1. refactor aggregate normalization to avoid data amplification before aggregate
2. remove useless aggreagte processing in ExtractAndNormalizeWindowExpression
3. only push distinct aggregate function children
TODO:
1. push down redundant expression in aggregate functions
2. refactor normalize repeat rule
3. move expression normalization and optimization after plan normalization to avoid unexpected expression optimization.
When user set default_storage_medium to true, the storage medium of all partitions should be SSD,
and cooldown time should be 9999-12-31 23:59:59.
So that it won't change to HDD.
But looks like sometimes it still change to HDD.
So I change the debug log to info to observer it.
1.some encrypt and decrypt functions have wrong blockEncryptionMode
2.topN node should compare tuples from intermediate_row_desc with first_sort_slot.tuple_id
3.must keep the limit if it's an uncorrelated in-subquery with limit on sort, like select a from t1 where a in ( select b from t2 order by xx limit yy )
make nereids generate more reasonable plans with table row count, but without column stats.
TODO: q5 and q7 is not good, because of column correlation
ps_suppkey and ps_partkey
If submit a query contains hms tbls which data files are compressed (bz2,lzo,lz4 ...), a error will occurs like this:
```[INTERNAL_ERROR]Only support csv data in utf8 codec``` .
This is because `org.apache.doris.planner.external.HiveScanNode` set `fileFormatType` as `TFileFormatType.FORMAT_CSV_PLAIN` whether the real compress algo of data files are. This pr try to fix this problem.
We shouldn't push top Project of JoinCluster in PushdownProjectThroughJoin
like
```
* Project (id + 1) if this project is top project of Join Cluster
* |
* Join
* / \
* Join Join
* / ....
* Join
```
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.
Fix some hive partition issues.
1. Fix be will crash when using hive partitions field of `date`, `timestamp`, `decimal` type.
2. Fix hdfs uri decode error when using `timestamp` partition filed which will cause some url-encoding for special chars, such as `%3A` will encode `:`.
* [Fix](multi-catalog) Fix sync hms event failed when start FE soon after.
* [Fix](multi-catalog) Fix sync hms event failed when start FE soon after.
---------
Co-authored-by: wangxiangyu@360shuke.com <wangxiangyu@360shuke.com>
Main changes:
1. If fqdn is enabled in the configuration file, when fe starts, localAddr will obtain fqdn instead of IP, priority_ Networks will fail
2. The IP and host names of Backend and Front are combined into one field, host. When fqdn is enabled, it represents the host name, and when not enabled, it represents the IP address
3. The communication between clusters directly uses fqdn, and various Connection pool add authentication mechanisms to prevent the IP address of the domain name from changing and the connection between nodes from making errors
4. No longer requires polling to verify if the IP has changed, delete fqdnManager
5. Change the method of verifying the legitimacy of nodes between FEs from obtaining client IP to displaying the identity of the transmitting node itself in the HTTP request header or the message body of the throttle
6. When processing the heartbeat, if BE finds that the host stored by itself is inconsistent with the host stored by the master, after verifying the legitimacy of the host, it will change its own host instead of directly reporting an error
7. Simplify the generation logic of fe name
Scope of influence:
1. Establishing communication connections between clusters
2. Determine whether it is the same node through attributes such as IP
3. Print Log
4. Information display
5. Address Splicing
6. k8s deployment
7. Upgrade compatibility
Test plan:
1. Change the IP address of the node, while keeping the fqdn unchanged, change the IP addresses of fe and be, and verify whether the cluster can read and write data normally
2. Use the master code to generate metadata, and use the previous metadata on the current pr to verify whether it is compatible with the old version (upgrading is no longer supported if fqdn has been enabled before)
3. Deploy fe and be clusters using k8s to verify whether the cluster can read and write data normally
4. According to https://doris.apache.org/zh-CN/docs/dev/admin-manual/cluster-management/fqdn?_highlight=fqdn#%E6%97%A7%E9%9B%86%E7%BE%A4%E5%90%AF%E7%94%A8fqdn Upgrading old clusters
5. Use streamload to specify the fqdn of fe and be to import data separately
6. Use different users to start transactions and write data using insert statements
1. fixed some error regressions (results error with big nstance_num due to incorrect order by).
2. if set parallel_fragment_exec_instance_num to 0, the concurrency in the Pipeline execution engine will automatically be set to half of the number of CPU cores.
3. add limit to parallel_fragment_exec_instance_num that it cannot be set to more than fe.conf::max_instance_num(Default: 128)
```
mysql [(none)]>set parallel_fragment_exec_instance_num = 514;
ERROR 1231 (42000): errCode = 2, detailMessage = Variable 'parallel_fragment_exec_instance_num' can't be set to the value of '514(Should not be set to more than 128)'
```
Only 2000 union will cause BE use a lot of memory, so that I enable other test in this PR only disable 2000 union case.
---------
Co-authored-by: yiguolei <yiguolei@gmail.com>
Add table level statistics, support SHOW TABLE STATS statement to show table level statistics.
Implement automatically analyze statistics, support ANALYZE... WITH AUTO ... statement to automatically analyze statistics.
TODO:
collate relevant p0 tests
Supplement the design description to README.md
Issue Number: close #xxx
- 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.
when I use mysql-jdbc 5.1.47 create a doris jdbc catalog, the largeint cannot select
When mysql-jdbc reads largeint, it will convert the format to string because it is too long
mysql> select `largeint` from type3;
ERROR 1105 (HY000): errCode = 2, detailMessage = (127.0.0.1)[INTERNAL_ERROR]Fail to convert jdbc type of java.lang.String to doris type LARGEINT on column: largeint. You need to check this column type between external table and doris table.
Support querying data from the Nebula graph database
This feature comes from the needs of commercial customers who have used Doris and Nebula, hoping to connect these two databases
changes mainly include:
* add New Graph Database JDBC Type
* Adapt the type and map the graph to the Doris type
Fix errors when read boolean type from external doris cluster by jdbc catalog:
```
ERROR 1105 (HY000): errCode = 2, detailMessage = (172.16.10.11)[INTERNAL_ERROR]Fail to convert jdbc type of java.lang.Integer to doris type BOOL on column: deleted.
You need to check this column type between external table and doris table.
```
MySQL Types and Return Values for GetColumnTypeName and GetColumnClassName are presented in https://dev.mysql.com/doc/connector-j/8.0/en/connector-j-reference-type-conversions.html.
However when tinyInt1isBit=false, GetColumnClassName of MySQL returns java.lang.Boolean, while that of Doris returns java.lang.Integer. In order to be compatible with both MySQL and Doris, Jdbc params should set tinyInt1isBit=true&transformedBitIsBoolean=true
This pr does three things:
1. add `delete_existing_files` property for outfile/export. If `delete_existing_files = true`, export/outfile will delete all files under file_path first.
2. add p2 test for export
3. modify docs
