This document discusses architectural anti-patterns related to data distribution and handling failures. It provides examples of anti-patterns when using SQL and NoSQL databases, including using tables as queues, logs, or caches instead of the proper tools. Alternatives are suggested such as using message queues, document databases, and key-value stores instead of forcing data models. The document advises to simplify data schemes, avoid over-engineering, and think about how to best structure data and applications.

1. Architectural Anti Patterns
Notes on Data Distribution and Handling Failures
Gleicon Moraes
http://zenmachine.wordpress.com
http://github.com/gleicon
@gleicon

2. Required Listening: King Crimson - Discipline

3. Anti Patterns
Evolution from SQL Anti Patterns (NoSQL:br May 2010)
More than just RDBMS
Large volumes of data
Distribution
Architecture
Research on other tools
Message Queues, DHT, Job Schedulers, NoSQL
Indexing, Map/Reduce
New revision since QConSP 2010: included Hierarchical
Sharding, Embedded lists and Distributed Global Locking

4. RDBMS Anti Patterns
Not all things fit on a relational database, single ou distributed
The eternal table-as-a-tree
Dynamic table creation
Table as cache
Table as queue
Table as log file
Stoned Procedures
Row Alignment
Extreme JOINs
Your scheme must be printed in an A3 sheet.
Your ORM issue full queries for Dataset iterations
Hierarchical Sharding
Embedded lists
Distributed global locking

5. Doing it wrong, Junior !

6. 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 !
Alternative: Document storage:
{ thread_id:1, title: 'the meeting', author: 'gleicon', replies:[
{
'author': elvis, text:'shout', replies:[{...}]
}
]
}

7. 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.
Alternatives:
- Document storage, modeling a facility as a document
- Key/Value, modeling each facility as a SET

8. 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
Alternatives:
- Really ?
- Memcached
- Redis + AOF + EXPIRE
- De-normalization

9. Table as queue
Problem: A table which holds messages to be completed.
Worse, they must be ordered by
time of creation.
Corolary: Job Scheduler table
Alternatives:
- RestMQ, Resque
- Any other message broker
- Redis (LISTS - LPUSH + RPOP)
- Use the right tool

10. 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.
Alternative:
- MongoDB capped collection
- Redis, and RRD pattern
- RIAK

11. 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.
Alternative:
- Now be careful so you dont use map/reduce as modern
stoned procedures. Unfit for real time search/processing
- Use your preferred language for business stuff, and let event
handling to pub/sub or message queues.

12. 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. But that's no
excuse.
Alternative:
- Quit being cheap. Quit feeling 'hacker' about padding
- Document based databases as MongoDB and CouchDB, has
no schema. New atributes are local to the document and can
be added easily.

13. 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.
Alternative:
- Document storage, as MongoDB
might help having important
information together.
- De-normalization
- Serialized objects

14. 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
Alternatives:
- De-normalization
- Another scheme ?
- Document store for flattening model
- Key/Value
- See 'Extreme JOINs'

15. 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
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 ?

16. Hierarchical Sharding
Problem: Genius at work. Distinct databases inside a RDBMS
ranging from A to Z, each database has tables for users
starting with the proper letter. Each table has user data.
Fictional example: e-mail accounts management
> show databases;
a b c d e f g h i j k l m n o p q r s t u w x z
> use a
> show tables;
...alberto alice alma ... (and a lot more)
There is no way to query anything in common for all users with
out application side processing. In this particular case this
sharding was uncalled for as relational databases have all
tools to deal with this particular case of 'different clients and
data'

17. Embedded Lists
Problem: As data complexity grows, one thinks that it's proper
for the application to handle different data structures
embedded in a single cell or row. The popular 'Let's use
commas to separe it'. Another name could be Stored CSV
> select group_field from that_email_sharded_database.user
"a@email1.net, b@email1.net,c@email2.net"
This is a bit complementar of 'Your scheme fits in a A3 sheet'.
This kind of optimization, while harmless at first sight, spreads
without control. Querying such fields yields unwanted results.
You might only use languages where string splitting is not
expensive or remember to compile RegExps before querying a
large data volume. Either learn to model your data, or resort to
K/V stores as Redis.

18. Distributed Global Locking
Problem: Someone learns java and synchronize. A bit later
genius thinks that a distributed synchronize would be
awesome. The proper place to do that would be the database
of course. Start with a reference counter in a table and end up
with this:
> select COALESCE(GET_LOCK('my_lock',0 ),0 )
Plain and simple, you might find it embedded in a magic class
called DistributedSynchronize or ClusterSemaphore. Locks,
transactions and reference counters (which may act as soft
locks) doesn't belongs to the database. While its they use is
questionable even in code, the matter of fact is that you are
doing it wrong, if you are doing like that.

19. No silver bullet
- Think about data
handling and your
system architecture
- Think outside the norm
- De-normalize
- Simplify
- Know stuff (Message
queues, NoSQL, DHT)

20. 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.

21. 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.

22. Stuff to think about
Think if the data you use aren't de-normalized somewhere
(cached)
Most of the anti-patterns signals that there are architectural
issues instead of only database issues.
The NoSQL route (or at least a partial NoSQL route) may
simplify it.
Are you dependent on cache ? Does your application fails
when there is no cache ? Does it just slows down ?
Think about the way to put and to get back your data from the
database (be it SQL or NoSQL).

23. Thanks
http://scienceblogs.com/evolgen/upload/2007/04/rube_back_scratch.gif
http://en.wikipedia.org/wiki/Rube_Goldberg_machine