In previous, when using file scan node(eq, querying hive table), the max number of scanner for each scan node
will be the `doris_scanner_thread_pool_thread_num`(default is 48).
And if the query parallelism is N, the total number of scanner would be 48 * N, which is too many.
In this PR, I change the logic, the max number of scanner for each scan node
will be the `doris_scanner_thread_pool_thread_num / query parallelism`. So that the total number of scanners
will be up to `doris_scanner_thread_pool_thread_num`.
Reduce the number of scanner can significantly reduce the memory usage of query.
Iceberg has its own metadata information, which includes count statistics for table data. If the table does not contain equli'ty delete, we can get the count data of the current table directly from the count statistics.
For load request, there are 2 tuples on scan node, input tuple and output tuple.
The input tuple is for reading file, and it will be converted to output tuple based on user specified column mappings.
And the broker load support different column mapping in different data description to same table(or partition).
So for each scanner, the output tuples are same but the input tuple can be different.
The previous implements save the input tuple in scan node level, causing different scanner using same input tuple,
which is incorrect.
This PR remove the input tuple from scan node and save them in each scanners.
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
Co-authored-by: Jerry Hu <mrhhsg@gmail.com>
1. Filtering is done at the sending end rather than the receiving end
2. Projection is done at the sending end rather than the receiving end
3. Each sender can use different shuffle policies to send data
For pipeline, olap table sink close is divided into three stages, try_close() --> pending_finish() --> close()
only after all node channels are done or canceled, pending_finish() will return false, close() will start.
this will avoid block pipeline on close().
In close, check the index channel intolerable failure status after each node channel failure,
if intolerable failure is true, the close will be terminated in advance, and all node channels will be canceled to avoid meaningless blocking.
Currently, there are many profiles using add child profile to orgnanize profile into blocks. But it is wrong. Child profile will have a total time counter. Actually, what we should use is just a label.
- MemoryUsage:
- HashTable: 23.98 KB
- SerializeKeyArena: 446.75 KB
Add a new macro ADD_LABEL_COUNTER to add just a label in the profile.
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Co-authored-by: yiguolei <yiguolei@gmail.com>
1 Currently, Node's total timer couter has timed twice(in Open and alloc_resource), this may cause timer in profile is not correct.
2 Add more timer to find more code which may cost much time.
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.
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.
Firstly, to reduce memory usage, we do not pre-allocate blocks, instead we lazily allocate block when upper call get_free_block. And when upper call return_free_block to return free block, we add the block to a queue for memory reuse, and we will free the blocks in the queue when the scanner_context was closed instead of destructed.
Secondly, to limit the memory usage of the scanner, we introduce a variable _free_blocks_capacity to indicate the current number of free blocks available to the scanners. The number of scanners that can be scheduled will be calculated based on this value.
ssb flat test
previous
lineorder 1.2G:
load time: 3s, query time: 0.355s
lineorder 5.8G:
load time: 330s, query time: 0.970s
load time: 349s, query time: 0.949s
load time: 349s, query time: 0.955s
load time: 360s, query time: 0.889s (pipeline enabled)
after
lineorder 1.2G:
load time: 3s, query time: 0.349s
lineorder 5.8G:
load time: 342s, query time: 0.929s
load time: 337s, query time: 0.913s
load time: 345s, query time: 0.946s
load time: 346s, query time: 0.865s (pipeline enabled)
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.
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>
make rows_read correct so that the scheduler could using this correctly.
use single scanner if has limit clause. Move it from fragment context to scannode.
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Co-authored-by: yiguolei <yiguolei@gmail.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* [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
