03 - Porcelli e Gleicon - A importância dos dados em sua arquitetura… uma visão muito além do...

A importância dos dados em sua arquitetura… uma visão muito além do SQL Server

Alexandre Porcelli - @porcelliGleicon Moraes - @gleicon

Alexandre Porcelli

Writer

Alexandre Porcelli

Organizer

Alexandre Porcelli

Commiter / Parser Developer

Alexandre PorcelliCreator & Dictator

Alexandre PorcelliCore Developer / API Designer

Gleicon Moraes

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

@gleicon

existe um mundo além do...

ou do...

além, até mesmo dos...

inclusive do...

próximo do mundo sombrio do...

nosql

uma nova escola

contexto

falta de capital

big data

história...

• Hierarchical (IMS): late 1960’s and 1970’s • Directed graph (CODASYL): 1970’s • Relational: 1970’s and early 1980’s • Entity-Relationship: 1970’s • Extended Relational: 1980’s • Semantic: late 1970’s and 1980’s• Object-oriented: late 1980’s and early 1990’s • Object-relational: late 1980’s and early 1990’s • Semi-structured (XML): late 1990’s to late 2000’s• The next big thing: ???

ref: What Goes Around Comes Around por Michael Stonebraker e Joey Hellerstein

modelos

next big thing?

definição...

abaixo ao banco de

dados relacional!

abaixo ao banco de dados relacional!

como bala de prata!

momento histórico...

resolver problemas específicos

estrutura de dados

chave-valor

modelo

família de colunas

modeloKeyspace

Família de Colunas

.

.

.

chavelinha

coluna coluna coluna. . .coluna

chavelinha

coluna coluna coluna. . .coluna coluna coluna

nome valortimestamp

Coluna

documento

modelo

grafo

visão geral

arquitetura

Architectural Anti PatternsNotes on Data Distribution and Handling Failures

Fail

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

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, queue, 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• Throttle Control

The eternal treeProblem: 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 - text1 - 0 - gleicon - hello world2 - 1 - elvis - shout !

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

Dynamic table creationProblem: 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 - stuff1 - 10 - 20 - cat food2 - 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

Table as cacheProblem: 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

Table as queueProblem: A table which holds messages to be completed. Worse, they must be ordered bytime of creation.

Corolary: Job Scheduler table

Alternatives: - RestMQ, Resque

- Any other message broker

- Redis (LISTS - LPUSH + RPOP)

- Use the right tool

Table as log fileProblem: 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

Stoned proceduresProblem: 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.

Row AlignmentProblem: 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.

Extreme JOINsProblem: 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

Your scheme fits in an A3 sheetProblem: 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_Bid - desc id - desc

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

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 ?

Hierarchical ShardingProblem: 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'

Embedded ListsProblem: 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'. You may find distinct separators as |, -, [] and so on.

> select group_field from that_email_sharded_database.user"a@email1.net, b@email1.net,c@email2.net"

> select flags from stupid_email_admin where id = 21|0|1|1|0|

Either learn to model your data, or resort to model keys on K/V stores. Or any other way to show up flags, as you are not programming in C over a RDBMS hopefully.

Distributed Global LockingProblem: Trying to emulate JAVA's synchronize in a distributed manner. As there is no primitive architectura block to do that, sounds like the proper place to do that would be a RDBMS.

May starts 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.

Throttle ControlProblem: To control and track access to a given resource, a sequence of statements is issued, varying from a update...select to a transaction block using a stored procedure:

> select count(access) from throttle_ctl where ip_addr like ...> update .... or begin ... commit

Apart from having IP addresses stored as string, each request would have to check on this block. It gets worse if throttle control is mixed with a table-based access.

Using memcached (or any other k/v) as the data is ephemeral would work as (after creating the entry and setting expire time):

if (add 'IPADDR:YYYY:MM:DD:HH', 1) < your_limit: do_stuff()

ferramentas

noSQL

key-valuecolumnfamily

document graph

key-valuecolumnfamily

document graph

newSQL

newSQLnoSQL

cada escolha uma

renúncia

padrões

how-to

acid

(

existe nosqlacid

)

A word about ops

Meaningful data • Email traffic accounting ~4500 msgs/sec in, ~2000 msgs

out

• 10 Gb SPAM/NOT SPAM tokenized data per day

• 300 Gb logfile data/day

• 80 Billion DNS queries per day

• ~1k req/sec tcptable queries.

• 0.8 Pb of data migrated over mixed quality network. Planned 3mo, executed 6mo, online, on production.

• traffic from 400mb/s to 3.1Gb/s

Stuff to think aboutThink 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.

Call it NoSQL, non relational, or any other name, but assemble a toolbox to deal with different data types.

Are you dependent on cache ? Does your application fails when there is no warm 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). Migrations are painful.

Stuff to think about- Without operational requirements, it's a personal choice about data organization- Without time pressure, any migration is easy, regardless data size.- Out of production environment, events flow in a different time flow- 'Normal Accidents' (Charles Perrow) - how resilient is your operation ? Are you ready to tackle your incidents ?- Everything breaks under scale (Benjamin Black)- "Picking up pennies in front of a steamroller" (Nassin N. Taleb)

Perguntas?

@porcelli

linkedin.com/in/alexandreporcelli

porcelli.com.br

Obrigado

github.com/porcelli

@gleicon

linkedin.com/in/gleicon

zenmachine.wordpress.com

github.com/gleicon