When `rewriteBatchedStatements=false`, the JDBC driver will not merge multiple insert statements into one larger insert statement. Therefore, during the batch insertion process, each insert statement needs to be sent to the MySQL server individually, leading to a higher number of network roundtrips. Network latency could potentially be a significant factor contributing to the performance degradation. For this reason, we propose to set this parameter to true by default, to enhance the performance of prepared statement batch inserts.
`Analyze database db_name ` command couldn't use current catalog, it is always using the internal catalog. This will cause the command failed to find the db. This pr is to fix this bug.
### 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.
Support alias function, Java UDF, Java UDAF for Nereids.
Implementation:
UDFs(alias function, Java UD(A)F) are saved in database object, we get it by FunctionDesc, which requires function name and arg types. So firstly we bind expressions of its children so that we can get the return type of args. Then we get the best selection.
Secondly:
For alias function:
The original function of the alias function is represented as original planner-style function, it's too hard to translate it to nereids-style expression hence we transfer it to the corresponding sql and parse it. Now we get the nereids-style function, and try to bind the function.
the bound function will also change the type by add cast node of its children to its expecting input types, so that if we travel a bound function more than one times, the cast node will be different. To solve the problem, we add a flag isAnalyzedFunction. it's set false by default and will be set true when return from the visitor function. If the flag is true, it will return immediately in visitor function.
Now we can ensure that the bound functions in children will be the same though we travel it more than one time. we can replace the alias function to its original function and bind the unbound functions.
For JavaUDF and JavaUDAF
JavaUDF and JavaUDAF can be recognized as a catalog function and hard to be entirely translated to Nereids-style function, we create a nereids expression object JavaUdf and JavaUdaf to wrap it.
All in all, now Nereids support UDFs and nesting them.
cast(A as date), where A is a string column. the min/max of result column stats should be calc like this:
convert A.minExpr to a date dateA, and then get double value from dateA.
add "explain memo plan select ..." to print memo from mysql client
dump column stats for FileScanNode, used in datalake.
PushdownJoinOtherCondition will pushdown expression in condition into project, it will block JoinReorder, so we need to pushdown project to help JoinReorder
After mergeGroup, the children of the plan are different from GroupExpr. To avoid optimizing out-dated group, we should construct new plan with groupExpr's children rather than plan's children
Use file system type and Conf as key to cache remote file system.
This could avoid get a new file system for each external table partition's location.
The time cost for fetching 100000 partitions with 1 file for each partition is reduced to 22s from about 15 minutes.
arrow is not support key column has null element , but doris default map key column is nullable , so need to deal with if doris map row if key column has null element , we put null to arrow
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.
Support hudi time travel in external table:
```
select * from hudi_table for time as of '20230712221248';
```
PR(https://github.com/apache/doris/pull/15418) supports to take timestamp or version as the snapshot ID in iceberg, but hudi only has timestamp as the snapshot ID. Therefore, when querying hudi table with `for version as of`, error will be thrown like:
```
ERROR 1105 (HY000): errCode = 2, detailMessage = Hudi table only supports timestamp as snapshot ID
```
The supported formats of timestamp in hudi are: 'yyyy-MM-dd HH:mm:ss[.SSS]' or 'yyyy-MM-dd' or 'yyyyMMddHHmmss[SSS]', which is consistent with the [time-travel-query.](https://hudi.apache.org/docs/quick-start-guide#time-travel-query)
## Partitioning Strategies
Before this PR, hudi's partitions need to be synchronized to hive through [hive-sync-tool](https://hudi.apache.org/docs/syncing_metastore/#hive-sync-tool), or by setting very complex synchronization parameters in [spark conf](https://hudi.apache.org/docs/syncing_metastore/#sync-template). These processes are exceptionally complex and unnecessary, unless you want to query hudi data through hive.
In addition, partitions are changed in time travel. We cannot guarantee the correctness of time travel through partition synchronization.
So this PR directly obtain partitions by reading hudi meta information. Caching and updating table partition information through hudi instant timestamp, and reusing Doris' partition pruning.
