we have some prunning path logical in cascades framework. However it do not work as we expected. if we do prunning on one Group, then maybe we need to do thousands of times optimization on its parent without any success result. This PR remove these prunning provisionally. We will add prunning back when we re-design it.
If BE crashed the error would be logged, and the analysis task would be mark as finished, which is incorrect.
In this PR, update analysis task according to the query state
1. Support mutli table for routine load
2. Multi-table dynamic setting table information
3. Add multi-table syntax rules
4. Add new multi-table execution plan
in join node, if it's broadcast_join
and shared hash table, some counter/timer about build hash table is useless,
so we could add those counter/timer in faker profile, and those will not display in web profile.
The precision handling for division with DECIMALV3 is as follows (excluding cases where division increases precision):
(p1, s1) / (p2, s2) ----> (p1 + s2, s1)
However, due to precision loss in division, it is considered to increase the precision of the left operand:
(p1, s1) / (p2, s2) =====> (p1 + s2, s1 + s2) / (p2, s2) ----> (p1 + s2, s1)
However, the legacy optimizer repeats the analyze and substitute steps for an expression, which can result in the accumulation of precision:
(p1, s1) / (p2, s2) =====> (p1 + s2, s1 + s2) / (p2, s2) =====> (p1 + s2 + s2, s1 + s2 + s2) / (p2, s2)
To address this, the previous approach was to forcibly convert the left operand of DECIMALV3 calculations. This results in rewriting the expression as:
(p1, s1) / (p2, s2) =====> cast((p1, s1) as (p1 + s2, s1 + s2)) / (p2, s2)
Then, during the substitution step, a check is performed. If it is a cast expression, the expression modified by the cast is extracted:
cast((p1, s1) as (p1 + s2, s1 + s2)) =====> (p1, s1)
protected Expr substituteImpl(ExprSubstitutionMap smap, ExprSubstitutionMap disjunctsMap, Analyzer analyzer) {
if (isImplicitCast()) {
return getChild(0).substituteImpl(smap, disjunctsMap, analyzer);
}
This way, there won't be repeated analysis, preventing the continuous increase in precision. However, if the left expression is a constant (literal), theoretically, the precision would continue to increase. Unfortunately, the code that was removed in this PR (#19926) obscured this issue.
for (Expr child : children) {
if (child instanceof DecimalLiteral && child.getType().isDecimalV3()) {
((DecimalLiteral)child).tryToReduceType();
}
}
An attempt will be made to reduce the precision of literals in the expressions. However, this code snippet can cause such a bug.
mysql [test]>select cast(1 as DECIMALV3(16, 2)) / cast(3 as DECIMALV3(16, 2));
+-----------------------------------------------------------+
| CAST(1 AS DECIMALV3(16, 2)) / CAST(3 AS DECIMALV3(16, 2)) |
+-----------------------------------------------------------+
| 0.00 |
+-----------------------------------------------------------+
1.00 / 3.00, due to reduced precision, becomes 1 / 3.
<--Describe your changes.-->
Fix some bugs of orc lazy materialization(#18615)
- Fix issue causing column size to continuously increase after `execute_conjuncts()` by calling `Block::erase_useless_column()`.
- Fix partition issues of orc lazy materialization.
- Fix lazy materialization will not be used when the predicate column is inconsistent with the orc file.
In some case of agg function, maybe running as streaming agg firstly,
this will call the add function when serialize, so need implement add function also.
Use consistent hash to collect BE only when the file cache is enabled. And move the consistent BE assign code to FederationBackendPolicy.
Fix explain split number and file size incorrect bug.
Only switch to bthread local when modifying the mem tracker in the thread context. No longer switches to bthread local by default when bthread starts
mem tracker increases brpc IOBufBlockMemory memory
remove thread mem tracker metrics
If a query hits a materialized view that has row storage enabled, but the row storage column is not present in the materialized view, it will result in a query crash. Therefore, it is necessary to include the row storage column when creating the materialized view, and serialize the row storage column during the execution of SchemaChange.
