![]() ![]() But, it doesn't tell you which element of the hash it was! The custom events include information on the actual hash element itself. For example, if you delete a Hash key, you get a HDEL notification that tells you which hash had a key deleted. Redis out-of-the-box keyspace notifications on their own are not sufficient as they don't generally provide enough information to invalidate the correct parts of the memory cache. NotificationDatabase - this publishes custom keyspace events which are required to keep any in-memory caches up-to-date.This means you can create new instances of RedisL1Database - passing in the Redis IDatabase instance you already have - and it'll continue to use any in-memory cache it had created previously for that database. The real beauty of this approach is of course that the same interface is implemented - the same interface you're already using - so you don't need to make *any* code changes to use this L1 caching solution. Operations via another client - Redis keyspace notifications are handled to detect that data has changed elsewhere, and is invalidated appropriately.KeyDelete, KeyExpire etc - deletes the in-memory data.For more complex data manipulation operations, we'd delete the in-memory key. ![]() StringAppend - in this trivial example, we'd append to the in-memory string, then pass the same operation to redis.So, what if we wrote a custom IDatabase implementation to handle all of the situations outlined above? In this case, we could solve all of the above problems: Now, StackExchange.Redis data access and update methods are defined on the IDatabase interface. When a key expires, it needs to be removed from the in-memory cache.In this case, the in-memory value on our client would be outdated. Another client may update or delete a value.When a key is deleted via KeyDelete, it needs to be removed from the in-memory cache. ![]() In this case, we need to invalidate the in-memory item. Redis allows us to operate on the data via functions such as StringAppend.In addition we are faced with the following issues: _memor圜ache.Add(key, strValue) // Add to memory return strValue Īlthough this is not difficult to implement, things quickly get MUCH more complicated when we attempt to follow the same pattern for other Redis data types. _cacheDatabase.StringSet(key, strValue) // Add to Redis String strValue = GetValueFromDataSource() // Get the value from eg. Return redisValue // It's in redis - return it RedisValue redisValue = _cacheDatabase.StringGet(key) Try and retrieve from memory if (_memor圜ache.ContainsKey(key)) This Stack Exchange meta answer explains in some detail the caching mechanisms they use: However, they have another trick up their sleeve - caching everything possible in-memory on the web server so that much of the time, they don't even have to talk to Redis. It's what Stack Exchange uses to aggressively cache *all* their data - and that's one of the busiest sites on the internet. The StackExchange.Redis library is free and open source, with a lively community. Redis is a very very fast cache, but it's not just a cache because it lets you operate on the data in that cache. Pages returned in around 500ms instead of the previous 2s - but when profiling, it was apparent that a large chunk of this 500ms was spent sending or receiving from Redis much of which was being received on every page request without being changed. Naturally, this speeded up the application hugely. ![]() We're using strings, sets, sorted sets, and hashes to cache all aspects of the app data. This model provides for better stability and a cleaner architecture however, this is at the cost of performance since every time you need SharePoint data you are at the mercy of network latency.įor this reason, we introduced Redis caching into the app, with StackExchange.Redis as the. For those that know about SharePoint, the SharePoint app model requires that apps do not execute within the SharePoint process, but on a separate app server. Back Storyįor the past couple of years I've been working on a SharePoint app, Repsor custodian. This is an account of how and why it was written. Visit the GitHub page, or get the NuGet package. NET application, you can drop this library in to accelerate it by caching data locally in-memory to avoid unnecessary calls to Redis when the data hasn't changed. What that means is that if you're using StackExchange.Redis in a. Using Leadtools using Leadtools.Caching using Leadtools.Codecs using Leadtools.Svg using Newtonsoft.Json using StackExchange.Redis using System using using System.IO namespace MyNamespace // Getting number of items in the cache.TLDR I wrote an open source library called StackRedis.L1. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |