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.
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.
This PR optimize topn query like `SELECT * FROM tableX ORDER BY columnA ASC/DESC LIMIT N`.
TopN is is compose of SortNode and ScanNode, when user table is wide like 100+ columns the order by clause is just a few columns.But ScanNode need to scan all data from storage engine even if the limit is very small.This may lead to lots of read amplification.So In this PR I devide TopN query into two phase:
1. The first phase we just need to read `columnA`'s data from storage engine along with an extra RowId column called `__DORIS_ROWID_COL__`.The other columns are pruned from ScanNode.
2. The second phase I put it in the ExchangeNode beacuase it's the central node for topn nodes in the cluster.The ExchangeNode will spawn a RPC to other nodes using the RowIds(sorted and limited from SortNode) read from the first phase and read row by row from storage engine.
After the second phase read, Block will contain all the data needed for the query
* [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
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)
We make all MemTrackers shared, in order to show MemTracker real-time consumptions on the web.
As follows:
1. nearly all MemTracker raw ptr -> shared_ptr
2. Use CreateTracker() to create new MemTracker(in order to add itself to its parent)
3. RowBatch & MemPool still use raw ptrs of MemTracker, it's easy to ensure RowBatch & MemPool destructor exec
before MemTracker's destructor. So we don't change these code.
4. MemTracker can use RuntimeProfile's counter to calc consumption. So RuntimeProfile's counter need to be shared
too. We add a shared counter pool to store the shared counter, don't change other counters of RuntimeProfile.
Note that, this PR doesn't change the MemTracker tree structure. So there still have some orphan trackers, e.g. RowBlockV2's MemTracker. If you find some shared MemTrackers are little memory consumption & too time-consuming, you could make them be the orphan, then it's fine to use the raw ptr.
Mainly contains the following modifications:
1. Use `std::unique_ptr` to replace some naked pointers
2. Modify some methods from member-method to local-static-function
3. Modify some methods do not need to be public to private
4. Some formatting changes: such as wrapping lines that are too long
5. Remove some useless variables
6. Add or modify some comments for easier understanding
No functional changes in this patch.
When creating table with OLAP engine, use can specify multi parition columns.
eg:
PARTITION BY RANGE(`date`, `id`)
(
PARTITION `p201701_1000` VALUES LESS THAN ("2017-02-01", "1000"),
PARTITION `p201702_2000` VALUES LESS THAN ("2017-03-01", "2000"),
PARTITION `p201703_all` VALUES LESS THAN ("2017-04-01")
)
Notice that load by hadoop cluster does not support multi parition column table.
NOTE: This patch would modify all Backend's data.
And this will cause a very long time to restart be.
So if you want to interferer your product environment,
you should upgrade backend one by one.
1. Refactoring be is to clarify the structure the codes.
2. Use unique id to indicate a rowset.
Nameing rowset with tablet_id and version will lead to
many conflicts among compaction, clone, restore.
3. Extract an rowset interface to encapsulate rowsets
with different format.
