Using Databases to pull your application weight

Using Databases to pull your application weight

This is the presentation that I took at south east ruby conference, October 5th 2017.

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Harisankar P S

October 05, 2017
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Transcript

  1. 2.

    { “name” => "Harisankar P S", “email” => ”mailme@hsps.in”, “twitter”

    => "coderhs", “facebook" => "coderhs", “github” => “coderhs”, “linkedin” => “coderhs”, }
  2. 3.

    One thing you notice about me is. I love Stickers!!

    So if you have stickers give them to me.
  3. 14.

    This talk is about all the things that I learned

    Which helped me Scale the application without having to spend a fortune in Hardware
  4. 22.

    This talk is about how we can offload couple of

    the jobs done by Rails to Database. You have a HULK, don’t feel scared to USE it.
  5. 23.

    I have done all these in production , so you

    don’t to feel scared to run this.
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    Today we are going to talk about • Query Planner

    • Indexing • Attribute Preloading • Materialised Views • Generating JSON • Synchronous Commit
  7. 30.
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    • SQL syntax is all about how the results should

    be • What you want in your result - SELECT id, name • Or some information about data like - SELECT average(price), max(price), min(price) Where is the decision on how the data should be fetch made.
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    • A query plan is created by the DB before

    the query you gave is executed. • Plan is the cost of running the query. The DB chooses the one with the least cost. • Query Plan assumes the plan it has is the ideal one
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    So we need to see what the query planner see

    Active Record has .explain method to help us there
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    So we check the query plan find where we are

    slowing down and then fix them and make the plan choose the faster method.
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    Tip: We can make the query plan display in JSON,

    YML & XML formats as well EXPLAIN (format YAML) select * from users
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    Indexes are a special lookup table that the database search

    engine can use to speed up data retrieval.
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    An Index is like a pointer to a particular row

    of a table. Where all the fields in the table are ordered.
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    Even if you have indexes if it find the sequential

    search to be cost less then it would go for that one.
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    Example: Lets say you have a column with a 10,000

    rows, but the the content is either short, medium, long. The database don’t use index as it finds sequential is faster
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    We should Index • Index Primary key • Index Foreign

    key • Index all columns you would be passing into where clause • Index the keys used to Join tables • Index the date column (if you are going to call it frequent, like rankings of a particular date) • Add partial index to scopes
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    Do not Index • Do not index tables with a

    lot of read, write • Do not index tables you know that will remain small, all through out its life time • Do not index columns where you will be manipulating lot of its values.
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    select * from tasks select * from tags inner join

    tasks_tags on tags.id = tasks_tags.tag_id where tasks_tags.task_id in (1,2,3,..) tasks = Task.find(:all, :include => :tags) 2 Queries Object for each tasks Rails Code
  23. 56.

    Fast Postgres Arrays: tasks = Task.find(:all, :select => "*, array(select

    tags.name from tags inner join tasks_tags on (tags.id = tasks_tags.tag_id) where tasks_tasks.task_id=tasks.id) as tag_names") 1 SQL query Rails doesn't have to create objects >3x faster
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    Database views? Database views are like the view in our

    rails. A rails view(an html page) shows data from multiple model in a single page Similarly we can show data from multiple table as a single table using the concept called views Why would we do that? Because it makes life easier
  25. 59.

    Instead of doing Every time you want the managers SELECT

    id, name, email FROM companies where role=‘manager’
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    CREATE VIEW company_managers AS SELECT id, name, email FROM companies

    WHERE role='manager'; You can create a view And simple do SELECT * FROM company_managers;
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    Note: • A schema of view lives in memory of

    a DB • The result is not stored in memory • Its is actually running our query to get the results • They are called pseudo tables
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    Materialised views are the next evolution of Database views. We

    store the result as well in a table • This was first introduced by Oracle • But now found in PostgreSQL, MicrosoftSQL, IBM DB2, etc. • MySQL doesn’t have it you can create it using open source extensions.
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    How can we use it in Ruby? Thanks to ActiveRecord

    its easy to access such pseudo tables
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    Create a migration to record the Materialised view We need

    a bit of SQL here class CreateAllTimesSalesMatView < ActiveRecord::Migration def up execute <<-SQL CREATE MATERIALIZED VIEW all_time_sales_mat_view AS SELECT sum(amount) as total_sale, DATE_TRUNC('day', invoice_adte) as date_of_sale FROM sales GROUP BY DATE_TRUNC('day', invoice_adte) SQL end def down execute("DROP MATERIALIZED VIEW IF EXISTS all_time_sales_view") end end
  31. 65.

    Create Active Record model I place these views at the

    location app/models/views class AllTimeSalesMatView < ActiveRecord::Base self.table_name = 'all_time_sales_mat_view' def readonly? true end def self.refresh ActiveRecord::Base.connection.execute('REFRESH MATERIALIZED VIEW CONCURRENTLY all_time_sales_mat_view') end end
  32. 67.

    First, Last and Find • They don’t work in your

    view as they operate on your tables primary key and a view doesn’t have it • If you want to use it then you need to one of the fields in your table as primary key class Model < ActiveRecord::Base self.primary_key = :id end
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    Benchmark • I created a table with 1 million random

    sales and random dates in a year. (Dates where bookmarked as well)
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    Take Away • Faster to fetch data. • Capture commonly

    used joins & filters. • Push data intensive processing from Ruby to Database. • Allow fast and live filtering of complex associations or calculation .fields. • We can index various fields in the table.
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    Pain Points • We will be using more RAM and

    Storage • Requires Postgres 9.3 for MatView • Requires Postgres 9.4 to refresh concurrently • Can’t have Live data • You can fix this by creating your own table and 
 updating it with the latest information
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    • Websites with simple HTML and plain javascript based AJAX

    is coming to an end • Its the era of new modern day JS frameworks • JSON is the glue that binds the fronted and our backend • So its natural to find more and more DB supporting the generation and storage of JSON.
  37. 73.

    To convert a single row to JSON select row_to_json(users) from

    users where id = 1 we use row_to_json() method in SQL
  38. 75.

    But for more practical use we write queries like select

    row_to_json(results) from ( select id, email from users ) as results {"id":1,"email":"hsps@redpanthers.co"}
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    A more complex one select row_to_json(result) from ( select id,

    email, ( select array_to_json(array_agg(row_to_json(user_projects))) from ( select id, name from projects where user_id=users.id order by created_at asc ) user_projects ) as projects from users where id = 1 ) result
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    { “id":1,"email":"hsps@redpanthers.co", "project":["id": 3, "name": “CSnipp"] } We did data

    preloading as well, instead of having the need to run another query separate from the first one. We got the data about projects as well.
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    json_build_object • Added in PostgreSQL 9.4 to make JSON creation

    a bit more simpler select json_build_object('foo',1,'bar',2); {"foo": 1, "bar": 2}
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    • For simple JSON creation you can use a gem

    called Surus • https://github.com/jackc/surus
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    Which lets you write code like User.find_json 1 User.find_json 1,

    columns: [:id, :name, :email] Post.find_json 1, include: :author User.find_json(user.id, include: {posts: {columns: [:id, :subject]}}) User.all_json User.where(admin: true).all_json User.all_json(columns: [:id, :name, :email], include: {posts: {columns: [:id, :subject]}}) Post.all_json(include: [:forum, :post])
  44. 83.

    But Like me if you want to keep as much

    stuff as possible in Ruby then. Create a materialised view for your complicated query And then use the gem to generate JSON =)
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    Benchmarks • In our case we saw request to a

    (.json) url which used to take 2 seconds, coming down to <= 200ms • Some benchmarks I found online mentions
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    • PostgreSQL sacrifices speed for durability and reliability • PostgreSQL

    is known for its slow writes and faster readers • It has slow writes as it waits for confirmation that what we inserted has been recorded to the Hard Disk. • You can disable this confirmation check to speed up your inserts if you are inserting a lot of rows every second
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    • Only issue now, is incase your DB crash it

    can’t recover the lost data not saved to Hard Disk • It won’t corrupt the data, but you might loose some rows of your data • Not to be used in cases when you want data integrity to be 100% • Use it where you don’t mind loosing some information or where you can rebuild it from outside your DB. Like logs, or raw information.
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    Postgres Config that we use # How much memory we

    have to cache the database, RAM_FOR_DATABASE * 3/4 effective_cache_size = <%= ram_for_database.to_i * 3/4 %>MB # Shared memory to hold data in RAM, RAM_FOR_DATABASE/4 shared_buffers = <%= ram_for_database.to_i / 3 %>MB # Work memory for queries (RAM_FOR_DATABASE/max_connections) ROUND DOWN 2^x work_mem = <%= 2**(Math.log(ram_for_database.to_i / expected_max_active_connections.to_i)/Math.log(2)).floor %>MB # Memory for vacuum, autovacuum, index creation, RAM/16 ROUND DOWN 2^x maintenance_work_mem = <%= 2**(Math.log(ram_for_database.to_i / 16)/Math.log(2)).floor %>MB # To ensure that we don't lose data, always fsync after commit synchronous_commit = on
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    # Size of WAL on disk, recommended setting: 16 checkpoint_segments

    = 16 # WAL memory buffer wal_buffers = 8MB # Ensure autovacuum is always turned on autovacuum = on # Set the number of concurrent disk I/O operations that PostgreSQL # expects can be executed simultaneously. effective_io_concurrency = 4
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    • Index data so that we don’t end up scanning

    the whole DB • Use arrays for data preloading • Simplify the way you fetch data from the DB using views • Move complicated JSON generation to the Databases • Disable synchronous commit when you feel like it won’t cause a problem
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    Conclusions • Know your tech stack • We should have

    control over all our moving parts • Try to bring about the best with your tech stack before you start throwing more money at it • SQL has been around for 40 years and its planning to say for a while longer =) • There is no golden rule. What worked for me might not work for your specific use case.
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    I blogged about this in detail. • http://blog.redpanthers.co/materialized-views- caching-database-query/ •

    http://blog.redpanthers.co/create-json-response- using-postgresql-instead-rails/ • http://blog.redpanthers.co/different-types-index- postgresql/ • http://blog.redpanthers.co/optimising-postgresql- database-query-using-indexes/
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    Thank you ந"# നnി ਤuਹਾਡਾ ਧ'ਨਵਾਦ ಧನ#$ದ ధన ଥା# େଯାଉ

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