* [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. Fix MC jni scanner OOM
2. add the second datetime type for MC SDK timestamp
3. make s3 uri case insensitive by the way
4. optimize max compute scanner parallel model
In doris regression-test/suites, a lot of test cases quit immediately only if "FINISHED", otherwise they will wait till timeout. For example:
while (max_try_secs--) {
String res = getJobState(tbName1)
if (res == "FINISHED") {
sleep(3000)
break
} else {
Thread.sleep(1000)
if (max_try_secs < 1) {
println "test timeout," + "state:" + res
assertEquals("FINISHED", res)
}
}
}
This PR added checks so that these test cases can quit immediately also if "CANCELLED", which is the only unchanging status other than "FINISHED".
Optimize the usage of fs benchmark tool:
1. Remove `Open` benchmark, it is useless.
2. Remove `Delete` benchmark, it is dangerous.
3. Add `SingleRead` benchmark, user can specify an exist file to test read operation:
`sh bin/run-fs-benchmark.sh --conf=conf/hdfs_read.conf --fs_type=hdfs --operation=single_read`
4. Modify the `run-fs-benchmark.sh`, remove `OPTS` section, use options in `fs_benchmark_tool` directly
5. Add some custom counters in the benchmark result, eg:
```
--------------------------------------------------------------------------------------------------------------------------------
Benchmark Time CPU Iterations UserCounters...
--------------------------------------------------------------------------------------------------------------------------------
HdfsReadBenchmark/iterations:1/repeats:3/manual_time/threads:1 6864 ms 2385 ms 1 ReadRate=200.936M/s
HdfsReadBenchmark/iterations:1/repeats:3/manual_time/threads:1 3919 ms 1828 ms 1 ReadRate=351.96M/s
HdfsReadBenchmark/iterations:1/repeats:3/manual_time/threads:1 3839 ms 1819 ms 1 ReadRate=359.265M/s
HdfsReadBenchmark/iterations:1/repeats:3/manual_time/threads:1_mean 4874 ms 2011 ms 3 ReadRate=304.054M/s
HdfsReadBenchmark/iterations:1/repeats:3/manual_time/threads:1_median 3919 ms 1828 ms 3 ReadRate=351.96M/s
HdfsReadBenchmark/iterations:1/repeats:3/manual_time/threads:1_stddev 1724 ms 324 ms 3 ReadRate=89.3768M/s
HdfsReadBenchmark/iterations:1/repeats:3/manual_time/threads:1_cv 35.37 % 16.11 % 3 ReadRate=29.40%
HdfsReadBenchmark/iterations:1/repeats:3/manual_time/threads:1_max 6864 ms 2385 ms 3 ReadRate=359.265M/s
HdfsReadBenchmark/iterations:1/repeats:3/manual_time/threads:1_min 3839 ms 1819 ms 3 ReadRate=200.936M/s
```
- For `open_read` and `single_read`, add `ReadRate` as `bytes per second`.
- For `create_write`, add `WriteRate` as `bytes per second`.
- For `exists` and `rename`, add `ExistsCost` and `RenameCost` as `time cost per one operation`.
In #10370, we try to opt string evaluate performance by rewrite the predicate using dict value. But it has to check if the string column is full dict encoding. So that we add a logic to read the last page of the string column to check it.
But it has some bad performance for cold data because it has to load the column's ordinal index and zone map index. In some scenario for example, select * from table where pk_col=1. If the query condition is primary key, the result maybe just a few rows but the result may have 100 columns, it will cost a lot of time to load these indices. We could find a lot of time is spending on block_init_time.
In my test, a table with 50 string columns and query with primary key.
The first read time will reduce from 220ms to 40ms.
1. query cache for chinese tokenizer is confusing when just converting w_char to char.
2. seperate query_type from inverted_index_reader to clean code.
The default value of RefreshCatalogStmt.invalidCache is false now, but the RefreshManager.RefreshTask does not invoke RefreshCatalogStmt.analyze() so it will not invalidate the cache. This pr mainly fix this problem
In the previous implementation, the check for groupby exprs was ignored. Add this necessary check to make sure it would work
You could reproduce it by runnning belowing sql:
CREATE TABLE t_push_filter_through_agg (col1 varchar(11451) not null, col2 int not null, col3 int not null)
UNIQUE KEY(col1)
DISTRIBUTED BY HASH(col1)
BUCKETS 3
PROPERTIES(
"replication_num"="1"
);
CREATE VIEW `view_i` AS
SELECT
`b`.`col1` AS `col1`,
`b`.`col2` AS `col2`
FROM
(
SELECT
`col1` AS `col1`,
sum(`cost`) AS `col2`
FROM
(
SELECT
`col1` AS `col1`,
sum(CAST(`col3` AS INT)) AS `cost`
FROM
`t_push_filter_through_agg`
GROUP BY
`col1`
) a
GROUP BY
`col1`
) b;
SELECT SUM(`total_cost`) FROM view_a WHERE `dt` BETWEEN '2023-06-12' AND '2023-06-18' LIMIT 1;
SQL may forward to master to execute when connecting to follower node, the result should be set to `StmtExecutor#proxyResultSet`
Before this PR, in above scenario , submit analyze sql by mysql client/jdbc whould return get malformed packet/ Communication failed.
set enable function pushdown default to false.
enable it in fuzzy mode to test this feature.
We should remove function pushdown in the future since we already have common expr pushdown.
Co-authored-by: yiguolei <yiguolei@gmail.com>
