A similar bug compares to #22140 .
When executing a query with hms catalog, the query maybe failed because some hdfs files are not existed. We should just distinguish this kind of errors and skip it.
```
errCode = 2, detailMessage = (xxx.xxx.xxx.xxx)[CANCELLED][INTERNAL_ERROR]failed to init reader for file hdfs://xxx/dwd_tmp.db/check_dam_table_relation_record_day_data/part-00000-c4ee3118-ae94-4bf7-8c40-1f12da07a292-c000.snappy.orc, err: [INTERNAL_ERROR]Init OrcReader failed. reason = Failed to read hdfs://xxx/dwd_tmp.db/check_dam_table_relation_record_day_data/part-00000-c4ee3118-ae94-4bf7-8c40-1f12da07a292-c000.snappy.orc: [INTERNAL_ERROR]Read hdfs file failed. (BE: xxx.xxx.xxx.xxx) namenode:hdfs://xxx/dwd_tmp.db/check_dam_table_relation_record_day_data/part-00000-c4ee3118-ae94-4bf7-8c40-1f12da07a292-c000.snappy.orc, err: (2), No such file or directory), reason: RemoteException: File does not exist: /xxx/dwd_tmp.db/check_dam_table_relation_record_day_data/part-00000-c4ee3118-ae94-4bf7-8c40-1f12da07a292-c000.snappy.orc at org.apache.hadoop.hdfs.server.namenode.INodeFile.valueOf(INodeFile.java:86)
at org.apache.hadoop.hdfs.server.namenode.INodeFile.valueOf(INodeFile.java:76)
at org.apache.hadoop.hdfs.server.namenode.FSDirStatAndListingOp.getBlockLocations(FSDirStatAndListingOp.java:158) at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getBlockLocations(FSNamesystem.java:1927)
at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.getBlockLocations(NameNodeRpcServer.java:738)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.getBlockLocations(ClientNamenodeProtocolServerSideTranslatorPB.java:426)
at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:524)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:1025) at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:876) at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:822) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1730)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2682)
```
Sometimes, the partitions of a hive table may on different storage, eg, some is on HDFS, others on object storage(cos, etc).
This PR mainly changes:
1. Fix the bug of accessing files via cosn.
2. Add a new field `fs_name` in TFileRangeDesc
This is because, when accessing a file, the BE will get a hdfs client from hdfs client cache, and different file in one query
request may have different fs name, eg, some of are `hdfs://`, some of are `cosn://`, so we need to specify fs name
for each file, otherwise, it may return error:
`reason: IllegalArgumentException: Wrong FS: cosn://doris-build-1308700295/xxxx, expected: hdfs://[172.xxxx:4007](http://172.xxxxx:4007/)`
Fix incorrect result if null partition fields in orc file.
### Root Cause
Theoretically, the underlying file of the hive partition table should not contain partition fields. But we found that in some user scenarios, the partition field will exist in the underlying orc/parquet file and are null values. As a result, the pushed down partition field which are null values. filter incorrectly.
### Solution
we handle this case by only reading non-partition fields. The parquet reader is already handled this way, this PR handles the orc reader.
Fix decimal type check for ColumnValueRange issue and use primitive_type in orc_reader. Because in #22842 the `CppType` of `PrimitiveTypeTraits<TYPE_DECIMALXXX> ` were changed.
[Fix](orc-reader) Fix filling partition or missing column used incorrect row count.
`_row_reader->nextBatch` returns number of read rows. When orc lazy materialization is turned on, the number of read rows includes filtered rows, so caller must look at `numElements` in the row batch to determine how
many rows were not filtered which will to fill to the block.
In this case, filling partition or missing column used incorrect row count which will cause be crash by `filter.size() != offsets.size()` in filter column step.
When orc lazy materialization is turned off, add `_convert_dict_cols_to_string_cols(block, nullptr)` if `(block->rows() == 0)`.
Truncate char or varchar columns if size is smaller than file columns or not found in the file column schema by session var `truncate_char_or_varchar_columns`.
Optimization "select count(*) from table" stmtement , push down "count" type to BE.
support file type : parquet ，orc in hive .
1. 4kfiles , 60kwline num
before: 1 min 37.70 sec
after: 50.18 sec
2. 50files , 60kwline num
before: 1.12 sec
after: 0.82 sec
### 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.
### 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.
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
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>
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.
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)`.
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.
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.
Add `MergeRangeFileReader` to merge small IO to optimize parquet&orc read performance.
`MergeRangeFileReader` is a FileReader that efficiently supports random access in format like parquet and orc.
In order to merge small IO in parquet and orc, the random access ranges should be generated when creating the
reader. The random access ranges is a list of ranges that order by offset.
The range in random access ranges should be reading sequentially, can be skipped, but can't be read repeatedly.
When calling read_at, if the start offset located in random access ranges, the slice size should not span two ranges.
For example, in parquet, the random access ranges is the column offsets in a row group.
When reading at offset, if [offset, offset + 8MB) contains many random access ranges,
the reader will read data in [offset, offset + 8MB) as a whole, and copy the data in random access ranges into small
buffers(name as box, default 1MB, 64MB in total). A box can be occupied by many ranges,
and use a reference counter to record how many ranges are cached in the box. If reference counter equals zero,
the box can be release or reused by other ranges. When there is no empty box for a new read operation,
the read operation will do directly.
## Effects
The runtime of ClickBench reduces from 102s to 77s, and the runtime of Query 24 reduces from 24.74s to 9.45s.
The profile of Query 24:
```
VFILE_SCAN_NODE (id=0):(Active: 8s344ms, % non-child: 83.06%)
- FileReadBytes: 534.46 MB
- FileReadCalls: 1.031K (1031)
- FileReadTime: 28s801ms
- GetNextTime: 8s304ms
- MaxScannerThreadNum: 12
- MergedSmallIO: 0ns
- CopyTime: 157.774ms
- MergedBytes: 549.91 MB
- MergedIO: 94
- ReadTime: 28s642ms
- RequestBytes: 507.96 MB
- RequestIO: 1.001K (1001)
- NumScanners: 18
```
1001 request IOs has been merged into 94 IOs.
## Remaining problems
1. Add p2 regression test in nest PR
2. Profiles are scattered in various codes and will be refactored in the next PR
3. Support ORC reader
Fix decimal v3 precision loss issues in the multi-catalog module.
Now it will use decimal v3 to represent decimal type in the multi-catalog module.
Regression Test: `test_load_with_decimal.groovy`
Currently, there are some useless includes in the codebase. We can use a tool named include-what-you-use to optimize these includes. By using a strict include-what-you-use policy, we can get lots of benefits from it.
Fix tow bugs:
1. Enabling file caching requires both `FE session` and `BE` configurations(enable_file_cache=true) to be enabled.
2. `ParquetReader` has not used `IOContext` previously, but `CachedRemoteFileReader::read_at` needs `IOContext` after PR(#17586).
PR(#17330) has changed the column type of kay and value from array to normal column, but orc&parquet reader still cast to array column, resulting in cast error.
See #17764 for details
I have tested:
- Unit test for local/s3/hdfs/broker file system: be/test/io/fs/file_system_test.cpp
- Outfile to local/s3/hdfs/broker.
- Load from local/s3/hdfs/broker.
- Query file on local/s3/hdfs/broker file system, with table value function and catalog.
- Backup/Restore with local/s3/hdfs/broker file system
Not test:
- cold & host data separation case.
