### Issue
Dictionary filtering is a mechanism that directly reads the dictionary encoding of a single string column filter condition for filter comparison. But dictionary filtered single string columns may be included in other multi-column filter conditions. This can cause problems.
For example:
`select * from multi_catalog.lineitem_string_date_orc where l_commitdate < l_receiptdate and l_receiptdate = '1995-01-01' order by l_orderkey, l_partkey, l_suppkey, l_linenumber limit 10;`
`l_receiptdate` is string filter column，it is included by multi-column filter condition `l_commitdate < l_receiptdate`.
### Solution
Resolve it by separating the multi-column filter conditions and executing it after the dictionary filter column is converted to string.
In some cases, it is necessary to unescape the original value, such as when converting a string to JSONB.
If not unescape, then later jsonb parse will be failed
### 1
In previous implementation, for each FileSplit, there will be a `TFileScanRange`, and each `TFileScanRange`
contains a list of `TFileRangeDesc` and a `TFileScanRangeParams`.
So if there are thousands of FileSplit, there will be thousands of `TFileScanRange`, which cause the thrift
data send to BE too large, resulting in:
1. the rpc of sending fragment may fail due to timeout
2. FE will OOM
For a certain query request, the `TFileScanRangeParams` is the common part and is same of all `TFileScanRange`.
So I move this to the `TExecPlanFragmentParams`.
After that, for each FileSplit, there is only a list of `TFileRangeDesc`.
In my test, to query a hive table with 100000 partitions, the size of thrift data reduced from 151MB to 15MB,
and the above 2 issues are gone.
### 2
Support when setting `max_external_file_meta_cache_num` <=0, the file meta cache for parquet footer will
not be used.
Because I found that for some wide table, the footer is too large(1MB after compact, and much more after
deserialized to thrift), it will consuming too much memory of BE when there are many files.
This will be optimized later, here I just support to disable this cache.
In parquet, min and max statistics may not be able to handle UTF8 correctly.
Current processing method is using min_value and max_value statistics introduced by PARQUET-1025 if they are used.
If not, current processing method is temporarily ignored. A better way is try to read min and max statistics if it contains
only ASCII characters. I will improve it in the future PR.
should set: enable_simdjson_reader=false in master as master enable_simdjson_reader=true by default.
Issue Number: close#21389
from rapidjson:
Query String
In addition to GetString(), the Value class also contains GetStringLength(). Here explains why:
According to RFC 4627, JSON strings can contain Unicode character U+0000, which must be escaped as "\u0000". The problem is that, C/C++ often uses null-terminated string, which treats \0 as the terminator symbol.
To conform with RFC 4627, RapidJSON supports string containing U+0000 character. If you need to handle this, you can use GetStringLength() to obtain the correct string length.
For example, after parsing the following JSON to Document d:
{ "s" : "a\u0000b" }
The correct length of the string "a\u0000b" is 3, as returned by GetStringLength(). But strlen() returns 1.
GetStringLength() can also improve performance, as user may often need to call strlen() for allocating buffer.
Besides, std::string also support a constructor:
string(const char* s, size_t count);
which accepts the length of string as parameter. This constructor supports storing null character within the string, and should also provide better performance.
Refactor the interface of create_file_reader
the file_size and mtime are merged into FileDescription, not in FileReaderOptions anymore.
Now the file handle cache can get correct file's modification time from FileDescription.
Add HdfsIO for hdfs file reader
pick from [Enhancement](multi-catalog) Add hdfs read statistics profile. #21442
1. fix concurrency bug of s3 fs benchmark tool, to avoid crash on multi thread.
2. Add `prefetch_read` operation to test prefetch reader.
3. add `AWS_EC2_METADATA_DISABLED` env in `start_be.sh` to avoid call ec2 metadata when creating s3 client.
4. add `AWS_MAX_ATTEMPTS` env in `start_be.sh` to avoid warning log of s3 sdk.
Fix error for broker load with orc file when time_zone is CST of which message is "Failed to create orc row reader. reason = Can't open /usr/share/zoneinfo/CST"
Co-authored-by: caiconghui1 <caiconghui1@jd.com>
* [Improve](dynamic schema) support filtering invalid data
1. Support dynamic schema to filter illegal data.
2. Expand the regular expression for ColumnName to support more column names.
3. Be compatible with PropertyAnalyzer and support legacy tables.
4. Default disable parse multi dimenssion array, since some bug unresolved
1. Add hdfs file handle cache for hdfs file reader
Copied from Impala, `https://github.com/apache/impala/blob/master/be/src/util/lru-multi-cache.h`. (Thanks for the Impala team)
This is a lru cache that can store multi entries with same key.
The key is build with {file name + modification time}
The value is the hdfsFile pointer that point to a certain hdfs file.
This cache is to avoid reopen same hdfs file mutli time, which can save
query time.
Add a BE config `max_hdfs_file_handle_cache_num` to limit the max number
of file handle cache, default is 20000.
2. Add file meta cache
The file meta cache is a lru cache. the key is {file name + modification time},
the value is the parsed file meta info of the certain file, which can save
the time of re-parsing file meta everytime.
Currently, it is only used for caching parquet file footer.
The test show that is cache is hit, the `FileOpenTime` and `ParseFooterTime` is reduce to almost 0
in query profile, which can save time when there are lots of files to read.
After supporting insert-only transactional hive full acid tables #19518, #19419, this PR support transactional hive full acid tables.
Support hive3 transactional hive full acid tables.
Hive2 transactional hive full acid tables need to run major compactions.
Fix some bugs of orc lazy materialization(#18615)
- Fix issue causing column size to continuously increase after `execute_conjuncts()` by calling `Block::erase_useless_column()`.
- Fix partition issues of orc lazy materialization.
- Fix lazy materialization will not be used when the predicate column is inconsistent with the orc file.
For routine load (kafka load), user can produce all data for different
table into single topic and doris will dispatch them into corresponding
table.
Signed-off-by: freemandealer <freeman.zhang1992@gmail.com>
Optimize the strategy of merging small IO to prevent severe read amplification, and turn off merged IO when file cache enabled.
Adjustable parameters:
```
// the max amplified read ratio when merging small IO
max_amplified_read_ratio=0.8
// the min segment size
file_cache_min_file_segment_size = 1048576
```
1. make ColumnObject exception safe
2. introduce FlushContext and construct schema at memtable flush stage to make segment independent from dynamic schema
3. add more test cases
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.
#18976 introduced merge small IO facility to optimize performance, and used by parquet reader.
This PR support this facility in orc reader. Current ORC reader implementation need to reposition parent present stream when reading lazy columns in lazy materialization facility. So let it works by removing `DCHECK_GE(offset, cached_data.end_offset)`.
/home/zcp/repo_center/doris_master/doris/be/src/olap/rowset/segment_v2/column_reader.cpp:895:21: runtime error: load of value 423208544, which is not a valid value for type 'doris::ReaderType'
/home/zcp/repo_center/doris_master/doris/be/src/vec/columns/column_decimal.cpp:260:33: runtime error: load of misaligned address 0x7fa3348b301c for type 'int64_t' (aka 'long'), which requires 8 byte alignment
/home/zcp/repo_center/doris_master/doris/be/src/olap/block_column_predicate.cpp:82:24: runtime error: variable length array bound evaluates to non-positive value 0
/home/zcp/repo_center/doris_master/doris/be/src/vec/columns/column_string.h:225:26: runtime error: null pointer passed as argument 2, which is declared to never be null
Fix partition field conjuncts not work.
Add predicate_partition_columns in _slot_id_to_filter_conjuncts(single slot conjuncts) to _filter_conjuncts, others should had been added from not_single_slot_filter_conjuncts.
Fix threes bugs of timestampv2 precision:
1. Hive catalog doesn't set the precision of timestampv2, and can't get the precision from hive metastore, so set the largest precision for timestampv2;
2. Jdbc catalog use datetimev1 to parse timestamp, and convert to timestampv2, so the precision is lost.
3. TVF doesn't use the precision from meta data of file format.
fix some wrong downcast founded by ubsan.
```cpp
doris/be/src/olap/bloom_filter_predicate.h:43:32: runtime error: downcast of address 0x7f8ec2b691a0 which does not point to an object of type 'doris::BloomFilterColumnPredicate<doris::TYPE_DATE>::SpecificFilter' (aka 'BloomFilterFunc<(doris::PrimitiveType)11U>')
0x7f8ec2b691a0: note: object is of type 'doris::BloomFilterFunc<(doris::PrimitiveType)12>'
e5 55 00 00 10 74 58 42 e5 55 00 00 00 00 10 00 8e 7f 00 00 20 07 6f cc 8e 7f 00 00 80 fe 68 cc
^~~~~~~~~~~~~~~~~~~~~~~
vptr for 'doris::BloomFilterFunc<(doris::PrimitiveType)12>'
```
1. TYPE_DATE/TYPE_DATETIME have same data format, so I change the cast about bloom filter to reinterpret cast.
```cpp
doris/be/src/vec/exec/format/orc/vorc_reader.h:281:17: runtime error: downcast of address 0x7f562f4c3180 which does not point to an object of type 'ColumnVector<int>'
0x7f562f4c3180: note: object is of type 'doris::vectorized::ColumnDecimal<doris::vectorized::Decimal<int> >'
74 65 00 00 20 91 70 f5 ca 55 00 00 02 00 00 00 00 00 00 00 f0 d4 4c 2f 56 7f 00 00 f0 d4 4c 2f
^~~~~~~~~~~~~~~~~~~~~~~
vptr for 'doris::vectorized::ColumnDecimal<doris::vectorized::Decimal<int> >'
```
2. doris use ColumnDecimal to store decimal elements.
Doris block does not support complex nested type now, but orc and parquet reader has generated complex nested column,
which makes the output of mysql client wrong and users confused.
Get the last modification time from file status, and use the combination of path and modification time to generate cache identifier.
When a file is changed, the modification time will be changed, so the former cache path will be invalid.
- Implements ORC lazy materialization, integrate with the implementation of https://github.com/apache/doris-thirdparty/pull/56 and https://github.com/apache/doris-thirdparty/pull/62.
- Refactor code: Move `execute_conjuncts()` and `execute_conjuncts_and_filter_block()` in `parquet_group_reader `to `VExprContext`, used by parquet reader and orc reader.
- Add session variables `enable_parquet_lazy_materialization` and `enable_orc_lazy_materialization` to control whether enable lazy materialization.
- Modify `build.sh` to update apache-orc submodule or download package every time.
Fix dict cols not be converted back to string type in some cases, which includes introduced by #19039.
For dict cols, we will convert dict cols to int32 type firstly, then convert back to string type after read block.
The block will be reuse it, so it is necessary to convert it back.
Add file cache metrics and management.
1. Get file cache metrics
> If the performance of file cache is not efficient, there are currently no metrics to investigate the cause. In practice, hit ratio, disk usage, and segments removed status are very important information.
API: `http://be_host:be_webserver_port/metrics`
File cache metrics for each base path start with `doris_be_file_cache_` prefix. `hits_ratio` is the hit ratio of the cache since BE startup; `removed_elements` is the num of removed segment files since BE startup; Every cache path has three queues: index, normal and disposable. The capacity ratio of the three queues is 1:17:2.
```
doris_be_file_cache_hits_ratio{path="/mnt/datadisk1/gaoxin/file_cache"} 0.500000
doris_be_file_cache_hits_ratio{path="/mnt/datadisk1/gaoxin/small_file_cache"} 0.500000
doris_be_file_cache_removed_elements{path="/mnt/datadisk1/gaoxin/file_cache"} 0
doris_be_file_cache_removed_elements{path="/mnt/datadisk1/gaoxin/small_file_cache"} 0
doris_be_file_cache_normal_queue_max_size{path="/mnt/datadisk1/gaoxin/file_cache"} 912680550400
doris_be_file_cache_normal_queue_max_size{path="/mnt/datadisk1/gaoxin/small_file_cache"} 8500000000
doris_be_file_cache_normal_queue_max_elements{path="/mnt/datadisk1/gaoxin/file_cache"} 217600
doris_be_file_cache_normal_queue_max_elements{path="/mnt/datadisk1/gaoxin/small_file_cache"} 102400
doris_be_file_cache_normal_queue_curr_size{path="/mnt/datadisk1/gaoxin/file_cache"} 14129846
doris_be_file_cache_normal_queue_curr_size{path="/mnt/datadisk1/gaoxin/small_file_cache"} 14874904
doris_be_file_cache_normal_queue_curr_elements{path="/mnt/datadisk1/gaoxin/file_cache"} 18
doris_be_file_cache_normal_queue_curr_elements{path="/mnt/datadisk1/gaoxin/small_file_cache"} 22
...
```
2. Release file cache
> Frequent segment files swapping can seriously affect the performance of file cache. Adding a deletion interface helps users clean up the file cache.
API: `http://be_host:be_webserver_port/api/file_cache?op=release&base_path=${file_cache_base_path}`
Return the number of released segment files. If `base_path` is not provide in url, all cache paths will be released.
It's thread-safe to call this api, so only the segment files not been read currently can be released.
```
{"released_elements":22}
```
3. Specify the base path to store cache data
> Currently, regression testing lacks test cases of file cache, which cannot guarantee the stability of file cache. This interface is generally used in regression testing scenarios. Different queries use different paths to verify different usage cases and performance.
User can set session variable `file_cache_base_path` to specify the base path to store cache data. `file_cache_base_path="random"` as default, means chosing a random path from cached paths to store cache data. If `file_cache_base_path` is not one of the base paths in BE configuration, a random path is used.
Using both `MergeRangeFileReader` and `BufferedStreamReader` simultaneously would waste a lot of memory,
so turn off prefetch data in `BufferedStreamReader` when using MergeRangeFileReader.
Issue Number: About #19038, we found in this case, l_orderkey has many nulls,
so we can filter it by null count statistics in the row group and page level,
then it can improve a lot of performance in this case.
We found qt_q11 in regression test test_external_catalog_hive is very slow.
The result is only one record, so other data should be filtered out in the parquet lazy read situation.
Then we found currently the parquet reader read many records because we can only skip parquet page. But in order to skip parquet page, currently we need to read page header, then it will caused prefetch data. Therefore, prefetch data in this case may be not good.
So there are two issues:
Skip whole row group in this case.
Prefetching data in this case may be not good, need to improve it.
This PR resolve issues 1.
