In this article MongoDB == NoSQL, that is not the case. Different NoSQL solutions have different use cases.
Also IMHO MongoDB is pretty SQLish in the data model, so you are actually comparing two implementations of a similar data model here, and one may be superior to the other one or the other way around I guess. No surprise.
A more interesting attempt is IMHO to check how the difference in the data model of some NoSQL solution can lead to very different performances.
For instance Clustrix VS Redis can be interesting. Examples:
1) A lot of writes against a table where you require then to get things ordered by insertion time. With Redis is is just LPUSH + LRANGE. Try to do a read/write test where many clients are writing and reading at the same time (real world), against a table (or Redis list) with millions of elements.
2) Range queries when there are a lot of writes against this indexes. For instance a table with a score (we are modeling an online game leaders board), a lot of inserts of new scores. Get ranges between random intervals at the same time. Again, many clients writing, many reading.
Comments
In this article MongoDB == NoSQL, that is not the case. Different NoSQL solutions have different use cases. Also IMHO MongoDB is pretty SQLish in the data model, so you are actually comparing two implementations of a similar data model here, and one may be superior to the other one or the other way around I guess. No surprise.
A more interesting attempt is IMHO to check how the difference in the data model of some NoSQL solution can lead to very different performances.
For instance Clustrix VS Redis can be interesting. Examples:
1) A lot of writes against a table where you require then to get things ordered by insertion time. With Redis is is just LPUSH + LRANGE. Try to do a read/write test where many clients are writing and reading at the same time (real world), against a table (or Redis list) with millions of elements.
2) Range queries when there are a lot of writes against this indexes. For instance a table with a score (we are modeling an online game leaders board), a lot of inserts of new scores. Get ranges between random intervals at the same time. Again, many clients writing, many reading.