Merge pull request #301735 from flang-msft/fxl---remove-old-aspnet-tutorials-2

fxl---Remove old tutorials

Author: denrea

articles/azure-cache-for-redis/cache-overview.md

@@ -24,9 +24,9 @@ Azure Cache for Redis improves application performance by supporting common appl
 
 | Pattern      | Description                                        |
 | ------------ | -------------------------------------------------- |
-| [Data cache](../redis/web-app-cache-aside-leaderboard.md) | Databases are often too large to load directly into a cache. It's common to use the [cache-aside](/azure/architecture/patterns/cache-aside) pattern to load data into the cache only as needed. When the system makes changes to the data, the system can also update the cache, which is then distributed to other clients. Additionally, the system can set an expiration on data, or use an eviction policy to trigger data updates into the cache.|
-| [Content cache](../redis/aspnet-output-cache-provider.md) | Many web pages are generated from templates that use static content such as headers, footers, banners. These static items shouldn't change often. Using an in-memory cache provides quick access to static content compared to backend datastores. This pattern reduces processing time and server load, allowing web servers to be more responsive. It can allow you to reduce the number of servers needed to handle loads. Azure Cache for Redis provides the Redis Output Cache Provider to support this pattern with ASP.NET.|
-| [Session store](../redis/aspnet-session-state-provider.md) | This pattern is commonly used with shopping carts and other user history data that a web application might associate with user cookies. Storing too much in a cookie can have a negative effect on performance as the cookie size grows and is passed and validated with every request. A typical solution uses the cookie as a key to query the data in a database. When you use an in-memory cache, like Azure Cache for Redis, to associate information with a user is faster than interacting with a full relational database. |
+| Data cache | Databases are often too large to load directly into a cache. It's common to use the [cache-aside](/azure/architecture/patterns/cache-aside) pattern to load data into the cache only as needed. When the system makes changes to the data, the system can also update the cache, which is then distributed to other clients. Additionally, the system can set an expiration on data, or use an eviction policy to trigger data updates into the cache.|
+| Content cache | Many web pages are generated from templates that use static content such as headers, footers, banners. These static items shouldn't change often. Using an in-memory cache provides quick access to static content compared to backend datastores. This pattern reduces processing time and server load, allowing web servers to be more responsive. It can allow you to reduce the number of servers needed to handle loads. Azure Cache for Redis provides the Redis Output Cache Provider to support this pattern with ASP.NET.|
+| Session store | This pattern is commonly used with shopping carts and other user history data that a web application might associate with user cookies. Storing too much in a cookie can have a negative effect on performance as the cookie size grows and is passed and validated with every request. A typical solution uses the cookie as a key to query the data in a database. When you use an in-memory cache, like Azure Cache for Redis, to associate information with a user is faster than interacting with a full relational database. |
 | Job and message queuing | Applications often add tasks to a queue when the operations associated with the request take time to execute. Longer running operations are queued to be processed in sequence, often by another server. This method of deferring work is called task queuing. Azure Cache for Redis provides a distributed queue to enable this pattern in your application.|
 | Distributed transactions | Applications sometimes require a series of commands against a backend data-store to execute as a single atomic operation. All commands must succeed, or all must be rolled back to the initial state. Azure Cache for Redis supports executing a batch of commands as a single [transaction](https://redis.io/topics/transactions). |
 
@@ -79,7 +79,7 @@ Consider the following options when choosing an Azure Cache for Redis tier:
 - **Memory**: The Basic and Standard tiers offer 250 MB – 53 GB; the Premium tier 6 GB - 1.2 TB; the Enterprise tier 1 GB - 2 TB, and the Enterprise Flash tier 300 GB - 4.5 TB.  To create larger sized cache instances, you can use [scale out](cache-how-to-scale.md). For more information, see [Azure Cache for Redis Pricing](https://azure.microsoft.com/pricing/details/cache/).
 - **Performance**: Caches in the Premium and Enterprise tiers are deployed on hardware that has faster processors, giving better performance compared to the Basic or Standard tier. The Enterprise tier typically has the best performance for most workloads, especially with larger cache instances. For more information, see [Performance testing](cache-best-practices-performance.md).
 - **Dedicated core for Redis server**: All caches except C0 run dedicated vCPUs. The Basic, Standard, and Premium tiers run open source Redis, which by design uses only one thread for command processing. On these tiers, having more vCPUs usually improves throughput performance because Azure Cache for Redis uses other vCPUs for I/O processing or for OS processes. However, adding more vCPUs per instance might not produce linear performance increases. Scaling out usually boosts performance more than scaling up in these tiers. Both the Enterprise and Enterprise Flash tiers run on Redis Enterprise, which is able to utilize multiple vCPUs per instance, which can also significantly increase performance over other tiers. For Enterprise and Enterprise flash tiers, scaling up is recommended before scaling out.
-- **Network performance**: If you have a workload that requires high throughput, the Premium or Enterprise tier offers more bandwidth compared to Basic or Standard. Also within each tier, larger size caches have more bandwidth because of the underlying VM that hosts the cache. Higher bandwidth limits help you avoid network saturation that cause timeouts in your application. For more information, see [Performance testing](cache-best-practices-performance.md).
+- **Network performance**: If you have a workload that requires high throughput, the Premium or Enterprise tier offers more bandwidth compared to Basic or Standard. Also within each tier, larger size caches have more bandwidth because of the underlying VM that hosts the cache. Higher bandwidth limits help you avoid network saturation that causes timeouts in your application. For more information, see [Performance testing](cache-best-practices-performance.md).
 - **Maximum number of client connections**: The Premium and Enterprise tiers offer the maximum numbers of clients that can connect to Redis, offering higher numbers of connections for larger sized caches. Clustering increases the total amount of network bandwidth available for a clustered cache.
 - **High availability**: Azure Cache for Redis provides multiple [high availability](cache-high-availability.md) options. It guarantees that a Standard, Premium, or Enterprise cache is available according to our [SLA](https://azure.microsoft.com/support/legal/sla/cache/v1_0/). The SLA only covers connectivity to the cache endpoints. The SLA doesn't cover protection from data loss. We recommend using the Redis data persistence feature in the Premium and Enterprise tiers to increase resiliency against data loss.
 - **Data persistence**: The Premium and Enterprise tiers allow you to persist the cache data to an Azure Storage account and a Managed Disk respectively. Underlying infrastructure issues might result in potential data loss. We recommend using the Redis data persistence feature in these tiers to increase resiliency against data loss. Azure Cache for Redis offers both RDB and AOF (preview) options. Data persistence can be enabled through Azure portal and CLI. For the Premium tier, see [How to configure persistence for a Premium Azure Cache for Redis](cache-how-to-premium-persistence.md).

articles/redis/.openpublishing.redirection.redis.json

@@ -35,7 +35,21 @@
             "source_path_from_root": "/articles/redis/tutorial-write-behind.md",
             "redirect_url": "/azure/azure-functions/functions-bindings-cache",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/redis/aspnet-output-cache-provider.md",
+            "redirect_url": "../../aspnet/core/performance/caching/overview",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/redis/aspnet-session-state-provider.md",
+            "redirect_url": "../../aspnet/core/performance/caching/overview",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/redis/web-app-cache-aside-leaderboard.md",
+            "redirect_url": "../../aspnet/core/performance/caching/overview",
+            "redirect_document_id": false
         }
     ]
-}  
-  
+}

articles/redis/TOC.yml

@@ -216,16 +216,10 @@ items:
     items:
     - name: Use ASP.NET core output cache
       href: aspnet-core-output-cache-provider.md
-    - name: Use session state provider
-      href: aspnet-session-state-provider.md
-    - name: Use output cache provider
-      href: aspnet-output-cache-provider.md
   - name: Java Spring
     items:
     - name: Use Spring Boot Initializer
       href: /azure/developer/java/spring-framework/configure-spring-boot-initializer-java-app-with-redis-cache
-  - name: Create leaderboards
-    href: web-app-cache-aside-leaderboard.md
   - name: Create Web App with Redis cache - Bicep
     displayName: ARM, Resource Manager, Template
     href: web-app-bicep-with-redis-cache-provision.md