Optimize the strategy of merging small IO to prevent severe read amplification, and turn off merged IO when file cache enabled.
Adjustable parameters:
```
// the max amplified read ratio when merging small IO
max_amplified_read_ratio=0.8
// the min segment size
file_cache_min_file_segment_size = 1048576
```
Add fast path for col like '%%' or col like '%' or regexp '\\.*'
(1) like about 34% speed up when use count() test
support col like '%%' , col like '%', col not like '%%' , col not like '%'
(2) regexp about 37% speed up when use count() test
support col regexp '\\.', col not regexp '\\.'
Q1: select count() From hits where url like '%';
Q2: select count() From hits where url regexp '\\.*';
Support json array with nereids bool
now :
```
set enable_nereids_planner=true;
mysql> SELECT json_array(1, "abc", NULL, TRUE, '10:00:00');
+----------------------------------------------+
| json_array(1, 'abc', NULL, TRUE, '10:00:00') |
+----------------------------------------------+
| [1,"abc",null,false,"10:00:00"] |
+----------------------------------------------+
1 row in set (0.02 sec)
```
nereids boolean is "true"/"false" is not '0' /'1' , so we always get false
Parallel scanning can result in some read amplification, for example, select * from xx where limit 1 actually requires only one row of data. However, due to parallel scanning of multiple tablets, read amplification occurs, leading to performance bottlenecks in high-concurrency scenarios. This PR Adding a SessionVariable to enforce serial scanning can help mitigate this issue.
1. make ColumnObject exception safe
2. introduce FlushContext and construct schema at memtable flush stage to make segment independent from dynamic schema
3. add more test cases
Test on SSB 100g:
select lo_suppkey, count(distinct lo_linenumber) from lineorder group by lo_suppkey;
exec time: 4.388s
create materialized view:
create materialized view customer_uv as select lo_suppkey, bitmap_union(to_bitmap(lo_linenumber)) from lineorder group by lo_suppkey;
select lo_suppkey, count(distinct lo_linenumber) from lineorder group by lo_suppkey;
exec time: 12.908s
test with the patch, exec time: 5.790s
This PR refactors the old way of writing data to JDBC External Table & JDBC Catalog, mainly including the following tasks
1. Continuing the work of @BePPPower 's PR #18594, changing the logic of splicing Inster sql to operating off-heap memory and using preparedStatement.set to write data logic to complete
2. Supplement the support written by largeint type, mainly to adapt to Java.Math.BigInteger, which uses binary operations
3. Delete the splicing SQL logic in the JDBC External Table & JDBC Catalog related written code
ToDo: Binary type，like bit,binary, blob...
Finally, special thanks to @BePPPower , @AshinGau for his work
Co-authored-by: Tiewei Fang <43782773+BePPPower@users.noreply.github.com>
before:
F0530 11:02:41.989699 1154607 assert_cast.h:54] Bad cast from type:doris::vectorized::IDataType const* to doris::vectorized::DataTypeAggState const*
after:
F0530 11:24:28.390286 1292475 assert_cast.h:46] Bad cast from type:doris::vectorized::DataTypeNullable* to doris::vectorized::DataTypeAggState const*
1. Fix create catalog with resource replay bug.
If user create catalog using `create catalog hive with resource xxx`, when replaying edit log,
there is a bug that resource may be dropped, causing NPE and FE will fail to start.
In this PR, I add a new FE config `disallow_create_catalog_with_resource`, default is true.
So that `with resource` will not be allowed, and it will be deprecated later.
And also fix the replay bug to avoid NPE.
2. Fix issue when creating 2 hive catalogs to connect with and without kerberos authentication.
When user create 2 hive catalogs, one use simple auth, the other use kerberos auth.
The query may fail with error like: `Server asks us to fall back to SIMPLE auth, but this client is configured to only allow secure connections.`
So I add a default property for hive catalog: `"ipc.client.fallback-to-simple-auth-allowed" = "true"`.
Which means this property will be added automatically when user creating hive catalog, to avoid such problem.
3. Fix calling `hdfsExists()` issue
When calling `hdfsExists()` with non-zero return code, should check if it encounters error or is file not found.
3. Some code refactor
Avoid import `org.apache.parquet.Strings`
recurrent:
./run-regression-test.sh --run -suiteParallel 1 -actionParallel 1 -parallel 1 -d query_p0/sql_functions/window_functions
select /*+ SET_VAR(query_timeout = 600) */ subq_0.`c1` as c0 from (select ref_1.`s_name` as c0, ref_1.`s_suppkey` as c1, ref_1.`s_address` as c2, ref_1.`s_address` as c3 from regression_test_query_p0_sql_functions_window_functions.tpch_tiny_supplier as ref_1 where (ref_1.`s_name` is NULL) or (ref_1.`s_acctbal` is not NULL)) as subq_0 where (subq_0.`c3` is NULL) or (subq_0.`c2` is not NULL)
reason:
FunctionIsNull and FunctionIsNotNull execute returns a const column, but their VectorizedFnCall::is_constant returns false, which causes problems with const handling when VCompoundPred::execute.
This pr converts const column to full column in VCompoundPred execute. In the future, there will be a more thorough solution to such problems.
* [Improve](performance) introduce SchemaCache to cache TabletSchame & Schema
1. When the system is under high-concurrency load with wide table point queries, the frequent memory allocation and deallocation of Schema become evident system bottlenecks. Additionally, the initialization of TabletSchema and Schema also becomes a CPU hotspot.Therefore, the introduction of a SchemaCache is implemented to cache these resources for reuse.
2. Make some variables wrapped with std::unique<unique_ptr>
Performance:
| 状态 | QPS | 平均响应时间 (avg) | P99 响应时间 |
|------------------|-----|------------------|-------------|
| 开启 SchemaCache | 501 | 20ms | 34ms |
| 关闭 SchemaCache | 321 | 31ms | 61ms |
* handle schema change with schema version
* remove useless header
* rebase
before
mysql [(none)]>select cast("10:10:10" as time);
+-------------------------------+
| CAST('10:10:10' AS TIMEV2(0)) |
+-------------------------------+
| 00:00:00 |
+-------------------------------+
after
mysql [(none)]>select cast("10:10:10" as time);
+-------------------------------+
| CAST('10:10:10' AS TIMEV2(0)) |
+-------------------------------+
| 10:10:10 |
+-------------------------------+
In the past, we supported this syntax.
mysql [(none)]>select cast("2023:05:01 13:14:15" as time);
+------------------------------------------+
| CAST('2023:05:01 13:14:15' AS TIMEV2(0)) |
+------------------------------------------+
| 13:14:15 |
+------------------------------------------+
However, "10:10:10" is also a valid datetime.
mysql [(none)]>select cast("10:10:10" as datetime);
+-----------------------------------+
| CAST('10:10:10' AS DATETIMEV2(0)) |
+-----------------------------------+
| 2010-10-10 00:00:00 |
+-----------------------------------+
So here, the order of parsing has been adjusted.
Refactoring the filtering conditions in the current ExecNode from an expression tree to an array can simplify the process of adding runtime filters. It eliminates the need for complex merge operations and removes the requirement for the frontend to combine expressions into a single entity.
By representing the filtering conditions as an array, each condition can be treated individually, making it easier to add runtime filters without the need for complex merging logic. The array can store the individual conditions, and the runtime filter logic can iterate through the array to apply the filters as needed.
This refactoring simplifies the codebase, improves readability, and reduces the complexity associated with handling filtering conditions and adding runtime filters. It separates the conditions into discrete entities, enabling more straightforward manipulation and management within the execution node.
before: the node will wait to retrieve all data from child, then send data to parent.
now: for data from child that does not require sorting, it can be sent to parent immediately.
#18976 introduced merge small IO facility to optimize performance, and used by parquet reader.
This PR support this facility in orc reader. Current ORC reader implementation need to reposition parent present stream when reading lazy columns in lazy materialization facility. So let it works by removing `DCHECK_GE(offset, cached_data.end_offset)`.
/home/zcp/repo_center/doris_master/doris/be/src/olap/rowset/segment_v2/column_reader.cpp:895:21: runtime error: load of value 423208544, which is not a valid value for type 'doris::ReaderType'
/home/zcp/repo_center/doris_master/doris/be/src/vec/columns/column_decimal.cpp:260:33: runtime error: load of misaligned address 0x7fa3348b301c for type 'int64_t' (aka 'long'), which requires 8 byte alignment
/home/zcp/repo_center/doris_master/doris/be/src/olap/block_column_predicate.cpp:82:24: runtime error: variable length array bound evaluates to non-positive value 0
/home/zcp/repo_center/doris_master/doris/be/src/vec/columns/column_string.h:225:26: runtime error: null pointer passed as argument 2, which is declared to never be null
* Revert "[fix](sink) fix END_OF_FILE error for pipeline caused by VDataStreamSender eof (#20007)"
This reverts commit 2ec1d282c5e27b25d37baf91cacde082cca4ec31.
* [fix](revert) data stream sender stop sending data to receiver if it returns eos early (#19847)"
This reverts commit c73003359567067ea7d44e4a06c1670c9ec37902.
