cherry-pick： https://github.com/apache/doris/pull/40820
example: select append_trailing_char_if_absent('it','a') would return
null in original design, it can not return null when folding constant on
fe any time
ddl_p0/test_create_table_like_nereids.groovy test result is unstable.
Should wait util rollup join finished then we can compare the explain
rollup result.
## Proposed changes
commitId: 3b7b29b8
pr: https://github.com/apache/doris/pull/39305
replace COS with OSS in the TeamCity pipeline to improve stability
pick from master #37952
---------
## Proposed changes
Issue Number: close #xxx
<!--Describe your changes.-->
## Proposed changes
Awaitility: Waits until a specific condition is met, which makes the
tests more reliable and less prone to intermittent failures. It ensures
that the test only proceeds when the expected condition is true.
Thread.sleep: Introduces a fixed delay, which can lead to flaky tests.
If the condition is met sooner than the sleep time, the test
unnecessarily waits. Conversely, if the condition is met after the sleep
time, the test will fail.
(cherry picked from commit 8d249a2562b15825a06a53a5a530a532a0ca2454)
## Proposed changes
Issue Number: close#37812
<!--Describe your changes.-->
this is brought by https://github.com/apache/doris/pull/33800
if mv is partitioned materialzied view,
the data will be wrong by using the hited materialized view when the
paritions in related base partiton table are deleted, created and so on.
this fix the problem.
if **SET enable_materialized_view_union_rewrite=true;** this will use
the materializd view and make sure the data is corrent
if **SET enable_materialized_view_union_rewrite=false;** this will query
base table directly to make sure the data is right
* [enhancement](Nereids) Enable parse sql from sql cache (#33262)
Before this pr, the query must pass through parser, analyzer, rewriter, optimizer and translator, then we can check whether this query can use sql cache, if the query is too long, or the number of join tables too big, the plan time usually >= 500ms.
This pr reduce this time by skip the fashion plan path, because we can reuse the previous physical plan and query result if no any changed. In some cases we should not parse sql from sql cache, e.g. table structure changed, data changed, user policies changed, privileges changed, contains non-deterministic functions, and user variables changed.
In my test case: query a view which has lots of join and union, and the tables has empty partition, the query latency is about 3ms. if not parse sql from sql cache, the plan time is about 550ms
## Features
1. use Config.sql_cache_manage_num to control how many sql cache be reused in on fe
2. if explain plan appear some plans contains `LogicalSqlCache` or `PhysicalSqlCache`, it means the query can use sql cache, like this:
```sql
mysql> set enable_sql_cache=true;
Query OK, 0 rows affected (0.00 sec)
mysql> explain physical plan select * from test.t;
+----------------------------------------------------------------------------------+
| Explain String(Nereids Planner) |
+----------------------------------------------------------------------------------+
| cost = 3.135 |
| PhysicalResultSink[53] ( outputExprs=[c1#0, c2#1] ) |
| +--PhysicalDistribute[50]@0 ( stats=3, distributionSpec=DistributionSpecGather ) |
| +--PhysicalOlapScan[t]@0 ( stats=3 ) |
+----------------------------------------------------------------------------------+
4 rows in set (0.02 sec)
mysql> select * from test.t;
+------+------+
| c1 | c2 |
+------+------+
| 1 | 2 |
| -2 | -2 |
| NULL | 30 |
+------+------+
3 rows in set (0.05 sec)
mysql> explain physical plan select * from test.t;
+-------------------------------------------------------------------------------------------+
| Explain String(Nereids Planner) |
+-------------------------------------------------------------------------------------------+
| cost = 0.0 |
| PhysicalSqlCache[2] ( queryId=78511f515cda466b-95385d892d6c68d0, backend=127.0.0.1:9050 ) |
| +--PhysicalResultSink[52] ( outputExprs=[c1#0, c2#1] ) |
| +--PhysicalDistribute[49]@0 ( stats=3, distributionSpec=DistributionSpecGather ) |
| +--PhysicalOlapScan[t]@0 ( stats=3 ) |
+-------------------------------------------------------------------------------------------+
5 rows in set (0.01 sec)
```
(cherry picked from commit 03bd2a337d4a56ea9c91673b3bd4ae518ed10f20)
* fix
* [fix](Nereids) fix some sql cache consistence bug between multiple frontends (#33722)
fix some sql cache consistence bug between multiple frontends which introduced by [enhancement](Nereids) Enable parse sql from sql cache #33262, fix by use row policy as the part of sql cache key.
support dynamic update the num of fe manage sql cache key
(cherry picked from commit 90abd76f71e73702e49794d375ace4f27f834a30)
* [fix](Nereids) fix bug of dry run query with sql cache (#33799)
1. dry run query should not use sql cache
2. fix test sql cache in cloud mode
3. enable cache OneRowRelation and EmptyRelation in frontend to skip parse sql
(cherry picked from commit dc80ecf7f33da7b8c04832dee88abd09f7db9ffe)
* remove cloud mode
* remove @NotNull
this pr can improve the performance of the nereids planner, in plan stage.
1. refactor expression rewriter to pattern match, so the lots of expression rewrite rules can criss-crossed apply in a big bottom-up iteration, and rewrite until the expression became stable. now we can process more cases because original there has no loop, and sometimes only process the top expression, like `SimplifyArithmeticRule`.
2. replace `Collection.stream()` to `ImmutableXxx.Builder` to avoid useless method call
3. loop unrolling some codes, like `Expression.<init>`, `PlanTreeRewriteBottomUpJob.pushChildrenJobs`
4. use type/arity specified-code, like `OneRangePartitionEvaluator.toNereidsLiterals()`, `PartitionRangeExpander.tryExpandRange()`, `PartitionRangeExpander.enumerableCount()`
5. refactor `ExtractCommonFactorRule`, now we can extract more cases, and I fix the deed loop when use `ExtractCommonFactorRule` and `SimplifyRange` in one iterative, because `SimplifyRange` generate right deep tree, but `ExtractCommonFactorRule` generate left deep tree
6. refactor `FoldConstantRuleOnFE`, support visitor/pattern match mode, in ExpressionNormalization, pattern match can criss-crossed apply with other rules; in PartitionPruner, visitor can evaluate expression faster
7. lazy compute and cache some operation
8. use int field to compare date
9. use BitSet to find disableNereidsRules
10. two level loop usually faster then build Multimap when bind slot in Scope, so I revert the code
11. `PlanTreeRewriteBottomUpJob` don't need to clearStatePhase any more
### test case
100 threads parallel continuous send this sql which query an empty table, test in my mac machine(m2 chip, 8 core), enable sql cache
```sql
select count(1),date_format(time_col,'%Y%m%d'),varchar_col1
from tbl
where partition_date>'2024-02-15' and (varchar_col2 ='73130' or varchar_col3='73130') and time_col>'2024-03-04'
and time_col<'2024-03-05'
group by date_format(time_col,'%Y%m%d'),varchar_col1
order by date_format(time_col,'%Y%m%d') desc, varchar_col1 desc,count(1) asc
limit 1000
```
before this pr: 3100 peak QPS, about 2700 avg QPS
after this pr: 4800 peak QPS, about 4400 avg QPS
(cherry picked from commit 7338683fdbdf77711f2ce61e580c19f4ea100723)
