## Proposed changes
Change `use_cnt` mechanism for incremental (auto partition) channels and
streams, it's now dynamically counted.
Use `close_wait()` of regular partitions as a synchronize point to make
sure all sinks are in close phase before closing any incremental (auto
partition) channels and streams.
Add dummy (fake) partition and tablet if there is no regular partition
in the auto partition table.
Backport #35287
Co-authored-by: zhaochangle <zhaochangle@selectdb.com>
* [Fix](auto inc) Fix multiple replica partial update auto inc data inconsistency problem (#34788)
* **Problem:** For tables with auto-increment columns, updating partial columns can cause data inconsistency among replicas.
**Cause:** Previously, the implementation for updating partial columns in tables with auto-increment columns was done independently on each BE (Backend), leading to potential inconsistencies in the auto-increment column values generated by each BE.
**Solution:** Before distributing blocks, determine if the update involves partial columns of a table with an auto-increment column. If so, add the auto-increment column to the last column of the block. After distributing to each BE, each BE will check if the data key for the partial column update exists. If it exists, the previous auto-increment column value is used; if not, the auto-increment column value from the last column of the block is used. This ensures that the auto-increment column values are consistent across different BEs.
* 2
* [Fix](regression-test) Fix auto inc partial update unstable regression test (#34940)
* [Fix](partial-update) Fix partial update fail when the datetime default value is 'current_time' (#32926)
* Problem: When importing data that includes datetime with a default value of current time for partial column updates, the import fails.
Reason: Partial column updates do not handle the logic for datetime default values.
Solution: During partial column updates, when the default value is set to current time, read the current time from the runtime state and write it into the data.
* [Enhancement](partial update)Add timezone case for partial update timestamp #33177
* [fix](partial update) Support partial update when the date default value is 'current_date'. This PR is a extension of PR #32926. (#33394)
fix legacy planner grammer
fix nereids planner parsing
fix cases
forbid auto range partition with null column
fix CreateTableStmt with auto partition and some partition items.
1 and 2 are about #31585
doc pr: apache/doris-website#488
Problem:
When partially updating columns without specifying the auto-increment column, and the imported data contains new keys, an error stating the auto-increment column could not be found occurs.
Reason:
The logic for partial column updates does not account for new keys in auto-increment columns. Since auto-increment columns can be generated by the system, it's possible to omit this column data during import. However, partial column updates treat this as a regular column, expecting it to be nullable or have a default value for automatic filling, overlooking the fact that auto-increment columns can also be auto-filled. This oversight leads to the error.
Solution:
Incorporate a check for auto-increment columns into the partial column update logic, and include the logic for generating auto-increment column values in the process of completing partial updates.
At present, `load_to_singlt_tablet` import implementation refers to simple random number remainder, which cannot achieve true averaging. This will lead to uneven disk IO and uneven use of cluster resources. To solve this problem, we are preparing to implement round-robin for each partition tablet imported each time, in order to achieve average load to each tablet.
When generating the load query plan, the tablet index record currently imported is passed to BE.
Add a deamon task in FE to regularly clean up the `loadTabletRecordMap`. The map will get the bucket_number of the partition and update the `load_tablet_index` when `getCurrentLoadTabletIndex`.
1. expand the semantics of variable strict_mode to control the behavior for stream load: if strict_mode is true, the stream load can only update existing rows; if strict_mode is false, the stream load can insert new rows if the key is not present in the table
2. when inserting a new row in non-strict mode stream load, the unmentioned columns should have default value or be nullable
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.
Currently, there are some useless includes in the codebase. We can use a tool named include-what-you-use to optimize these includes. By using a strict include-what-you-use policy, we can get lots of benefits from it.
There are many type definitions in BE. Should unify the type system and simplify the development.
---------
Co-authored-by: yiguolei <yiguolei@gmail.com>
This pr implements the list default partition referred in related #15507.
It's similar as GreenPlum's default's partition which would store all data not satisfying prior partition key's
constraints and optimizer wouldn't filter default partition which means default partition would be scanned
each time you try to select data from one table with default partition.
User could either create one table with default partition or alter add one default partition.
```sql
PARTITION LIST(key) {
PARTITION p1 values in (xx,xx),
PARTITION DEFAULT
}
ALTER TABLE XXX ADD PARTITION DEFAULT
```
We don't support automatically migrate data inside default partition which meets newly added partition key's
constraint to newly add partition when alter add new partition. User should select default partition using new
constraints as predicate and insert them to new partition.
```sql
insert into tbl select * from tbl partition default where partition_key=xx;
```
The background is described in this issue: #15723,
where users used Apache Druid to satisfy such lambada requirements before.
We will not make Doris dropping data not belonged to current time window automatically like Druid,
which is not flexible. We demand a ability to support mutable/immutable partition, the PR works this way:
1. Support mutable property for a partition.
2. The mutable property of a partition is passed from FE to BE in a load procedure
3. If a record's partition is immutable, we mark this row as "un selected" which will not be included in computation of 'max_filter_ratio',
so that data write to immutable partition will be neglected and not cause load failure.
Use Example:
1. Add immutable partition or modify an partition to be immutable:
- alter table test_tbl add [temporary] partition xxx values less than ('xxx') ('mutable' = 'true');
- alter table test_tbl modify partition xx set ('mutable' = 'false');
2. Write 5 records into table, two of then belongs to immutable partition
Issue Number: close#16351
Dynamic schema table is a special type of table, it's schema change with loading procedure.Now we implemented this feature mainly for semi-structure data such as JSON, since JSON is schema self-described we could extract schema info from the original documents and inference the final type infomation.This speical table could reduce manual schema change operation and easily import semi-structure data and extends it's schema automatically.
* [Schema Change] support fast add/drop column (#49)
* [feature](schema-change) support fast schema change. coauthor: yixiutt
* [schema change] Using columns desc from fe to read data. coauthor: Lchangliang
* [feature](schema change) schema change optimize for add/drop columns.
1.add uniqueId field for class column.
2.schema change for add/drop columns directly update schema meta
Co-authored-by: yixiutt <yixiu@selectdb.com>
Co-authored-by: SWJTU-ZhangLei <1091517373@qq.com>
[Feature](schema change) fix write and add regression test (#69)
Co-authored-by: yixiutt <yixiu@selectdb.com>
[schema change] be ssupport that delete use newest schema
add delete regression test
fix regression case (#107)
tmp
[feature](schema change) light schema change exclude rollup and agg/uniq/dup key type.
[feature](schema change) fe olapTable maxUniqueId write in disk.
[feature](schema change) add rpc iface for sc add column.
[feature](schema change) add columnsDesc to TPushReq for ligtht sc.
resolve the deadlock when schema change (#124)
fix columns from fe don't has bitmap_index flag (#134)
add update/delete case
construct MATERIALIZED schema from origin schema when insert
fix not vectorized compaction coredump
use segment cache
choose newest schema by schema version when compaction (#182)
[bugfix](schema change) fix ligth schema change problem.
[feature](schema change) light schema change add alter job. (#1)
fix be ut
[bug] (schema change) unique drop key column should not light schema
change
[feature](schema change) add schema change regression-test.
fix regression test
[bugfix](schema change) fix multi alter clauses for light schema change. (#2)
[bugfix](schema change) fix multi clauses calculate column unique id (#3)
modify PushTask process (#217)
[Bugfix](schema change) fix jobId replay cause bdbje exception.
[bug](schema change) fix max col unique id repeatitive. (#232)
[optimize](schema change) modify pendingMaxColUniqueId generate rule.
fix compaction error
* fix be ut
* fix snapshot load core
fix unique_id error (#278)
[refact](fe) remove redundant code for light schema change. (#4)
[refact](fe) remove redundant code for light schema change. (#4)
format fe core
format be core
fix be ut
modify fe meta version
fix rebase error
flush schema into rowset_meta in old table
[refactor](schema change) refact fe light schema change. (#5)
delete the change of schemahash and support get max version schema
* modify for review
* fix be ut
* fix schema change test
Early Design Documentation: https://shimo.im/docs/DT6JXDRkdTvdyV3G
Implement a new way of memory statistics based on TCMalloc New/Delete Hook,
MemTracker and TLS, and it is expected that all memory new/delete/malloc/free
of the BE process can be counted.
Modify the implementation of MemTracker:
1. Simplify a lot of useless logic;
2. Added MemTrackerTaskPool, as the ancestor of all query and import trackers, This is used to track the local memory usage of all tasks executing;
3. Add cosume/release cache, trigger a cosume/release when the memory accumulation exceeds the parameter mem_tracker_consume_min_size_bytes;
4. Add a new memory leak detection mode (Experimental feature), throw an exception when the remaining statistical value is greater than the specified range when the MemTracker is destructed, and print the accurate statistical value in HTTP, the parameter memory_leak_detection
5. Added Virtual MemTracker, cosume/release will not sync to parent. It will be used when introducing TCMalloc Hook to record memory later, to record the specified memory independently;
6. Modify the GC logic, register the buffer cached in DiskIoMgr as a GC function, and add other GC functions later;
7. Change the global root node from Root MemTracker to Process MemTracker, and remove Process MemTracker in exec_env;
8. Modify the macro that detects whether the memory has reached the upper limit, modify the parameters and default behavior of creating MemTracker, modify the error message format in mem_limit_exceeded, extend and apply transfer_to, remove Metric in MemTracker, etc.;
Modify where MemTracker is used:
1. MemPool adds a constructor to create a temporary tracker to avoid a lot of redundant code;
2. Added trackers for global objects such as ChunkAllocator and StorageEngine;
3. Added more fine-grained trackers such as ExprContext;
4. RuntimeState removes FragmentMemTracker, that is, PlanFragmentExecutor mem_tracker, which was previously used for independent statistical scan process memory, and replaces it with _scanner_mem_tracker in OlapScanNode;
5. MemTracker is no longer recorded in ReservationTracker, and ReservationTracker will be removed later;
In some scenarios, users cannot find a suitable hash key to avoid data skew, so we need to provide an additional data distribution for olap table to avoid data skew
example:
CREATE TABLE random_table
(
siteid INT DEFAULT '10',
citycode SMALLINT,
username VARCHAR(32) DEFAULT '',
pv BIGINT SUM DEFAULT '0'
)
AGGREGATE KEY(siteid, citycode, username)
DISTRIBUTED BY random BUCKETS 10
PROPERTIES("replication_num" = "1");
Co-authored-by: caiconghui1 <caiconghui1@jd.com>
# Proposed changes
Issue Number: close#6238
Co-authored-by: HappenLee <happenlee@hotmail.com>
Co-authored-by: stdpain <34912776+stdpain@users.noreply.github.com>
Co-authored-by: Zhengguo Yang <yangzhgg@gmail.com>
Co-authored-by: wangbo <506340561@qq.com>
Co-authored-by: emmymiao87 <522274284@qq.com>
Co-authored-by: Pxl <952130278@qq.com>
Co-authored-by: zhangstar333 <87313068+zhangstar333@users.noreply.github.com>
Co-authored-by: thinker <zchw100@qq.com>
Co-authored-by: Zeno Yang <1521564989@qq.com>
Co-authored-by: Wang Shuo <wangshuo128@gmail.com>
Co-authored-by: zhoubintao <35688959+zbtzbtzbt@users.noreply.github.com>
Co-authored-by: Gabriel <gabrielleebuaa@gmail.com>
Co-authored-by: xinghuayu007 <1450306854@qq.com>
Co-authored-by: weizuo93 <weizuo@apache.org>
Co-authored-by: yiguolei <guoleiyi@tencent.com>
Co-authored-by: anneji-dev <85534151+anneji-dev@users.noreply.github.com>
Co-authored-by: awakeljw <993007281@qq.com>
Co-authored-by: taberylyang <95272637+taberylyang@users.noreply.github.com>
Co-authored-by: Cui Kaifeng <48012748+azurenake@users.noreply.github.com>
## Problem Summary:
### 1. Some code from clickhouse
**ClickHouse is an excellent implementation of the vectorized execution engine database,
so here we have referenced and learned a lot from its excellent implementation in terms of
data structure and function implementation.
We are based on ClickHouse v19.16.2.2 and would like to thank the ClickHouse community and developers.**
The following comment has been added to the code from Clickhouse, eg:
// This file is copied from
// https://github.com/ClickHouse/ClickHouse/blob/master/src/Interpreters/AggregationCommon.h
// and modified by Doris
### 2. Support exec node and query:
* vaggregation_node
* vanalytic_eval_node
* vassert_num_rows_node
* vblocking_join_node
* vcross_join_node
* vempty_set_node
* ves_http_scan_node
* vexcept_node
* vexchange_node
* vintersect_node
* vmysql_scan_node
* vodbc_scan_node
* volap_scan_node
* vrepeat_node
* vschema_scan_node
* vselect_node
* vset_operation_node
* vsort_node
* vunion_node
* vhash_join_node
You can run exec engine of SSB/TPCH and 70% TPCDS stand query test set.
### 3. Data Model
Vec Exec Engine Support **Dup/Agg/Unq** table, Support Block Reader Vectorized.
Segment Vec is working in process.
### 4. How to use
1. Set the environment variable `set enable_vectorized_engine = true; `(required)
2. Set the environment variable `set batch_size = 4096; ` (recommended)
### 5. Some diff from origin exec engine
https://github.com/doris-vectorized/doris-vectorized/issues/294
## Checklist(Required)
1. Does it affect the original behavior: (No)
2. Has unit tests been added: (Yes)
3. Has document been added or modified: (No)
4. Does it need to update dependencies: (No)
5. Are there any changes that cannot be rolled back: (Yes)
