I am running a crm application which uses mysql database. My application generating lots of data in mysql. Now i want to give my customer a reporting section where admin can view real time report, they should be able to filter at real time. Basically i want my data to be slice and dice at real time fast as possible.
I have implemented the reporting using mysql and php. But now as data is too much query takes too much time and page does not load. After few read i came across few term like Nosql, mongoDb , cassandra , OLAP , hadoop etc but i was confuse which to choose. Is there any mechanism which would transfer my data from mysql to nosql on which i can run my reporting query ans serve my customer keeping my mysql database as it is ?
It doesn't matter what database / datastore technology you use for reporting: you still will have to design it to extract the information you need efficiently.
Improving performance by switching from MySQL to MongoDB or one of the other scalable key/value store systems is like solving a pedestrian traffic jam by building a railroad. It's going to take a lot of work to make it help the situation. I suggest you try getting things to work better in MySQL first.
First of all, you need to take a careful look at which SQL queries in your reporting system are causing trouble. You may be able to optimize their performance by adding indexes or doing other refactoring. That should be your first step. MySQL has a slow query log. Look at it.
Secondly, you may be able to add resources (RAM, faster disks, etc) to MySQL, and you may be able to tune it for higher performance. There's a book called High Performance MySQL that offers a sound methodology for doing this.
Thirdly, many people who need to add a reporting function to their busy application use MySQL replication. That is, they configure one or two slave MySQL servers to accept copies of all data from the master server.
http://dev.mysql.com/doc/refman/5.5/en/replication-howto.html
They then use the slave server or servers to run reporting queries. The slaves are ordinarily a few seconds or minutes behind the master (that is, they're slightly out of date). But it usually is good enough to give users the illusion of real-time reporting.
Notice that if you use MongoDB or some other technology you will also have to replicate your data.
I will throw this link out there for you to read which actually gives certain use cases: http://www.mongodb.com/use-cases/real-time-analytics but I will speak for a more traditional setup of just MongoDB.
I have used both MySQL and MongoDB for analytical purposes and I find MongoDB better suited, if not needing a little bit of hacking to get it working well.
The great thing about MongoDB when it comes to retreiving analytical data is that it does not require the IO/memory to write out a separate result set each time. This makes reads on a single member of a replica set extremely scalable since you just add your analytical collections to the working set (a.k.a memory) and serve straight from those using batch responses (this is the default implementation of the drivers).
So with MongoDB replication rarely gives an advantage in terms of read/write, and in reality with MySQL I have found it does not either. If it does then you are doing the wrong queries which will not scale anyway; at which point you install memcache onto your database servers and, look, you have stale data being served from memory in a NoSQL fashion anyway...whoop, I guess.
Okay, so we have some basic ideas set out; time to talk about that hack. In order to get the best possible speed out of MongoDB, and since it does not have JOINs, you need to flatten your data so that no result set will even be needed your side.
There are many tactics for this, but the one I will mention here is: http://docs.mongodb.org/ecosystem/use-cases/pre-aggregated-reports/ pre-aggregated reports. This method also works well in SQL techs since it essentially is the in the same breath as logically splitting tables to make queries faster and lighter on a large table.
What you do is you get your analytical data, split it into a demomination such as per day or month (or both) and then you aggregate your data across those ranges in a de-normalised manner, essentially, all one row.
After this you can show reports straight from a collection without any need for a result set making for some very fast querying.
Later on you could add a map reduce step to create better analytics but so far I have not needed to, I have completed full video based anlytics without such need.
This should get you started.
TiDB may be a good fit https://en.pingcap.com/tidb/, it is MySQL compatible, good at real-time analytics, and could replicate the data from MySQL through binlog.
Related
I have to implement a reporting/statistic tool in php for one of my application. The amount of data is really huge, about 120 million records. The reports should be generated real time since the user can select many filters before generating a report, so no way to pre-generate it, let say on nightly bases.
The MySql database I use, can't handle this amount of data due the data aggregation and joins (for filtering). Even after trying to denormalize the tables it is really slow.
My question is there are any dedicated open source reporting statistics tool that I can use it from PHP? Even if it is not written in PHP but can be linked with a library.
I also read about non-sql databases but since they are not relational is really hard to do the joins on them and when comes to aggregation they are not really good (as far as I saw on mongoDB).
Thank you for your advice.
Best regards,
Feri
If you look for an open source reporting engine, Jasper Reports and Pentaho are the way to go, however, both run on Java and are better used loosely coupled (simply as Report Servers). There is an (partial) implementation of Jasper Reports in PHP called PHPJasperXML, depending of what you want, it may be worth to take a look at this project and use it.
We are building a social website using PHP (Zend Framework), MySQL, server running Apache.
There is a requirement where in dashboard the application will fetch data for different events (there are about 12 events) on which this dashboard for user will be updated. We expect the total no of users to be around 500k to 700k. While at one time on average about 20% users would be online (for peak time we expect 50% users to be online).
So the problem is the event data as per our current design will be placed in a MySQL database. I think running a few hundred thousands queries concurrently on MySQL wouldn't be a good idea even if we use Amazon RDS. So we are considering to use both DynamoDB (or Redis or any NoSQL db option) along with MySQL.
So the question is: Having data both in MySQL and any NoSQL database would give us this benefit to have this power of scalability for our web application? Or we should consider any other solution?
Thanks.
You do not need to duplicate your data. One option is to use the ElastiCache that amazon provides to give your self in memory caching. This will get rid of your database calls and in a sense remove that bottleneck, but this can be very expensive. If you can sacrifice rela time updates then you can get away with just slowing down the requests or caching data locally for the user. Say, cache the next N events if possible on the browser and display them instead of making another request to the servers.
If it has to be real time then look at the ElastiCache and then tweak with the scaling of how many of them you require to handle your estimated amount of traffic. There is no point in duplicating your data. Keep it in a single DB if it makes sense to keep it there, IE you have some relational information that you need and then also have a variable schema system then you can use both databases, but not to load balance them together.
I would also start to think of some bottle necks in your architecture and think of how well your application will/can scale in the event that you reach your estimated numbers.
I agree with #sean, there’s no need to duplicate the database. Have you thought about a something with auto-scalability, like Xeround. A solution like that can scale out automatically across several nodes when you have throughput peaks and later scale back in, so you don’t have to commit to a larger, more expansive instance just because of seasonal peaks.
Additionally, if I understand correctly, no code changes are required for this auto-scalability. So, I’d say that unless you need to duplicate your data on both MySQL and NoSQL DB’s for reasons other than scalability-related issues, go for a single DB with auto-scaling.
I'm the webmaster for a major US university. We have a great deal of requests on our website, which I've built and been in charge of for the last 7 years or so. I've been building ever-more-complex features into our website and it's always been my practice to put as much of the programming burden on our multi-processor Microsoft SQL server as possible - using stored procedures, views, etc, and fill-in what can't be done with PHP, ASP, or Perl from the IIS web server. Both servers are very powerful and capable machines. Since I've been doing this alone for so long without anyone else to brainstorm with, I'm curious if my approach is ideal for even higher load situations we'll have in the future.
My question is: Is it better practice to place more of the load burden on the SQL server using nested SELECT statements, views, stored procedures and aggregate functions, or should I be pulling multiple simpler queries and processing through them using server-side compile-time scripts like PHP? Keep on keepin' on or come up with a better way?
I've recently become more interested in performance after I did some load traces and learned just how much I've been putting on the shoulders of the SQL server. Both the web server and SQL servers are fast and responsive throughout the day, and almost without regard for how much I put on them, but I'd like to be ready and have trained myself and upgraded my existing code optimized best practices in mind by the time it becomes important.
Thanks for your advice and input.
You put each layer in your stack to use in the domain it fits best.
There is no use in having your database server send 1000 rows and using PHP to filter them if a WHERE-clause or GROUP-clause would suffice. It's not optimal to call the database to add two integers (SELECT 5+9 works fine, but php can do it itself, and you save the roundtrip).
You will probably want to look into scalability: what parts of your application can be divided unto multiple processes? If you're still just using 2 layers (script & db), there is a lot of room for scaling there. But always start with the bottleneck first.
Some examples: host static contents on CDN, use caching for your pages, read about nginx and memcached, use nosql (mongoDB), consider sharding, consider replication.
My opinion is that it's generally (mostly) best to favor letting the web servers do the processing. Two points:
First is scalability. Once your application gets enough usage, you'll need to start worrying about load balancing. And it's a lot easier to drop in a couple of extra web servers pointing to a common database than it is to set up a distributed database cluster. So best to take as much strain away from the Database as you can and keep it on a single machine for as long as possible.
The second point i'd like to make is about optimizing the queries. This will depend a lot on the queries you are using, and the database backend. When i first started working with databases, i fell into the trap of making elaborate SQL queries with multiple JOINs that fetched exactly the data i wanted, even if it was from four or five different tables. I reasoned that "That's what the database is there for - lets get it to do the hard work"
I quickly found that these queries took way too long to execute, and often ended up blocking the database from other requests. While it may seam inefficient to split your query into multiple requests (for example in a for loop), you'll often find that executing multiple small queries with fast indexes will make your application run far more smoothly than trying to pass all the hard work to the database
Firstly, you might want to check if there is any load which can be removed entirely by client side caching (.js, .css, static HTML and images), and use of technologies such as AJAX to do partial updates of screens - this will remove load on both web and sql servers.
Secondly, see if there is sql load which can be reduced by web server caching - e.g. static or low refresh data - if you have a lot of 'content' pages on your systems, have a look at common CMS caching techniques which will scale to allow many more users to view the same data without rebuilding the page or hitting the database.
I tend to do as much as possible outside the db, viewing db calls as expensive/time-intensive.
For example, when performing a select on a user table with fields name_given and name_family, I could fatten the query to return a column called full_name built by concatenation. But that kind of thing can be easily done in a model on your server-side scripting language (PHP, Ruby, etc).
Of course, there are cases when the db is the more "natural" place to perform an operation. But, in general, I incline more towards putting the load on the web server and optimize there with many of the techniques noted in other answers.
I'm creating and app that will rely on a database, and I have all intention on using a flat file db, is there any serious reasons to stay away from this?
I'm using mimesis (http://mimesis.110mb.com)
it's simpler than using mySQL, which I have to admit I have little experience with.
I'm wondering about the security of the db. but the files are stored as php and it seems to be a solid database solution.
I really like the ease of backing up and transporting the databases, which I have found harder with mySQL. I see that everyone seems to prefer the mySQL way - and it likely is faster when it comes to queries but other than that is there any reason to stay away from flat-file dbs and (finally) properly learn mysql ?
edit
Just to let people know,
I ended up going with mySQL, and am using the CodeIgniter framework. Still like the flat file db, but have now realized that it's way more complex for this project than necessary.
Use SQLite, you get a database with many SQL features and yet it's only a single file.
Greetings, I'm the creator of Mimesis. Relational databases and SQL are important in situations where you have massive amounts of data that needs to be handled. Are flat files superior to relation databases? Well, you could ask Google, as their entire archiving system works with flat files, and its the most popular search engine on Earth. Does Mimesis compare to their system? Likely not.
Mimesis was created to solve a particular niche problem. I only use free websites for my online endeavors. Plenty of free sites offer the ability to use PHP. However, they don't provide free SQL database access. Therefore, I needed to create a database that would store data, implement locking, and work around file permissions. These were the primary design parameters of Mimesis, and it succeeds on all of those.
If you need an idea of Mimesis's speed, if you navigate to the first page it will tell you what country you're viewing the site from. This free database is taken from the site ip2nation.com and ported into a Mimesis ffdb. It has hundreds if not thousands of entries.
Furthermore, the hit counter on the main page has already tracked over 7000 visitors. These are UNIQUE visits, which means that the script has to search the database to see if the IP address that's visiting already exists, and also performs a count of the total IPs.
If you've noticed the main page loads up pretty quickly and it has two fairly intensive Mimesis database scripts running on the backend. The way Mimesis stores data is done to speed up read and write procedures and also translation procedures. Most ffdb example scripts or other ffdb scripts out there use a simple CVS file or other some such structure for storing data. Mimesis actually interprets binary data at some levels to augment its functionality. Mimesis is somewhat of a hybrid between a flat file database and a relational database.
Most other ffdb scripts involve rewriting the COMPLETE file every time an update is made. Mimesis does not do this, it rewrites only the structural file and updates the actual row contents. So that even if an error does occur you only lose new data that's added, not any of the older data. Mimesis also maintains its history. Unless the table is refreshed the data that rows had previously is still contained within.
I could keep going on about all the features, but this isn't intended as a "Mimesis is the greatest database ever" rant. Moreso, its intended to open people's eyes to the fact that SQL isn't the ONLY technology available, and that flat files, when given proper development paradigms are superior to a relational database, taking into account they are more specialized.
Long live flat files and the coders who brave the headaches that follow.
The answer is "Fine" if you only NEED a flat-file structure. One test: Would a single simple spreadsheet handle all needs? If not, you need a relational structure, not a flat file.
If you're not sure, perhaps you can start flat-file. SQLite is a great app for getting started.
It's not good to learn you made the wrong choice, if you figure it out too far along in the process. But if you understand the importance of a relational structure, and upsize early on if needed, then you are fine.
I really like the ease of backing up
and transporting the databases, which
I have found harder with mySQL.
Use SQLite as mentioned in another answer. There is only one file to backup, or set up periodic dumps of the MySQL databases to SQL files. This is a relatively simple thing to do.
I see that everyone seems to prefer
the mySQL way - and it likely is
faster when it comes to queries
Speed is definitely a consideration. Databases tend to be a lot faster, because the data is organized better.
other than that is there any reason to
stay away from flat-file dbs and
(finally) properly learn mysql ?
There sure are plenty of reasons to use a database solution, but there are arguments to be made for flat files. It is always good to learn things other than what you "usually" use.
Most decisions depend on the application. How many concurrent users are you going to have? Do you need transaction support?
Wanted to inform that Mimesis has moved from the original URL to http://mimesis.site11.com/
Furthermore, I am shifting the focus of Mimesis from an ffdb to a key-value store. It's more sensible Given the types of information I'm storing and the methods I use to retrieve it. There was also a grave error present in the coding of Mimesis (which I've since fixed). However, I'm still in the testing phase of the new key-value store type. I've also been side-tracked by other things. Locking has also been changed from the use of file creation to directory creation as the mutex mechanism.
Interoperability. MySQL can be interfaced by basically any language that counts. Mimesis is unlikely to be usable outside PHP.
This becomes significant the moment you try to use profilers, or modify data from the outside.
You might also look at http://lukeplant.me.uk/resources/flatfile/ for the PHP Flatfile Package.
The issue with going flatfile is that in order to adjust the situation for further development you have to alter a significant amount of code in order to improve the foundation of the system. Whereas if it was a pure SQL system it would require little to no modification to proceed in the future.
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