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DB 2 08 Predicate Evaluation Summer 2018 Torsten Grust Universitt - PowerPoint PPT Presentation

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01 DB 2 08 Predicate Evaluation Summer 2018 Torsten Grust Universitt Tbingen, Germany 02 1 Q Predicate (or Filter) Evaluation SQL's WHERE / HAVING / FILTER clauses use expressions of type Boolean ( predicates ) to filter

  1. 01 DB 2 08 – Predicate Evaluation Summer 2018 Torsten Grust Universität Tübingen, Germany

  2. 02 1 ┆ Q ₇ — Predicate (or Filter) Evaluation SQL's WHERE / HAVING / FILTER clauses use expressions of type Boolean ( predicates ) to filter rows. Predicates may use Boolean connectives ( AND , OR , NOT ) to build complex filters from simple predicate building blocks: SELECT t.a, t.b FROM ternary AS t WHERE t.a % 2 = 0 AND [ OR ] t.c < 1 -- either AND or OR Evaluate predicate for every row t scanned. Here: assume that evaluation of the predicate is not supported by a specific index. ( ⚠ Index support for predicates is essential → see upcoming chapters.)

  3. 03 Using EXPLAIN on Q ₇ !"#$%&' (!)*+,! ,!$!-. t.a, t.b /)+0 ternary %, t -- 1000 rows 12!)! t.a % 2 = 0 %'3 t.c < 1; ┌───────────────────────────────────────────────────────────────────┐ │ QUERY PLAN │ ├───────────────────────────────────────────────────────────────────┤ | " " | │ Seq Scan on ternary t (cost=… rows=1 …) (actual time=… rows=5 …) │ │ Filter: ((c < '1'::double precision) AND ((a % 2) = 0)) # │ │ Rows Removed by Filter: 995 │ │ Planning time: 2.125 ms $ │ │ Execution time: 1.894 ms │ └───────────────────────────────────────────────────────────────────┘ Filter predicate evaluated during Seq Scan . Estimated selectivity of predicate ¹ / ₁₀₀₀ (real: ⁵ / ₁₀₀₀ ).

  4. 04 t.a % 2 = 0 AND t.c < 1 : An Expression of Type bool In the absence of index support, use the regular expression interpreter to evaluate predicates: SCAN_FETCHSOME(t, [a, c]) SCAN_VAR(c) ───────╮ CONST(1) ────────────╮ FUNCEXPR_STRICT(<, •, •) ─╮ BOOL_AND_STEP_FIRST( •) # if • = >?@AB , immediately yield >?@AB SCAN_VAR(a) ───────╮ # ( ∧ semantics: >?@AB ∧ D = >?@AB ) CONST(2) ────────────╮ FUNCEXPR_STRICT(%, •, •) ─╮ CONST(0) ──────────╮ │ FUNCEXPR_STRICT(=, •, •) ─╮ BOOL_AND_STEP_LAST( •) # yield • ( ∧ semantics: EFGB ∧ D = D ) Uses “jumps” in program to implement Boolean shortcut .

  5. 05 Heuristic Predicate Simplification Predicate evaluation effort is multiplied by the number of rows processed. Even small simplifcations add up. PostgreSQL performs basic predicate simplifications: Reduce constant expressions to true / false . Apply basic identities (e.g., NOT(NOT( D )) ≡ D and ( D AND I ) OR ( D AND J ) ≡ D AND ( I OR J ) ). Remove duplicate clauses (e.g., D AND D ≡ D ) Apply De Morgan's laws. ⚠ These are heuristics (expected to improve evaluation time): selectivity is not yet taken into account.

  6. 06 Machine-Generated Queries and Predicate Simplification Automatically generated SQL text may differ significantly from human-authored queries. Consider a web search form: ┌─────────────────────────────┒ 1. User enters search keys for │ ⮾ Search ternary... ┃ columns M and/or N . ├─────────────────────────────┨ │ ┌─────────────┐ ┃ 2. Web form maps missing keys to │ M : │⌕ 42 ⠄⠄⠄⠄⠄⠄ │ ┃ 'R$$ (interpret as wildcard). │ └─────────────┘ ┃ │ ┌─────────────┐ ┃ 3. DBMS executes parameterized query: │ N : │⌕ ⠄⠄⠄⠄⠄⠄⠄⠄⠄⠄ │ ┃ │ └─────────────┘ ┃ ,!$!-. t.* │ ╭──────╮ ┃ /)+0 ternary AS t *↗ ┃ │ │ SUBMIT 12!)! (t.a = : T +) : T &, 'R$$ ) │ ╰──────╯ ┃ %'3 (t.c = : W +) : W &, 'R$$ ) ┕━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛

  7. 07 Heuristics May Not Be Enough Heuristics only go so far. The (estimated) cost of evaluation may suggest better predicate rewrites: SELECT t.* (expected) N[\] FROM ternary_10m AS t WHERE length (btrim(t.b, '0…9')) < length (t.b) D₁ ┝━━━━━ OR t.a % 1000 <> 0 D₂ ┝━ With Boolean shortcut it makes a difference which disjunct is evaluated first. (Both predicates not selective, *₁ : 85.9%, *₂ : 99.9% of 10 ⁷ rows pass.) ⇒ Many optimizer decisions indeed are cost-based .

  8. 08 2 ┆ Q ₇ — Predicate (or Filter) Evaluation " SELECT t.a, t.b FROM ternary AS t WHERE t.a % 2 = 0 AND [ OR ] t.c < 1 -- either AND or OR MonetDB can evaluate basic predicates on individual column BATs (here: a and c ) ➊ but then needs to 1. derive the result of composite predicates ➋ and 2. propagate the filter effect to all output columns (here: a , b ) ➌ to form the final selection result.

  9. 09 Using EXPLAIN on Q ₇ (Boolean Connective: OR ) sql> !"#$%&' ,!$!-. t.a, t.b /)+0 ternary %, t 12!)! t.a % 2 = 0 +) t.c < 1; ⋮ ternary :bat[:oid] := sql.tid(sql, "sys", "ternary"); a0 :bat[:int] := sql.bind(sql, "sys", "ternary", "a", 0:int); a :bat[:int] := algebra.projection(ternary, a0); e1 :bat[:int] := batcalc.%(a, 2:int); # a % 2 ➊ p1 :bat[:oid] := algebra.thetaselect(e1, 0:int, "=="); # D₁ ≡ a % 2 = 0 c0 :bat[:dbl] := sql.bind(sql, "sys", "ternary", "c", 0:int); c :bat[:dbl] := algebra.projection(ternary, c0); ➊ p2 :bat[:oid] := algebra.thetaselect(c, 1:dbl, "<"); # D₂ ≡ c < 1 ➋ or :bat[:oid] := bat.mergecand(p1, p2); # D₁ ∨ D₂ b0 :bat[:str] := sql.bind(sql, "sys", "ternary", "b", 0:int); ➌ bres :bat[:str] := algebra.projectionpath(or, ternary, b0); # result col b ➌ ares :bat[:int] := algebra.projection(or, a); # result col a ⋮

  10. 10 Result of a Predicate ≡ Selection Vectors ┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈ \hihN]j[k lhN][m\ ┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈    ┊ ┌┄┄┄┄┬────┐ ┊ ┊ ┊ head │ tail │ ┊ ┊ ┝━━━━┽────┤ ┊  | ⋯⋯⋯⋯⋯┊ 0@0 │ 0@0 │  ┌┄┄┄┄┬────┐ │ ┊ 1@0 │ 1@0 │ ┌┄┄┄┄┬────┐ ┊ head │ tail │ mergecand(•,•) ┊ 2@0| 3@0 │ ┊ head │ tail │ ┝━━━━┽────┤ ╱ ╲ └┄┄┄┄┴────┘ ┝━━━━┽────┤ ┊ 0@0 │ 1@0 │   ┊ 0@0 │ 0@0 │ ┊ 1@0 │ 3@0 │⋯⋯ thetaselect(•,0:int,==) thetaselect(•,1:dbl,<) ⋯⋯┊ 1@0 │ 1@0 │ └┄┄┄┄┴────┘ │ │ └┄┄┄┄┴────┘ ┌┄┄┄┄┬────┐ │ │ ┌┄┄┄┄┬────┐ ┊ head │ tail │⋯⋯⟦ a % 2 ⟧ ⟦ c ⟧⋯⋯┊ head │ tail │ ┝━━━━┽────┤ ┝━━━━┽────┤ ┊ 0@0 │ 1 │ ┊ 0@0 │ 0.8 │ ┊ 1@0 │ 0 │ ┊ 1@0 │ 0.9 │ ┊ 2@0 │ 1 │ ┊ 2@0 │ 1.0 │ ┊ 3@0 │ 0 │ ┊ 3@0 │ 1.1 │ └┄┄┄┄┴────┘ └┄┄┄┄┴────┘

  11. 11 Selection Vectors (also: Candidate Lists) Selection vector ~ : BAT of type bat[:oid] . Ä @0 ∈ ~ ⇔ Ä th input row satisfies filter predicate. Use algebra.projection( ~ , WÅÇ ) to propagate filter effect to column WÅÇ . Implement Boolean connectives for predicate Dᵢ with ~ᵢ : D₁ OR D₂ : bat.mergecand( ~₁ , ~₂ ) D₁ AND D₂ : algebra.projectionpath( ~₂ , ~₁ ,•) with algebra.projectionpath( ~₂ , ~₁ ,•) ≡ algebra.projection( ~₂ , algebra.projection( ~₁ ,•)) .

  12. 12 3 ┆ Implementing Selection in Tight Loops Under a layer of C macros, the core of MonetDB's filtering routine A := thetaselect( WÅÇ :bat[:int],  :int, Ñ ) resembles: int thetaselect(int * A , int * WÅÇ , int  , Ñ ) { int SIZE = ‹ ÖGÜáBF à> FàâA äÖ WÅÇ ›; /* input cardinality */ int out = 0; ã[m (int i = 0; i < SIZE; i += 1) { ⎜ jã ( WÅÇ [i] Ñ  ) { /* test filter condition */ ⎜ ⎜ ~ [out] = i; /* build selection vector */ ⎜ ⎜ out += 1; ⎜ ⎣ } ⎣ } return out; /* output cardinality */ }

  13. 13 Instruction Pipelining in Modern CPUs Control flow branches ( for , but particularly if ) are a challenge for modern pipelining CPUs: 1* (write back to register) ┊ 0!0 (read/write memory) ┊ ┊ !" (execute) ┊ ┊ ┊ &3 (instruction decode, branch flag test) ┊ ┊ ┊ ┊ &/ (instruction fetch) ┊ ┊ ┊ ┊ ┊ ⬛⬛ ⬛⬛ ⬛⬛ ⬛⬛ ⬛⬛ # Ä instruction ⬛⬛ ⬛⬛ ⬛⬛ ⬛⬛ ⬛⬛ # Ä＋ 1 ⬛⬛ ⬛⬛ ⬛⬛ ⬛⬛ ⬛⬛ # Ä＋ 2 AE?@@ ( ⬚⬚ ): ins # Ä +3 ⬛⬛ ⬛⬛ ⬛⬛ ⬛⬛ ⬛⬛ ⬚⬚ # Ä＋ 3 cannot fetch its opcode ⬛⬛ ⬛⬛ ⬛⬛ ⬛⬛ ⬛⬛ # Ä＋ 4 ( &3 ) while ins # Ä time accesses memory ( 0!0 )

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