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Super Graph Codebase Explained
Super Graph code is made up of a number of packages. We have done our best to keep each package small and focused. Let us begin by looking at some of these packages.
- qcode - GraphQL lexer and parser.
- psql - SQL generator
- serv - HTTP Endpoint, Configs, CLI, etc
- rails - Rails cookie and session store decoders
QCODE
This package contains the core of the GraphQL compiler it handling the lexing and parsing of the GraphQL query transforming it into an internal representation called
QCode.
This is the first step of the compiling process the func NewCompiler(c Config) function creates a new instance of this compiler which has it's own config.
Keep in mind QCode has no knowledge of the Database structure it is designed to be a fast GraphQL parser. Care is taken to keep memory allocations to a minimum.
const (
opQuery
opMutate
...
)
type QCode struct {
Type QType
Selects []Select
...
}
type Select struct {
ID int32
ParentID int32
Args map[string]*Node
Name string
FieldName string
Cols []Column
Where *Exp
OrderBy []*OrderBy
DistinctOn []string
Paging Paging
Children []int32
Functions bool
Allowed map[string]struct{}
PresetMap map[string]string
PresetList []string
}
But before the incoming GraphQL query can be turned into QCode it must first be tokenzied by the lexer lex.go. As the tokenzier walks the bytes of the query it generates tokens item structs which are then consumed by the next step the parser parse.go.
type item struct {
typ itemType
pos Pos
end Pos
}
For exmple a simple query like query getUser { user { id } } will be converted into several tokens like below.
item{itemQuery, 0, 4} // query
item{itemName, 6, 12} // getUser
item{itemObjOpen, 16, 20} // {
...
These tokens are then fed into the parser parse.go the parser does the work of generating an abstract syntax tree (AST) from the tokens. This AST is an internal representation (data structure) and is not exposed outside the package. Since the AST is a tree a stack stack.go is used to walk the tree and generate the QCode AST. The QCode data structure is also a tree (represented as an array). This is then returned to the caller of the compile function.
type Operation struct {
Type parserType
Name string
Args []Arg
Fields []Field
}
type Field struct {
ID int32
ParentID int32
Name string
Alias string
Args []Arg
Children []int32
}
PSQL
This package is responsible for generating Postgres SQL from the QCode AST. There are various GraphQL query types (Query, Mutation, etc). And several more sub types like single root or multi-root queries, various types of mutations (insert, update delete, bulk insert, etc). This package is designed to be able to generate SQL for all of those types.
In addition to QCode variable data is also passed to the compile function within this package. Variables are decoded to derive what is being inserted and what kind of insert is it single or bulk. This information is not available in the GraphQL query its passed in seperatly via variables. This package is able to put all this together and generate the right SQL code.
The entry point of this package is in query.go. The database schema must be passed in the config object when creating a new compiler instance NewCompiler. The functions to extract this schema from the database are also part of this package tables.go. The GetTables functions fetches all the tables from the database and GetColumns fetches columns and relationship information.
func NewCompiler(conf Config) *Compiler {
return &Compiler{conf.Schema, conf.Vars}
}
func (co *Compiler) Compile(qc *qcode.QCode, w io.Writer, vars Variables) (uint32, error) {
switch qc.Type {
case qcode.QTQuery:
return co.compileQuery(qc, w)
case qcode.QTInsert, qcode.QTUpdate, qcode.QTDelete, qcode.QTUpsert:
return co.compileMutation(qc, w, vars)
}
return 0, fmt.Errorf("Unknown operation type %d", qc.Type)
}
GraphQL, input is first converted to QCode.
query {
user {
id
}
posts {
title
}
}
SQL, in reality the generated SQL is far more complex single it has to be very efficient, leverage the power of Postgres, support RBAC (Role based access control) and all of this must be done in a single SQL query.
SELECT users.id, posts.title FROM users, posts;
SERV
The serv package constains most of code that turns the above compiler into an HTTP service. It also includes authentication middleware, remote join resolvers, config parsering, database migrations and seeding commands.
Another big feature that this package handles is the allow.list management code. In production mode parsing the allow list file and registering prepared statements to adding GraphQL queries to this file in development mode.
Currently the following global variables are referrenced across the package. In future I'd prefer to move these into a context struct and pass that around instead.
var (
logger zerolog.Logger // logger for everything but errors
errlog zerolog.Logger // logger for errors includes line numbers
conf *config // parsed config
confPath string // path to the config file
db *pgxpool.Pool // database connection pool
schema *psql.DBSchema // database tables, columns and relationships
qcompile *qcode.Compiler // qcode compiler
pcompile *psql.Compiler // postgres sql compiler
)
Testing
There are several unit tests and benchmark tests parse_test.go) included. There are also scripts included for memory pprof_cpu.sh and cpu pprof_cpu.sh profiling.
// Test to ensure synthetic tables gnerate the correct SQL
func syntheticTables(t *testing.T) {
gql := `query {
me {
email
}
}`
sql := `SELECT json_object_agg('me', json_0) FROM (SELECT row_to_json((SELECT "json_row_0" FROM (SELECT ) AS "json_row_0")) AS "json_0" FROM (SELECT "users"."email" FROM "users" WHERE ((("users"."id") = '{{user_id}}' :: bigint)) LIMIT ('1') :: integer) AS "users_0" LIMIT ('1') :: integer) AS "sel_0"`
resSQL, err := compileGQLToPSQL(gql, nil, "user")
if err != nil {
t.Fatal(err)
}
if string(resSQL) != sql {
t.Fatal(errNotExpected)
}
}
You can run tests within each package or across the entire app. It is usually the fastest to first write a test and then build the feature to satisfy it.
go test -v ./...
Memory profiling can help find where allocations are happining within the package code.
$ cd ./psql
$ ./pprof_mem.sh
goos: darwin
goarch: amd64
pkg: github.com/dosco/super-graph/psql
BenchmarkCompile-8 52567 19401 ns/op 3918 B/op 61 allocs/op
BenchmarkCompileParallel-8 219548 5684 ns/op 3938 B/op 61 allocs/op
PASS
ok github.com/dosco/super-graph/psql 2.582s
Type: alloc_space
Time: Nov 29, 2019 at 11:59pm (EST)
Entering interactive mode (type "help" for commands, "o" for options)
(pprof) top
Showing nodes accounting for 880.59MB, 80.63% of 1092.14MB total
Dropped 33 nodes (cum <= 5.46MB)
Showing top 10 nodes out of 35
flat flat% sum% cum cum%
22MB 2.01% 2.01% 903.57MB 82.73% github.com/dosco/super-graph/qcode.(*Compiler).Compile
0 0% 2.01% 862.98MB 79.02% github.com/dosco/super-graph/psql.BenchmarkCompileParallel.func1
0 0% 2.01% 862.98MB 79.02% testing.(*B).RunParallel.func1
461.95MB 42.30% 44.31% 760.53MB 69.64% github.com/dosco/super-graph/qcode.(*Compiler).compileQuery
396.63MB 36.32% 80.63% 396.63MB 36.32% github.com/dosco/super-graph/util.NewStack
0 0% 80.63% 252.07MB 23.08% github.com/dosco/super-graph/qcode.(*Compiler).compileArgs
0 0% 80.63% 228.15MB 20.89% testing.(*B).runN
0 0% 80.63% 227.63MB 20.84% github.com/dosco/super-graph/psql.BenchmarkCompile
0 0% 80.63% 227.63MB 20.84% testing.(*B).launch
0 0% 80.63% 187.04MB 17.13% github.com/dosco/super-graph/psql.(*Compiler).Compile
Benchmarking
Most packages contain benchmark tests to ensure new features don't introduce a significant regression to performance.
$ cd ./psql
$ go test -v -run=xx -bench=.
goos: darwin
goarch: amd64
pkg: github.com/dosco/super-graph/psql
BenchmarkCompile-8 60775 19076 ns/op 3919 B/op 61 allocs/op
BenchmarkCompileParallel-8 207847 5172 ns/op 3937 B/op 61 allocs/op
PASS
ok github.com/dosco/super-graph/psql 2.530s
Reach out
If you'd like me to explain other parts of the code please reach out over Twitter or Discord. I'll keep adding to this doc as I get time.