MySQL vs MongoDB 1000 reads

MongoDB is not magically faster. If you store the same data, organised in basically the same fashion, and access it exactly the same way, then you really shouldn't expect your results to be wildly different. After all, MySQL and MongoDB are both GPL, so if Mongo had some magically better IO code in it, then the MySQL team could just incorporate it into their codebase.

People are seeing real world MongoDB performance largely because MongoDB allows you to query in a different manner that is more sensible to your workload.

For example, consider a design that persisted a lot of information about a complicated entity in a normalised fashion. This could easily use dozens of tables in MySQL (or any relational db) to store the data in normal form, with many indexes needed to ensure relational integrity between tables.

Now consider the same design with a document store. If all of those related tables are subordinate to the main table (and they often are), then you might be able to model the data such that the entire entity is stored in a single document. In MongoDB you can store this as a single document, in a single collection. This is where MongoDB starts enabling superior performance.

In MongoDB, to retrieve the whole entity, you have to perform:

One index lookup on the collection (assuming the entity is fetched by id)

Retrieve the contents of one database page (the actual binary json document)

So a b-tree lookup, and a binary page read. Log(n) + 1 IOs. If the indexes can reside entirely in memory, then 1 IO.

In MySQL with 20 tables, you have to perform:

One index lookup on the root table (again, assuming the entity is fetched by id)

With a clustered index, we can assume that the values for the root row are in the index

20+ range lookups (hopefully on an index) for the entity's pk value

These probably aren't clustered indexes, so the same 20+ data lookups once we figure out what the appropriate child rows are.

So the total for mysql, even assuming that all indexes are in memory (which is harder since there are 20 times more of them) is about 20 range lookups.

These range lookups are likely comprised of random IO — different tables will definitely reside in different spots on disk, and it's possible that different rows in the same range in the same table for an entity might not be contiguous (depending on how the entity has been updated, etc).

So for this example, the final tally is about 20 times more IO with MySQL per logical access, compared to MongoDB.

This is how MongoDB can boost performance in some use cases.

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Do you have concurrency, i.e simultaneous users ? If you just run 1000 times the query straight, with just one thread, there will be almost no difference. Too easy for these engines :)

BUT I strongly suggest that you build a true load testing session, which means using an injector such as JMeter with 10, 20 or 50 users AT THE SAME TIME so you can really see a difference (try to embed this code inside a web page JMeter could query).

I just did it today on a single server (and a simple collection / table) and the results are quite interesting and surprising (MongoDb was really faster on writes & reads, compared to MyISAM engine and InnoDb engine).

This really should be part of your test : concurrency & MySQL engine. Then, data/schema design & application needs are of course huge requirements, beyond response times. Let me know when you get results, I'm also in need of inputs about this!

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Source: https://github.com/webcaetano/mongo-mysql

10 rows

mysql insert: 1702ms
mysql select: 11ms

mongo insert: 47ms
mongo select: 12ms

100 rows

mysql insert: 8171ms
mysql select: 10ms

mongo insert: 167ms
mongo select: 60ms

1000 rows

mysql insert: 94813ms (1.58 minutes)
mysql select: 13ms

mongo insert: 1013ms
mongo select: 677ms

10.000 rows

mysql insert: 924695ms (15.41 minutes)
mysql select: 144ms

mongo insert: 9956ms (9.95 seconds)
mongo select: 4539ms (4.539 seconds)

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https://github.com/reoxey/benchmark

benchmark

speed comparison of MySQL & MongoDB in GOLANG1.6 & PHP5

system used for benchmark: DELL cpu i5 4th gen 1.70Ghz * 4 ram 4GB GPU ram 2GB

Speed comparison of RDBMS vs NoSQL for INSERT, SELECT, UPDATE, DELETE executing different number of rows 10,100,1000,10000,100000,1000000

Language used to execute is: PHP5 & Google fastest language GO 1.6

________________________________________________
GOLANG with MySQL (engine = MyISAM)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
            INSERT
------------------------------------------------
num of rows             time taken
------------------------------------------------
10                      1.195444ms
100                     6.075053ms
1000                    47.439699ms
10000                   483.999809ms
100000                  4.707089053s
1000000                 49.067407174s


            SELECT
------------------------------------------------
num of rows             time taken
------------------------------------------------
1000000                 872.709µs


        SELECT & DISPLAY
------------------------------------------------
num of rows             time taken
------------------------------------------------
1000000                 20.717354746s


            UPDATE
------------------------------------------------
num of rows             time taken
------------------------------------------------
1000000                 2.309209968s
100000                  257.411502ms
10000                   26.73954ms
1000                    3.483926ms
100                     915.17µs
10                      650.166µs


            DELETE
------------------------------------------------
num of rows             time taken
------------------------------------------------
1000000                 6.065949ms
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


________________________________________________
GOLANG with MongoDB
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
            INSERT
------------------------------------------------
num of rows             time taken
------------------------------------------------
10                      2.067094ms
100                     8.841597ms
1000                    106.491732ms
10000                   998.225023ms
100000                  8.98172825s
1000000                 1m 29.63203158s


            SELECT
------------------------------------------------
num of rows             time taken
------------------------------------------------
1000000                 5.251337439s


        FIND & DISPLAY (with index declared)
------------------------------------------------
num of rows             time taken
------------------------------------------------
1000000                 21.540603252s


            UPDATE
------------------------------------------------
num of rows             time taken
------------------------------------------------
1                       1.330954ms
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

________________________________________________
PHP5 with MySQL (engine = MyISAM)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
            INSERT
------------------------------------------------
num of rows             time taken
------------------------------------------------
 10                     0.0040680000000001s
 100                    0.011595s
 1000                   0.049718s
 10000                  0.457164s
 100000                 4s
 1000000                42s


            SELECT
------------------------------------------------
num of rows             time taken
------------------------------------------------
 1000000                <1s


            SELECT & DISPLAY
------------------------------------------------
num of rows             time taken
------------------------------------------------
  1000000               20s
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

________________________________________________
PHP5 with MongoDB 
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
            INSERT
------------------------------------------------
num of rows             time taken
------------------------------------------------
10                      0.065744s
100                     0.190966s
1000                    0.2163s
10000                   1s
100000                  8s
1000000                 78s


            FIND
------------------------------------------------
num of rows             time taken
------------------------------------------------
1000000                 <1s


            FIND & DISPLAY
------------------------------------------------
num of rows             time taken
------------------------------------------------
1000000                 7s


            UPDATE
------------------------------------------------
num of rows             time taken
------------------------------------------------
1000000                 9s

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man,,, the answer is that you're basically testing PHP and not a database.

don't bother iterating the results, whether commenting out the print or not. there's a chunk of time.

   foreach ($cursor as $obj)
    {
        //echo $obj["thread_title"] . "<br><Br>";
    }

while the other chunk is spend yacking up a bunch of rand numbers.

function get_15_random_numbers()
{
    $numbers = array();
    for($i=1;$i<=15;$i++)
    {
        $numbers[] = mt_rand(1, 20000000) ;

    }
    return $numbers;
}

then theres a major difference b/w implode and in.

and finally what is going on here. looks like creating a connection each time, thus its testing the connection time plus the query time.

$m = new Mongo();

vs

$db = new AQLDatabase();

so your 101% faster might turn out to be 1000% faster for the underlying query stripped of jazz.

urghhh.

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Here is a little research that explored RDBMS vs NoSQL using MySQL vs Mongo, the conclusions were inline with @Sean Reilly's response. In short, the benefit comes from the design, not some raw speed difference. Conclusion on page 35-36:

RDBMS vs NoSQL: Performance and Scaling Comparison

The project tested, analysed and compared the performance and scalability of the two database types. The experiments done included running different numbers and types of queries, some more complex than others, in order to analyse how the databases scaled with increased load. The most important factor in this case was the query type used as MongoDB could handle more complex queries faster due mainly to its simpler schema at the sacrifice of data duplication meaning that a NoSQL database may contain large amounts of data duplicates. Although a schema directly migrated from the RDBMS could be used this would eliminate the advantage of MongoDB’s underlying data representation of subdocuments which allowed the use of less queries towards the database as tables were combined. Despite the performance gain which MongoDB had over MySQL in these complex queries, when the benchmark modelled the MySQL query similarly to the MongoDB complex query by using nested SELECTs MySQL performed best although at higher numbers of connections the two behaved similarly. The last type of query benchmarked which was the complex query containing two JOINS and and a subquery showed the advantage MongoDB has over MySQL due to its use of subdocuments. This advantage comes at the cost of data duplication which causes an increase in the database size. If such queries are typical in an application then it is important to consider NoSQL databases as alternatives while taking in account the cost in storage and memory size resulting from the larger database size.

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Honestly even if MongoDB is slower, MongoDB definitely makes me and you code faster.... no need to worry about silly table columns, row or entity migrations...

With MongoDB, you just instantiate a class and save!

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From MongoDB Offical site

Observing some of the high-level query behaviors of the two systems, we can see that MySQL is faster at selecting a large number of records, while MongoDB is significantly faster at inserting or updating a large number of records.

Reference

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On Single Server, MongoDb would not be any faster than mysql MyISAM on both read and write, given table/doc sizes are small 1 GB to 20 GB. MonoDB will be faster on Parallel Reduce on Multi-Node clusters, where Mysql can NOT scale horizontally.

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