Johnny cache not invalidating. Milk Your Caching for All It's Worth - How to Add Django Database Caching in 5 Minutes.



Johnny cache not invalidating

Johnny cache not invalidating

Is johnny-cache for you? I've been pleasantly surprised with the amount of interest in johnny-cache since Jeremy and I released it this past weekend. A lot of the comments revealed that perhaps the documentation is missing an important discussion on the repercussions of using Johnny. They are also pretty positive about the name: Using Johnny is really adopting a particular caching strategy.

This strategy isn't always a win; it can impact performance negatively: Loading from memcached is going to smoke even your db's queryset cache with respects to latency while giving you cheap and easy horizontal scalability.

It's not often you get these two coming hand in hand. Every time you do a query that hits cache, your database doesn't have to accept a connection, allocate cursors, examine your query, execute it, and return the result.

This is a fairly heavy cognitive load to lift off of your database servers. If you were using something akin to MySQL's queryset cache before, you can pretty much turn it off. Not only do you get that memory back for loading indexes, performing queries, etc, but you can now horizontally scale your query cache with ease.

Scaling reads across a pool of RODB databases is no picnic, either. For a read-heavy application, Johnny can alleviate so much read traffic that you can potentially just scale reads in memcached. Even if you need to horizontally scale reads across an rodb pool, they now have a shared queryset cache, such that reads on one slave saves reads to another.

Still, writes eventually happen, and when they do, Johnny will blow away the cache depending on the table written to. The implications of this are that Johnny's effectiveness is reduced if you: For a simple one page, one query, one table scenario where you are receiving about 1 write per second. Typical webapps are going to read far more often than they write, and serve a few pages far more often than the other pages on the site. For these apps, Johnny will probably work quite well.

Even in cases where it doesn't fly, it's probably a good starting point. But due to the magic of the internet, I don't have to rely solely on hypothetical and anecdotal evidence.

Someone running such an application tried Johnny out and wrote a nice little blog post about his results. His chart even suggests that his application is quite write heavy.

It also looks pretty similar to what we saw when we pushed the primordial version of Johnny live last year.

The post itself is pretty fascinating; the readers digest translation is that he already had some caching in place, but installed Johnny, set it up, and his query count still dropped pretty dramatically illustrated. Note that it wasn't just cache hits that dropped; Johnny can cache some queries that MySQL can't, and there are other classes of queries that are impossible to cache but are easily avoided. Despite that initial positive result, he noticed that his CPU utilization and context-switching increased, likely because memcached and mysql and I perhaps even his app server were running on the same box.

So, where to take Johnny from here? Johnny is version '0. The first improvement would be a way to allow application authors to keep Johnny from caching result sets from tables that receive very heavy write traffic, like a log table. Although monkeypatching was really the only way to achieve the level of integration and simplicity we needed, you always have to acknowledge that there will be cases where people only want to use your code some of the time, or maybe most of the time, but not all of the time.

Some kind of model annotation or table blacklist might suffice here, but I want to think through this and its invalidation implications a bit more before deciding on how to do it. Another improvement I want is increased access to the generational keys Johnny maintains.

I recognize cases where you might want to use Johnny's invalidation to consistently cache higher level objects like html fragments or even entire pages. I'm still trying to work out how to increase this idea's usefulness when you introduce pagination.

Every application has its own set of circumstances and requirements, and probably its own optimal caching strategy, but if you're a perfectionist with a deadline, Johnny might just get you a whole lot of bang for fairly little buck.

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Johnny cache not invalidating

Is johnny-cache for you? I've been pleasantly surprised with the amount of interest in johnny-cache since Jeremy and I released it this past weekend. A lot of the comments revealed that perhaps the documentation is missing an important discussion on the repercussions of using Johnny. They are also pretty positive about the name: Using Johnny is really adopting a particular caching strategy.

This strategy isn't always a win; it can impact performance negatively: Loading from memcached is going to smoke even your db's queryset cache with respects to latency while giving you cheap and easy horizontal scalability. It's not often you get these two coming hand in hand. Every time you do a query that hits cache, your database doesn't have to accept a connection, allocate cursors, examine your query, execute it, and return the result. This is a fairly heavy cognitive load to lift off of your database servers.

If you were using something akin to MySQL's queryset cache before, you can pretty much turn it off. Not only do you get that memory back for loading indexes, performing queries, etc, but you can now horizontally scale your query cache with ease. Scaling reads across a pool of RODB databases is no picnic, either. For a read-heavy application, Johnny can alleviate so much read traffic that you can potentially just scale reads in memcached. Even if you need to horizontally scale reads across an rodb pool, they now have a shared queryset cache, such that reads on one slave saves reads to another.

Still, writes eventually happen, and when they do, Johnny will blow away the cache depending on the table written to. The implications of this are that Johnny's effectiveness is reduced if you: For a simple one page, one query, one table scenario where you are receiving about 1 write per second.

Typical webapps are going to read far more often than they write, and serve a few pages far more often than the other pages on the site. For these apps, Johnny will probably work quite well. Even in cases where it doesn't fly, it's probably a good starting point. But due to the magic of the internet, I don't have to rely solely on hypothetical and anecdotal evidence. Someone running such an application tried Johnny out and wrote a nice little blog post about his results.

His chart even suggests that his application is quite write heavy. It also looks pretty similar to what we saw when we pushed the primordial version of Johnny live last year. The post itself is pretty fascinating; the readers digest translation is that he already had some caching in place, but installed Johnny, set it up, and his query count still dropped pretty dramatically illustrated.

Note that it wasn't just cache hits that dropped; Johnny can cache some queries that MySQL can't, and there are other classes of queries that are impossible to cache but are easily avoided. Despite that initial positive result, he noticed that his CPU utilization and context-switching increased, likely because memcached and mysql and I perhaps even his app server were running on the same box.

So, where to take Johnny from here? Johnny is version '0. The first improvement would be a way to allow application authors to keep Johnny from caching result sets from tables that receive very heavy write traffic, like a log table.

Although monkeypatching was really the only way to achieve the level of integration and simplicity we needed, you always have to acknowledge that there will be cases where people only want to use your code some of the time, or maybe most of the time, but not all of the time. Some kind of model annotation or table blacklist might suffice here, but I want to think through this and its invalidation implications a bit more before deciding on how to do it.

Another improvement I want is increased access to the generational keys Johnny maintains. I recognize cases where you might want to use Johnny's invalidation to consistently cache higher level objects like html fragments or even entire pages. I'm still trying to work out how to increase this idea's usefulness when you introduce pagination. Every application has its own set of circumstances and requirements, and probably its own optimal caching strategy, but if you're a perfectionist with a deadline, Johnny might just get you a whole lot of bang for fairly little buck.

Johnny cache not invalidating

Deploying route to substance can be concerned with uncertainty. Up the winners, and case johnny cache not invalidating and more often. Irrevocably, there are a routine of her sounds concerned to facilitate this equal of dating, including Johnny Exclusive jihnny, Django Cache-Botand Django Road Machine.

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1 Comments

  1. The post itself is pretty fascinating; the readers digest translation is that he already had some caching in place, but installed Johnny, set it up, and his query count still dropped pretty dramatically illustrated. Because the generation keys are invalidated on write, and a transaction commit does not go down the same code path as our invalidation, there are a number of scenarios involving transactions that could cause problems. By setting the getattr magic method in the CachedManager, you can effectively handle all your lazy chaining needs see this post by Zach Smith on this awesome Django tip.

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