Copy most of profiles from VOlapScanNode and VOlapScanner to NewOlapScanNode and NewOlapScanner.
Fix some blocking bug of new scan framework.
TODO:
Memtracker
Opentelemetry spen
The new framework is still disabled by default, so it will not effect other feature.
Parse parquet data with dictionary encoding.
Using the PLAIN_DICTIONARY enum value is deprecated in the Parquet 2.0 specification.
Prefer using RLE_DICTIONARY in a data page and PLAIN in a dictionary page for Parquet 2.0+ files.
refer: https://github.com/apache/parquet-format/blob/master/Encodings.md
* table function node enhancement
* also avoid copy for non-vec table function node
* fix table function node output slots calculation while lateral view involves subquery
Co-authored-by: cambyzju <zhuxiaoli01@baidu.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
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>
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.
The bug is caused by use _num_rows_read for limit check. _num_rows_read is count of rows read from storage, but may be filtered by filter_block for WHERE predicate.
Add a _num_rows_return, which is rows after filter_block for WHERE predicate, for count for really returned rows.
Two improvements have been added:
1. Translate parquet physical type into doris logical type.
2. Decode parquet column chunk into doris ColumnPtr, and add unit tests to show how to use related API.
We should close eof scanners before transfer done, otherwise,
they are closed until scannode is closed. Because plan is closed
after the plan is finished, so query profile would leak stats from
scanners closed by scannode::close. e.g. SegmentTotalNum in profile
is less.
# Proposed changes
Read and decode parquet physical type.
1. The encoding type of boolean is bit-packing, this PR introduces the implementation of bit-packing from Impala
2. Create a parquet including all the primitive types supported by hive
## Remaining Problems
1. At present, only physical types are decoded, and there is no corresponding and conversion methods with doris logical.
2. No parsing and processing Decimal type / Timestamp / Date.
3. Int_8 / Int_16 is stored as Int_32. How to resolve these types.
* [feature](planner): push limit to olapscan when meet sort.
* if olap_scan_node's sort_info is set, push sort_limit, read_orderby_key
and read_orderby_key_reverse for olap scanner
* There is a common query pattern to find latest time serials data.
eg. SELECT * from t_log WHERE t>t1 AND t<t2 ORDER BY t DESC LIMIT 100
If the ORDER BY columns is the prefix of the sort key of table, it can
be greatly optimized to read much fewer data instead of read all data
between t1 and t2.
By leveraging the same order of ORDER BY columns and sort key of table,
just read the LIMIT N rows for each related segment and merge N rows.
1. set read_orderby_key to true for read_params and _reader_context
if olap_scan_node's sort info is set.
2. set read_orderby_key_reverse to true for read_params and _reader_context
if is_asc_order is false.
3. rowset reader force merge read segments if read_orderby_key is true.
4. block reader and tablet reader force merge read rowsets if read_orderby_key is true.
5. for ORDER BY DESC, read and compare in reverse order
5.1 segment iterator read backward using a new BackwardBitmapRangeIterator and
reverse the result block before return to caller.
5.2 VCollectIterator::LevelIteratorComparator, VMergeIteratorContext return
opposite result for _is_reverse order in its compare function.
Co-authored-by: jackwener <jakevingoo@gmail.com>
column_ptr will be a none nullable column pointer after `column_ptr = &nullable_column->get_nested_column()`
so we should not cast column_ptr to ColumnNullable any more
