Optimization "select count(*) from table" stmtement , push down "count" type to BE.
support file type : parquet ，orc in hive .
1. 4kfiles , 60kwline num
before: 1 min 37.70 sec
after: 50.18 sec
2. 50files , 60kwline num
before: 1.12 sec
after: 0.82 sec
* [Fix](rowset) When a rowset is cooled down, it is directly deleted. This can result in data query misses in the second phase of a two-phase query.
related pr #20732
There are two reasons for moving the logic of delayed deletion from the Tablet to the StorageEngine. The first reason is to consolidate the logic and unify the delayed operations. The second reason is that delayed garbage collection during queries can cause rowsets to remain in the "stale rowsets" state, preventing the timely deletion of rowset metadata, It may cause rowset metadata too large.
* not use unused rowsets
* [Bug](topn opt) Fix Two-Phase read when some rowset swept
If this is a Two-Phase read query, and we need to delay the release of Rowset by row->update_delayed_expired_timestamp() to expand the lifespan of rowsets. This is necessary to avoid data loss during the second phase reading, where some stale rowsets may be swept and result in missing data.
When FE is old version, be is new version, issue a schema change(add column) and
then query, old version of FE query without schema version could result in reading
stale schema from schema cache
* [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
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.
/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
Co-authored-by: yiguolei <yiguolei@gmail.com>
Currently, exec node save exprcontext**, but the object is in object pool, the code is very unclear. we could just use exprcontext*.
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.
Sometimes, `show load profile` will only show part of the insert opertion's profile.
This is because we assume that for all load operation(including insert), there is only one fragment in the plan.
But actually, there will be more than 1 fragment in plan. eg:
`insert into tbl1 select * from tbl1 limit 1` will have 2 fragments.
This PR mainly changes:
1. modify the `show load profile`
Before: `show load profile "/queryid/taskid/instanceid";`
After: `show load profile "/queryid/taskid/fragmentid/instanceid";`
2. Modify the display of `ReadColumns` in OlapScanNode
Because for wide table, the line of `ReadColumns` may be too long for show in profile.
So I wrap it and each line contains at most 10 columns names.
3. Fix tvf not working with pipeline engine, follow up #18376
We want to use file cache for caching cold data in S3.
When reading them, we want to know where the data come from and the time taken to read the datas.
So we support the metrics in olap scan node.
And for clearing the information, i also update the fields about the metrics.
1. add PassNullPredicate to fix topn wrong result for NULL values
2. refactor RuntimePredicate to avoid using TCondition
3. refactor using ordering_exprs in fe and vsort_node
In the past, only simple predicates (slot=const), and, like, or (only bitmap index) could be pushed down to the storage layer. scan process:
Read part of the column first, and calculate the row ids with a simple push-down predicate.
Use row ids to read the remaining columns and pass them to the scanner, and the scanner filters the remaining predicates.
This pr will also push-down the remaining predicates (functions, nested predicates...) in the scanner to the storage layer for filtering. scan process:
Read part of the column first, and use the push-down simple predicate to calculate the row ids, (same as above)
Use row ids to read the columns needed for the remaining predicates, and use the pushed-down remaining predicates to reduce the number of row ids again.
Use row ids to read the remaining columns and pass them to the scanner.
remove duplicate type definition in function context
remove unused method in function context
not need stale state in vexpr context because vexpr is stateless and function context saves state and they are cloned.
remove useless slot_size in all tuple or slot descriptor.
remove doris_udf namespace, it is useless.
remove some unused macro definitions.
init v_conjuncts in vscanner, not need write the same code in every scanner.
using unique ptr to manage function context since it could only belong to a single expr context.
Issue Number: close #xxx
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Co-authored-by: yiguolei <yiguolei@gmail.com>
background：
At the moment, match query must with inverted index,
problem description:
After drop inverted index which is the only index in table, there still can use match query for this index column.
fix it:
The index should be updated on BE regardless of whether the indexes_desc from FE is empty.
There are 2 kinds for scanner thread pool, local and remote.
Local is for local file read, specially for olap scanner.
Remote is for other external data source, such as file scanner, jdbc scanner.
This PR mainly changes:
For olap scanner, use cold or hot rowset to decide whether to use local or remote pool.
For other scanner, user remote pool by default.
Add a new BE config doris_max_remote_scanner_thread_pool_thread_num, default is 512,
indicate the max thread number of the remote scanner thread pool
This will alleviate the problem of interaction between olap queries with load job and external queries.
Add cache for inverted index query match bitmap to accelerate common query keyword, especially for keyword matching many rows.
Tests result:
- large result: matching 99% out of 247 million rows shows 8x speed up.
- small result: matching 0.1% out of 247 million rows shows 2x speed up.
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
The origin scan pools are in exec_env.
But after enable new_load_scan_node by default, the scan pool in exec_env is no longer used.
All scan task will be submitted to the scan pool in scanner_scheduler.
BTW, reorganize the scan pool into 3 kinds:
local scan pool
For olap scan node
remote scan pool
For file scan node
limited scan pool
For query which set cpu resource limit or with small limit clause
TODO:
Use bthread to unify all IO task.
Some trivial issues:
fix bug that the memtable flush size printed in log is not right
Add RuntimeProfile param in VScanner
* [feature-wip](inverted index)inverted index api: reader
* [feature-wip](inverted index) Fulltext query syntax with MATCH/MATCH_ALL/MATCH_ALL
* [feature-wip](inverted index) Adapt to index meta
* [enhance] add more metrics
* [enhance] add fulltext match query check for column type and index parser
* [feature-wip](inverted index) Support apply inverted index in compound predicate which except leaf node of and node
Current bitmap index only can apply pushed down predicates which in AND conditions. When predicates in OR conditions and other complex compound conditions, it will not be pushed down to the storage layer, this leads to read more data.
Based on that situation, this pr will do:
1. this pr in order to support bitmap index apply compound predicates, query sql like:
select * from tb where a > 'hello' or b < 100;
select * from tb where a > 'hello' or b < 100 or c > 'ok';
select * from tb where (a > 'hello' or b <100) and (a < 'world' or b > 200);
select * from tb where (not a> 'hello') or b < 100;
...
above sql，column a and b and c has created bitmap_index.
2. this optimization can reduce reading data by index
3. set config enable_index_apply_compound_predicates to use this optimization
1.Support in bitmap syntax, like 'where k1 in (select bitmap_column from tbl)';
2.Support bitmap runtime filter. Generate a bitmap filter using the right table bitmap and push it down to the left table storage layer for filtering.
When calling select on remote files, download cache files to local disk.
When calling alter table on remote files, read files directly from remote storage. So if tablet is too large, it will not take up too many local disk when creating local cache file.
