range:.

Client applications can subscribe to a channel, and other applications or services can publish messages to the channel. Subscribing applications will then receive these messages and can process them. Redis provides the SUBSCRIBE command for client applications to use to subscribe to channels.

This command expects the name of one or more channels on which the application will accept messages. The StackExchange library includes the ISubscription interface, which enables a. NET Framework application to subscribe and publish to channels.

You create an ISubscription object by using the GetSubscriber method of the connection to the Redis server. Then you listen for messages on a channel by using the SubscribeAsync method of this object.

The following code example shows how to subscribe to a channel named "messages:blogPosts":. The first parameter to the Subscribe method is the name of the channel. This name follows the same conventions that are used by keys in the cache.