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We are facing performance related issues in our current MySQL DB. Our application is pretty heavy on a few tables ~20. We run lot of aggregation queries on this table as well as writes. Most of our teams are developers and we don't have access to a dba which might help in retuning our current db and make things work faster.
Moving to NoSql is an option. But seriously thinking what are the higher limits in terms of
Volumes (Current volumes per day ~50GB)
Structured or Raw Data? (Structured Data)
IO stats on DB - ( Current rate is 60 KB/Sec)
Record writes - (now 3000 rows/sec)
Question arise
Is 50GB is high enough to consider NoSql? Some documentation recommends more than a TB
The data should be raw data, which can be further processed to get structured and use in application
MySql scales out at 3000 rows/secs, not sure MySql can be further tuned
HBase seems to be promising for Analytic application.
Would like to get some guidelines on limits of RDBMS one can think of moving to NoSQL
This is such a broad topic so don't believe there are any "right" answers but maybe a few general recommendations would help:
I think you should think of this challenge in terms of picking the right tool for the problem. All databases have their pros and cons and in some challenges the best approach is to use an entire toolbox to get the job done.
Note that moving your data, or even just parts of it, to different datastores is rarely a non-trivial effort. Use this chance to rethink about your data model before implementing it.
Getting this job done should also take into account more requirements, such your growth plans for example. It looks you're at this crossroads because your original assumptions->choices are no longer en par with reality. If you want to delay the next time you're at the same place, you should use this opportunity to do so.
Lastly keep in mind that the job really done only after you do something with all that captured data - or else I'd recommend you use the infinitely-scalable write-to-/dev/null design pattern ;) Put differently, unless your data is write-only, you'd want to make sure that whatever SQL/NoSQL/NewSQL/other datastore that you choose can also get you the data/information/knowledge inside your use case's acceptable time frames.
It will probably worth it given your current infrastructure, but keep in mind that it's going to be a huge task, since you're going to need to redesign the whole process. HBase can help you, as it has some neat features, like realtime counters (which in some cases eliminates the needing of periodic rollups), or per-client buffering (which can allow you to scale to the >100k writes per second), but, be warned it cannot be queried in the same way you query a relational database, so, you're going to need to carefully plan it to make it work for you.
It seems that your main issue is with the raw data writes, sure, you can definitely rely on HBase for that, and then do the rollups every X min to store the data in your RDBMS so it can be queried as usual. But given you're doing them every minute, which is a very short gap, why don't you keep the data in memory and flush it the rolled up tables every minute?. Sure, you could loss data, but I don't know how critic is for you loosing one minute of data, and that alone could help you a lot.
Anyway, the best advice I can think of: read a book, understand how HBase works first, dig into the pros & cons, and think about how it can suit your specific needings. This is crucial because a good implementation is what is going to determine if it's a success or a total failure.
Some resources:
HBase: The Definitive Guide
HBase Administration Cookbook
HBase Reference guide (free)
The usual case. I have a simple app that will allow people to upload photos and follow other people. As a result, every user will have something like a "wall" or an "activity feed" where he or she sees the latest photos uploaded from his/her friends (people he or she follows).
Most of the functionalities are easy to implement. However, when it comes to this history activity feed, things can easily turn into a mess because of pure performance reasons.
I have come to the following dilemma here:
i can easily design the activity feed as a normalized part of the database, which will save me writing cycles, but will enormously increase the complexity when selecting those results for each user (for each photo uploaded within a certain time period, select a certain number, whose uploaders I am following / for each person I follow, select his photos )
An optimization option could be the introduction of a series of threshold constraints which, for instance would allow me to order the people I follow on the basis of the date of their last upload, even exclude some, to save cycles, and for each user, select only the 5 (for example) last uploaded photos.
The second approach is to introduce a completely denormalized schema for the activity feed, in which every row represents a notification for one of my followers. This means that every time I upload a photo, the DB will put n rows in this "drop bucket", n meaning the number of people I follow, i.e. lots of writing cycles. If I have such a table, though, I could easily apply some optimization techniques such as clever indexing, as well as pruning entries older than a certain period of time (queue).
Yet, a third approach that comes to mind, is even a less denormalized schema where the server side application will take some part of the complexity off the DB. I saw that some social apps such as friendfeed, heavily rely on the storage of serialized objects such as JSON objects in the DB.
I am definitely still mastering the skill of scalable DB design, so I am sure that there are many things I've missed, or still to learn. I would highly appreciate it if someone could give me at least a light in the right direction.
If your application is successful, then it's a good bet that you'll have more reads than writes - I only upload a photo once (write), but each of my friends reads it whenever they refresh their feed. Therefore you should optimize for fast reads, not fast writes, which points in the direction of a denormalized schema.
The problem here is that the amount of data you create could quickly get out of hand if you have a large number of users. Very large tables are hard on the db to query, so again there's a potential performance issue. (There's also the question of having enough storage, but that's much more easily solved).
If, as you suggest, you can delete rows after a certain amount of time, then this could be a good solution. You can reduce that amount of time (up to a point) as you grow and run into performance issues.
Regarding storing serialized objects, it's a good option if these objects are immutable (you won't change them after writing) and you don't need to index them or query on them. Note that if you denormalize your data, it probably means that you have a single table for the activity feed. In that case I see little gain in storing blobs.
If you're going the serialized objects way, consider using some NoSQL solution, such as CouchDB - they're better optimized for handling that kind of data, so in principle you should get better performance for the same hardware setup.
Note that I'm not suggesting that you move all your data to NoSQL - only for that part where it's a better solution.
Finally, a word of caution, spoken from experience: building an application that can scale is hard and takes time better spent elsewhere. You should spend your times worrying about how to get millions of users to your app before you worry about how you're going to serve those millions - the first is the more difficult problem. When you get to the point that you're hugely successful, you can re-architect and rebuild your application.
There are many options you can take
Add more hardware, Memory, CPU -- Enter cloud hosting
Hows 24GB of memory sound? Most of your importantly accessed DB information can fit just in memory.
Choose a host with expandable SSDs.
Use an events based system in your application to write the "history" of all users. So it will be like so: id, user_id, event_name, date, event_parameters' -- an example would be: 1, 8, CHANGED_PROFILE_PICTURE, 26-03-2011 12:34, <id of picture> and most important of all, this table will be in memory. No longer need to worry about write performance. After the records go past i.e. 3 days they can be purged into another table (in non-memory) and included into the query results, if the user chooses to go back that far. By having all this in one table you remove having to do multiple queries and SELECTs to build up this information.
Consider using INNODB for the history/feeds table.
Good Resources to read
Exploring the software behind Facebook, the world’s largest site
Digg: 4000% Performance Increase by Sorting in PHP Rather than MySQL
Caching & Performance: Lessons from Facebook
I would probably start with using a normalized schema so that you can write quickly and compactly. Then use non transactional (no locking) reads to pull the information back out making sure to use a cursor so that you can process the results as they're coming back as opposed to waiting for the entire result set. Since it doesn't sound like the information has any particular critical implications you don't really need to worry about a lock of the concerns that would normally push you away from transactional reads.
These kind of problems are why currently NOSql solutions used these days. What I did in my previos projecs is really simple. I don't keep user->wall user->history which contains purely feed'ids in memory stores(my favorite is redis). so in every insert I do 1 insert operation on database and (n*read optimization) insert operation in memory store. I design memory store to optimize my reads. if I want to filter user history (or wall) for videos I put a push feedid to a list like user::{userid}::wall::videos.
Well ofcourse you can purely build the system in memstores aswell but its nice to have 2 systems doing what they are doing the best.
edit :
checkout these applications to get an idea:
http://retwis.antirez.com/
http://twissandra.com/
I'm reading more and more about NoSQL solutions and people suggesting them, however no one ever mentions drawbacks of such choice.
Most obvious for me is lack of transactions - imagine if you lost a few records every now and then (there are cases reporting this happens often).
But, what I'm surprised with is that no one mentions MySQL being used as NoSQL - here's a link for some reading.
In the end, no matter what solution you choose (relational database or NoSQL storage), they scale in similar manner - by sharding data across network (naturally, there are more choices but this is the most obvious one). Since NoSQL does less work (no SQL layer so CPU cycles aren't wasted on interpreting SQL), it's faster, but it can hit the roof too.
As Elad already pointed out - building an app that's scalable from the get go is a painful process. It's better that you spend time focusing on making it popular and then scale it out.
Hi Friends
i am using MySQL DB for one of my Product, about 250 schools are singed for it now, its about 1500000 insertion per hour and about 12000000 insertion per day, i think my current setup like just a single server may crash with in hours, and the read is also same as write, how can i make it crash free DB server, the main problem i am facing now is the slow of both writing and reading data how can i over come that,it is very difficult for me to get a solution.guys please help me..which is the good model for doing the solution?
It is difficult to get both fast reads and writes simultaneously. To get fast reads you need to add indexes. To get fast writes you need to have few indexes. And to get both to be fast they must not lock each other.
Depending on your needs, one solution is to have two databases. Write new data to your live database and every so often when it is quiet you can synchronize the data to another database where you can perform queries. The disadvantage of this approach is that data you read will be a little old. This may or may not be a problem depending on what it is you need to do.
~500 inserts per second is nothing to sneeze at indeed.
For a flexible solution, you may want to implement some sort of sharding. Probably the easiest solution is to separate schools into groups upfront and store data for different groups of schools on different servers. E.g., data for schools 1-10 is stored on server A, schools 11-20 on server B, etc. This is almost infinitely scalable, assuming that there are few relationships between data from different schools.
Also you could just try throwing more horsepower at the problem and invest into a RAID of SSD drives and, assuming that you have enough processing power, you should be OK. Of course, if it's a huge database, the capacity of SSD drives may not be enough.
Finally, see if you can cut down on the number of insertions, for example by denormalizing the database. Say, instead of storing attendance for each student in a separate row put attendance of the entire class as a vector in a single row. Of course, such changes will heavily limit your querying capabilities.
My laid back advice is:
Build you application lightweight. Don't use an high level database abstraction layer like Active Record. They suck at scaling.
Learn a lot about mysql permformance.
Learn about mysql replication.
Learn about load balancing.
Learn about in memory caches. (memcached)
Hire an administrator (with decent mysql knowledge) or web app performance guru/consultant.
The concrete strategy depends on your application and how it is used. Mysql replication, may or may not be appropriate (same applies for the mentioned sharding strategy). But it's a rather simple way to achive some scaling, because it doesn't impact your application design too much. In memory caches can keep away some load from your databases, but they need some work to apply and some trade offs. In the end you need a good overall understanding how to handle a database driven application under heavy load. If you have a tight deadline, add external manpower, because you won't do this right within 6 weeks without experience.
Right now I'm developing the prototype of a web application that aggregates large number of text entries from a large number of users. This data must be frequently displayed back and often updated. At the moment I store the content inside a MySQL database and use NHibernate ORM layer to interact with the DB. I've got a table defined for users, roles, submissions, tags, notifications and etc. I like this solution because it works well and my code looks nice and sane, but I'm also worried about how MySQL will perform once the size of our database reaches a significant number. I feel that it may struggle performing join operations fast enough.
This has made me think about non-relational database system such as MongoDB, CouchDB, Cassandra or Hadoop. Unfortunately I have no experience with either. I've read some good reviews on MongoDB and it looks interesting. I'm happy to spend the time and learn if one turns out to be the way to go. I'd much appreciate any one offering points or issues to consider when going with none relational dbms?
The other answers here have focused mainly on the technical aspects, but I think there are important points to be made that focus on the startup company aspect of things:
Availabililty of talent. MySQL is very common and you will probably find it easier (and more importantly, cheaper) to find developers for it, compared to the more rarified database systems. This larger developer base will also mean more tutorials, a more active support community, etc.
Ease of development. Again, because MySQL is so common, you will find it is the db of choice for a great many systems / services. This common ground may make any external integration a little easier.
You are preparing for a situation that may never exist, and is manageable if it does. Very few businesses (nevermind startups) come close to MySQL's limits, and with all due respect (and I am just guessing here); the likelihood that your startup will ever hit the sort of data throughput to cripple a properly structured, well resourced MySQL db is almost zero.
Basically, don't spend your time ( == money) worrying about which db to use, as MySQL can handle a lot of data, is well proven and well supported.
Going back to the technical side of things... Something that will have a far greater impact on the speed of your app than choice of db, is how efficiently data can be cached. An effective cache can have dramatic effects on reducing db load and speeding up the general responsivness of an app. I would spend your time investigating caching solutions and making sure you are developing your app in such a way that it can make the best use of those solutions.
FYI, my caching solution of choice is memcached.
So far no one has mentioned PostgreSQL as alternative to MySQL on the relational side. Be aware that MySQL libs are pure GPL, not LGPL. That might force you to release your code if you link to them, although maybe someone with more legal experience could tell you better the implications. On the other side, linking to a MySQL library is not the same that just connecting to the server and issue commands, you can do that with closed source.
PostreSQL is usually the best free replacement of Oracle and the BSD license should be more business friendly.
Since you prefer a non relational database, consider that the transition will be more dramatic. If you ever need to customize your database, you should also consider the license type factor.
There are three things that really have a deep impact on which one is your best database choice and you do not mention:
The size of your data or if you need to store files within your database.
A huge number of reads and very few (even restricted) writes. In that case more than a database you need a directory such as LDAP
The importance of of data distribution and/or replication. Most relational databases can be more or less well replicated, but because of their concept/design do not handle data distribution as well... but will you handle as much data that does not fit into one server or have access rights that needs special separate/extra servers?
However most people will go for a non relational database just because they do not like learning SQL
What do you think is a significant amount of data? MySQL, and basically most relational database engines, can handle rather large amount of data, with proper indexes and sane database schema.
Why don't you try how MySQL behaves with bigger data amount in your setup? Make some scripts that generate realistic data to MySQL test database and and generate some load on the system and see if it is fast enough.
Only when it is not fast enough, first start considering optimizing the database and changing to different database engine.
Be careful with NHibernate, it is easy to make a solution that is nice and easy to code with, but has bad performance with large amount of data. For example whether to use lazy or eager fetching with associations should be carefully considered. I don't mean that you shouldn't use NHibernate, but make sure that you understand how NHibernate works, for example what "n + 1 selects" -problem means.
Measure, don't assume.
Relational databases and NoSQL databases can both scale enormously, if the application is written right in each case, and if the system it runs on is properly tuned.
So, if you have a use case for NoSQL, code to it. Or, if you're more comfortable with relational, code to that. Then, measure how well it performs and how it scales, and if it's OK, go with it, if not, analyse why.
Only once you understand your performance problem should you go searching for exotic technology, unless you're comfortable with that technology or want to try it for some other reason.
I'd suggest you try out each db and pick the one that makes it easiest to develop your application. Go to http://try.mongodb.org to try MongoDB with a simple tutorial. Don't worry as much about speed since at the beginning developer time is more valuable than the CPU time.
I know that many MongoDB users have been able to ditch their ORM and their caching layer. Mongo's data model is much closer to the objects you work with than relational tables, so you can usually just directly store your objects as-is, even if they contain lists of nested objects, such as a blog post with comments. Also, because mongo is fast enough for most sites as-is, you can avoid dealing the complexities of caching and generally deliver a more real-time site. For example, Wordnik.com reported 250,000 reads/sec and 100,000 inserts/sec with a 1.2TB / 5 billion object DB.
There are a few ways to connect to MongoDB from .Net, but I don't have enough experience with that platform to know which is best:
Norm: http://wiki.github.com/atheken/NoRM/
MongoDB-CSharp: http://github.com/samus/mongodb-csharp
Simple-MongoDB: http://code.google.com/p/simple-mongodb/
Disclaimer: I work for 10gen on MongoDB so I am a bit biased.
I'm wondering if some other non-relational database would be a good fit for activity streams - sort of like what you see on Facebook, Flickr (http://www.flickr.com/activity), etc. Right now, I'm using MySQL but it's pretty taxing (I have tens of millions of activity records) and since they are basically read-only once written and always viewed chronologically, I was thinking that an alternative DB might work well.
The activities are things like:
6 PM: John favorited Bacon
5:30 PM: Jane commented on Snow Crash
5:15 PM: Jane added a photo of Bacon to her album
The catch is that unlike Twitter and some other systems, I can't just simply append activities to lists for each user who is interested in the activity - if I could it looks like Redis would be a good fit (with its list operations).
I need to be able to do the following:
Pull activities for a set or subset of people who you are following ("John" and "Jane"), in reverse date order
Pull activities for a thing (like "Bacon") in reverse date order
Filter by activity type ("favorite", "comment")
Store at least 30 million activities
Ideally, if you added or removed a person who you are following, your activity stream would reflect the change.
I have been doing this with MySQL. My "activities" table is as compact as I could make it, the keys are as small as possible, and the it is indexed appropriately. It works, but it just feels like the wrong tool for this job.
Is anybody doing anything like this outside of a traditional RDBMS?
Update November 2009: It's too early to answer my own question, but my current solution is to stick with MySQL but augment with Redis for fast access to the fresh activity stream data. More information in my answer here: How to implement the activity stream in a social network...
Update August 2014: Years later, I'm still using MySQL as the system of record and using Redis for very fast access to the most recent activities for each user. Dealing with schema changes on a massive MySQL table has become a non-issue thanks to pt-online-schema-change
I'd really, really, suggest stay with MySQL (or a RDBMS) until you fully understand the situation.
I have no idea how much performance or much data you plan on using, but 30M rows is not very many.
If you need to optimise certain range scans, you can do this with (for example) InnoDB by choosing a (implicitly clustered) primary key judiciously, and/or denormalising where necessary.
But like most things, make it work first, then fix performance problems you detect in your performance test lab on production-grade hardware.
EDIT:Some other points:
key/value database such as Cassandra, Voldermort etc, do not generally support secondary indexes
Therefore, you cannot do a CREATE INDEX
Most of them also don't do range scans (even on the main index) because they're using hashing to implement partitioning (which they mostly do).
Therefore they also don't do range expiry (DELETE FROM tbl WHERE ts < NOW() - INTERVAL 30 DAYS)
Your application must do ALL of this itself or manage without it; secondary indexes are really the killer
ALTER TABLE ... ADD INDEX takes quite a long time in e.g. MySQL with a large table, but at least you don't have to write much code to do it. In a "nosql" database, it will also take a long time BUT also you have to write heaps and heaps of code to maintain the new secondary index, expire it correctly, AND modify your queries to use it.
In short... you can't use a key/value database as a shortcut to avoid ALTER TABLE.
I am also planning on moving away from SQL. I have been looking at CouchDB, which looks promising. Looking at your requirements, I think all can be done with CouchDB views, and the list api.
It seems to me that what you want to do -- Query a large set of data in several different ways and order the results -- is exactly and precisely what RDBMeS were designed for.
I doubt you would find any other datastore that would do this as well as a modern commercial DBMS (Oracle, SQLServer, DB2 etc.) or any opn source tool that would accomplish
this any better than MySql.
You could have a look at Googles BigTable, which is really a relational database but
it can present an 'object'y personality to your program. Its exceptionaly good for free format text
searches, and complex predicates. As the whole thing (at least the version you can download) is implemented in Python I doubt it would beat MySql in a query marathon.
For a project I once needed a simple database that was fast at doing lookups and which would do lots of lookups and just an occasional write. I just ended up writing my own file format.
While you could do this too, it is pretty complex, especially if you need to support it from a web server. With a web server, you would at least need to protect every write to the file and make sure it can be read from multiple threads. The design of this file format is something you should work out as good as possible with plenty of testing and experiments. One minor bug could prove fatal for a web project in this style, but if you get it working, it can work real well and extremely fast.
But for 99.999% of all situations, you don't want such a custom solution. It's easier to just upgrade the hardware, move to Oracle, SQL Server or InterBase, use a dedicated database server, use faster hard disks, install more memory, upgrade to a 64-bit system. Those are the more generic tricks to improve performance with the least effort.
I'd recommend learning about message queue technology. There are several open-source options available, and also robust commercial products that would serve up the volume you describe as a tiny snack.
CouchDB is schema-free, and it's fairly simple to retrieve a huge amount of data quickly, because you are working only with indexes. You are not "querying" the database each time, you are retrieving only matching keys (which are pre-sorted making it even faster).
"Views" are re-indexed everytime new data is entered into the database, but this takes place transparently to the user, so while there might be potential delay in generating an updated view, there will virtually never be any delay in retrieving results.
I've just started to explore building an "activity stream" solution using CouchDB, and because the paradigm is different, my thinking about the process had to change from the SQL thinking.
Rather than figure out how to query the data I want and then process it on the page, I instead generate a view that keys all documents by date, so I can easily create multiple groups of data, just by using the appropriate date key, essentially running several queries simultaneously, but with no degradation in performance.
This is ideal for activity streams, and I can isolate everything by date, or along with date isolation I can further filter results of a particular subtype, etc - by creating a view as needed, and because the view itself is just using javascript and all data in CouchDB is JSON, virtually everything can be done client-side to render your page.