NoSQL and SQL Anti Patterns

NoSQL
SQL anti patterns and NoSQL alternatives

Gleicon Moraes

http://zenmachine.wordpress.com
http://github.com/gleicon

SQL Anti patterns
and related stuff

The eternal tree (rows refer to the table itself - think
threaded discussion)
Dynamic table creation (and dynamic query building)
Table as cache (lets save it in another table)
Table as queue (wtf)
Table as log file (table cleaning slave required)
Stoned Procedures (living la vida business)
Row Alignment (the careful gentleman)
Extreme JOINs (app requires a warmed up cache)
Your scheme must be printed in an A3 sheet.
Your ORM issue full queries for Dataset iterations

The eternal tree
Problem: Most threaded discussion example uses something
like a table which contains all threads and answers, relating to
each other by an id. Usually the developer will come up with
his own binary-tree version to manage this mess.

id - parent_id -author - text
1 - 0 - gleicon - hello world
2 - 1 - elvis - shout !

NoSQL alternative: Document storage:
{ thread_id:1, title: 'the meeting', author: 'gleicon', replies:[
{
'author': elvis, text:'shout', replies:[{...}]
}
]
}

Dynamic table creation
Problem: To avoid huge tables, one must come with a
"dynamic schema". For example, lets think about a document
management company, which is adding new facilities over the
country. For each storage facility, a new table is created:

item_id - row - column - stuff
1 - 10 - 20 - cat food
2 - 12 - 32 - trout

Now you have to come up with "dynamic queries", which will
probably query a "central storage" table and issue a huge join
to check if you have enough cat food over the country.

NoSQL alternative:
- Document storage, modeling a facility as a document
- Key/Value, modeling each facility as a SET

Table as cache
Problem: Complex queries demand that a result be stored in a
separated table, so it can be queried quickly. Worst than views

NoSQL alternative:
- Really ?
- Memcached
- Redis + AOF + EXPIRE
- Denormalization

Table as queue
Problem: A table which holds messages to be completed.
Worse, they must be ordered.

NoSQL alternative:
- RestMQ, Resque
- Any other message broker
- Redis (LISTS - LPUSH + RPOP)
- Use the right tool

Table as log file
Problem: A table in which data gets written as a log file. From
time to time it needs to be purged. Truncating this table once
a day usually is the first task assigned to new DBAs.

NoSQL alternative:
- MongoDB capped collection
- Redis, and a RRD pattern
- RIAK

Stoned procedures
Problem: Stored procedures hold most of your applications
logic. Also, some triggers are used to - well - trigger important
data events.

SP and triggers has the magic property of vanishing of our
memories and being impossible to keep versioned.

NoSQL alternative:
- Now be careful so you dont use map/reduce as stoned
procedures.
- Use your preferred language for business stuff, and let event
handling to pub/sub or message queues.

Row Alignment
Problem: Extra rows are created but not used, just in case.
Usually they are named as a1, a2, a3, a4 and called padding.

There's good will behind that, specially when version 1 of the
software needed an extra column in a 150M lines database
and it took 2 days to run an ALTER TABLE.

NoSQL alternative:
- Document based databases as MongoDB and CouchDB,
where new atributes are local to the document. Also, having no
schema helps

- Column based databases may be not the best choice if
column creation need restart/migrations

Extreme JOINs
Problem: Business stuff modeled as tables. Table inheritance
(Product -> SubProduct_A). To find the complete data for a
user plan, one must issue gigantic queries with lots of JOINs.

NoSQL alternative:
- Document storage, as MongoDB
- Denormalization
- Serialized objects

Your scheme fits in an A3 sheet
Problem: Huge data schemes are difficult to manage. Extreme
specialization creates tables which converges to key/value
model. The normal form get priority over common sense.

Product_A Product_B
id - desc id - desc

NoSQL alternative:
- Denormalization
- Another scheme ?
- Document store for flattening model
- Key/Value

Your ORM ...
Problem: Your ORM issue full queries for dataset iterations,
your ORM maps and creates tables which mimics your
classes, even the inheritance, and the performance is bad
because the queries are huge, etc, etc

NoSQL alternative:
Apart from denormalization and good old common sense,
ORMs are trying to bridge two things with distinct impedance.

There is nothing to relational models which maps cleanly to
classes and objects. Not even the basic unit which is the
domain(set) of each column. Black Magic ?

No silver bullet
- Consider alternatives

- Think outside the norm

- Denormalize

- Simplify

Cycle of changes - Product A
1. There was the database model
2. Then, the cache was needed. Performance was no good.
3. Cache key: query, value: resultset
4. High or inexistent expiration time [w00t]

(Now there's a turning point. Data didn't need to change often.
Denormalization was a given with cache)

5. The cache needs to be warmed or the app wont work.
6. Key/Value storage was a natural choice. No data on MySQL
anymore.

Cycle of changes - Product B
1. Postgres DB storing crawler results.
2. There was a counter in each row, and updating this counter
caused contention errors.
3. Memcache for reads. Performance is better.
4. First MongoDB test, no more deadlocks from counter
update.
5. Data model was simplified, the entire crawled doc was
stored.

Stuff to think about
Think if the data you use aren't denormalized (cached)

Most of the anti-patterns contain signs that the NoSQL route
(or at least a partial NoSQL route) may simplify.

Are you dependent on cache ? Does your application fails
when there is no cache ? Does it just slows down ?

Are you ready to think more about your data ?

Think about the way to put and to get back your data from the
database (be it SQL or NoSQL).

Extra - MongoDB and Redis
The next two slides are here to show what is like to use
MongoDB and Redis for the same task.

There is more to managing your data than stuffing it inside a
database. You gotta plan ahead for searches and migrations.

This example is about storing books and searching between
them. MongoDB makes it simpler, just liek using its query
language. Redis requires that you keep track of tags and ids to
use SET operations to recover which books you want.

Check http://rediscookbook.org and http://cookbook.mongodb.
org/ for recipes on data handling.

MongoDB/Redis recap - Books
MongoDB Redis
{
'id': 1,
SET book:1 {'title' : 'Diving into Python',
'title' : 'Diving into Python',
'author': 'Mark Pilgrim'}
'author': 'Mark Pilgrim',
SET book:2 { 'title' : 'Programing Erlang',
'tags': ['python','programming', 'computing']
'author': 'Joe Armstrong'}
}
SET book:3 { 'title' : 'Programing in Haskell',
'author': 'Graham Hutton'}
{
'id':2,
'title' : 'Programing Erlang', SADD tag:python 1
'author': 'Joe Armstrong', SADD tag:erlang 2
'tags': ['erlang','programming', 'computing', SADD tag:haskell 3
'distributedcomputing', 'FP'] SADD tag:programming 1 2 3
} SADD tag computing 1 2 3
SADD tag:distributedcomputing 2
{ SADD tag:FP 2 3
'id':3,
'title' : 'Programing in Haskell',
'author': 'Graham Hutton',
'tags': ['haskell','programming', 'computing',
'FP']
}

MongoDB/Redis recap - Books
MongoDB Redis
Search tags for erlang or haskell:
SINTER 'tag:erlang' 'tag:haskell'
db.books.find({"tags":
0 results
{ $in: ['erlang', 'haskell']
}
SINTER 'tag:programming' 'tag:computing'
})
3 results: 1, 2, 3
Search tags for erlang AND haskell (no
SUNION 'tag:erlang' 'tag:haskell'
results)
2 results: 2 and 3
SDIFF 'tag:programming' 'tag:haskell'
{ $all: ['erlang', 'haskell']
2 results: 1 and 2 (haskell is excluded)
}
})

This search yields 3 results
{ $all: ['programming', 'computing']
}
})

NoSQL and SQL Anti Patterns

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