Consider the following example:
CREATE TABLE test(p DateTime, k int) ENGINE MergeTree PARTITION BY toDate(p) ORDER BY k;
INSERT INTO test VALUES ('2020-09-01 00:01:02', 1), ('2020-09-01 20:01:03', 2), ('2020-09-02 00:01:03', 3);
- SELECT count() FROM test WHERE toDate(p) >= '2020-09-01' AND p <= '2020-09-01 00:00:00'
In this case rpn will be (FUNCTION_IN_RANGE, FUNCTION_UNKNOWN (due to strict), FUNCTION_AND)
and for optimize_trivial_count_query we cannot use index if there is at least one FUNCTION_UNKNOWN.
since there is no post processing and return count() based on only the first predicate is wrong.
Before this patch FUNCTION_UNKNOWN was allowed for optimize_trivial_count_query, and the result was wrong.
And two examples above just to show the difference, the behaviour hadn't been changed with this patch:
- SELECT * FROM test WHERE toDate(p) >= '2020-09-01' AND p <= '2020-09-01 00:00:00'
In this case will be (FUNCTION_IN_RANGE, FUNCTION_IN_RANGE (due to non-strict), FUNCTION_AND)
so it will prune everything out and nothing will be read.
- SELECT * FROM test WHERE toDate(p) >= '2020-09-01' AND toUnixTimestamp(p)%5==0
In this case will be (FUNCTION_IN_RANGE, FUNCTION_UNKNOWN, FUNCTION_AND)
and all, two, partitions will be scanned, but due to filtering later none of rows will be matched.
Existing code that looks up marks that match the query has a pathological
case, when most of the part does in fact match the query.
The code works by recursively splitting a part into ranges and then discarding
the ranges that definitely do not match the query, based on primary key.
The problem is that it requires visiting every mark that matches the query,
making the complexity of this sort of look up O(n).
For queries that match exact range on the primary key, we can find
both left and right parts of the range with O(log 2) complexity.
This change implements exactly that.
To engage this optimization, the query must:
* Have a prefix list of the primary key.
* Have only range or single set element constraints for columns.
* Have only AND as a boolean operator.
Consider a table with `(service, timestamp)` as the primary key.
The following conditions will be optimized:
* `service = 'foo'`
* `service = 'foo' and timestamp >= now() - 3600`
* `service in ('foo')`
* `service in ('foo') and timestamp >= now() - 3600 and timestamp <= now`
The following will fall back to previous lookup algorithm:
* `timestamp >= now() - 3600`
* `service in ('foo', 'bar') and timestamp >= now() - 3600`
* `service = 'foo'`
Note that the optimization won't engage when PK has a range expression
followed by a point expression, since in that case the range is not continuous.
Trace query logging provides the following messages types of messages,
each representing a different kind of PK usage for a part:
```
Used optimized inclusion search over index for part 20200711_5710108_5710108_0 with 9 steps
Used generic exclusion search over index for part 20200711_5710118_5710228_5 with 1495 steps
Not using index on part 20200710_5710473_5710473_0
```
Number of steps translates to computational complexity.
Here's a comparison for before and after for a query over 24h of data:
```
Read 4562944 rows, 148.05 MiB in 45.19249672 sec., 100966 rows/sec., 3.28 MiB/sec.
Read 4183040 rows, 135.78 MiB in 0.196279627 sec., 21311636 rows/sec., 691.75 MiB/sec.
```
This is especially useful for queries that read data in order
and terminate early to return "last X things" matching a query.
See #11564 for more thoughts on this.
