Skip to content

Commit

Permalink
Add a blog post for LIKE optimizations
Browse files Browse the repository at this point in the history
  • Loading branch information
xumingming committed Jan 31, 2024
1 parent 280fc86 commit b18effe
Show file tree
Hide file tree
Showing 2 changed files with 151 additions and 0 deletions.
146 changes: 146 additions & 0 deletions website/blog/2024-01-27-like-optimization.mdx
Original file line number Diff line number Diff line change
@@ -0,0 +1,146 @@
---
slug: like
title: "Improve LIKE's performance"
authors: [xumingming]
tags: [tech-blog,performance]
---

## What is LIKE?

<a href="https://prestodb.io/docs/current/functions/comparison.html#like">LIKE</a> is a very useful operation,
it is used to do string pattern matching, the following examples are from Presto doc:

```
SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name)
WHERE name LIKE '%b%'
--returns 'abc' and 'bcd'
SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name)
WHERE name LIKE '_b%'
--returns 'abc'
SELECT * FROM (VALUES ('a_c'), ('_cd'), ('cde')) AS t (name)
WHERE name LIKE '%#_%' ESCAPE '#'
--returns 'a_c' and '_cd'
```

These examples show the basic usage of LIKE:

- Use `%` to match zero or more characters.
- Use `_` to match exactly one character.
- If we need to match `%` and `_` literally, we can specify escape char to escape them.

When we use Velox as the backend to evaluate Presto's query, LIKE operation is translated
into Velox's function call, e.g. `name LIKE '%b%'` is translated to
`like(name, '%b%')`. Internally Velox converts the pattern string into a regular
expression and then uses regular expression library <a href="https://github.com/google/re2">RE2</a>
to do the pattern matching. RE2 is a very good regular expression library, it is fast
and safe which gives Velox LIKE a good performance. But some popularly used simple patterns
can be optimized to use simple C++ string functions to implement directly,
e.g. Pattern `hello%` matches inputs that start with `hello`, which can be implemented by
memory comparing the prefix bytes of inputs:

```
// Match the first 'length' characters of string 'input' and prefix pattern.
bool matchPrefixPattern(
StringView input,
const std::string& pattern,
size_t length) {
return input.size() >= length &&
std::memcmp(input.data(), pattern.data(), length) == 0;
}
```

It is much faster than using RE2, benchmark shows it gives us a 750x speedup. We can do similar
optimizations for some other patterns:

- `%hello`: matches inputs that start with `hello`. It can be optimized by memory comparing the suffix bytes of the inputs.
- `%hello%`: matches inputs that contain `hello`. It can be optimized by using `std::string_view::find` to check whether inputs contain `hello`.

These simple patterns are straightforward to optimize, there are some more
relaxed patterns that are not so straightforward:

- `hello_velox%`: matches inputs that start with 'hello', followed by any character, then followed by 'velox'.
- `%hello_velox`: matches inputs that end with 'hello', followed by any character, then followed by 'velox'.
- `%hello_velox%`: matches inputs that contains both 'hello' and 'velox', and there is a single character separating them.

Although these patterns look similar to previous ones, but they are not so straightforward
to optimize, `_` here matches any single character, we can not simply use memory comparison to
do the matching. And if user's input is not pure ASCII, `_` might match more than one byte which
makes the implementation even more complex. And also note that the patterns above are just for
illustrative purpose, actual patterns can be more complex, e.g. `h_e_l_l_o`, so trivial algorithm
will not work.

## Optimizing Relaxed Patterns

We optimized these patterns as follows. First, we split the patterns into a list of sub patterns, e.g.
`hello_velox%` is split into sub-patterns: `hello`, `_`, `velox`, `%`, because there is
a `%` at the end, we determine it as a `kRelaxedPrefix` pattern, which means we need to some prefix
matching, but it is not a trivial prefix matching, we need to match three sub-patterns:

- kLiteralString: hello
- kSingleCharWildcard: _
- kLiteralString: velox

For `kLiteralString` we simply do a memory comparison:

```
if (subPattern.kind == SubPatternKind::kLiteralString &&
std::memcmp(
input.data() + start + subPattern.start,
patternMetadata.fixedPattern().data() + subPattern.start,
subPattern.length) != 0) {
return false;
}
```

Note that since it is a memory comparison, it handles both pure ASCII inputs and inputs that
contains Unicode characters.

Matching `_` is more complex considering that there are variable length multi-bytes character in
unicode inputs. Fortunately there are existing libraries which provides unicode related operations:
<a href="https://juliastrings.github.io/utf8proc/">utf8proc</a>. It provides functions that tells
us whether a byte in input is the start of a character or not, how many bytes current character
consists of etc. So to match a sequence of `_` our algorithm is:

```
if (subPattern.kind == SubPatternKind::kSingleCharWildcard) {
// Match every single char wildcard.
for (auto i = 0; i < subPattern.length; i++) {
if (cursor >= input.size()) {
return false;
}
auto numBytes = unicodeCharLength(input.data() + cursor);
cursor += numBytes;
}
}
```

Here `cursor` is the index in the input we are trying to match, `unicodeCharLength` is
a function which wraps utf8proc function to determine how many bytes current character consists of,
so the logic is basically repeatedly calculate size of current character and skip it.

It seems not that complex, but we should note that this logic is not effective for pure ASCII input,
for pure ASCII input, every character is one byte, to match a sequence of `_`, we don't need to
calculate the size of each character, don't need the for loop, actually we don't need to explicitly
match `_` for pure ASCII input at all, following is the whole logic for ASCII input:

```
for (const auto& subPattern : patternMetadata.subPatterns()) {
if (subPattern.kind == SubPatternKind::kLiteralString &&
std::memcmp(
input.data() + start + subPattern.start,
patternMetadata.fixedPattern().data() + subPattern.start,
subPattern.length) != 0) {
return false;
}
}
```

It only matches the kLiteralString pattern at the right position of the inputs, `_` is automatically
matched(actually skipped), no need to match it explicitly. With this optimization we get 40x speedup
for kRelaxedPrefix patterns, 100x speedup for kRelaxedSuffix patterns.

Thank you <a href="https://github.com/mbasmanova">Maria Basmanova</a> for spending a lot of time
reviewing the code.
5 changes: 5 additions & 0 deletions website/blog/authors.yml
Original file line number Diff line number Diff line change
Expand Up @@ -54,3 +54,8 @@ raulcd:
url: https://github.com/raulcd
image_url: https://github.com/raulcd.png

xumingming:
name: James Xu
title: Software Engineer @ Alibaba
url: https://github.com/xumingming
image_url: https://github.com/xumingming.png

0 comments on commit b18effe

Please sign in to comment.