Windows Azure Caching Architecture Overview

Windows Azure Caching is a fully managed caching service that helps improve the performance and scalability of your applications. It stores frequently accessed data in a fast, in-memory cache.

This allows your applications to quickly retrieve data without having to query a database or other storage system, reducing latency and improving responsiveness.

By using Windows Azure Caching, you can reduce the load on your database and other storage systems, making them more efficient and reliable.

Windows Azure Caching supports multiple cache modes, including Additive, Cache-Aside, and Refresh-Avoiding, each designed to work with different application architectures.

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Azure caching is a powerful tool, but it's not a one-size-fits-all solution. Considerations for using Azure caching include the type of data you're storing and how often it's accessed.

Azure Cache for Redis is a high-performance caching solution that provides availability, scalability, and security. It typically runs as a service spread across one or more dedicated machines.

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Before deciding to use Azure caching, think about the trade-offs. Caching can improve performance, scalability, and availability, but it's not a substitute for a persistent data store.

For example, if you have data that's read frequently but modified infrequently, caching can be a good solution. This is because caching reduces the latency and contention associated with handling large volumes of concurrent requests in the original data store.

However, caching shouldn't be used as the authoritative store of critical information. Always ensure that all changes that your application can't afford to lose are saved to a persistent data store.

Azure caching can be shared by client applications that have the appropriate access key, making it a convenient solution for large-scale applications.

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Azure makes it easy to scale an application infrastructure, but data storage can be a bottleneck. An in-memory distributed cache can help alleviate this issue by reducing expensive database reads by as much as 90 percent.

This cache scales in a linear fashion, making it a great solution for high-availability and scalability. Unlike a relational database, it generally won't become a scalability bottleneck, even with 90 percent of read traffic going to the cache instead of the database.

A shared cache service can also provide scalability by using a cluster of servers to distribute data. This approach is beneficial for applications that need to share data across instances. However, it's slower to access and may add complexity to the solution.

Implementing high availability and scalability in your application is crucial to ensure it can handle a large number of users and transactions without breaking down. This can be achieved by using an in-memory distributed cache, which can reduce expensive database reads by as much as 90 percent.

An in-memory distributed cache scales in a linear fashion, meaning it generally won’t become a scalability bottleneck, even if 90 percent of the read traffic goes to the cache instead of the database. This is in contrast to a relational database, which can become a bottleneck as the load increases.

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You can easily add more cache servers as your transaction load increases, making it a great option for applications that need to scale quickly. For example, a 2-node cluster can handle 50,000 reads per second, while a 5-node cluster can handle 120,000 reads per second.

To ensure high availability, consider using a shared caching approach, which locates the cache in a separate location, typically hosted as part of a separate service. This ensures that different application instances see the same view of cached data.

However, shared caching can be slower to access because it's no longer held locally to each application instance. Additionally, implementing a separate cache service might add complexity to the solution.

In some cases, storing dynamic information directly in the cache can be beneficial, especially when the data is non-critical and doesn't require auditing. This can help reduce the overhead of storing and retrieving data from a persistent data store.

However, in systems that implement eventual consistency, using a cache-aside pattern can lead to issues, such as reading and populating the cache with an old value. To mitigate this, ensure that the instance of the application that populates the cache has access to the most recent and consistent version of the data.

Here's a summary of the scalability benefits of using an in-memory distributed cache:

Polling relational databases to detect data changes is a bad idea, as it involves many unnecessary database reads, which can lead to performance and scalability issues.

Database events, such as SqlDependency or OracleDependency, are another approach, but they add overhead to the database, making it choke under heavy transaction load.

Message queues are good for situations where the recipients might not receive events for a long time or where applications are distributed across the WAN, but they might not perform or scale like an in-memory distributed cache in a high-transaction environment.

An in-memory distributed cache is a good solution for runtime data sharing in a high-transaction environment, as it lets you share data at run time in a variety of ways, all of which are asynchronous.

Here are the different ways an in-memory distributed cache can share data at run time:

Topic-based events are general purpose and aren't tied to any data changes in the cache, making them a good choice for a publish/subscribe model.

You can provision a cache by using the Azure portal, which offers a number of predefined configurations.

The smallest cache available is 250 MB, without replication and no availability guarantees, while the largest is 53 GB, running as a dedicated service with SSL communications and master/subordinate replication.

You can configure the eviction policy of the cache through the Azure portal, which gives you control over how the cache handles data eviction.

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Managing expiration in Redis is crucial to prevent stale data from accumulating in your cache. You can set a default expiration policy when configuring the cache, but you can also specify the expiration period for individual objects when storing them programmatically in the cache.

Data in the cache can become stale if it's not accessed within a certain time period, causing it to be removed from the cache. This can be prevented by setting a sliding expiration period, which causes the item to be removed from the cache if it isn't accessed within the specified time.

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The expiration period should be carefully considered to balance the benefits of using the cache with the risk of data becoming stale. If the period is too short, objects will expire too quickly, while a period that's too long can cause the cache to fill up and lead to eviction.

Some caching implementations provide additional eviction policies, including most-recently-used, first-in-first-out, and explicit removal based on a triggered event. You can also use the TTL command to query how much more time a key has before it expires.

Here are some common expiration policies:

You can also use the EXPIRE command to set the expiration time to a specific date and time. This can be useful for implementing more complex expiration policies.

Redis is a distributed in-memory database with an extensive command set that supports many common scenarios.

You can use Redis as a simple cache server or as a more complex database, depending on your needs.

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Azure Cache for Redis provides access to Redis servers hosted at an Azure datacenter, acting as a façade that provides access control and security.

You can provision a cache by using the Azure portal, which provides a number of predefined configurations ranging from a 53 GB cache to a 250 MB cache.

Most administrative tasks are performed through the Azure portal, so many of the administrative commands available in the standard version of Redis aren't available.

You can monitor the performance of the cache through the Azure portal's graphical display, which shows metrics such as connections, requests, reads, writes, cache hits, and cache misses.

You can create alerts that send email messages to an administrator if critical metrics fall outside of an expected range.

Building a custom Redis cache involves using Azure Virtual Machines to host your own Redis servers, which can be complex and require creating multiple VMs for replication and clustering.

A minimal clustered replication topology for high availability and scalability requires at least six VMs organized as three pairs of primary/subordinate servers.

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You're responsible for monitoring, managing, and securing the service if you implement your own Redis cache.

To automatically expire keys in a Redis cache, you can specify a timeout, and you can query the remaining lifetime of a key using the TTL command.

The TTL command is available in the StackExchange library as the IDatabase.KeyTimeToLive method.

You can set up a Redis cache in Azure using the Azure Redis cache, which provides a simple and secure way to access Redis servers hosted at an Azure datacenter.

You can provision a cache by using the Azure portal, which offers a range of predefined configurations, from a 53 GB cache to a 250 MB cache.

The Azure portal also allows you to configure the eviction policy and control access to the cache by adding users to roles, such as Owner, Contributor, and Reader.

Most administrative tasks are performed through the Azure portal, but if you need more advanced configuration, you can build and host your own Redis servers using Azure Virtual Machines.

If you need a custom Redis cache, you can build and host your own Redis servers using Azure Virtual Machines.

You'll need to create multiple VMs to act as primary and subordinate nodes for replication, with a minimal clustered replication topology requiring at least six VMs organized as three pairs of primary/subordinate servers.

Each primary/subordinate pair should be located close together to minimize latency.

You can locate each set of pairs in different Azure datacenters in different regions to place cached data near the applications that use it.

For an example of building and configuring a Redis node, see Running Redis on a CentOS Linux VM in Azure.

If you implement your own Redis cache, you're responsible for monitoring, managing, and securing the service.

A custom Redis setup can be complex, requiring multiple VMs and careful planning to ensure high availability and scalability.

To implement a Redis client, you can use the Azure portal to provision a cache by selecting a predefined configuration. These configurations range from a 53 GB cache running as a dedicated service to a 250 MB cache running on shared hardware.

You can choose from various roles, including Owner, Contributor, and Reader, to control access to the cache. Members of the Owner role have complete control over the cache and its contents.

The Azure portal provides a convenient graphical display to monitor the performance of the cache, allowing you to view metrics such as connections, requests, reads, and writes. You can also create alerts to send email messages to an administrator if critical metrics fall outside of an expected range.

Azure Cache for Redis is compatible with many APIs used by client applications, making it a quick migration path to caching in the cloud.

Azure Caching Architecture is designed to provide high availability, linear scalability, and data replication and reliability. It's perfect for high-traffic apps that can't afford downtime.

There are two deployment topologies for Caching: Dedicated and Co-located. Dedicated means the cache is hosted on a Windows Azure role, while Co-located means it's hosted on a separate role from the application.

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A self-healing peer-to-peer cache cluster is used to maintain elasticity and high availability. This cluster adjusts itself whenever nodes are added or removed, and connection failover capability is within the cache clients, ensuring they can continue working even if a cache server goes down.

Here are the three important aspects of in-memory distributed cache:

Dynamic configuration is also a key feature, where cache servers propagate configuration details to cache clients at runtime, including any changes.

Distributed applications typically implement either a private cache or a shared cache to store data locally or across multiple machines.

A distributed cache can be implemented client-side or server-side, depending on the application's needs.

In a distributed cache, data is stored across multiple machines, making it a great solution for high-traffic applications that require linear scalability.

The scalability of a distributed cache is linear, meaning it can handle increased traffic without becoming a bottleneck.

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Some distributed caches, like Azure Caching, allow you to add more cache servers as your transaction load increases.

Azure Caching can reduce expensive database reads by up to 90 percent, making it a great solution for applications with high database traffic.

A distributed cache can be faster and more scalable than a relational database, making it a great solution for high-traffic applications.

Here are some common caching topologies used in distributed architectures:

These caching topologies can help you achieve high availability, linear scalability, and data replication and reliability in your distributed architecture.

High-traffic applications require a distributed cache that can handle high availability, linear scalability, and data replication and reliability.

Some distributed caches, like Azure Caching, provide self-healing peer-to-peer cache clusters, connection failover, and dynamic configuration to ensure high availability and linear scalability.

By choosing the right caching topology and distributed cache architecture, you can build high-traffic applications that scale linearly and provide fast and reliable performance to your users.

Partitioning a Redis is a way to distribute data across multiple servers, which can improve performance and scalability. This is especially useful in large-scale applications.

You can provision a cache by using the Azure portal, which provides a number of predefined configurations, ranging from a 53 GB cache to a 250 MB cache.

Partitioning a Redis can be a complex task, but Azure Cache for Redis makes it easier by providing a façade that provides access control and security. This means you can control access to the cache by adding users to the roles provided, such as Owner, Contributor, and Reader.

The Azure portal also provides a convenient graphical display that enables you to monitor the performance of the cache, including the number of connections being made, the number of requests being performed, and the volume of reads and writes. This can help you determine the effectiveness of the cache and make adjustments as needed.

In a co-located topology, you use a percentage of available memory on existing web or worker roles for Caching. This approach is cost-effective and makes use of existing memory on a role within a cloud service.

A co-located cache is distributed across all instances of the web role, which also hosts the web front-end for the cloud service. This allows for efficient use of resources.

This type of topology is particularly useful for cloud services with multiple instances of web roles, as it enables caching across all instances. This can improve application performance and reduce latency.

The cache is configured to use only a percentage of the physical memory on each instance of the web role, which helps to prevent memory overload. This means you can make the most of the available memory without compromising performance.

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The runners section in Azure caching architecture is a crucial part of configuring native support for Azure Blob Storage.

To access Azure Blob Storage, you'll need to specify the account name, which is the name of the Azure Blob Storage account used to access the storage.

In Azure, a collection of objects is called a container, not a bucket like in S3 and GCS.

You can omit the account key from the configuration by using Azure workload or managed identities.

Here's a breakdown of the parameters that define native support for Azure Blob Storage:

The StorageDomain parameter is optional, and its default value is blob.core.windows.net.

In Visual Studio, caching is configured in the Caching tab of the properties of the role that hosts caching.

To configure caching, you'll need to make underlying changes to the ServiceConfiguration.cscfg file, which determines the topology used (dedicated or co-located) and the number of named caches and their settings.

You can customize any of the preconfigured settings according to your requirements, such as changing the configuration from co-located to dedicated role-based caching or changing the expiration settings of the preconfigured named caches.

To reconfigure the expiration settings or set of named caches, use the Caching tab of the cloud service project.

You must also reconfigure the provider settings to match whatever cache configuration you create, using the Settings tab of the cloud service project.

To configure cache cluster settings before deploying, you'll need to open the Web.config file in the Orchard.Web project and add the necessary settings for output caching and database caching.

Here's an example configuration for output caching and database caching:

For multi-tenancy scenarios, each setting can optionally be prefixed with a tenant name followed by a colon, such as SomeTenant:Orchard.Azure.OutputCache.HostIdentifier.

This means that the caching providers will always first look for a setting specific for the current tenant, and if no such setting exists, they'll fallback to the default non-prefixed setting.

To deploy the web site, you'll need to deploy the cloud service project, which will configure the cache cluster settings and provider settings according to your configuration.

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You can customize the configuration of Windows Azure Caching to suit your needs. This includes changing the configuration from co-located to dedicated role-based caching by adding a dedicated caching role to the cloud service.

To do this, you'll need to reconfigure the cloud service project. You can use the Caching tab to reconfigure the expiration settings or set of named caches.

Here are some specific things you can customize:

Don't forget to also reconfigure the provider settings to match your new cache configuration, using the Settings tab of the cloud service project.

In multi-tenancy scenarios, each setting can be prefixed with a tenant name followed by a colon.

This allows for specific configuration settings for each tenant, which will be used over default settings.

The tenant name prefix is used to identify settings for a particular tenant, such as SomeTenant:Orchard.Azure.OutputCache.HostIdentifier.

When a caching provider reads configuration settings, it will first look for a setting specific to the current tenant.

If no tenant-specific setting exists, it will fall back to the default non-prefixed setting.

In an Azure Web Site configuration, multiple tenants can use different cache service instances, such as OutputCache on the Windows Azure Cache Service.

Each tenant can have its own cache service instance, allowing for separate caching configurations.

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You can customize any of the preconfigured settings according to your requirements. For example, you might want to change the configuration from co-located to dedicated role-based caching by adding a dedicated caching role to the cloud service.

To reconfigure the expiration settings or set of named caches, use the Caching tab of the cloud service project. This is where you can change the topology used and the number of named caches and their settings.

You can also use a NuGet package to configure other roles to use Caching. This includes modifying the web.config to contain a properly configured dataCacheClients section. The following example dataCacheClients section specifies that the role that hosts Caching is named “CacheWorker1”.

Here are some specific settings you can customize:

These changes can be made by reconfiguring the cloud service project, specifically using the Caching tab and Settings tab.

Azure makes it easy to scale an application infrastructure, allowing you to add more Web roles, worker roles or virtual machines (VMs) when you anticipate higher transaction load.

Data storage can be a bottleneck that could keep you from being able to scale your app, but an in-memory distributed cache can help by caching as much data as you want and reducing expensive database reads by as much as 90 percent.

This reduces transactional pressure on the database, allowing it to perform faster and take on a greater transaction load.

An in-memory distributed cache scales in a linear fashion, which means it generally won’t become a scalability bottleneck, even though 90 percent of the read traffic might go to the cache instead of the database.

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You can easily add more cache servers as your transaction load increases, making it a flexible solution for scalable applications.

Figure 1 shows how to direct apps to the cache, making it easy to integrate into your application.

This performance data shows that an in-memory distributed cache can handle a high volume of reads and writes, making it a reliable solution for scalable applications.

Windows Azure Caching is a powerful tool that can help improve the performance of your applications. The following examples demonstrate how to configure and use Windows Azure Caching.

The following sections show Windows Azure Caching configuration and code examples. This indicates that the official documentation provides detailed examples to help developers get started.

You can find Windows Azure Caching configuration examples in the official documentation. These examples will guide you through the process of setting up and using the caching service.

Windows Azure Caching is a great way to improve the performance of your applications by reducing the load on your database. By caching frequently accessed data, you can significantly improve the responsiveness of your application.

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To get started with Windows Azure Caching, you can refer to the official documentation for configuration and code examples. This will help you understand the basics of Windows Azure Caching and how to implement it in your application.

Windows Azure Caching is a powerful tool that can help improve the performance of your applications.

Azure Cache for Redis is a high-performance caching solution that provides availability, scalability, and security.

It typically runs as a service spread across one or more dedicated machines, attempting to store as much information as it can in memory to ensure fast access.

This architecture is intended to provide low latency and high throughput by reducing the need to perform slow I/O operations.

Azure Cache for Redis is compatible with many of the various APIs that are used by client applications.

You can share caches with client applications that have the appropriate access key, making it a great option for teams working on multiple projects.

Azure Cache for Redis is an implementation of the open source Redis cache that runs as a service in an Azure datacenter.