1. use rlock in most logic instead of wrlock
2. filter stale rowset's delete bitmap in save meta
3. add a delete_bitmap lock to handle compaction and publish_txn confict
Co-authored-by: yixiutt <yixiu@selectdb.com>
There are currently many types of ScanNodes in Doris. And most of the logic of these ScanNodes is the same, including:
Runtime filter
Predicate pushdown
Scanner generation and scheduling
So I intend to unify the common logic of all ScanNodes.
Different data sources only need to implement different Scanners for data access.
So that the future optimization for scan can be applied to the scan of all data sources,
while also reducing the code duplication.
This PR mainly adds 4 new class:
VScanner
All Scanners' parent class. The subclasses can inherit this class to implement specific data access methods.
VScanNode
The unified ScanNode, and is responsible for common logic including RuntimeFilter, predicate pushdown, Scanner generation and scheduling.
ScannerContext
ScannerContext is responsible for recording the execution status
of a group of Scanners corresponding to a ScanNode.
Including how many scanners are being scheduled, and maintaining
a producer-consumer blocks queue between scanners and scan nodes.
ScannerContext is also the scheduling unit of ScannerScheduler.
ScannerScheduler schedules a ScannerContext at a time,
and submits the Scanners to the scanner thread pool for data scanning.
ScannerScheduler
Unified responsible for all Scanner scheduling tasks
Test:
This work is still in progress and default is disabled.
I tested it with jmeter with 50 concurrency, but currently the scanner is just return without data.
The QPS can reach about 9000.
I can't compare it to origin implement because no data is read for now. I will test it when new olap scanner is ready.
Co-authored-by: morningman <morningman@apache.org>
1. Spark can set the timestamp precision by the following configuration:
spark.sql.parquet.outputTimestampType = INT96(NANOS), TIMESTAMP_MICROS, TIMESTAMP_MILLIS
DATETIME V1 only keeps the second precision, DATETIME V2 keeps the microsecond precision.
2. If using DECIMAL V2, the BE saves the value as decimal128, and keeps the precision of decimal as (precision=27, scale=9). DECIMAL V3 can maintain the right precision of decimal
Stream load will ignore invisible columns if no http header columns
specified, but in some case user cannot get all columns if columns
changed frequently。
Add a hidden_columns header to support hidden columns import。User can
set hidden_columns such as __DORIS_DELETE_SIGN__ and add this column
in stream load data so we can delete this line.
For example:
curl -u root -v --location-trusted -H "hidden_columns: __DORIS_DELETE_SIGN__" -H
"format: json" -H "strip_outer_array: true" -H "jsonpaths: [\"$.id\",
\"$.name\",\"$.__DORIS_DELETE_SIGN__\"]" -T 1.json
http://{beip}:{be_port}/api/test/test1/_stream_load
Co-authored-by: yixiutt <yixiu@selectdb.com>
when config::enable_simdjson_parser=true in vec streamload, may lead to core dump when json input invalid format string like '{ "a', or all the fields is null like '{}', this may lead to simdjson lib throw some unhandled expection like `Objects and arrays can only be iterated when they are first encountered`.We should take care of these cases
Signed-off-by: eldenmoon <15605149486@163.com>
The column id check in VSlotRef::execute function before is too strict for fuzzy test to continuously produce random query. Temporarily loosen the check logic.
Moreover, there exists some careless call to VExpr::get_const_col, it might return a nullptr but not every function call checks if it's valid. It's an underlying problem.
some feature:
1. add min max key in segment footer to speed up get_row_ranges_by_keys
2. do not load pk bloom filter in query
Co-authored-by: yixiutt <yixiu@selectdb.com>
In engine_clone_task.cpp, it use tablet->tablet_schema() to create rowset, but in the method, it need a lock that already locked in engine_clone_task.cpp:514. It use cloned_tablet_meta->tablet_schema() originally, but modified in #11131. It need to revert to use cloned_tablet_meta->tablet_schema().
Currently we use rapidjson to parse json document, It's fast but not fast enough compare to simdjson.And I found that the simdjson has a parsing front-end called simdjson::ondemand which will parse json when accessing fields and could strip the field token from the original document, using this feature we could reduce the cost of string copy(eg. we convert everthing to a string literal in _write_data_to_column by sprintf, I saw a hotspot from the flamegrame in this function, using simdjson::to_json_string will strip the token(a string piece) which is std::string_view and this is exactly we need).And second in _set_column_value we could iterate through the json document by for (auto field: object_val) {xxx}, this is much faster than looking up a field by it's field name like objectValue.FindMember("k1").The third optimization is the at_pointer interface simdjson provided, this could directly get the json field from original document.
Disable Chunk Allocator in Vectorized Allocator, this will reduce memory cache.
For high concurrent queries, using Chunk Allocator with vectorized Allocator can reduce the impact of gperftools tcmalloc central lock.
Jemalloc or google tcmalloc have core cache, Chunk Allocator may no longer be needed after replacing gperftools tcmalloc.
