I've got a large ECommerce website running LAMP and was wondering how best to easily implement Memcached?
Store all queries in memcached for a certain period - sounds pointless
Store only certain important data like product information into Memcached and make sure the proper updates can expire it correctly - sounds like an end to end solution.
Store complex query results which do not change often - involves a lot of static code
Trying to get an overview of what changes I should make to take the best advantage of memcached.
Thanks :)
I'd let your users decide.
In other words rather than trying to second guess what will work best, I'd rework ALL the database queries to use memcached along the lines of;
Can memcache answer this query? If
so - return the results from cache.
If not 1), pull results from
database and write back to memcached
so the next time it's in the cache.
Ensure all your updates / inserts /
deletes invalidate the appropriate
cache keys.
Now given that 3) might be complex, I'd use that factor to choose which queries to load through the cache - if it's hard and/or time consuming to invalidate the cache, don't cache back those queries to start with.
Because memcached will automatically dump the least recently used keys when the store approaches capacity, you can set everything to never expire and just allow available resources to determine what is currently in the cache. This will largely be determined by user behaviour (which products are popular etc) and hence my first comment about letting the users decide.
It's also worth saying that you should ensure your MySQL database is well tuned first as that can often be an easier win. Query caching, checking heavy queries with Explain to tune your indexes etc, all of this can have a greater impact.
There is no way to get optimization tailored specifically to your system here.
Either you put the name of the OS system you use, or pay someone to analyze what you have.
There is no "common threads" here. (besides, to cache queries, you can do it in the level of the DB with enough memory)
Related
I'm hoping to develop a LAMP application that will centre around a small table, probably less than 100 rows, maybe 5 fields per row. This table will need to have the data stored within accessed rapidly, maybe up to once a second per user (though this is the 'ideal', in practice, this could probably drop slightly). There will be a number of updates made to this table, but SELECTs will far outstrip UPDATES.
Available hardware isn't massively powerful (it'll be launched on a VPS with perhaps 512mb RAM) and it needs to be scalable - there may only be 10 concurrent users at launch, but this could raise to the thousands (and, as we all hope with these things, maybe 10,000s, but this level there will be more powerful hardware available).
As such I was wondering if anyone could point me in the right direction for a starting point - all the data retrieved will be the same for all users, so I'm trying to investigate if there is anyway of sharing this data across all users, rather than performing 10,000 identical selects a second. Soooo:
1) Would the mysql_query_cache cache these results and allow access to the data, WITHOUT requiring a re-select for each user?
2) (Apologies for how broad this question is, I'd appreciate even the briefest of reponses greatly!) I've been looking into the APC cache as we already use this for an opcode cache - is there a method of caching the data in the APC cache, and just doing one MYSQL select per second to update this cache - and then just accessing the APC for each user? Or perhaps an alternative cache?
Failing all of this, I may look into having a seperate script which handles the queries and outputs the data, and somehow just piping this one script's data to all users. This isn't a fully formed thought and I'm not sure of the implementation, but perhaps a combo of AJAX to pull the outputted data from... "Somewhere"... :)
Once again, apologies for the breadth of these question - a couple of brief pointers from anyone would be very, very greatly appreciated.
Thanks again in advance
If you're doing something like an AJAX chat which polls the server constantly, you may want to look at node.js instead, which keeps an open connection between server and browser. This way, you can have changes pushed to the user when they happen and you won't need to do all that redundant checking once per second. This can scale very well to thousands of users and is written in javascript on the server-side, so not too difficult.
The problem with using the MySQL cache is that the entire table cache gets invalidated on any write to that table. You're better off using a caching solution like memcached or APC if you're trying to control that behavior more precisely. And yes, APC would be able to cache that information.
One other thing to keep in mind is that you need to know when to invalidate the cache as well, so you don't have stale data.
You can use apc,xcache or memcache for database query caching or you can use vanish or squid for gateway caching...
I am considering enabling Memcache support for my large-scale REST service. However I have some questions regarding best approaches for these key-value stores.
The setup:
A database wrapper which has functions for select, update and etc.
A REST framework which contains all the API functions (getUser, createUser and etc.)
In my head, the ideal approach would be to integrate the Memcache in the database wrapper so, for example, every SQL query would get md5-hashed and saved in the cache (this is btw what most online resources suggests). However, there is obviously a problem with this approach: if a search query has been cached, and one of the users from the search result has been updated after the cached result, this wont reflect in the next request (because it is now in the cache).
As I see it I have several ways of handeling this:
Implement the Memcache in the REST framework for each function (getUser, createUser etc) and thereby explicit handle the updating of the cache etc. if users gets updated. This could end up in redundant code.
Let the cached values expire very quickly and live with the fact that some requests shows old cached values.
Do a more advanced implementation of the Memcache in the database wrapper so that I can identify which parts(e.g. users) to update in e.g. a search request.
Could you guide me to which of the following, or a complete another approach, to take?
Thanks in advance.
Enabling cache for a web application is not something to take lightly.
Maybe you have done that already bit... I recommend you first come up with a goal based on business needs or forcast (ex: must accept 1000 requests per seconds) then properly stress-test your system to have numbers before you start changing anything and then identify your bottleneck.
http://en.wikipedia.org/wiki/Performance_tuning
I usually use profiling tools such as HXProf (by facebook).
https://github.com/facebook/xhprof
Caching all your data to mirror your database might not be the best approach.
Find out how big you can allocate for your cache. If your architecture only allow you to allocate 100MB for your memcache, then it will affect your decision about what you cache and how long you cache it.
The best cache is to cache forever. But we all know that data changes. You can start by caching data that is requested often and requires the most resources to fetch.
Always try to make sure you are not working on improving something that will get you low improvement.
Without understanding your architecture in depth, it would be hazardous for anyone to recommend a caching strategy that best fit your needs.
Maybe you should cache the resutling output of your web services instead? Using a reverse proxy for example (What #Darrel is talking about) or using output buffering...
http://en.wikipedia.org/wiki/Reverse_proxy
http://php.net/manual/en/book.outcontrol.php
Optimize your database queries before you think about caching. Make sure your use a PHP Op cache (like APC) and all those things that are standard practice.
http://phplens.com/lens/php-book/optimizing-debugging-php.php
http://blog.digitalstruct.com/2008/01/31/performance-tuning-overview/
If you want to cache data and prevent stale/old data from being served, the trick is to identify your data (primary key maybe?) and when the data is updated or deleted, you delete or update the cache for that identifyer.
<?php
// After inserting into DB, you can also put it in the cache
$memcache->set($userId, $userData);
// After updating or deleting the user, you update or delete the data
$memcache->delete($userId);
A lot of site will show stale data. When I am on stackoverflow and my reputation is increased and then I got in the stackoverflow chat, the reputation shown is my old reputation. When I got a reputation of 20 (reputation required to chat) I still could not chat for another 5 minutes because the chat system had my old reputation data and did not yet know my reputation had increased enough to allow me to chat. Some data can be stale while other type of data should never be stale. Consider that when caching data.
Conclusion
Your approaches can all be valid depending on the factors that I talk about above. In fact, you can use a combination of those for all the different type of data you want to cache and how long it is acceptable to show old data for them. Maybe the categories or list of countries (since they do not change often) can be cached for a long time while the reputation (or whatever data changes all the time for all users) should be cached for a short period only.
I have about 10 tables with ~10,000 rows each which need to be pulled very often.
For example, list of countries, list of all schools in the world, etc.
PHP can't persist this stuff in memory (to my knowledge) so I would have to query the server for a SELECT * FROM TABLE every time. Should I use memcached here? At first though it's a clear absolutely yes, but at second thought, wouldn't mysql already be caching for me and this would be almost redundant?
I don't have too much understanding of how mysql caches data (or if it even does cache entire tables).
You could use MySQL query cache, but then you are still using DB resources to establish the connection and execute the query. Another option is opcode caching if your pages are relatively static. However I think memcached is the most flexible solution. For example if you have a list of countries which need to be accessed from various code-points within your application, you could pull the data from the persistent store (mysql), and store them into memcached. Then the data is available to any part of your application (including batch processes and cronjobs) for any business requirement.
I'd suggest reading up on the MySQL query cache:
http://dev.mysql.com/doc/refman/5.6/en/query-cache.html
You do need some kind of a cache here, certainly; layers of caching within and surrounding the database are considerably less efficient than what memcached can provide.
That said, if you're jumping to the conclusion that the Right Thing is to cache the query itself, rather than to cache the content you're generating based on the query, I think you're jumping to conclusions -- more analysis is needed.
What data, other than the content of these queries, is used during output generation? Would a page cache or page fragment cache (or caching reverse-proxy in front) make more sense? Is it really necessary to run these queries "often"? How frequently does the underlying data change? Do you have any kind of a notification event when that happens?
Also, SELECT * queries without a WHERE clause are a "code smell" (indicating that something probably is being done the Wrong Way), especially if not all of the data pulled is directly displayed to the user.
I don't really have any experience with caching at all, so this may seem like a stupid question, but how do you know when to cache your data? I wasn't even able to find one site that talked about this, but it may just be my searching skills or maybe too many variables to consider?
I will most likely be using APC. Does anyone have any examples of what would be the least amount of data you would need in order to cache it? For example, let's say you have an array with 100 items and you use a foreach loop on it and perform some simple array manipulation, should you cache the result? How about if it had a 1000 items, 10000 items, etc.?
Should you be caching the results of your database query? What kind of queries should you be caching? I assume a simple select and maybe a couple joins statement to a mysql db doesn't need caching, or does it? Assuming the mysql query cache is turned on, does that mean you don't need to cache in the application layer, or should you still do it?
If you instantiate an object, should you cache it? How to determine whether it should be cached or not? So a general guide on what to cache would be nice, examples would also be really helpful, thanks.
When you're looking at caching data that has been read from the database in APC/memcache/WinCache/redis/etc, you should be aware that it will not be updated when the database is updated unless you explicitly code to keep the database and cache in synch. Therefore, caching is most effective when the data from the database doesn't change often, but also requires a more complex and/or expensive query to retrieve that data from the database (otherwise, you may as well read it from the database when you need it)... so expensive join queries that return the same data records whenever they're run are prime candidates.
And always test to see if queries are faster read from the database than from cache. Correct database indexing can vastly improve database access times, especially as most databases maintain their own internal cache as well, so don't use APC or equivalent to cache data unless the database overheads justify it.
You also need to be aware of space usage in the cache. Most caches are a fixed size and you don't want to overfill them... so don't use them to store large volumes of data. Use the apc.php script available with APC to monitor cache usage (though make sure that it's not publicly accessible to anybody and everybody that accesses your site.... bad security).
When holding objects in cache, the object will be serialized() when it's stored, and unserialized() when it's retrieved, so there is an overhead. Objects with resource attributes will lose that resource; so don't store your database access objects.
It's sensible only to use cache to store information that is accessed by many/all users, rather than user-specific data. For user session information, stick with normal PHP sessions.
The simple answer is that you cache data when things get slow. Obviously for any medium to large sized application, you need to do much more planning than just a wait and see approach. But for the vast majority of websites out there, the question to ask yourself is "Are you happy with the load time". Of course if you are obsessive about load time, like myself, you are going to want to try to make it even faster regardless.
Next, you have to identify what specifically is the cause of the slowness. You assumed that your application code was the source but its worth examining if there are other external factors such as large page file size, excessive requests, no gzip, etc. Use a site like http://tools.pingdom.com/ or an extension like yslow as a start for that. (quick tip make sure keepalives and gzip are working).
Assuming the problem is the duration of execution of your application code, you are going to want to profile your code with something like xdebug (http://www.xdebug.org/) and view the output with kcachegrind or wincachegrind. That will let you know what parts of your code are taking long to run. From there you will make decisions on what to cache and how to cache it (or make improvements in the logic of your code).
There are so many possibilities for what the problem could be and the associated solutions, that it is not worth me guessing. So, once you identify the problem you may want to post a new question related to solving that specific problem. I will say that if not used properly, the mysql query cache can be counter productive. Also, I generally avoid the APC user cache in favor of memcached.
I've seen several database cache engines, all of them are pretty dumb (i.e.: keep this query cached for X minutes) and require that you manually delete the whole cache repository after a INSERT / UPDATE / DELETE query has been executed.
About 2 or 3 years ago I developed an alternative DB cache system for a project I was working on, the idea was basically to use regular expressions to find the table(s) involved in a particular SQL query:
$query_patterns = array
(
'INSERT' => '/INTO\s+(\w+)\s+/i',
'SELECT' => '/FROM\s+((?:[\w]|,\s*)+)(?:\s+(?:[LEFT|RIGHT|OUTER|INNER|NATURAL|CROSS]\s*)*JOIN\s+((?:[\w]|,\s*)+)\s*)*/i',
'UPDATE' => '/UPDATE\s+(\w+)\s+SET/i',
'DELETE' => '/FROM\s+((?:[\w]|,\s*)+)/i',
'REPLACE' => '/INTO\s+(\w+)\s+/i',
'TRUNCATE' => '/TRUNCATE\s+(\w+)/i',
'LOAD' => '/INTO\s+TABLE\s+(\w+)/i',
);
I know that these regexs probably have some flaws (my regex skills were pretty green back then) and obviously don't match nested queries, but since I never use them that isn't a problem for me.
Anyway, after finding the involved tables I would alphabetically sort them and create a new folder in the cache repository with the following naming convention:
+table_a+table_b+table_c+table_...+
In case of a SELECT query, I would fetch the results from the database, serialize() them and store them in the appropriate cache folder, so for instance the results of the following query:
SELECT `table_a`.`title`, `table_b`.`description` FROM `table_a`, `table_b` WHERE `table_a`.`id` <= 10 ORDER BY `table_a`.`id` ASC;
Would be stored in:
/cache/+table_a+table_b+/079138e64d88039ab9cb2eab3b6bdb7b.md5
The MD5 being the query itself. Upon a consequent SELECT query the results would be trivial to fetch.
In case of any other type of write query (INSERT, REPLACE, UPDATE, DELETE and so on) I would glob() all the folders that had +matched_table(s)+ in their name all delete all the file contents. This way it wouldn't be necessary to delete the whole cache, just the cache used by the affected and related tables.
The system worked pretty well and the difference of performance was visible - although the project had many more read queries than write queries. Since then I started using transactions, FK CASCADE UPDATES / DELETES and never had the time to perfect the system to make it work with these features.
I've used MySQL Query Cache in the past but I must say the performance doesn't even compare.
I'm wondering: am I the only one who sees beauty in this system? Is there any bottlenecks I may not be aware of? Why do popular frameworks like CodeIgniter and Kohana (I'm not aware of Zend Framework) have such rudimentary DB cache systems?
More importantly, do you see this as a feature worth pursuing? If yes, is there anything I could do / use to make it even faster (my main concerns are disk I/O and (de)serialization of query results)?
I appreciate all input, thanks.
I can see the beauty in this solution, however, I belive it only works for a very specific set of applications. Scenarios where it is not applicable include:
Databases which utilize cascading deletes/updates or any kind of triggers. E.g., your DELETE to table A may cause a DELETE from table B. The regex will never catch this.
Accessing the database from points which do not go through you cache invalidation scheme, e.g. crontab scripts etc. If you ever decide to implement replication across machines (introduce read-only slaves), it may also disturb the cache (because it does not go through cache invalidation etc.)
Even if these scenarios are not realistic for your case it does still answer the question of why frameworks do not implement this kind of cache.
Regarding if this is worth pursuing, it all depends on your application. Maybe you care to supply more information?
The solution, as you describe it, is at risk for concurrency issues. When you're receiving hundreds of queries per second, you're bound to hit a case where an UPDATE statement runs, but before you can clear your cache, a SELECT reads from it, and gets stale data. Additionally, you may run in to issues when several UPDATEs hit the same set of rows in a short time period.
In a broader sense, best practice with caching is to cache the largest objects possible. E.g., rather than having a bunch of "user"-related rows cached all over the place, it's better to just cache the "user" object itself.
Even better, if you can cache whole pages (e.g., you show the same homepage to everyone; a profile page appears identical to almost everyone, etc.), that's even better. One cache fetch for a whole, pre-rendered page will dramatically outperform dozens of cache fetches for row/query level caches followed by re-rending the page.
Long story short: profile. If you take the time to do some measurement, you'll likely find that caching large objects, or even pages, rather than small queries used to build those things, is a huge performance win.
While I do see the beauty in this - especially for environments where resources are limited and can not easily be extended, like on shared hosting - I personally would fear complications in the future: What if somebody, newly hired and unaware of the caching mechanism, starts using nested queries? What if some external service starts updating the table, with the cache not noticing?
For a specialized, defined project that urgently needs a speedup that cannot be helped by adding processor power or RAM, this looks like a great solution. As a general component, I find it too shaky, and would fear subtle problems in the long run that stem from people forgetting that there is a cache to be aware of.
I suspect that the regexes may not provide for every case - certainly they don't seem to deal with the scenario of mixing base table names and the tables themselves. e.g. consider
update stats.measures set amount=50 where id=1;
and
use stats;
update measures set amount=50 where id=1;
Then there's PL/SQL.
Then there's the fact that it depends on every client opting in to an advisory control mechanism i.e. it pre-supposes that all the database access is from machines implementing the caching control mechanism on a shared filesystem.
(as a small point - wouldn't it be simpler to just check the modification times on the data files to determine if the cached version of a query on a defined set of tables is still current, rather then trying to identify if the cache control mechanism has spotted an update - it would certainly be a lot more robust)
Stepping back a bit, implementing this from scratch using a robust architecture would mean that all queries would have to be intercepted by the control mechanism. The control mechanism would probably need a more sophisticated query parser. It certainly requires a common storgae substrate for all the instances of the control mechanism. It probably needs an understanding of the data dictionary - all things which are already implemented by the database itself.
You state that "I've used MySQL Query Cache in the past but I must say the performance doesn't even compare."
I find this rather odd. Certainly when dealing with large result sets from queries, my experience is that loading the data into the heap from a database is a lot faster than unserializing large arrays - although large result sets are rather atypical of web based applications.
When I've tried to speed up database access (after fixing everything else of course) then I've gone down the route of replicating and partitioning data across multiple DBMS instances.
C.
This is related to the problem of session splitting when working with multiple databases in a master-slave configuration. Basically, a similar set of regular expressions are used to determine which tables (or even which rows) are being read from or written to. The system keeps track of which tables were written to and when, and when a read to one of those tables comes up, it's routed to the master. If a query is reading from a table whose data needn't be up-to-the-second accurate, then it's routed to the slave. Generally, information only really needs to be current when it's something a user changed themselves (i.e., editing a user's profile).
They talk about this a good bit in the O'Reilly book High Performance MySQL. I used it quite a bit when developing a system for handling session splits back in the day.
The improvement you describe is to avoid invalidating caches that are guaranteed to not have been affected by an update because they draw data from a different table.
That is of course nice, but I am not sure if it is fine-grained enough to make a real difference. You would still be invaliding lots of caches that did not really need to be (because the update was on the table, but on different rows).
Also, even this "simple" scheme relies on being able to detect the relevant tables by looking at the SQL query string. This can be difficult to do in the general case, because of views, table aliases, and multiple catalogs.
It is very difficult to automatically (and efficiently) detect whether a cache needs to be invalidated. Because of that, you can either use a very simple scheme (such as invalidating on every update, or per table, as in your system, which does not work too well when there are many updates), or a very hand-crafted cache for the specific application with deep hooks into the query logic (probably difficult to write and hard to maintain), or accept that the cache can contain stale data and just refresh it periodically.