MongoDB vs. Cassandra vs. MySQL for real-time advertising platform

There are several ways to achieve this with all of the technologies listed. It is more a question of how you use them. Your ideal solution may use a combination of these, with some consideration for usage patterns. I don't feel that the information out there is that dated because the concepts at play are very fundamental. There may be new NoSQL databases and fixes to existing ones, but your question is primarily architectural.

NoSQL solutions like MongoDB and Cassandra get a lot of attention for their insert performance. People tend to complain about the update/insert performance of relational databases but there are ways to mitigate these issues.

Starting with MySQL you could review O'Reilly's High Performance MySQL, optimise the schema, add more memory perhaps run this on different hardware from the rest of your app (assuming you used MySQL for that), or partition/shard data. Another area to consider is your application. Can you queue inserts and updates at the application level before insertion into the database? This will give you some flexibility and is probably useful in all cases. Depending on how your final schema looks, MySQL will give you some help with extracting the data as long as you are comfortable with SQL. This is a benefit if you need to use 3rd party reporting tools etc.

MongoDB and Cassandra are different beasts. My understanding is that it was easier to add nodes to the latter but this has changed since MongoDB has replication etc built-in. Inserts for both of these platforms are not constrained in the same manner as a relational database. Pulling data out is pretty quick too, and you have a lot of flexibility with data format changes. The tradeoff is that you can't use SQL (a benefit for some) so getting reports out may be trickier. There is nothing to stop you from collecting data in one of these platforms and then importing it into a MySQL database for further analysis.

Based on your requirements there are tools other than NoSQL databases which you should look at such as Flume. These make use of the Hadoop platform which is used extensively for analytics. These may have more flexibility than a database for what you are doing. There is some content from Hadoop World that you might be interested in.

Characteristics of MySQL:

• Database locking (MUCH easier for financial transactions)
• Consistency/security (as above, you can guarantee that, for instance, no changes happen between the time you read a bank account balance and you update it).
• Data organization/refactoring (you can have disorganized data anywhere, but MySQL is better with tables that represent "types" or "components" and then combining them into queries -- this is called normalization).
• MySQL (and relational databases) are more well suited for arbitrary datasets and requirements common in AGILE software projects.

Characteristics of Cassandra:

• Speed: For simple retrieval of large documents. However, it will require multiple queries for highly relational data – and "by default" these queries may not be consistent (and the dataset can change between these queries).
• Availability: The opposite of "consistency". Data is always available, regardless of being 100% "correct".^[1]
• Optional fields (wide columns): This CAN be done in MySQL with meta tables etc., but it's for-free and by-default in Cassandra.

Cassandra is key-value or document-based storage. Think about what that means. TYPICALLY I give Cassandra ONE KEY and I get back ONE DATASET. It can branch out from there, but that's basically what's going on. It's more like accessing a static file. Sure, you can have multiple indexes, counter fields etc. but I'm making a generalization. That's where Cassandra is coming from.

MySQL and SQL is based on group/set theory -- it has a way to combine ANY relationship between data sets. It's pretty easy to take a MySQL query, make the query a "key" and the response a "value" and store it into Cassandra (e.g. make Cassandra a cache). That might help explain the trade-off too, MySQL allows you to always rearrange your data tables and the relationships between datasets simply by writing a different query. Cassandra not so much. And know that while Cassandra might PROVIDE features to do some of this stuff, it's not what it was built for.

MongoDB and CouchDB fit somewhere in the middle of those two extremes. I think MySQL can be a bit verbose^[2] and annoying to deal with especially when dealing with optional fields, and migrations if you don't have a good model or tools. Also with scalability, I'm sure there are great technologies for scaling a MySQL database, but Cassandra will always scale, and easily, due to limitations on its feature set. MySQL is a bit more unbounded. However, NoSQL and Cassandra do not do joins, one of the critical features of SQL that allows one to combine multiple tables in a single query. So, complex relational queries will not scale in Cassandra.

[1] Consistency vs. availability is a trade-off within large distributed dataset. It takes a while to make all nodes aware of new data, and eg. Cassandra opts to answer quickly and not to check with every single node before replying. This can causes weird edge cases when you base you writes off previously read data and overwriting data. For more information look into the CAP Theorem, ACID database (in particular Atomicity) as well as Idempotent database operations. MySQL has this issue too, but the idea of high availability over correctness is very baked into Cassandra and gives it many of its scalability and speed advantages.

[2] SQL being "verbose" isn't a great reason to not use it – plus most of us aren't going to (and shouldn't) write plain-text SQL statements.

Nosql solutions are better than Mysql, postgresql and other rdbms techs for this task. Don't waste your time with Hbase/Hadoop, you've to be an astronaut to use it. I recommend MongoDB and Cassandra. Mongo is better for small datasets (if your data is maximum 10 times bigger than your ram, otherwise you have to shard, need more machines and use replica sets). For big data; cassandra is the best. Mongodb has more query options and other functionalities than cassandra but you need 64 bit machines for mongo. There are some works around for analytics in both sides. There is atomic counters in both sides. Both can scale well but cassandra is much better in scaling and high availability. Both have php clients, both have good support and community (mongo community is bigger).

Cassandra analytics project sample:Rainbird http://www.slideshare.net/kevinweil/rainbird-realtime-analytics-at-twitter-strata-2011

mongo sample: http://www.slideshare.net/jrosoff/scalable-event-analytics-with-mongodb-ruby-on-rails

http://axonflux.com/how-superfeedr-built-analytics-using-mongodb

doubleclick developers developed mongo http://www.informationweek.com/news/software/info_management/224200878