Chris Lea - What does NoSQL Mean for You

What Does NoSQL Mean for You? Chris Lea (mt) Media Temple FOWA Dublin 2010

For Starters: What does it mean at all?

For Starters: What does it mean at all? “NoSQL is a blanket term used to describe structured storage that doesn’t rely on SQL to be accessed in a useful way”. -- Chris Lea

For Starters: What does it mean at all? “NoSQL” DOES NOT mean “SQL is Bad”

MySQL does what I need, why should I care?

MySQL does what I need, why should I care? “If I’d asked my customers what they wanted, they’d have said a faster horse.” -- Henry Ford

MySQL does what I need, why should I care? RDBMS NoSQL Designed for generic Designed to solve workloads speciﬁc problems Large (and growing) Trades features for feature sets performance

(the NoSQL umbrella)

Key / Value Caches • Redis (the NoSQL umbrella) • Memcached

Key / Value Caches • Redis (the NoSQL umbrella) • Memcached Key / Value Stores • Tokyo cabinet • Memcachedb • Project Voldemort • Cassandra

Key / Value Caches • Redis (the NoSQL umbrella) • Memcached Key / Value Stores • Tokyo cabinet • Memcachedb • Project Voldemort • Cassandra Tabular • HBase • Hypertable

Key / Value Caches • Redis (the NoSQL umbrella) • Memcached Key / Value Stores Document • Tokyo cabinet • Memcachedb • CouchDB • Project Voldemort • MongoDB • Cassandra • Jackrabbit Tabular • HBase • Hypertable

Should I be Thinking about NoSQL?

Should I be Thinking about NoSQL? Probably need RDBMS. Yes Can you sanely do what you need in the app? No Do you need transactions? Yes No Think about NoSQL.

NoSQL Systems Typically Don’t do Transactions or Joins

NoSQL Systems Typically Don’t do Transactions or Joins • If you really need transactions, stick with RDBMS • Not having joins turns out to be not such a big deal

NoSQL Systems Typically Don’t do Transactions or Joins MongoDB is an excellent use case example

Why MongoDB? • Comfortable if you are coming from MySQL • Written in C++ means all machine code • no Erlang / Java / virtual machines • Tools like mongo (shell), mongodump, mongostat, mongoimport • Native drives in languages you care about • no Thrift / REST / code generation steps

Why MongoDB? • No complex transactions • If you don’t use them, this is a non-issue • No joins • This turns out to not be a big deal generally, because we’re going to rethink our data modeling

Why MongoDB? Transactions and joins are a huge computational overhead, even if you don’t use them! • No complex transactions • If you don’t use them, this is a non-issue • No joins • This turns out to not be a big deal generally, because we’re going to rethink our data modeling

Thinking About Your Data (RDBMS) • Look at data, determine logical groupings • (hope structure never changes) • Make tables based on groups, link with ID ﬁelds • Break up data on insert, put into appropriate tables • Use joins on select to re-assemble data • Create indexes as needed for fast queries

Thinking About Your Data (RDBMS) user_t comment_t comment_id user_id post_t post_id user_name post_id comment_body user_id post_title post_body

Thinking About Your Data (RDBMS) This leads to queries such as: SELECT post_title,post_body,post_id FROM post_t,user_t WHERE user_t.user_name = “Lorraine” AND post_t.user_id = user_t.user_id LIMIT 1; SELECT comment_body FROM comment_t WHERE comment_t.post_id = $post_id;

Thinking About Your Data (MongoDB) • Figure out how you will eventually use the data • Store it that way • Create indexes as needed for fast queries

Thinking About Your Data (MongoDB) from pymongo import Connection connection = Connection() db = connection['blog'] posts = db['posts'] post = {"author": "Lorraine", "title": "Who on Earth lets Chris Lea Talk on Stage?", "post": "Seriously. That's just not cool.", "comments": ["Is he really that bad?", "Yes, he really is."], "date": datetime.datetime.utcnow()} posts.insert(post)

Thinking About Your Data (MongoDB) from pymongo import Connection connection = Connection() db = connection['blog'] posts = db['posts'] post = posts.ﬁnd_one({“author”: “Lorraine”})

Say Goodbye to Schemas from pymongo import Connection connection = Connection() db = connection['blog'] posts = db['posts'] post = {"author": "Lorraine", "title": "Who on Earth lets Chris Lea Talk on Stage?", "post": "Seriously. That's just not cool.", "comments": ["Is he really that bad?", "Yes, he really is."], "date": datetime.datetime.utcnow()} posts.insert(post)

Say Goodbye to Schemas from pymongo import Connection connection = Connection() db = connection['blog'] posts = db['posts'] post = {"author": "Lorraine", "title": "Who on Earth lets Chris Lea Talk on Stage?", "post": "Seriously. That's just not cool.", "comments": ["Is he really that bad?", "Yes, he really is."], "tags": ["fowa", "nosql", "nerds"], "date": datetime.datetime.utcnow()} posts.insert(post)

Say Goodbye to Schemas from pymongo import Connection connection = Connection() db = connection['blog'] If you want new ﬁelds... just start posts = db['posts'] using them! post = {"author": "Lorraine", "title": "Who on Earth lets Chris Lea Talk on Stage?", "post": "Seriously. That's just not cool.", "comments": ["Is he really that bad?", "Yes, he really is."], "tags": ["fowa", "nosql", "nerds"], "date": datetime.datetime.utcnow()} posts.insert(post)

Enjoy a Wealth of Query Options from pymongo import Connection connection = Connection() db = connection['blog'] posts = db['posts'] posts.ﬁnd_one({“author”: “Lorraine”})

Enjoy a Wealth of Query Options from pymongo import Connection connection = Connection() db = connection['blog'] posts = db['posts'] posts.ﬁnd({“author”: “Lorraine”}).limit(5)

Enjoy a Wealth of Query Options from pymongo import Connection connection = Connection() db = connection['blog'] posts = db['posts'] posts.ﬁnd({“author”: /^Lor/})

Enjoy a Wealth of Query Options from pymongo import Connection connection = Connection() db = connection['blog'] posts = db['posts'] posts.ﬁnd({“author”: {$not: “Lorraine”} })

Enjoy a Massive Performance Jump • Mileage will vary, but 10x is not uncommon • For reads and writes • Writes happen at near disk native speed • Logging to MongoDB is perfectly acceptable • Reads for active data near Memcached speeds

Enjoy a Massive Performance Jump Ability to write bad queries is enormously reduced!

Ability to write bad queries is enormously reduced! • No joins means need for complex indexes reduced • Chances of index / query mismatches vastly lower • Disk I/O much less complex, and therefore much faster

Caveats for MongoDB • Really should use 64bit machines for production • 32bit has 2G limit per collection (table) • Happiest with lots of RAM relative to active data • Under heavy development • Features / drivers / docs changing rapidly

Questions?

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Chris Lea - What does NoSQL Mean for You

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