Support delta encoding and rle(bool) to read Glue data
add delta bit pack decoder,
add delta length byte array decoder,
add delta byte array decoder.
add rle bool decoder.
We find some data type is read with delta encoding on AWS Glue, so it should be supported.
The definition of delta encoding can refer to the delta encoding in parquet.
Fix three bugs:
1. `repeated_parent_def_level ` should be the definition of its repeated parent.
2. Failed to parse schema like `decimal(p, s)`
3. Fill wrong offsets for array type
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
---------
Co-authored-by: yiguolei <yiguolei@gmail.com>
fix heap-use-after-free
The OrcReader has a internal FileInputStream, If the file is empty, the memory of FileInputStream will leak.
Besides, there is a Statistics instance in FileInputStream. FileInputStream maybe delete if the orc reader
is inited failed, but Statistics maybe used when orc reader is closed, causing heap-use-after-free error.
Potential memory leak
When init file scanner in file scan node, the file scanner prepare failed, the memory of file scanner will leak.
* [Optimize](simd json reader) Cached search results for previous row (keyed as index in JSON object) - used as a hint.
`_simdjson_set_column_value` could become a hot spot while parsing json in simdjson mode,
introduce `_prev_positions` to cache results for previous row (keyed as index in JSON object) due to the json name field order,
should be quite the same between each lines
* fix case
A const reference member variables as class member stores a temporary object, which cannot be got after the temporary object being destroyed, cause be core dump while enable debug level log
_broker_addr has been destroyed in BrokerFileReader
1. Enhencement:
For single-charset column separator，csv_reader use another method of `split value`.
2. BugFix
Set `json` file format loading to be sensitive.
Support parsing map&struct type in parquet&orc reader.
## Remaining Problems
1. Doris use array type to build the key and value column of a `map`, but doesn't fill the offsets in value column, so the offsets in value column is wasted.
2. Parquet support reading only key or value column in `map`, this PR hasn't supported yet.
3. Parquet support reading partial columns in `struct`, this PR hasn't supported yet.
Orc doesn't fill null values in new batch, but the former batch has been release.
Other types like int/long/timestamp... are flat types without pointer in them,
so other types do not need to be handled separately like string.
Support to decode nested array column in parquet reader:
1. FE should generate the right nested column type. FE doesn't check the nesting depth and legality, like map\<array\<int\>, int\>.
2. `ParquetColumnReader` has removed the filtering of page index to support nested array type.
It's too difficult to skip values in nested complex types. Maybe we should support the filtering of page index and lazy read in later PR.
3. `ExternalFileScanNode` has a bug in creating default value expression.
4. Maybe it's slow to read repetition levels in a while loop. I'll optimize this in next PR.
5. Array column has temporary `SchemaElement` in its thrift definition,
we have removed them and keep its parent in former implementation.
The remaining parent should inherit the repetition and definition level of its child.
Improve performance of parquet reader filter calculation.
- Use `filter_data` instead of `(*filter_ptr)` to merge filter to improve performance.
- Use mutable column filter func instead of original new column filter func which introduced by #16850.
- Avoid column ref-count increasing which caused unnecessary copying by passing column pointer ref.
Issue Number: close#17003
## Problem summary
The linker couldn't find some symbols because the implementation of a template member function doris::vectorized::Decoder::init_decimal_converter is missing in the header file in which the corresponding declaration is placed.
In previous implementation, when querying tvf, FE will get schema from BE.
And BE will try to open the first file to get its schema info, but for orc or parquet format,
if the file is empty, it will return error.
But even for an empty file, we can still get schema info from file's footer.
So we should handle the empty file to get schema info correctly.
Also modify the catalog doc to add some FAQ.
Set column names from path to lower case in case-insensitive case.
This is for Iceberg columns from path. Iceberg columns are case sensitive,
which may cause error for table with partitions.
To support schema evolution, Iceberg add schema information to Parquet file metadata.
But for early iceberg version, it doesn't write any schema information to Parquet file.
This PR is to support read parquet without schema information.
Hive 1.x may write orc file with internal column name (_col0, _col1, _col2...).
This will cause query result be NULL because column name in orc file doesn't match
with column name in Doris table schema. This pr is to support query Hive orc files with internal column names.
For now, we haven't see any problem in Parquet file, will send new pr to fix parquet if any problem show up in the future.
Issue Number: close#16351
Dynamic schema table is a special type of table, it's schema change with loading procedure.Now we implemented this feature mainly for semi-structure data such as JSON, since JSON is schema self-described we could extract schema info from the original documents and inference the final type infomation.This speical table could reduce manual schema change operation and easily import semi-structure data and extends it's schema automatically.
Tow improvements:
1. Refactor rowgroup&page filtering in `ParquetReader`, and use the operator overloading of Doris native c++ type to process comparison.
2. Support decimal/decimal v3/date/datev2/datetime/datetimev2
The performance of ClickBench Q30 is affected by batch_size:
| batch_size | 1024 | 4096 | 20480 |
| -- | -- | -- | -- |
| Q30 query time | 2.27 | 1.08 | 0.62 |
Because aggregation operator will create a new result block for each batch block, and Q30 has 90 columns, which is time-consuming. Larger batch_size will decrease the number of aggregation blocks, so the larger batch_size will improve performance.
Doris internal reader will read at least 4064 rows even if batch_size < 4064, so this PR keep the process of reading external table the same as internal table.
Read parquet file failed:
```
ERROR 1105 (HY000): errCode = 2, detailMessage = [INTERNAL_ERROR]Read parquet file xxx failed, reason = [CORRUPTION]The number of rows are not equal among parquet columns
```
This error may be thrown when reading non-predicate columns in lazy-read, for example:
A row group with 1000 rows has tow non-predicate columns.
Column A has one page, Column B has two pages with 500 rows for each page.
The read range of `ParquetColumnReader` is [0, 400), and the rows between [0, 450) are all filtered by predicate columns.
So column A can skip the first page, and reach the EOF, while column B can also skip the first page, but doesn't read the EOF.
Support set skip line number for stream load to load csv file.
Usage `-H skip_lines:number`:
```
curl --location-trusted -u root: -T test.csv -H skip_lines:5 -XPUT http://127.0.0.1:8030/api/testDb/testTbl/_stream_load
```
Skip line number also can be used in mysql load as below:
```sql
LOAD DATA
LOCAL
INFILE '${mysql_load_skip_lines}'
INTO TABLE ${tableName}
COLUMNS TERMINATED BY ','
IGNORE 2 LINES
PROPERTIES ("auth" = "root:");
```
External hms catalog table column names in doris are all in lower case,
while iceberg table or spark-sql created hive table may contain upper case column name,
which will cause empty query result. This pr is to fix this bug.
1. For parquet file, transfer all column names to lower case while parse parquet metadata.
2. For orc file, store the origin column names and lower case column names in two vectors, use the suitable names in different cases.
3. FE side, change the column name back to the origin column name in iceberg while doing convertToIcebergExpr.
This pr #15836 change the way to use parquet reader by first open() then init_reader().
But we forgot to call open() for iceberg delete file, which cause coredump.
Support iceberg schema evolution for parquet file format.
Iceberg use unique id for each column to support schema evolution.
To support this feature in Doris, FE side need to get the current column id for each column and send the ids to be side.
Be read column id from parquet key_value_metadata, set the changed column name in Block to match the name in parquet file before reading data. And set the name back after reading data.
`date_time_v2` will check scale when constructed datatimev2:
```
LOG(FATAL) << fmt::format("Scale {} is out of bounds", scale);
```
This [PR](https://github.com/apache/doris/pull/15510) has fixed this issue, but parquet does not use constructor to create `TypeDescriptor`, leading the `scale = -1` when reading datetimev2 data.
The main purpose of this pr is to import `fileCache` for lakehouse reading remote files.
Use the local disk as the cache for reading remote file, so the next time this file is read,
the data can be obtained directly from the local disk.
In addition, this pr includes a few other minor changes
Import File Cache:
1. The imported `fileCache` is called `block_file_cache`, which uses lru replacement policy.
2. Implement a new FileRereader `CachedRemoteFilereader`, so that the logic of `file cache` is hidden under `CachedRemoteFilereader`.
Other changes:
1. Add a new interface `fs()` for `FileReader`.
2. `IOContext` adds some statistical information to count the situation of `FileCache`
Co-authored-by: Lightman <31928846+Lchangliang@users.noreply.github.com>
A deleted file may belong to multiple data files. Each data file will read a full amount of deleted files,
so a deleted file may be read repeatedly. The deleted files can be cached, and multiple data files
can reuse the first read content.
The performance is improved by 60% in the case of single thread, and by 30% in the case of multithreading.
**Optimize**
PR #14470 has used `Expr` to filter delete rows to match current data file,
but the rows in the delete file are [sorted by file_path then position](https://iceberg.apache.org/spec/#position-delete-files)
to optimize filtering rows while scanning, so this PR remove `Expr` and use binary search to filter delete rows.
In addition, delete files are likely to be encoded in dictionary, it's time-consuming to decode `file_path`
columns into `ColumnString`, so this PR use `ColumnDictionary` to read `file_path` column.
After testing, the performance of iceberg v2's MOR is improved by 30%+.
**Fix Bug**
Lazy-read-block may not have the filter column, if the whole group is filtered by `Expr`
and the batch_eof is generated from next batch.
