Commit Graph

7 Commits

Author SHA1 Message Date
Robert Schulze
7b378dbad3
Remove broken lockless variant of re2 2023-09-14 16:40:42 +00:00
Robert Schulze
74937cf27b
Reject DoS-prone hyperscan regexes 2023-02-09 17:17:35 +00:00
Li Yin
4088c0a7f3 Automated function registration
Automated register all functions with below naming convention by
iterating through the symbols:
void DB::registerXXX(DB::FunctionFactory &)
2022-07-29 15:39:50 +08:00
Robert Schulze
ad12adc31c
Measure and rework internal re2 caching
This commit is based on local benchmarks of ClickHouse's re2 caching.

Question 1: -----------------------------------------------------------
Is pattern caching useful for queries with const LIKE/REGEX
patterns? E.g. SELECT LIKE(col_haystack, '%HelloWorld') FROM T;

The short answer is: no. Runtime is (unsurprisingly) dominated by
pattern evaluation + other stuff going on in queries, but definitely not
pattern compilation. For space reasons, I omit details of the local
experiments.

(Side note: the current caching scheme is unbounded in size which poses
a DoS risk (think of multi-tenancy). This risk is more pronounced when
unbounded caching is used with non-const patterns ..., see next
question)

Question 2: -----------------------------------------------------------
Is pattern caching useful for queries with non-const LIKE/REGEX
patterns? E.g. SELECT LIKE(col_haystack, col_needle) FROM T;

I benchmarked five caching strategies:
1. no caching as a baseline (= recompile for each row)
2. unbounded cache (= threadsafe global hash-map)
3. LRU cache (= threadsafe global hash-map + LRU queue)
4. lightweight local cache 1 (= not threadsafe local hashmap with
   collision list which grows to a certain size (here: 10 elements) and
   afterwards never changes)
5. lightweight local cache 2 (not threadsafe local hashmap without
   collision list in which a collision replaces the stored element, idea
   by Alexey)

... using a haystack of 2 mio strings and
A). 2 mio distinct simple patterns
B). 10 simple patterns
C)  2 mio distinct complex patterns
D)  10 complex patterns

Fo A) and C), caching does not help but these queries still allow to
judge the static overhead of caching on query runtimes.

B) and D) are extreme but common cases in practice. They include
queries like "SELECT ... WHERE LIKE (col_haystack, flag ? '%pattern1%' :
'%pattern2%'). Caching should help significantly.

Because LIKE patterns are internally translated to re2 expressions, I
show only measurements for MATCH queries.

Results in sec, averaged over on multiple measurements;

1.A): 2.12
  B): 1.68
  C): 9.75
  D): 9.45

2.A): 2.17
  B): 1.73
  C): 9.78
  D): 9.47

3.A): 9.8
  B): 0.63
  C): 31.8
  D): 0.98

4.A): 2.14
  B): 0.29
  C): 9.82
  D): 0.41

5.A) 2.12 / 2.15 / 2.26
  B) 1.51 / 0.43 / 0.30
  C) 9.97 / 9.88 / 10.13
  D) 5.70 / 0.42 / 0.43
(10/100/1000 buckets, resp. 10/1/0.1% collision rate)

Evaluation:

1. This is the baseline. It was surprised that complex patterns (C, D)
   slow down the queries so badly compared to simple patterns (A, B).
   The runtime includes evaluation costs, but as caching only helps with
   compilation, and looking at 4.D and 5.D, compilation makes up over 90%
   of the runtime!

2. No speedup compared to 1, probably due to locking overhead. The cache
   is unbounded, and in experiments with data sets > 2 mio rows, 2. is
   the only scheme to throw OOM exceptions which is not acceptable.

3. Unique patterns (A and C) lead to thrashing of the LRU cache and very
   bad runtimes due to LRU queue maintenance and locking. Works pretty
   well however with few distinct patterns (B and D).

4. This scheme is tailored to queries B and D where it performs pretty
   good. More importantly, the caching is lightweight enough to not
   deteriorate performance on datasets A and C.

5. After some tuning of the hash map size, 100 buckets seem optimal to
   be in the same ballpark with 10 distinct patterns as 4. Performance
   also does not deteriorate on A and C compared to the baseline.
   Unlike 4., this scheme behaves LRU-like and can adjust to changing
   pattern distributions.

As a conclusion, this commit implementes two things:

1. Based on Q1, pattern search with const needle no longer uses
   caching. This applies to LIKE and MATCH + a few (exotic) other SQL
   functions. The code for the unbounded caching was removed.

2. Based on Q2, pattern search with non-const needles now use method 5.
2022-05-30 20:00:35 +02:00
Robert Schulze
7232f47c68
Fix Bug 37114 - ilike on FixedString(N) columns produces wrong results
The main fix is in MatchImpl.h where the "case_insensitive" parameter is
added to Regexps::get().

Also made "case_insensitive" a non-default template parameter to reduce
the risk of future bugs.

The remainder of this commit are minor random code improvements.

resoves #37114
2022-05-11 14:30:21 +02:00
Artem Zuikov
b3eafc1106 hide symbols in nameless namespace 2020-09-07 21:00:37 +03:00
Alexey Milovidov
466da445e1 Every function in its own file, part 13 2020-05-07 02:21:13 +03:00