[Enhancement] Constant LIKE/REGEXP pattern is recompiled for every clone of the expression (per scanner and per segment iterator)

Aperta
  • Qualcosa non va?
#69,916 1 commento 0 reazioni 0 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 1 giorno

Nessuno ha ancora preso questa issue.

Valutazione

Difficoltà
4/5
Tempo stimato
3-5 giorni
Idoneità per principianti
45/100
Tipo di issue
Funzionalità
Chiarezza
Abbastanza chiara
Stato di attività
Attiva
Stack tecnologico
cpp, sql
Ambito
databases, performance

Direzione di ricerca

Start from FunctionLike and FunctionRegexpLike in the backend expr code, where THREAD_LOCAL open compiles the pattern. Read VExprContext::clone and SegmentIterator::_construct_compound_expr_context to see per-clone opens. Implement FRAGMENT_LOCAL compilation storing state in FunctionContext, then reuse via hs_clone_scratch. Validate with the provided REGEXP SQL on a many-segment table; done when open phase no longer scales with segments.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Search before asking
  • I had searched in the issues and found no similar issues.
Version

master (checked at 5b2c066b4d), branch-4.1, 4.1.5

What's wrong?

FunctionLike and FunctionRegexpLike build their state, including the Hyperscan database (hs_compile) or the RE2/Boost regex for a constant pattern, only in their THREAD_LOCAL open. VExprContext::clone opens THREAD_LOCAL again for every clone, and a pushed-down predicate is cloned once per scanner and once per segment iterator (SegmentIterator::_construct_compound_expr_context). A scan over N segments therefore compiles the same constant pattern about N times.

A case-insensitive alternation such as (?i)(alpha|bravo|...|juliet)[.](exe|dll)$ takes milliseconds to compile. On a table with about ten thousand segments, the open phase (SegmentIteratorInitTimer in the profile) dominates the query and costs minutes of CPU. Matching itself (ExprFilterEvalTime) stays in the tens of milliseconds.

LikeSearchState::clone, used by LikeColumnPredicate, also runs hs_prepare again for each clone.

What you expected to happen

A constant pattern is compiled once per fragment, and its clones reuse it.

How to reproduce
CREATE TABLE t (k BIGINT, s STRING) DUPLICATE KEY(k) DISTRIBUTED BY HASH(k) BUCKETS 64
PROPERTIES ('replication_num' = '1', 'disable_auto_compaction' = 'true');
-- 60 times:
INSERT INTO t SELECT number, concat('file', cast(number AS STRING), '.exe') FROM numbers('number' = '5000');

SELECT count(*) FROM t WHERE s REGEXP 'file1[.]exe$';
SELECT count(*) FROM t WHERE s REGEXP '(?i)(alpha|bravo|charlie|delta|echo|foxtrot|golf|hotel|india|juliet|file1)[.](exe|dll)$';

With about 3.9k rowsets on one BE, the second query takes about 150 ms against about 72 ms for the first: about 77 ms of compiling. With the change below, both take about 47 ms. The gap grows linearly with the segment count.

Anything else?

Proposed change:

  • In the FRAGMENT_LOCAL open, compile a constant pattern and store it as fragment state. FunctionContext::clone() already shares that state.
  • In the THREAD_LOCAL open, share the fragment's Hyperscan database, which is read-only once compiled, and give each state its own scratch through hs_clone_scratch.
  • Keep compiling RE2 and Boost per state, because RE2 locks its lazily built DFA cache on every search, so one shared RE2 would serialise matching across scanner threads.
  • Non-constant patterns keep the current per-call path.
  • LikeSearchState::clone shares the database the same way.

Scope: this applies to LIKE/REGEXP, the only functions that compile a single pattern with Hyperscan. regexp_count, regexp_replace, regexp_extract and split_by_regexp also compile per THREAD_LOCAL open, but they use RE2. Building an RE2 is much cheaper than hs_compile, and sharing one RE2 across clones would contend on its DFA cache lock, so they are better left compiling per clone.

Related: #68977

Are you willing to submit PR?
  • Yes I am willing to submit a PR!
Code of Conduct
Lingua principale
Java
Stelle
16.2k
Fork
4k
Merge medio
1g 17h
PR unite (30g)
640

Preparare l'ambiente

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Altre issue di apache/doris

Tutte le issue di apache/doris

Issue simili

Altre issue su Java

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.