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Exploring Ruby on Rails and PostgreSQL

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An overview of Ruby, jRuby, Rails, Torquebox, and PostgreSQL that was presented as a 3 hour class to other programmers at The Ironyard (http://theironyard.com) in Greenville, SC in July of 2013. The …

An overview of Ruby, jRuby, Rails, Torquebox, and PostgreSQL that was presented as a 3 hour class to other programmers at The Ironyard (http://theironyard.com) in Greenville, SC in July of 2013. The Rails specific sections are mostly code samples that were explained during the session so the real focus of the slides is Ruby, "the rails way" / workflow / differentiators and PostgreSQL.

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  • This is a bad diagram because I tried to use the built in tools in power point. I need to update it. In reality the View arrow should be going back through Rack to the Browser.
  • Transcript

    • 1. Exploring Ruby on Rails and PostgreSQL
    • 2. Who am I? • I’m Barry Jones • Application Developer since ’98 – Java, PHP, Groovy, Ruby, Perl, Python – MySQL, PostgreSQL, SQL Server, Oracle, MongoDB • Efficiency and infrastructure nut • Believer in “right tool for the job” – There is no silver bullet, programming is about tradeoffs
    • 3. The Silver Bullet Ruby on Rails and PostgreSQL j/k  But it’s close
    • 4. What do we look for in a language? • Balance – Can it do what I need it to do? • Web: Ruby/Python/PHP/Perl/Java/C#/C/C++ – Efficient to develop with it? • Ruby/Python/PHP – Libraries/tools/ecosystem to avoid reinventing the wheel? • Ruby/Python/PHP/Java/Perl – Is it fast? • Ruby/Python/Java/C#/C/C++ – Is it stable? • Ruby/Python/PHP/Perl/Java/C#/C/C++ – Do other developers use it? • At my company? In the area? Globally? – Cost effective? • Ruby/Python/PHP/Perl/C/C++ – Can it handle my architectural approach well? • Ruby/Python/Java/C# handle just about everything • CGI languages (PHP/Perl/C/C++) are very bad fits for frameworks, long polling, evented programming – Will it scale? • Yes. This is a subjective question because web servers scale horizontally naturally – Will my boss let me use it? • .NET shop? C# • Java shop? Java (Groovy, Clojure, Scala), jRuby, jython • *nix shop? Ruby, Python, Perl, PHP, C, C++ • Probable Winners: Ruby and Python
    • 5. What stands out about Ruby? • Malleability – Everything is an object – Objects can be monkey patched • Great for writing Domain Specific Languages – Puppet – Chef – Capistrano – Rails “this is a string object”.length class String def palindrome? self == self.reverse end end “radar”.palindrome?
    • 6. How is monkey patching good? • Rails adds web specific capabilities to Ruby – “ “.blank? == true • Makes using 3rd party libraries much easier – Aspect Oriented Development • Not dependent on built in hooks – Queued processing record = Record.find(id) record.delay.some_intense_logic • DelayedJob • Resque • Sidekiq • Stalker – Cross integrations Email.deliver • MailHopper – Automatically deliver all email in the background • Gems that specifically enhance other gems
    • 7. How is monkey patching…bad? • If any behavior is modified by a monkey patch there is a chance something will break • On a positive note, if you’re writing tests and following TDD or BDD the tests should catch any problems • On another positive note, the ruby community is very big on testing
    • 8. Why was Ruby created? • Created by Yukirio Matsumoto • "I wanted a scripting language that was more powerful than Perl, and more object-oriented than Python.” • "I hope to see Ruby help every programmer in the world to be productive, and to enjoy programming, and to be happy. That is the primary purpose of Ruby language.” – Google Tech Talk in 2008
    • 9. Ruby Version Manager • cd into directory autoselects correct version of ruby and gemset • Makes running multiple projects with multiple versions of ruby and gem dependencies on one machine dead simple .rvmrc file rvm rubytype-version-patch@gemset Examples: rvm ruby-1.9.3-p327@myproject rvm jruby-1.7.4@myjrubyproject rvm ree-1.8.7@oldproject
    • 10. Bundler and Gemfile $ bundle install Using rake (10.0.4) Using i18n (0.6.1) Using multi_json (1.7.2) Using activesupport (3.2.13) Using builder (3.0.4) Using activemodel (3.2.13) Using erubis (2.7.0) Using journey (1.0.4) Using rack (1.4.5) Using rack-cache (1.2) Using rack-test (0.6.2) … Your bundle is complete! Use `bundle show [gemname]` to see where a bundled gem is installed. source 'https://rubygems.org' source 'http://gems.github.com' # Application infrastructure gem 'rails', '3.2.13' gem 'devise' gem 'simple_form' gem 'slim' gem 'activerecord-jdbc-adapter’ gem 'activerecord-jdbcpostgresql- adapter' gem 'jdbc-postgres' gem 'jruby-openssl' gem 'jquery-rails' gem 'torquebox', '2.3.0' gem 'torquebox-server', '~> 2.3.0'
    • 11. Foreman Not Ruby specific but written in ruby Used with Heroku Drop in a Procfile $ foreman start  CTRL + C to stop everything Procfile web: bundle exec thin start -p $PORT worker: bundle exec rake resque:work QUEUE=* clock: bundle exec rake resque:scheduler
    • 12. jRuby: Why? Ruby isn’t perfect • Some gems can create memory leaks – esp. if they were written with native C • Does not have kernel level threading – Global Interpreter Lock • Everything is an object means unnecessary processing happens when doing things like adding numbers leading to a performance hit
    • 13. jRuby: So how does it fix things? I hate writing Java…but the JVM is a work of art • Java infrastructure is virtually bulletproof – Most mature way to deploy a web application – Enterprisey  • JVM’s garbage collector is best of breed and eliminates the potential memory leak issues • JVM’s Just-In-Time compiler continually optimizes code the longer it runs making it faster • JVM gives Ruby kernel level threading • jRuby inspects your Ruby code to see if you’re doing anything it would prefer you didn’t…and turns it off if you’re not – Eg. If you aren’t overloading the + operator on int’s, it will convert them to basic types instead of running as objects • Include and use very mature Java libraries directly in your Ruby code – Significantly expands your toolbelt – Allows easy integration into existing Java environments
    • 14. The Sidekiq Test Sidekiq is a multithreaded background worker that provides tremendous concurrency benefits Creating 1,000,000 objects in 50 concurrent threads Ruby jRuby
    • 15. The App Server Test CPU Usage
    • 16. The App Server Test Free Memory
    • 17. The App Server Test Latency
    • 18. The App Server Test Throughput
    • 19. Update and Clarification • As of this posting to Slideshare, Torquebox has a mature version 3 and a prototype version 4 that operates in a “web server only” mode. Ruby is at version 2.1.0 with dramatic improvements to memory performance with forking which allows higher concurrency. • At this time, jruby still wins but it’s much closer. Based on chatter from the #jruby IRC channels, a major new release of both jRuby and Torquebox are expected to dramatically improve their performance thanks to recent Java updates. The expected timeline was late 2014 last I heard. • Independent benchmarks can be found here: http://www.techempower.com/benchmarks/#section=data- r9&hw=peak&test=json
    • 20. RUBY ON RAILS Let’s take a break before covering…
    • 21. What do we look for in a framework? • Please don’t suck – Rails does not suck • Does it follow Model-View-Controller? – Yes – Since Rails 1 it’s been the standard bearer for how to do MVC on the web, copied in almost every language • Does it help me avoid repeating myself (DRY)? – Yes • Is it self documenting? – Yes, it has a set of rules that generally make most documentation unnecessary • Is it flexible enough to bend to my application needs? – Yes • Do other people use it? – Good gosh yes • Will it work with my database? – Yes • Is it still going to be around in X years? – Ruby has Rails – Python has Django – Groovy has Grails – C# has MVC – PHP has fragmented framework Hell (aka – who knows?) – Java has a few major players (Struts 2, Play, etc)
    • 22. Rails: The Basics Browser Rack Router Controller + Models View
    • 23. Rails: Rack Watch this excellent walkthrough of Rack Middleware: http://railscasts.com/episodes/151-rack- middleware Summary: It’s a layer of ruby code that passes requests into your app and sends responses back out. You can add layers to do pre/post processing on all requests prior to beginning ANY of your application code.
    • 24. Rails: Models / ActiveRecord class Post < ActiveRecord::Base belongs_to :category has_many :tags, through: :posts_tags validates :title, presence: true before_save :create_slug, only: :create scope :newest_first, order(‘created_at DESC’) scope :active, where(‘active = ?’,true) scope :newest_active, newest_first.active scope :search, lambda do |text| where(‘title LIKE ?’,”%#{text}%”) end def create_slug self.slug = title.downcase.squish.sub(‘ ‘,’-’) end end post = Post.new(title: ‘Some title’) post.save! OR post = Post.create(title: ‘Some title’) post.slug # some-title post.id # 1 post.created_at # Created datetime post.updated_at # Updated datetime post.title = ‘New title’ post.save! # Relations post.tags.first post.tags.count post.category.name post = Post.include(:tags) # Eager load post = Post.search(‘some’).newest_active.first
    • 25. Rails: Migrations class CreateInitialTables < ActiveRecord::Migration def up create_table :posts do |t| t.string :title t.text :body t.string :slug t.integer :category_id t.timestamps end # … create more tables… add_index :tags, [:name,:something], unique: true execute “UPDATE posts SET field = ‘value’ WHERE stuff = ‘happens’” end def down drop_table :posts end def change add_column :posts, :user_id, :integer end end $ rake db:migrate
    • 26. Rails: Controllers Class PostsController < ApplicationController before_filter :authenticate, only: :destroy def index # GET /posts end def new # GET /posts/new end def create # POST /posts end def show # GET /posts/:id end def edit # GET /posts/:id/edit end def update # PUT /posts/:id end def destroy # DELETE /posts/:id end end # Routes resources :posts OR limit it resources :posts, only: [:create,:new]
    • 27. Rails: Views /app/views /layouts /application.html.erb /posts /new.html.slim /new.json.rabl /index.xml.erb /_widget.html.erb # slim example .post h2=post.title .body.grid-8=post.body # erb example <div class=“post”> <h2><%=post.title%></h2> <div class=“body grid-8”> <%=post.body%> </div> </div>
    • 28. Rails: Testing with rspec Describe Post do describe ‘a basic test’ do subject { FactoryGirl.build(:post,title: ‘Some title’) } it ‘should be valid’ do should_not be_nil subject.valid?.should be_true end end describe ‘something with a complicated dependency’ do before do Post.stub(:function_to_override){ true } end end describe ‘a test with API hits’ do use_vcr_cassette ‘all_a_twitter’, record: :new_episodes end end
    • 29. POSTGRESQL Let’s take a break before we talk about…
    • 30. How do you pronounce it? Answer Response Percentage post-gres-q-l 2379 45% post-gres 1611 30% pahst-grey 24 0% pg-sequel 50 0% post-gree 350 6% postgres-sequel 574 10% p-g 49 0% database 230 4% Total 5267
    • 31. What IS PostgreSQL? • Fully ACID compliant • Feature rich and extensible • Fast, scalable and leverages multicore processors very well • Enterprise class with quality corporate support options • Free as in beer • It’s kind’ve nifty
    • 32. Laundry List of Features • Multi-version Concurrency Control (MVCC) • Point in Time Recovery • Tablespaces • Asynchronous replication • Nested Transactions • Online/hot backups • Genetic query optimizer multiple index types • Write ahead logging (WAL) • Internationalization: character sets, locale-aware sorting, case sensitivity, formatting • Full subquery support • Multiple index scans per query • ANSI-SQL:2008 standard conformant • Table inheritance • LISTEN / NOTIFY event system • Ability to make a Power Point slide run out of room
    • 33. What are we covering today? • Full text-search • Built in data types • User defined data types • Automatic data compression • A look at some other cool features and extensions, depending how we’re doing on time
    • 34. Full-text Search • What about…? – Solr – Elastic Search – Sphinx – Lucene – MySQL • All have their purpose – Distributed search of multiple document types • Sphinx – Client search performance is all that matters • Solr – Search constantly incoming data with streaming index updates • Elastic Search excels – You really like Java • Lucene – You want terrible search results that don’t even make sense to you much less your users • MySQL full text search = the worst thing in the world
    • 35. Full-text Search • Complications of stand alone search engines – Data synchronization • Managing deltas, index updates • Filtering/deleting/hiding expired data • Search server outages, redundancy – Learning curve – Character sets match up with my database? – Additional hardware / servers just for search – Can feel like a black box when you get a support question asking “why is/isn’t this showing up?”
    • 36. Full-text Search • But what if your needs are more like: – Search within my database – Avoid syncing data with outside systems – Avoid maintaining outside systems – Less black box, more control
    • 37. Full-text Search • tsvector – The text to be searched • tsquery – The search query • to_tsvector(‘the church is AWESOME’) @@ to_tsquery(SEARCH) • @@ to_tsquery(‘church’) == true • @@ to_tsquery(‘churches’) == true • @@ to_tsquery(‘awesome’) == true • @@ to_tsquery(‘the’) == false • @@ to_tsquery(‘churches & awesome’) == true • @@ to_tsquery(‘church & okay’) == false • to_tsvector(‘the church is awesome’) – 'awesom':4 'church':2 • to_tsvector(‘simple’,’the church is awesome’) – 'are':3 'awesome':4 'church':2 'the':1
    • 38. Full-text Search • ALTER TABLE mytable ADD COLUMN search_vector tsvector • UPDATE mytable SET search_vector = to_tsvector(‘english’,coalesce(title,’’) || ‘ ‘ || coalesce(body,’’) || ‘ ‘ || coalesce(tags,’’)) • CREATE INDEX search_text ON mytable USING gin(search_vector) • SELECT some, columns, we, need FROM mytable WHERE search_vector @@ to_tsquery(‘english’,‘Jesus & awesome’) ORDER BY ts_rank(search_vector,to_tsquery(‘english’,‘Jesus & awesome’)) DESC • CREATE TRIGGER search_update BEFORE INSERT OR UPDATE ON mytable FOR EACH ROW EXECUTE PROCEDURE tsvector_update_trigger(search_vector, ’english’, title, body, tags)
    • 39. Full-text Search • CREATE FUNCTION search_trigger RETURNS trigger AS $$ begin new.search_vector := setweight(to_tsvector(‘english’,coalesce(new.title,’’)),’A’) || setweight(to_tsvector(‘english’,coalesce(new.body,’’)),’D’) || setweight(to_tsvector(‘english’,coalesce(new.tags,’’)),’B’); return new; end $$ LANGUAGE plpgsql; • CREATE TRIGGER search_vector_update BEFORE INSERT OR UPDATE OF title, body, tags ON mytable FOR EACH ROW EXECUTE PROCEDURE search_trigger();
    • 40. Full-text Search • A variety of dictionaries – Various Languages – Thesaurus – Snowball, Stem, Ispell, Synonym – Write your own • ts_headline – Snippet extraction and highlighting
    • 41. Datatypes: ranges • int4range, int8range, numrange, tsrange, tstzrange, daterange • SELECT int4range(10,20) @> 3 == false • SELECT numrange(11.1,22.2) && numrange(20.0,30.0) == true • SELECT int4range(10,20) * int4range(15,25) == 15-20 • CREATE INDEX res_index ON schedule USING gist(during) • ALTER TABLE schedule ADD EXCLUDE USING gist (during WITH &&) ERROR: conflicting key value violates exclusion constraint ”schedule_during_excl” DETAIL: Key (during)=([ 2010-01-01 14:45:00, 2010-01-01 15:45:00 )) conflicts with existing key (during)=([ 2010-01-01 14:30:00, 2010-01-01 15:30:00 )).
    • 42. Datatypes: hstore • properties – {“author” => “John Grisham”, “pages” => 535} – {“director” => “Jon Favreau”, “runtime” = 126} • SELECT … FROM mytable WHERE properties -> ‘director’ LIKE ‘%Favreau’ – Does not use an index • WHERE properties @> (‘author’ LIKE “%Grisham”) – Uses an index to only check properties with an ‘author’ • CREATE INDEX table_properties ON mytable USING gin(properties)
    • 43. Datatypes: arrays • CREATE TABLE sal_emp(name text, pay_by_quarter integer[], schedule text[][]) • CREATE TABLE tictactoe ( squares integer[3][3] ) • INSERT INTO tictactoe VALUES (‘{{1,2,3},{4,5,6},{7,8,9}}’) • SELECT squares[1:2][1:1] == {{1},{4}} • SELECT squares[2:3][2:3] == {{5,6},{8,9}}
    • 44. Datatypes: JSON • Validate JSON structure • Convert row to JSON • Functions and operators very similar to hstore
    • 45. Datatypes: XML • Validates well-formed XML • Stores like a TEXT field • XML operations like Xpath • Can’t index XML column but you can index the result of an Xpath function
    • 46. Data compression with TOAST • TOAST = The Oversized Attribute Storage Technique • TOASTable data is automatically TOASTed • Example: – stored a 2.2m XML document – storage size was 81k
    • 47. User created datatypes • Built in types – Numerics, monetary, binary, time, date, interval, boolean, enumerated, geometric, network address, bit string, text search, UUID, XML, JSON, array, composite, range – Add-ons for more such as UPC, ISBN and more • Create your own types – Address (contains 2 streets, city, state, zip, country) – Define how your datatype is indexed – GIN and GiST indexes are used by custom datatypes
    • 48. Further exploration: PostGIS • Adds Geographic datatypes • Distance, area, union, intersection, perimeter • Spatial indexes • Tools to load available geographic data • Distance, Within, Overlaps, Touches, Equals, Contains, Crosses • SELECT name, ST_AsText(geom) FROM nyc_subway_stations WHERE name = ‘Broad St’ • SELECT name, boroname FROM nyc_neighborhoods WHERE ST_Intersects(geom, ST_GeomFromText(‘POINT(583571 4506714)’,26918) • SELECT sub.name, nh.name, nh.borough FROM nyc_neighborhoods AS nh JOIN nyc_subway_stations AS sub ON ST_Contains(nh.geom, sub.geom) WHERE sub.name = ‘Broad St”
    • 49. Further exploration: Functions • Can be used in queries • Can be used in stored procedures and triggers • Can be used to build indexes • Can be used as table defaults • Can be written in PL/pgSQL, PL/Tcl, PL/Perl, PL/Python out of the box • PL/V8 is available an an extension to use Javascript
    • 50. Further exploration: PLV8 • CREATE OR REPLACE FUNCTION plv8_test(keys text[], vals text[]) RETURNS text AS $$ var o = {}; for(var i = 0; i < keys.length; i++) { o[keys[i]] = vals[i]; } return JSON.stringify(o); $$ LANGUAGE plv8 IMMUTABLE STRICT; SELECT plv8_test(ARRAY[‘name’,’age’],ARRAY[‘Tom’,’29’]); • CREATE TYPE rec AS (i integer, t text); CREATE FUNCTION set_of_records RETURNS SETOF rec AS $$ plv8.return_next({“i”: 1,”t”: ”a”}); plv8.return_next({“i”: 2,”t”: “b”}); $$ LANGUAGE plv8; SELECT * FROM set_of_records();
    • 51. Further exploration: Async commands / indexes • Fine grained control within functions – PQsendQuery – PQsendQueryParams – PQsendPrepare – PQsendQueryPrepared – PQsendDescribePrepared – PQgetResult – PQconsumeInput • Per connection asynchronous commits – set synchronous_commit = off • Concurrent index creation to avoid blocking large tables – CREATE INDEX CONCURRENTLY big_index ON mytable (things)
    • 52. ARCHITECTURE And finally…
    • 53. Biggest Issue with Frameworks • Framework Dependency • Trying to do everything in application code • Race conditions • Package dependency
    • 54. Old School • Service Oriented Architecture – Getting more popular because of REST – Had been happening for years prior with WSDL • Database managed your data – Constraints, triggers, functions, stored procedures – If it was in the database…it was valid • Nothing has changed…this is still the best way
    • 55. If you really leverage your database… • You can easily break your application into logical parts • You don’t need to create APIs through your core code base when direct DB access there • You can use a different language for certain things if it makes sense to do so – Node.js is great for APIs – Using a library that only runs on Windows • Database can provide granular access controls
    • 56. Architecture: Before
    • 57. Architecture: After
    • 58. Architecture: Scaled
    • 59. THANKS!
    • 60. Credits / Sources • NOTE: Some code samples in this presentation have minor alterations for presentation clarity (such as leaving out dictionary specifications on some search calls, etc) • http://www.postgresql.org/docs/9.2/static/index.html • http://workshops.opengeo.org/postgis-intro/ • http://stackoverflow.com/questions/15983152/how-can-i-find-out-how-big-a- large-text-field-is-in-postgres • https://devcenter.heroku.com/articles/heroku-postgres-extensions-postgis-full- text-search • http://railscasts.com/episodes/345-hstore?view=asciicast • http://www.slideshare.net/billkarwin/full-text-search-in-postgresql • http://sourceforge.net/apps/mediawiki/postgres-xc/index.php?title=Main_Page • http://railscasts.com/episodes/151-rack-middleware • http://joshrendek.com/2012/11/sidekiq-vs-resque/ • http://torquebox.org/news/2011/10/06/torquebox-2x-performance/ • http://jruby.org/ • https://rvm.io/ • http://ddollar.github.io/foreman/ • http://en.wikipedia.org/wiki/Ruby_(programming_language) • http://bundler.io/ • http://www.techempower.com/benchmarks/#section=data-r9&hw=peak&test=json

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