![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
Adam Retter
adam@evolvedbinary.com
Declarative Amsterdam @ Online
2020-10-09
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@adamretter
Compiling XQuery
to
Native Machine Code
About Me
-
Director and CTO of Evolved Binary
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XQuery / XSLT / Schema / RelaxNG
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Scala / Java / C++ / Rust*
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Concurrency and Scalability
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Creator of FusionDB multi-model database (5 yrs.)
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Contributor to Facebook's RocksDB (5 yrs.)
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Core contributor to eXist-db XML Database (15 yrs.)
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Founder of EXQuery, and creator of RESTXQ
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Was a W3C XQuery WG Invited expert
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/6607296/eXist-book-logo-red.jpg)
Framing the Problem
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FusionDB inherited eXist-db's XQuery Engine
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Several bugs/issues
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Especially: Context Item and Expression Precedence
-
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EOL - Parser implemented in Antlr 2 (1997-2005)
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Mixes concerns - Grammar with Java inlined in same file
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Hard to optimise - lack of clean AST for transform/rewrite
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Hard to maintain - lack of docs / support
-
-
We made some small incremental improvements
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Performance Mismatch
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XQuery Engine is high-level (Java)
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Operates over an inefficient DOM abstraction
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FDB Core Storage is low-level (C++)
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![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
Resolving the Problem
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We need/want a new XQuery Engine for FusionDB
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Must be able to support all XQuery Grammars
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Query, Full-Text, Update, Scripting, etc.
-
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Must offer excellent error reporting with context
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Where did the error occur?
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Why did it occur?
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Should suggest possible fixes (e.g. Rust Compiler)
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Must be able to exploit FDB storage performance
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Should exploit modern techniques
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Should be able to utilise modern hardware
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Would like a clean AST without inline code
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Enables AST -> AST transformations (Rewrite optimisation)
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Enables Target Independence
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![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
Anatomy of an XQuery Engine
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/7800274/query-compilation.png)
Parser Concerns
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Many Different Algorithms
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LR - LALR / GLR
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LL - (Predictive) Recursive Descent / ALL(*) / PEG
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Others - Earley / Leo-Earley / Pratt / Combinators / Marpa
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Implementation trade-offs
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Parser Generators, e.g. Flex/Bison
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Pro - Speed of prototyping/impl.
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Cons - Not just EBNF - Learn additional Grammar Syntax / Poor Error Messages
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Parser Combinators, e.g. FastParse
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Pro - Speed of prototyping/impl.
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Con - Defined in code
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Bespoke
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Pro - Complete Control
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Con - Huge amount of work
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![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
Our New XQuery Parser
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Bespoke
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We really care about error messages
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We really care about performance
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We wanted a clean AST
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Can also produce a CST
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Separate Lexer and Parser
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Lexer is Intelligent
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Data type conversion, e.g.
IntegerLiteral
-> native integer type -
Recognises Sub-tokens, e.g.
PredefinedEnityRef
within StringLiteral
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Predictive Recursive Descent
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Implemented in Rust
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
Our New XQuery Compiler
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Mostly it's just - LLVM!
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Previously called: Low Level Virtual Machine
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Open Source set of compiler and toolchain technologies
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Front-end for language and Back-end for target
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Separated by IR (Intermediate Representation) code
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![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/7800892/llvm-arch.png)
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Our New XQuery Parser produces an AST
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Our Language Front-end generates IR from the AST
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Anatomy of our New XQuery Engine
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/7800925/query-compilation-llvm.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
declare function local:fib($num as xs:integer) as xs:integer {
if ($num = 0) then
0
else if ($num = 1) then
1
else
local:fib($num - 1) + local:fib($num - 2)
};
local:fib(33)
Computing nth Fibonacci Number
- XQuery Code
Computing nth Fibonacci Number
- Lexer Output
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
DeclareWord(TokenContext(Extent(0, 7), Location(1, 1)))
FucntionWord(TokenContext(Extent(8, 16), Location(1, 9)))
QName(TokenContextWithSubTokens { token_context: TokenContext(Extent(17, 26), Location(1, 18)), sub_tokens: [NCName(TokenContext(Extent(17, 22), Location(1, 18))), NCName(TokenContext(Extent(23, 26), Location(1, 24)))] })
LeftParenChar(TokenContext(Extent(26, 27), Location(1, 27)))
DollarSignChar(TokenContext(Extent(27, 28), Location(1, 28)))
NCName(TokenContext(Extent(28, 31), Location(1, 29)))
NCName(TokenContext(Extent(32, 34), Location(1, 33)))
QName(TokenContextWithSubTokens { token_context: TokenContext(Extent(35, 45), Location(1, 36)), sub_tokens: [NCName(TokenContext(Extent(35, 37), Location(1, 36))), NCName(TokenContext(Extent(38, 45), Location(1, 39)))] })
RightParenChar(TokenContext(Extent(45, 46), Location(1, 46)))
NCName(TokenContext(Extent(47, 49), Location(1, 48)))
QName(TokenContextWithSubTokens { token_context: TokenContext(Extent(50, 60), Location(1, 51)), sub_tokens: [NCName(TokenContext(Extent(50, 52), Location(1, 51))), NCName(TokenContext(Extent(53, 60), Location(1, 54)))] })
LeftCurlyBracketChar(TokenContext(Extent(61, 62), Location(1, 62)))
IfWord(TokenContext(Extent(75, 77), Location(2, 13)))
LeftParenChar(TokenContext(Extent(78, 79), Location(2, 16)))
DollarSignChar(TokenContext(Extent(79, 80), Location(2, 17)))
NCName(TokenContext(Extent(80, 83), Location(2, 18)))
EqualsSignChar(TokenContext(Extent(84, 85), Location(2, 22)))
IntegerLiteral(TokenContextWithBigUint { token_context: TokenContext(Extent(86, 87), Location(2, 24)), value: BigUint { data: [] } })
RightParenChar(TokenContext(Extent(87, 88), Location(2, 25)))
ThenWork(TokenContext(Extent(89, 93), Location(2, 27)))
IntegerLiteral(TokenContextWithBigUint { token_context: TokenContext(Extent(110, 111), Location(3, 17)), value: BigUint { data: [] } })
ElseWord(TokenContext(Extent(124, 128), Location(4, 13)))
IfWord(TokenContext(Extent(129, 131), Location(4, 18)))
LeftParenChar(TokenContext(Extent(132, 133), Location(4, 21)))
DollarSignChar(TokenContext(Extent(133, 134), Location(4, 22)))
NCName(TokenContext(Extent(134, 137), Location(4, 23)))
EqualsSignChar(TokenContext(Extent(138, 139), Location(4, 27)))
IntegerLiteral(TokenContextWithBigUint { token_context: TokenContext(Extent(140, 141), Location(4, 29)), value: BigUint { data: [1] } })
RightParenChar(TokenContext(Extent(141, 142), Location(4, 30)))
ThenWork(TokenContext(Extent(143, 147), Location(4, 32)))
IntegerLiteral(TokenContextWithBigUint { token_context: TokenContext(Extent(164, 165), Location(5, 17)), value: BigUint { data: [1] } })
ElseWord(TokenContext(Extent(178, 182), Location(6, 13)))
QName(TokenContextWithSubTokens { token_context: TokenContext(Extent(199, 208), Location(7, 17)), sub_tokens: [NCName(TokenContext(Extent(199, 204), Location(7, 17))), NCName(TokenContext(Extent(205, 208), Location(7, 23)))] })
LeftParenChar(TokenContext(Extent(208, 209), Location(7, 26)))
DollarSignChar(TokenContext(Extent(209, 210), Location(7, 27)))
NCName(TokenContext(Extent(210, 213), Location(7, 28)))
HyphenMinusChar(TokenContext(Extent(214, 215), Location(7, 32)))
IntegerLiteral(TokenContextWithBigUint { token_context: TokenContext(Extent(216, 217), Location(7, 34)), value: BigUint { data: [1] } })
RightParenChar(TokenContext(Extent(217, 218), Location(7, 35)))
PlusSignChar(TokenContext(Extent(219, 220), Location(7, 37)))
QName(TokenContextWithSubTokens { token_context: TokenContext(Extent(221, 230), Location(7, 39)), sub_tokens: [NCName(TokenContext(Extent(221, 226), Location(7, 39))), NCName(TokenContext(Extent(227, 230), Location(7, 45)))] })
LeftParenChar(TokenContext(Extent(230, 231), Location(7, 48)))
DollarSignChar(TokenContext(Extent(231, 232), Location(7, 49)))
NCName(TokenContext(Extent(232, 235), Location(7, 50)))
HyphenMinusChar(TokenContext(Extent(236, 237), Location(7, 54)))
IntegerLiteral(TokenContextWithBigUint { token_context: TokenContext(Extent(238, 239), Location(7, 56)), value: BigUint { data: [2] } })
RightParenChar(TokenContext(Extent(239, 240), Location(7, 57)))
RightCurlyBracketChar(TokenContext(Extent(249, 250), Location(8, 9)))
SemicolonChar(TokenContext(Extent(250, 251), Location(8, 10)))
QName(TokenContextWithSubTokens { token_context: TokenContext(Extent(260, 269), Location(9, 9)), sub_tokens: [NCName(TokenContext(Extent(260, 265), Location(9, 9))), NCName(TokenContext(Extent(266, 269), Location(9, 15)))] })
LeftParenChar(TokenContext(Extent(269, 270), Location(9, 18)))
IntegerLiteral(TokenContextWithBigUint { token_context: TokenContext(Extent(270, 272), Location(9, 19)), value: BigUint { data: [33] } })
RightParenChar(TokenContext(Extent(272, 273), Location(9, 21)))
Computing nth Fibonacci Number
- Parser Output
(1 of 2)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/7801731/abstract-syntax-tree-part1.png)
Computing nth Fibonacci Number
- Parser Output
(2 of 2)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/7801730/abstract-syntax-tree-part2.png)
Generating IR for LLVM
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We use a Visitor Pattern over our AST
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LLVM provides an API (C++)
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Various bindings for other languages
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Inkwell(s) for Rust looks promising
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Starting point is the
IRBuilder
class -
IRBuilder
adds instructions to yourModule
class -
The
NamedValues
class holds function parameters, variables, etc.
-
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LLVM provides many possible Optimisations for IR
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See: https://llvm.org/docs/Passes.html
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Of particular interest (for XQuery):
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Tail Call Elimination
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Dead Code/Argument/Type/Global/Loop Elimination
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![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
Computing nth Fibonacci Number - IR
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
;; defines our fibonacci function as local_fib
;; t1 = $num
define i64 @"local_fib"(i64 %".1")
{
local_fib_body:
;; t3 = ($num = 0)
%".3" = icmp eq i64 %".1", 0
;; if t3 then goto local_fib_body.ifbody1 else goto local_fib_body.ifelsebody1
br i1 %".3", label %"local_fib_body.ifbody1", label %"local_fib_body.ifelsebody1"
local_fib_body.ifbody1:
;; return 0
ret i64 0
local_entry.ifelsebody1:
;; t4 = ($num = 1)
%".4" = icmp eq i64 %".1", 1
;; if t4 then goto local_fib_body.ifbody2 else goto local_fib_body.ifelsebody2
br i1 %".4", label %"local_fib_body.ifbody2", label %"local_fib_body.ifelsebody2"
local_fib_body.ifbody2:
;; return 1
ret i64 1
local_entry.ifelsebody2:
;; t6 = ($num - 1)
%".6" = sub i64 %".1", 1
;; t7 = ($num - 2)
%".7" = sub i64 %".1", 2
;; t8 = local_fib( t6 ) ; ie local_fib( n - 1 )
%".8" = call i64 (i64) @"local_fib"(i64 %".6")
;; t9 = local_fib( t7 ) ; ie local_fib( n - 2 )
%".9" = call i64 (i64) @"local_fib"(i64 %".7")
;; t10 = (t8 + t9)
%".10" = add i64 %".8", %".9"
;; return t10
ret i64 %".10"
}
Where we are at today...
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Still early stage!
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Lexer is OK
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Parser is in-progress
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We have experimental IR Generation and JIT
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Lots still to do...
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Thinking about the future...
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LLVM can target WebAssembly
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Then we can run/call XQuery from Web-browser and Node.js
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LLVM can target the GPU
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![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/1461650/Screen_Shot_2015-06-05_at_13.12.39.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/119594/images/6233248/FusionDB_transparent_no-text.png)
fusiondb.com
Compiling XQuery to Native Machine Code
By Adam Retter
Compiling XQuery to Native Machine Code
Presentation given for Declarative Amsterdam - 9 October 2020 - Online
- 1,500