Fix of PR #23582
Some Fe codes are deleted by [Improvement](pipeline) Cancel outdated query if original fe restarts #23582 , need to be added back;
Fix mac build failed caused by wrong thrift declaration order.
The reason is that sql cache just use partitionKey , latestVersion and latestTime to check if the cache should be returned, if we delete some partition(s) which is not the latest updated partition, all above values are not changed, so the cache will hit.
Use a field to save the partition num of these tables and sum the partition nums and send it to BE, there are two situations which contains delete-partition ops:
- just delete some partition(s), so the sum of partition num will be lower than before.
- delete some partition(s) coexists with add some partition(s), so the latest time or latest version will be higher than before.
New structure for delete sub predicate.
Delete sub predicate uses a string type condition_str to stored temporarily now and fields will be extracted from it using std::regex, which may introduces stack overflow when matching a extremely large string(bug of libc).
Now we attempt to use a new PB structure to hold the delete sub predicate, to avoid that problem.
message DeleteSubPredicatePB {
optional int32 column_unique_id = 1;
optional string column_name = 2;
optional string op = 3;
optional string cond_value = 4;
}
Currently, 2 versions of sub predicate will both be filled. For query, we use the v2, and during compaction we still use v1. The old rowset meta with delete predicates which had sub predicate v1 will be attempted to convert to v2 when read from PB. Moreover, efforts will be made to rewrite these meta with the new delete sub predicate.
Make preparation to use column unique id to specify a column globally.
Using the column unique id rather than the column name to identify a column is vital for flexible schema change. The rewritten delete predicate will attach column unique id.
1. do not split compress data file
Some data file in hive is compressed with gzip, deflate, etc.
These kinds of file can not be splitted.
2. Support lz4 block codec
for hive scan node, use lz4 block codec instead of lz4 frame codec
4. Support snappy block codec
For hadoop snappy
5. Optimize the `count(*)` query of csv file
For query like `select count(*) from tbl`, only need to split the line, no need to split the column.
Need to pick to branch-2.0 after this PR: #22304
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/)`
* support int128 in jsonb
* fix jsonb int128 write
* fix jsonb to json int128
* fix json functions for int128
* add nereids function jsonb_extract_largeint
* add testcase for json int128
* change docs for json int128
* add nereids function jsonb_extract_largeint
* clang format
* fix check style
* using int128_t = __int128_t for all int128
* use fmt::format_to instead of snprintf digit by digit for int128
* clang format
* delete useless check
* add warn log
* clang format
Iceberg has its own metadata information, which includes count statistics for table data. If the table does not contain equli'ty delete, we can get the count data of the current table directly from the count statistics.
## Proposed changes
Refactor thoughts: close#22383
Descriptions about `enclose` and `escape`: #22385
## Further comments
2023-08-09:
It's a pity that experiment shows that the original way for parsing plain CSV is faster. Therefor, the refactor is only applied on enclose related code. The plain CSV parser use the original logic.
Fallback of performance is unavoidable anyway. From the `CSV reader`'s perspective, the real weak point may be the write column behavior, proved by the flame graph.
Trimming escape will be enable after fix: #22411 is merged
Cases should be discussed:
1. When an incomplete enclose appears in the beginning of a large scale data, the line delimiter will be unreachable till the EOF, will the buffer become extremely large?
2. What if an infinite line occurs in the case? Essentially, `1.` is equivalent to this.
Only support stream load as trial in this PR, avoid too many unrelated changes. Docs will be added when `enclose` and `escape` is available for all kinds of load.
Assume that there is a hive catalog named hive_ctl, a hive db named db1 and a table named tbl1, if we connect a slave FE and execute following commands:
1. `switch hive_ctl`
2. `show partitions from db1.tbl1`
Then we will meet the error like this:
```
MySQL [(none)]> show partitions from db1.tbl1;
ERROR 1049 (42000): errCode = 2, detailMessage = Unknown database 'default_cluster:db1'
```
The reason is that the slave FE will forward the `ShowPartitionStmt` to master FE but we do not sync the default catalog information, so the parser can not find the db and throws this exception. This is just one case, some other simillar cases will failed too.
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`.
This PR was originally #16940 , but it has not been updated for a long time due to the original author @Cai-Yao . At present, we will merge some of the code into the master first.
thanks @Cai-Yao @yiguolei
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
In this PR, we introduce TOKENIZE function for inverted index, it is used as following:
```
SELECT TOKENIZE('I love my country', 'english');
```
It has two arguments, first is text which has to be tokenized, the second is parser type which can be **english**, **chinese** or **unicode**.
It also can be used with existing table, like this:
```
mysql> SELECT TOKENIZE(c,"chinese") FROM chinese_analyzer_test;
+---------------------------------------+
| tokenize(`c`, 'chinese') |
+---------------------------------------+
| ["来到", "北京", "清华大学"] |
| ["我爱你", "中国"] |
| ["人民", "得到", "更", "实惠"] |
+---------------------------------------+
```
