1. Introduction to Compilers Jianlin Feng School of Software SUN YAT-SEN UNIVERSITY

2. Computers run 0/1 strings (machine language program) 0010001000000100 0010010000000100 0001011001000010 0011011000000011 1111000000100101 0000000000000101 0000000000000110 0000000000000000 A machine language program that adds two numbers First 4 bits for opcode Last 12 bits for operands Source: Louden and Lambert’s book: Programming Languages

3. Programmers write more readable character strings An assembly language program that adds two numbers, from Louden and Lambert’s book.

4. Even more readable character strings: high-level languages Imperative Languages: specifies HOW  Fortran  ALGOL  PASCAL  C  C++  Java Declarative Languages: specifies WHAT  SQL, ML, Prolog

5. Models of Computation in Languages Underlying most programming languages is a model of computation: Procedural: Fortran (1957) Functional: Lisp (1958) Object oriented: Simula (1967) Logic: Prolog (1972) Relational algebra: SQL (1974) Source: A. V. Aho. Lectures of Programming Languages and Translators

6. Programming Languages Evolve: Java as an Example Java 1.0, 1996  Object-oriented  The language of choice for internet applet programs. Java 8, 2014  Changing computing background: multicore and processing big data.  Java 8 streams support database-queries style of programming  Java 8 incorporates many ideas from functional programming.

7. What is a compiler? A Compiler is a translator  between computers and programmers More generally speaking, a Compiler is a translator between source strings and target strings.  between assembly language and Fortran  between Java and Java Bytecode  between Java and SQL

8. Assembly language vs Fortran Source: Stephen A. Edwards. Lectures of Programming Languages and Translators

9. The Structure of a Compiler 1. Lexical Analysis 2. Syntax Analysis (or Parsing) 3. Semantic Analysis 4. Intermediate Code Generation 5. Code Optimization 6. Code Generation

10. Translation of an assignment statement (1)

11. Translation of an assignment statement (2)

12. Translation of SQL query SELECT S.sname FROM Reserves R, Sailors S WHERE R.sid=S.sid AND R.bid=100 AND S.rating>5 Reserves Sailors sid=sid bid=100 rating > 5 sname Query can be converted to relational algebra Relational Algebra converts to tree, joins form branches Each operator has implementation choices Operators can also be applied in different order!  (sname)  (bid=100  rating > 5) (Reserves  Sailors)

13. Cost-based Query Sub-System Query Parser Query Optimizer Plan Generator Plan Cost Estimator Query Executor Catalog Manager Usually there is a heuristics-based rewriting step before the cost-based steps. Schema Statistics Select * From Blah B Where B.blah = blah Queries

14. Motivating Example Cost: 500+500*1000 I/Os By no means the worst plan! Misses several opportunities:  selections could be`pushed’ down  no use made of indexes Goal of optimization: Find faster plans that compute the same answer. SELECT S.sname FROM Reserves R, Sailors S WHERE R.sid=S.sid AND R.bid=100 AND S.rating>5 Sailors Reserves sid=sid bid=100 rating > 5 sname (Page-Oriented Nested loops) (On-the-fly) Plan:

15. 500,500 IOs Alternative Plans – Push Selects (No Indexes) Sailors Reserves sid=sid bid=100 rating > 5 sname (Page-Oriented Nested loops) (On-the-fly) Sailors Reserves sid=sid rating > 5 sname (Page-Oriented Nested loops) (On-the-fly) bid=100 (On-the-fly) 250,500 IOs

16. Alternative Plans – Push Selects (No Indexes) Sailors Reserves sid=sid rating > 5 sname (Page-Oriented Nested loops) (On-the-fly) bid=100 (On-the-fly) SailorsReserves sid=sid bid = 100 sname (Page-Oriented Nested loops) (On-the-fly) rating > 5 (On-the-fly) 500 + 1000 + 250 + 250*10 250,500 IOs 4250 IOs