I'm building a news rating script for this website that has a lot of users. I'm trying to make this websites as efficient as possible and now I'm wondering what would be the most efficient way to keep track of the votes. Of course I don't want users to vote more than once.
My first though was to store it in a my MySQL database, but I'm worried this would have a negative influence in my website's speed because this table would get quite big.
Would storing it in a database still be the best solution or are there any better solutions.
If you plan on having > 1,000,000 records you should make sure the table's structure is efficient (which shouldnt be hard for your example) and that you index it correctly.
Memcached would be the simplest way to implement caching and is easy to scale if your site grows and more servers are necessary.
With a properly indexed vote table, you can keep reasonable performance regardless of how large your table is (of course, beyond a certain point, your tables will be too large to fit in cache, but that would involve having a very large number of users and items).
Add in some per-user caching (on the client, in $_SESSION, using memcached) and you can get a quite fast "no" response time).
Since you can't use memcached I would say this. A decent database server ( decent hardware + decent db implementation) should be able to handle this quite well. A single table with a physical index on article-id and a second entry representing the vote will handle a few googillion (yes I made up the word) articles easily :P
Rationale :
Database servers maintain statistics -- read: self-tuning -- and only hot items (index + row-entries) remain in-memory.
Moral:
Don't worry about such things unless they become a problem -- i.e., If your company is the size of facebook I would worry.
Memcached would be a very good way to do this. you need to synchronize from memcached once in a while (I would do this using the pull model using a cron script on you mysql server).
Did you see this?
http://destiney.com/php#Destiney_rated_images
Demo here: http://ratedsite.com/
Related
I'm attempting to write a search functionality for a website, and I've decided upon an approach of using MySQL temporary tables to handle the data input, via the query below:
CREATE TEMPORARY TABLE `patternmatch`
(`pattern` VARCHAR(".strlen($queryLengthHere)."))
INSERT INTO `patternmatch` VALUES ".$someValues
Where $someValues is a set of data with the layout ('some', 'search', 'query') - or basically what the user searched. I then search my main table images based on the data within table patternmatch like so:
SELECT images.* FROM images JOIN patternmatch ON (images.name LIKE patternmatch.pattern)
I then apply a heuristic or scoring system based on how well each result matched the input and display the results by that heuristic etc.
What I'm wondering is how much overhead does creating a temporary table require? I understand that they only exist in session, and are dropped as soon as the session is ended, but if I have hundreds of thousands of searches per second, what sort of performance issues might I encounter? Is there any better way of implementing a search functionality?
What you stated is totally correct, the temporary table will only be visible to the current user/connection. Still, there is some overhead and some other problems such as:
For each of the thousands of searches you are going to create and fill that table (and drop it later) - not per user, per search. Because each search most likely will re-execute the script, and "per session" does not mean PHP session - it means database session (open connection).
You will need the CREATE TEMPORARY TABLES privilege, which you might not have.
Still, that table really should have MEMORY type, which steals your RAM more than it looks like. Because even having VARCHAR, MEMORY tables use fixed length row-storage.
If your heuristics later need to refer to that table twice (like SELECT xyz FROM patternmatch AS pm1, patternmatch AS pm2 ...) - this is not possible with MEMORY tables.
Next, it would be easier for you - and also for the database - to add the LIKE '%xyz%' directly to your images tables WHERE clause. It will do the same without the overhead of creating a TEMP TABLE and joining it.
In any case - no matter which way you go - that WHERE will be horribly slow. Even if you add an index on images.name you most likely will need LIKE '%xyz%' instead of LIKE 'xyz%', so that index will not get used.
I'm asking whether a session-specific temporary table to handle the search input by the user (created on a search, dropped on the end of a session) is an appropriate way of handling a search functionality.
No. :)
Alternative options
MySQL has a build-in Fulltext-Search (since 5.6 also for InnoDB) that even can give you that scoring: I highly recommend giving it a read and a try. You can be sure that the database knows better than you how to do that search efficiently.
If you are going to use MyISAM instead of InnoDB, be aware of the often overlooked limitation that FULLTEXT searches only return anything if the number of results is less than 50% of the total table rows.
Other things that you might want to look at, are for example Solr (Nice introduction read to that topic itself would be the beginning of http://en.wikipedia.org/wiki/Apache_Solr ). We are using it in our company and it does a great job, but it requires quite some learning.
Summary
The solution to your current problem itself (the search) is to use the FULLTEXT capabilities.
If I have hundreds of thousands of searches per second, what sort of performance issues might I encounter? Is there any better way of implementing a search functionality?
To give you a number, 10.000 calls per second is not "trivial" already - with hundreds of thousands of searches per second the sort of performance issues you will encounter are everywhere in your set-up. You are going to need a couple of servers, load balancing and tons of other amazing tech crap. And one of this will be for example Solr ;)
Creating temporary tables on disk is relatively expensive. In your scenario it sounds like it'll be slower than it's worth.
It's usually only worthwhile to create temporary tables in memory. But you need to know you have enough memory available at all times. If you plan to support so many searches per second this is not a good solution.
MySQL has full-text searching built-in. It's good for small systems. This would likely perform far better than your temp table and JOIN. But if you want to support thousands of searches per second I would not recommend it. It could consume too much of your overall database performance. Plus you're then forced to use MyISAM for storage which might have its own issues in your scenario.
For so many searches you'll want to offload the work to another system. Plenty of searching systems with scoring already exist. Take a look at ElasticSearch, Solr/Lucene, Redis, etc.
From the code you give, I really don't think tmp tables are needed, nor is FULLTEXT searching. But ... about tmp table performance:
The creation/cleanup of the tmp table is not written to transaction logs, so it will be relatively quick for the OS to do the I/O involved. If the temporary tables will be small and short-lived, and you have lots of buffers available for the OS, the disk realistically wont even be touched. If you think it will be anyways, get an SSD drive, and get more RAM.
But if you are realistic that you are looking at hundreds of thousands of searches per second then you have a big engineering project on hand. Why not just do:
select images.* from images where name in ('some', 'search', 'query')
?
I am trying to display overall ratings on the front of my site and although my site and DB are small now, I think this query and process might bog things down when this table gets large.
Right now, I have ratings employed that are easy because my query is saying: find all records for this ID, pull all ratings and average them.
What I now want to do is a query that says: Find ALL records regardless of ID, sort by ID, average any existing "RATINGS" for each record, hold the rating somewhere with associated ID then have a cutoff at the top 10 records.
This middle area in the real world would be like "scratch paper" and I don't know how to address this middle ground. Well, I do, but all those remaining results that fall outside of the ten that I want seem like a wasted process??
I don't know.. I recall a guy talking about memcache or something once. Is that the "scratch pad" i am looking for??
Thanks,
Rob
Memcached certainly could be used for that. Its a memory based (so fast) way to store and data, and is widely used.
Being ram based, its not persisted if your system restarts, so the most common practise is to use it alongside a relational database to store data/structures that are relatively expensive to produce, this saves constantly recreating them - certainly caching results of complicated database queries as you suggest is a common use.
Memcached runs as a service on your server. For PHP to be able to access it, you need install the pecl memcache extension. In your code, you instantiate its memcache class and then use the set() and get() methods to save and load data under known keys. It will automatically serialize/deserialize objects, so you can set and get native php/objects/arrays etc.
Slight aside: I agree with what you are saying, averages and overviews are often slow because of the amount of data to churn. But I'd be wary of optimising this too much before its an issue - you may change other things before this is an issue that mean you no longer have to solve this, or you may never have enough users/data for this to be a problem, and its often not the things you think that impact performance most anyway, which you can't know until you have issues.
I'd argue that your time is better spent building traffic/users etc first!
I was wondering if it's faster to process data in MySQL or a server language like PHP or Python. I'm sure native functions like ORDER will be faster in MySQL due to indexing, caching, etc, but actually calculating the rank (including ties returning multiple entries as having the same rank):
Sample SQL
SELECT TORCH_ID,
distance AS thisscore,
(SELECT COUNT(distinct(distance))+1 FROM torch_info WHERE distance > thisscore) AS rank
FROM torch_info ORDER BY rank
Server
...as opposed to just doing a SELECT TORCH_ID FROM torch_info ORDER BY score DESC and then figure out rank in PHP on the web server.
Edit: Since posting this, my answer has changed completely, partly due to the experience I've gained since then and partly because relational database systems have gotten significantly better since 2009. Today, 9 times out of 10, I would recommend doing as much of your data crunching in-database as possible. There are three reasons for this:
Databases are highly optimized for crunching data—that's their entire job! With few exceptions, replicating what the database is doing at the application level is going to be slower unless you invest a lot of engineering effort into implementing the same optimizations that the DB provides to you for free—especially with a relatively slow language like PHP, Python, or Ruby.
As the size of your table grows, pulling it into the application layer and operating on it there becomes prohibitively expensive simply due to the sheer amount of data transferred. Many applications will never reach this scale, but if you do, it's best to reduce the transfer overhead and keep the data operations as close to the DB as possible.
In my experience, you're far more likely to introduce consistency bugs in your application than in your RDBMS, since the DB can enforce consistency on your data at a low level but the application cannot. If you don't have that safety net built-in, so you have to be more careful to not make mistakes.
Original answer: MySQL will probably be faster with most non-complex calculations. However, 90% of the time database server is the bottleneck, so do you really want to add to that by bogging down your database with these calculations? I myself would rather put them on the web/application server to even out the load, but that's your decision.
In general, the answer to the "Should I process data in the database, or on the web server question" is, "It depends".
It's easy to add another web server. It's harder to add another database server. If you can take load off the database, that can be good.
If the output of your data processing is much smaller than the required input, you may be able to avoid a lot of data transfer overhead by doing the processing in the database. As a simple example, it'd be foolish to SELECT *, retrieve every row in the table, and iterate through them on the web server to pick the one where x = 3, when you can just SELECT * WHERE x = 3
As you pointed out, the database is optimized for operation on its data, using indexes, etc.
The speed of the count is going to depend on which DB storage engine you are using and the size of the table. Though I suspect that nearly every count and rank done in mySQL would be faster than pulling that same data into PHP memory and doing the same operation.
Ranking is based on count, order. So if you can do those functions faster, then rank will obviously be faster.
A large part of your question is dependent on the primary keys and indexes you have set up.
Assuming that torchID is indexed properly...
You will find that mySQL is faster than server side code.
Another consideration you might want to make is how often this SQL will be called. You may find it easier to create a rank column and update that as each track record comes in. This will result in a lot of minor hits to your database, versus a number of "heavier" hits to your database.
So let's say you have 10,000 records, 1000 users who hit this query once a day, and 100 users who put in a new track record each day. I'd rather have the DB doing 100 updates in which 10% of them hit every record (9,999) then have the ranking query get hit 1,000 times a day.
My two cents.
If your test is running individual queries instead of posting transactions then I would recommend using a JDBC driver over the ODBC dsn because youll get 2-3 times faster performance. (im assuming your using an odbc dsn here in your tests)
How to increase the performance for mysql database because I have my website hosted in shared server and they have suspended my account because of "too many queries"
the stuff asked "index" or "cache" or trim my database
I don't know what does "index" and cache mean and how to do it on php
thanks
What an index is:
Think of a database table as a library - you have a big collection of books (records), each with associated data (author name, publisher, publication date, ISBN, content). Also assume that this is a very naive library, where all the books are shelved in order by ISBN (primary key). Just as the books can only have one physical ordering, a database table can only have one primary key index.
Now imagine someone comes to the librarian (database program) and says, "I would like to know how many Nora Roberts books are in the library". To answer this question, the librarian has to walk the aisles and look at every book in the library, which is very slow. If the librarian gets many requests like this, it is worth his time to set up a card catalog by author name (index on name) - then he can answer such questions much more quickly by referring to the catalog instead of walking the shelves. Essentially, the index sets up an 'alternative ordering' of the books - it treats them as if they were sorted alphabetically by author.
Notice that 1) it takes time to set up the catalog, 2) the catalog takes up extra space in the library, and 3) it complicates the process of adding a book to the library - instead of just sticking a book on the shelf in order, the librarian also has to fill out an index card and add it to the catalog. In just the same way, adding an index on a database field can speed up your queries, but the index itself takes storage space and slows down inserts. For this reason, you should only create indexes in response to need - there is no point in indexing a field you rarely search on.
What caching is:
If the librarian has many people coming in and asking the same questions over and over, it may be worth his time to write the answer down at the front desk. Instead of checking the stacks or the catalog, he can simply say, "here is the answer I gave to the last person who asked that question".
In your script, this may apply in different ways. You can store the results of a database query or a calculation or part of a rendered web page; you can store it to a secondary database table or a file or a session variable or to a memory service like memcached. You can store a pre-parsed database query, ready to run. Some libraries like Smarty will automatically store part or all of a page for you. By storing the result and reusing it you can avoid doing the same work many times.
In every case, you have to worry about how long the answer will remain valid. What if the library got a new book in? Is it OK to use an answer that may be five minutes out of date? What about a day out of date?
Caching is very application-specific; you will have to think about what your data means, how often it changes, how expensive the calculation is, how often the result is needed. If the data changes slowly, it may be best to recalculate and store the result every time a change is made; if it changes often but is not crucial, it may be sufficient to update only if the cached value is more than a certain age.
Setup a copy of your application locally, enable the mysql query log, and setup xdebug or some other profiler. The start collecting data, and testing your application. There are lots of guides, and books available about how to optimize things. It is important that you spend time testing, and collecting data first so you optimize the right things.
Using the data you have collected try and reduce the number of queries per page-view, Ideally, you should be able to get everything you need in less 5-10 queries.
Look at the logs and see if you are asking for the same thing twice. It is a bad idea to request a record in one portion of your code, and then request it again from the database a few lines later unless you are sure the value is likely to have changed.
Look for queries embedded in loop, and try to refactor them so you make a single query and simply loop on the results.
The select * you mention using is an indication you may be doing something wrong. You probably should be listing fields you explicitly need. Check this site or google for lots of good arguments about why select * is evil.
Start looking at your queries and then using explain on them. For queries that are frequently used make sure they are using a good index and not doing a full table scan. Tweak indexes on your development database and test.
There are a couple things you can look into:
Query Design - look into more advanced and faster solutions
Hardware - throw better and faster hardware at the problem
Database Design - use indexes and practice good database design
All of these are easier said than done, but it is a start.
Firstly, sack your host, get off shared hosting into an environment you have full control over and stand a chance of being able to tune decently.
Replicate that environment in your lab, ideally with the same hardware as production; this includes things like RAID controller.
Did I mention that you need a RAID controller. Yes you do. You can't achieve decent write performance without one - which needs a battery backed cache. If you don't have one, each write needs to physically hit the disc which is ruinous for performance.
Anyway, back to read performance, once you've got the machine with the same spec RAID controller (and same discs, obviously) as production in your lab, you can try to tune stuff up.
More RAM is usually the cheapest way of achieving better performance - make sure that you've got MySQL configured to use it - which means tuning storage-engine specific parameters.
I am assuming here that you have at least 100G of data; if not, just buy enough ram that your entire DB fits in ram then read performance is essentially solved.
Software changes that others have mentioned such as optimising queries and adding indexes are helpful too, but only once you've got a development hardware environment that enables you to usefully do performance work - i.e. measure performance of your application meaningfully - which means real hardware (not VMs), which is consistent with the hardware environment used in production.
Oh yes - one more thing - don't even THINK about deploying a database server on a 32-bit OS, it's a ruinous waste of good ram.
Indexing is done on the database tables in order to speed queries. If you don't know what it means you have none. At a minumum you should have indexes on every foriegn key and on most fileds that are used frequently in the where clauses of your queries. Primary keys should have indexes automatically assuming you set them up to begin with which I would find unlikely in someone who doesn't know what an index is. Are your tables normalized?
BTW, since you are doing a division in your math (why I haven't a clue), you should Google integer math. You may neot be getting correct results.
You should not select * ever. Instead, select only the data you need for that particular call. And what is your intention here?
order by votes*1000+((1440 - ($server_date - date))/60)2+visites600 desc
You may have poorly-written queries, and/or poorly written pages that run too many queries. Could you give us specific examples of queries you're using that are ran on a regular basis?
sure
this query to fetch the last 3 posts
select * from posts where visible = 1 and date > ($server_date - 86400) and dont_show_in_frontpage = 0 order by votes*1000+((1440 - ($server_date - date))/60)*2+visites*600 desc limit 3
what do you think?
I've been coding php for a while now and have a pretty firm grip on it, MySQL, well, lets just say I can make it work.
I'd like to make a stats script to track the stats of other websites similar to the obvious statcounter, google analytics, mint, etc.
I, of course, would like to code this properly and I don't see MySQL liking 20,000,000 to 80,000,000 inserts ( 925 inserts per second "roughly**" ) daily.
I've been doing some research and it looks like I should store each visit, "entry", into a csv or some other form of flat file and then import the data I need from it.
Am I on the right track here? I just need a push in the right direction, the direction being a way to inhale 1,000 psuedo "MySQL" inserts per second and the proper way of doing it.
Example Insert: IP, time(), http_referer, etc.
I need to collect this data for the day, and then at the end of the day, or in certain intervals, update ONE row in the database with, for example, how many extra unique hits we got. I know how to do that of course, just trying to give a visualization since I'm horrible at explaining things.
If anyone can help me, I'm a great coder, I would be more than willing to return the favor.
We tackled this at the place I've been working the last year so over summer. We didn't require much granularity in the information, so what worked very well for us was coalescing data by different time periods. For example, we'd have a single day's worth of real time stats, after that it'd be pushed into some daily sums, and then off into a monthly table.
This obviously has some huge drawbacks, namely a loss of granularity. We considered a lot of different approaches at the time. For example, as you said, CSV or some similar format could potentially serve as a way to handle a month of data at a time. The big problem is inserts however.
Start by setting out some sample schema in terms of EXACTLY what information you need to keep, and in doing so, you'll guide yourself (through revisions) to what will work for you.
Another note for the vast number of inserts: we had potentially talked through the idea of dumping realtime statistics into a little daemon which would serve to store up to an hours worth of data, then non-realtime, inject that into the database before the next hour was up. Just a thought.
For the kind of activity you're looking at, you need to look at the problem from a new point of view: decoupling. That is, you need to figure out how to decouple the data-recording steps so that delays and problems don't propogate back up the line.
You have the right idea in logging hits to a database table, insofar as that guarantees in-order, non-contended access. This is something the database provides. Unfortunately, it comes at a price, one of which is that the database completes the INSERT before getting back to you. Thus the recording of the hit is coupled with the invocation of the hit. Any delay in recording the hit will slow the invocation.
MySQL offers a way to decouple that; it's called INSERT DELAYED. In effect, you tell the database "insert this row, but I can't stick around while you do it" and the database says "okay, I got your row, I'll insert it when I have a minute". It is conceivable that this reduces locking issues because it lets one thread in MySQL do the insert, not whichever you connect to. Unfortuantely, it only works with MyISAM tables.
Another solution, which is a more general solution to the problem, is to have a logging daemon that accepts your logging information and just en-queues it to wherever it has to go. The trick to making this fast is the en-queueing step. This the sort of solution syslogd would provide.
In my opinion it's a good thing to stick to MySQL for registering the visits, because it provides tools to analyze your data. To decrease the load I would have the following suggestions.
Make a fast collecting table, with no indixes except primary key, myisam, one row per hit
Make a normalized data structure for the hits and move the records once a day to that database.
This gives you a smaller performance hit for logging and a well indexed normalized structure for querying/analyzing.
Presuming that your MySQL server is on a different physical machine to your web server, then yes it probably would be a bit more efficient to log the hit to a file on the local filesystem and then push those to the database periodically.
That would add some complexity though. Have you tested or considered testing it with regular queries? Ie, increment a counter using an UPDATE query (because you don't need each entry in a separate row). You may find that this doesn't slow things down as much as you had thought, though obviously if you are pushing 80,000,000 page views a day you probably don't have much wiggle room at all.
You should be able to get that kind of volume quite easily, provided that you do some stuff sensibly. Here are some ideas.
You will need to partition your audit table on a regular (hourly, daily?) basis, if nothing else only so you can drop old partitions to manage space sensibly. DELETEing 10M rows is not cool.
Your web servers (as you will be running quite a large farm, right?) will probably want to do the inserts in large batches, asynchronously. You'll have a daemon process which reads flat-file logs on a per-web-server machine and batches them up. This is important for InnoDB performance and to avoid auditing slowing down the web servers. Moreover, if your database is unavailable, your web servers need to continue servicing web requests and still have them audited (eventually)
As you're collecting large volumes of data, some summarisation is going to be required in order to report on it at a sensible speed - how you do this is very much a matter of taste. Make sensible summaries.
InnoDB engine tuning - you will need to tune the InnoDB engine quite significantly - in particular, have a look at the variables controlling its use of disc flushing. Writing out the log on each commit is not going to be cool (maybe unless it's on a SSD - if you need performance AND durability, consider a SSD for the logs) :) Ensure your buffer pool is big enough. Personally I'd use the InnoDB plugin and the file per table option, but you could also use MyISAM if you fully understand its characteristics and limitations.
I'm not going to further explain any of the above as if you have the developer skills on your team to build an application of that scale anyway, you'll either know what it means or be capable of finding it out.
Provided you don't have too many indexes, 1000 rows/sec is not unrealistic with your data sizes on modern hardware; we insert that many sometimes (and probably have a lot more indexes).
Remember to performance test it all on production-spec hardware (I don't really need to tell you this, right?).
I think that using MySQL is an overkill for the task of collecting the logs and summarizing them. I'd stick to plain log files in your case. It does not provide the full power of relational database management but it's quite enough to generate summaries. A simple lock-append-unlock file operation on a modern OS is seamless and instant. On the contrary, using MySQL for the same simple operation loads the CPU and may lead to swapping and other hell of scalability.
Mind the storage as well. With plain text file you'll be able to store years of logs of a highly loaded website taking into account current HDD price/capacity ratio and compressability of plain text logs