PR(https://github.com/apache/doris/pull/13404) introduced that ParquetReader
will break up batch insertion when encountering null values, which leads to the bad performance
compared to OrcReader.
So this PR has pushed null map into decode function, reduce the time of virtual function call
when encountering null values.
Further more, reuse hdfsFS among file readers to reduce the time of building connection to hdfs.
* [bugfix](VecDateTimeValue) eat the value of microsecond in function from_date_format_str
* add sql based regression test
Co-authored-by: xiaojunjie <xiaojunjie@baidu.com>
1. Fix issue #13115
2. Modify the method of `get_next_block` or `GenericReader`, to return "read_rows" explicitly.
Some columns in block may not be filled in reader, if the first column is not filled, use `block->rows()` can not return real row numbers.
3. Add more checks for broker load test cases.
Add more detail profile for ParquetReader:
ParquetColumnReadTime: the total time of reading parquet columns
ParquetDecodeDictTime: time to parse dictionary page
ParquetDecodeHeaderTime: time to parse page header
ParquetDecodeLevelTime: time to parse page's definition/repetition level
ParquetDecodeValueTime: time to decode page data into doris column
ParquetDecompressCount: counter of decompressing page data
ParquetDecompressTime: time to decompress page data
ParquetParseMetaTime: time to parse parquet meta data
This change serves the following purposes:
1. use ScanPredicate instead of TCondition for external table, it can reuse old code branch.
2. simplify and delete some useless old code
3. use ColumnValueRange to save predicate
Add `JSON` datatype, following features are implemented by this PR:
1. `CREATE` tables with `JSON` type columns
2. `INSERT` values containing `JSON` type value stored in `String`, which is represented as binary format(AKA `JSONB`) at BE
3. `SELECT` JSON columns
Detail design refers [DSIP-016: Support JSON type](https://cwiki.apache.org/confluence/display/DORIS/DSIP-016%3A+Support+JSON+type)
* add JSONB data storage format type
* fix JsonLiteral resolve bug
* add DataTypeJson case in data_type_factory
* add JSON syntax check in FE
* add operators for jsonb_document, currently not support comparison between any JSON type value
* add ColumnJson and DataTypeJson
* add JsonField to store JsonValue
* add JsonValue to convert String JSON to BINARY JSON and JsonLiteral case for vliteral
* add push_json for MysqlResultWriter
* JSON column need no zone_map_index
* Revert "JSON column need no zone_map_index"
This reverts commit f71d1ce1ded9dbae44a5d58abcec338816b70d79.
* add JSON writer and reader, ignore zone-map for JSON column
* add json_to_string for DataTypeJson
* add olap_data_convertor for JSON type
* add some enum
* add OLAP_FIELD_TYPE_JSON type, FieldTypeTraits for it and corresponding cases or functions
* fix column_json offsets overflow bug, format code
* remove useless TODOs, add CmpType cases for JSON type
* add license header
* format license
* format be codes
* resolve rebase master conflicts
* fix bugs for CREATE and meta related code
* refactor JsonValue constructors, add fe JSON cases and fix some bugs, reformat codes
* modification be codes along code review advice
* fix rebase conflicts with master
* add unit test for json_value and column_json
* fix rebase error
* rename json to jsonb
* fix some data convert bugs, set Mysql type to JSON
refactor some arguments for parquet reader
1. Add new parquet context to wrap reader arguments
2. Reduced some arguments for function call
Co-authored-by: jinzhe <jinzhe@selectdb.com>
Add some utils and provide the candidate row range (generated with skipped row range of each column)
to read for page index filter
this version support binary operator filter
todo:
- use context instead of structures in close()
- process complex type filter
- use this instead of row group minmax filter
- refactor _eval_binary() for row group filter and page index filter
Currently, Doris has a variety of readers for different file formats,
such as parquet reader, orc reader, csv reader, json reader and so on.
The interfaces of these readers are not unified, which makes it impossible to call them through a unified method.
In this PR, I added a `GenericReader` interface class, and other Readers will implement this interface class
to use the `get_next_block()` method.
This PR currently only modifies `arrow_reader` and `parquet reader`.
Other readers will be modified one by one in subsequent PRs.
We already separate Array Offset64 and String Offset(32bit) in PR: #12341
Now we limit: Offset inside IColumn, Offset64 only inside ColumnArray, to avoid abuse of them.
If we use the wrong one, it will compile failed.
In old Doris version string offsets are 32bit, but it is not enough for Array type.
If we change string offsets from 32bit to 64bit, there will be problem if we upgrade BE one by one. Because at the same time 32bit Offsets and 64 bit Offsets String will exist at the same time.
As a result, we separate the Codes for Array Offsets.
Co-authored-by: cambyzju <zhuxiaoli01@baidu.com>
Read and generate parquet array column.
When D=1, R=0, representing an empty array. Empty array is not a null value, so the NullMap for this row is false,
the offset for this row is [offset_start, offset_end) whose `offset_start == offset_end`,
and offset_end is the start offset of the next row, so there is no value in the nested primitive column.
When D=0, R=0, representing a null array, and the NullMap for this row is true.
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
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
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.
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.
# 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.
