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Hassan Abedi
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@ -18,41 +18,81 @@ The operators are: atom scan, semijoin, and natural join.
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### Example
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`Q(X) :- edge(X, X), labeled(X).` (labeled self-loops):
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The rule below returns the authors of every bestseller along with the book's price.
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It uses all three operators: `scan_atom` for the three input tables, `semijoin` to keep only authors of bestsellers, and `natural_join` to attach each
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book's price.
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```text
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Q(name, book, dollars) :- author(name, book), bestseller(book), price(book, dollars).
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```
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```rust
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use query_ops::atom::{AtomPattern, Term, scan_atom};
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use query_ops::join::semijoin;
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use query_ops::join::{natural_join, semijoin};
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use query_ops::table::Table;
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use query_ops::value::Value;
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fn s(x: &str) -> Value {
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Value::Str(x.to_string())
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}
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fn i(x: i64) -> Value {
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Value::Int(x)
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}
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fn main() {
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let edge = Table::from_rows(
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let author = Table::from_rows(
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2,
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vec![
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vec![Value::Int(1), Value::Int(2)],
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vec![Value::Int(3), Value::Int(3)], // self-loop on 3
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vec![Value::Int(2), Value::Int(2)], // self-loop on 2
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vec![s("Alice"), s("Foo")],
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vec![s("Bob"), s("Bar")],
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vec![s("Alice"), s("Baz")],
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vec![s("Carol"), s("Qux")],
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],
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);
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let bestseller = Table::from_rows(1, vec![vec![s("Foo")], vec![s("Baz")]]);
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let price = Table::from_rows(
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2,
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vec![
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vec![s("Foo"), i(25)],
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vec![s("Bar"), i(15)],
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vec![s("Baz"), i(30)],
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vec![s("Qux"), i(20)],
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],
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);
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let labeled = Table::from_rows(1, vec![vec![Value::Int(2)]]);
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let self_loops = scan_atom(
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&edge,
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let author_rel = scan_atom(
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&author,
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&AtomPattern {
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columns: vec![Term::Var("X".to_string()), Term::Var("X".to_string())],
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columns: vec![Term::Var("name".to_string()), Term::Var("book".to_string())],
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},
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);
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let labeled_x = scan_atom(
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&labeled,
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let bestseller_rel = scan_atom(
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&bestseller,
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&AtomPattern {
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columns: vec![Term::Var("X".to_string())],
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columns: vec![Term::Var("book".to_string())],
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},
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);
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let price_rel = scan_atom(
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&price,
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&AtomPattern {
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columns: vec![Term::Var("book".to_string()), Term::Var("dollars".to_string())],
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},
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);
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let result = semijoin(&self_loops, &labeled_x);
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assert_eq!(result.columns, vec!["X".to_string()]);
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assert_eq!(result.rows, vec![vec![Value::Int(2)]]);
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let authors_of_bestsellers = semijoin(&author_rel, &bestseller_rel);
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let result = natural_join(&authors_of_bestsellers, &price_rel);
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assert_eq!(
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result.columns,
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vec!["name".to_string(), "book".to_string(), "dollars".to_string()],
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);
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assert_eq!(
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result.rows,
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vec![
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vec![s("Alice"), s("Foo"), i(25)],
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vec![s("Alice"), s("Baz"), i(30)],
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],
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);
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}
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```
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@ -1,77 +1,91 @@
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//! Hand-written query plans composed from `scan_atom`, `semijoin`, and `natural_join`.
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//! Hand-written query plan composed from `scan_atom`, `semijoin`, and `natural_join`.
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//!
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//! Schema:
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//! - `edge(src, dst)`: directed edges
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//! - `labeled(node)`: a set of labeled nodes
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//! - `author(name, book)`: who wrote each book
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//! - `bestseller(book)`: the set of bestseller titles
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//! - `price(book, dollars)`: price of each book
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//!
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//! Two rules are executed against the same fixture:
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//! - `Q1(X) :- edge(X, X), labeled(X).` (labeled self-loops)
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//! - `Q2(X, Y) :- edge(X, Y), labeled(Y).` (edges whose destination is labeled)
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//! Rule:
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//! - `Q(name, book, dollars) :- author(name, book), bestseller(book), price(book, dollars).`
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//! ("Authors of bestsellers along with each book's price.")
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//!
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//! The plan first scans each input table, then narrows `author` to authors of
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//! bestsellers via a semijoin against `bestseller`, then attaches each book's
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//! price via a natural join against `price`.
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use query_ops::atom::{scan_atom, AtomPattern, Term};
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use query_ops::atom::{AtomPattern, Term, scan_atom};
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use query_ops::join::{natural_join, semijoin};
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use query_ops::table::Table;
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use query_ops::value::Value;
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fn var(name: &str) -> Term {
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Term::Var(name.to_string())
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fn s(x: &str) -> Value {
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Value::Str(x.to_string())
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}
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fn int(value: i64) -> Value {
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Value::Int(value)
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fn i(x: i64) -> Value {
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Value::Int(x)
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}
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#[test]
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fn labeled_self_loops_and_edges_into_labeled_nodes() {
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let edge = Table::from_rows(
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fn authors_of_bestsellers_with_price() {
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let author = Table::from_rows(
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2,
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vec![
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vec![int(1), int(2)],
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vec![int(2), int(3)],
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vec![int(3), int(3)],
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vec![int(4), int(1)],
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vec![int(2), int(2)],
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vec![s("Alice"), s("Foo")],
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vec![s("Bob"), s("Bar")],
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vec![s("Alice"), s("Baz")],
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vec![s("Carol"), s("Qux")],
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],
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);
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let labeled = Table::from_rows(1, vec![vec![int(2)], vec![int(3)]]);
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let self_loops = scan_atom(
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&edge,
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&AtomPattern {
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columns: vec![var("X"), var("X")],
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},
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);
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let labeled_x = scan_atom(
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&labeled,
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&AtomPattern {
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columns: vec![var("X")],
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},
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);
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let q1 = semijoin(&self_loops, &labeled_x);
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assert_eq!(q1.columns, vec!["X".to_string()]);
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assert_eq!(q1.rows, vec![vec![int(3)], vec![int(2)]]);
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let edge_xy = scan_atom(
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&edge,
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&AtomPattern {
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columns: vec![var("X"), var("Y")],
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},
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);
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let labeled_y = scan_atom(
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&labeled,
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&AtomPattern {
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columns: vec![var("Y")],
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},
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);
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let q2 = natural_join(&edge_xy, &labeled_y);
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assert_eq!(q2.columns, vec!["X".to_string(), "Y".to_string()]);
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assert_eq!(
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q2.rows,
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let bestseller = Table::from_rows(1, vec![vec![s("Foo")], vec![s("Baz")]]);
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let price = Table::from_rows(
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2,
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vec![
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vec![int(1), int(2)],
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vec![int(2), int(3)],
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vec![int(3), int(3)],
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vec![int(2), int(2)],
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vec![s("Foo"), i(25)],
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vec![s("Bar"), i(15)],
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vec![s("Baz"), i(30)],
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vec![s("Qux"), i(20)],
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],
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);
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let author_rel = scan_atom(
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&author,
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&AtomPattern {
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columns: vec![Term::Var("name".to_string()), Term::Var("book".to_string())],
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},
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);
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let bestseller_rel = scan_atom(
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&bestseller,
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&AtomPattern {
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columns: vec![Term::Var("book".to_string())],
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},
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);
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let price_rel = scan_atom(
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&price,
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&AtomPattern {
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columns: vec![
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Term::Var("book".to_string()),
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Term::Var("dollars".to_string()),
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],
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},
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);
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let authors_of_bestsellers = semijoin(&author_rel, &bestseller_rel);
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let result = natural_join(&authors_of_bestsellers, &price_rel);
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assert_eq!(
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result.columns,
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vec![
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"name".to_string(),
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"book".to_string(),
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"dollars".to_string()
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],
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);
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assert_eq!(
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result.rows,
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vec![
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vec![s("Alice"), s("Foo"), i(25)],
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vec![s("Alice"), s("Baz"), i(30)],
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],
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);
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}
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