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Bringing PostgreSQL closer to your Application Layer

4553a14dba1de0cb759092543a124e32?s=47 HUSSTECH
February 11, 2018

Bringing PostgreSQL closer to your Application Layer

Introducing SQLpy a lightweight tool to help you organise SQL queries and data access in Python projects. https://sqlpy.readthedocs.io

4553a14dba1de0cb759092543a124e32?s=128

HUSSTECH

February 11, 2018
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  1. Bringing PostgreSQL closer to your Application Layer 1

  2. I used to work at an Investment Bank 2

  3. ...definitely not this! 3

  4. ...more like this 4

  5. ...specifically 5

  6. Hi I’m Huss Co-founder & CTO @ 9fin Investment Banking

    Technology • Equities Trading • Bespoke Trader Desk tools • Risk and Control systems Aeronautical Engineering • Lots of aeroplane stuff • .. But no Databases! :( 9fin.com @9finHQ angel.co/9fin 6 @HUSSTECH
  7. Good choice! Where did it go? DBAs and Devs!? Never

    the twain shall meet 7 Bringing PostgreSQL closer to your Application Layer
  8. Software Archeology • BigCo EE: Silo groups with big databases

    • “Fat Client” architecture • 3 tier relatively recent ◦ Typically endpoints on enterprise message bus ◦ SOAP! 8
  9. BigCo Org Structure • Hierarchical • Long communication and decision

    chains • “shared services” idea emerges 2008+ • DBAs and Sysadmins moved here ◦ Away from business aligned teams • Now hard to diagnose issues quickly ◦ Everyone is not sitting together anymore 9
  10. WebCo 2.0 • Flatter structure - mixed teams, practitioners of

    many technologies • Databases are out-of-the-box “good enough” ◦ Heroku, RDS, et al • The DBA function merged into Sr. Devs usually • Explosion in dynamic languages with rich tooling for web development 10
  11. Object Relational Mapping • Model your entities using an ORM

    library for your application • Takes care of data retrieval and persistence • Great for bootstrapping ◦ Focus on app code not storage ◦ Strong control of your business objects • Rich libraries in many languages 11
  12. That awkward moment when you need SQL • At some

    point you’re going to want to take control of the query generation ◦ Performance tune ◦ Force an index use ◦ Use advanced DB features ▪ WITH RECURSIVE, Subqueries/CTEs, PARTITION OVER, JSON • Raw SQL support in ORMs varies Example: Cumulative total of revenue made at a fictional DVD rental business? 12 from flask import Flask from flask_sqlalchemy import SQLAlchemy from sqlalchemy.sql import func # setup SQLAlchemy and model object app = Flask(__name__) app.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql://xxx' db = SQLAlchemy(app) class Payment(db.Model): __tablename__ = 'payment' payment_id = db.Column(db.Integer, nullable=False, primary_key=True) rental_id = db.Column(db.SmallInteger, nullable=False) amount = db.Column(db.Numeric(scale=(4, 2), asdecimal=True), nullable=False) payment_date = db.Column(db.DateTime, nullable=False)
  13. That awkward moment when you need SQL 13 # set

    up two query objects one for a subquery one for the window function subquery = db.session.query(func.DATE(Payment.payment_date).label('payment_date'), func.sum(Payment.amount).label('amount') ).group_by(func.DATE(Payment.payment_date))\ .order_by(func.DATE(Payment.payment_date))\ .subquery('s') window = db.session.query(subquery.c.payment_date, subquery.c.amount, func.sum(subquery.c.amount).over( order_by=subquery.c.payment_date) ).select_entity_from(subquery).order_by(subquery.c.payment_date) • 10+ (readable) lines • This is quite nice syntax actually • But the semantics are halfway between Python object orientation and SQL declarative style How to do the same in SQL? SELECT payment_date, amount, sum(amount) OVER (ORDER BY payment_date) FROM ( SELECT CAST(payment_date AS DATE) AS payment_date, SUM(amount) AS amount FROM payment GROUP BY CAST(payment_date AS DATE)) p ORDER BY payment_date;
  14. SQLpy - a (not) new idea • Write SQL *next*

    to your application code • No query strings dotted all over source files • A light wrapper around your DB2.0 API • Bases on YeSQL by Kris Jenkins 14
  15. SQLpy + Models • Still like linking behaviours and actions

    via models? • Use the DAO pattern - define the models how you want ◦ Add extra methods to serialize or validate 15 class Hello(object): def __init__(self, queryfunc, cur, args): self._query = queryfunc self._result = self.result(cur, args) @property def result(self): return self._result @result.setter def result(self, cur, args): self._result = self._query(cur, 0, args) def refresh(self, cur, args): return self._query(cur, 0, args) def jsonout(self): return json.dumps(self.result)
  16. SQLpy + Functions • Prefer your app only make calls

    to stored procedures? • At least the list of sprocs used is all in one place 16 -- name: cool_proc SELECT public.cool_proc(); -- name: cooler_proc SELECT public.cooler_proc(%s, %s); sql = sqlpy.Queries('queries.sql') .... resultsA = sql.COOL_PROC(cur, 0) resultsB = sql.COOLER_PROC(cur, 0, args)
  17. SQLpy + Composition • Constructing SQL queries is legitimately dangerous!

    • How to do this in SQLpy, but not destroy the valid SQL syntax in queries file? • BUILT SQL ◦ Clauses added based on parameters passed to wrapping function ◦ Transparent and lightweight • Query still passes through the DB2.0 API sanitiser for safe execution 17
  18. SQLpy + Composition • Constructing SQL queries is legitimately dangerous!

    • How to do this in SQLpy, but not destroy the valid SQL syntax in queries file? • BUILT SQL ◦ Clauses added based on parameters passed to wrapping function ◦ Transparent and lightweight • Query still passes through the DB2.0 API sanitiser for safe execution 18 -- name: built_sql_statement$ -- a built up sql statement SELECT * FROM hello WHERE 1=1 AND id = %(id_low)s OR id = %(id_high)s AND message = %(msg)s; sql = sqlpy.Queries('queries.sql') .... kwargs = {'id_low': 1} results = sql.BUILT_SQL_STATEMENT(cur, 0, **kwargs) SELECT * FROM hello WHERE 1=1 AND id = 1;
  19. SQLpy + Your SQL • Essentially this is the one

    central idea behind SQLpy • Let’s play with PostgreSQL ◦ Correlate pg_stat_statements with the queries in SQLpy ◦ Because you know what was logged (pg_stat_statements) and what was run (sqlpy) ◦ See how your database is actually being used ◦ Critical data for indexing and optimization decisions 19 SQLpy pg_stat_statements REGEX All the query context!
  20. sqlpy.readthedocs.io • github.com/9fin/sqlpy (MIT) • Made for PostgreSQL first ◦

    Other DBs working in theory, but not tested! • Still <v1.0 ◦ ...so critical comments and other contributions welcome • Check out other ports for YeSQL ◦ Js, Go, C#, Ruby, Erlang, Clojure, PHP ◦ github.com/krisajenkins/yesql/#othe r-languages 20
  21. Thank You! References and Credits [slide#] [3] image: By Raiffeisenverband

    Salzburg reg. Gen. m. b. H., Schwarzstr. 13-15, 5024 Salzburg - Raiffeisenverband Salzburg reg. Gen. m. b. H., Schwarzstr. 13-15, 5024 Salzburg (transferred from de:Image:RVS Handelsraum.jpg), CC BY 2.0 at, https://commons.wikimedia.org/w/index.php?curid=5009720 [4] image: CC0 license [5] image: An unnamed colleague 21