External hms catalog table column names in doris are all in lower case,
while iceberg table or spark-sql created hive table may contain upper case column name,
which will cause empty query result. This pr is to fix this bug.
1. For parquet file, transfer all column names to lower case while parse parquet metadata.
2. For orc file, store the origin column names and lower case column names in two vectors, use the suitable names in different cases.
3. FE side, change the column name back to the origin column name in iceberg while doing convertToIcebergExpr.
remove json functions code
remove string functions code
remove math functions code
move MatchPredicate to olap since it is only used in storage predicate process
remove some code in tuple, Tuple structure should be removed in the future.
remove many code in collection value structure, they are useless
This pr #15836 change the way to use parquet reader by first open() then init_reader().
But we forgot to call open() for iceberg delete file, which cause coredump.
Issue Number: close #xxx
This pr fix two bugs:
_jdbc_scanner may be nullptr in vjdbc_connector.cpp, so we use another method to count jdbc statistic. close [Enhencement](jdbc scanner) add profile for jdbc scanner #15914
In the batch insertion scenario, oracle database does not support syntax insert into tables values (...),(...); , what it supports is:
insert all
into table(col1,col2) values(c1v1, c2v1)
into table(col1,col2) values(c1v2, c2v2)
SELECT 1 FROM DUAL;
Support iceberg schema evolution for parquet file format.
Iceberg use unique id for each column to support schema evolution.
To support this feature in Doris, FE side need to get the current column id for each column and send the ids to be side.
Be read column id from parquet key_value_metadata, set the changed column name in Block to match the name in parquet file before reading data. And set the name back after reading data.
This PR optimize topn query like `SELECT * FROM tableX ORDER BY columnA ASC/DESC LIMIT N`.
TopN is is compose of SortNode and ScanNode, when user table is wide like 100+ columns the order by clause is just a few columns.But ScanNode need to scan all data from storage engine even if the limit is very small.This may lead to lots of read amplification.So In this PR I devide TopN query into two phase:
1. The first phase we just need to read `columnA`'s data from storage engine along with an extra RowId column called `__DORIS_ROWID_COL__`.The other columns are pruned from ScanNode.
2. The second phase I put it in the ExchangeNode beacuase it's the central node for topn nodes in the cluster.The ExchangeNode will spawn a RPC to other nodes using the RowIds(sorted and limited from SortNode) read from the first phase and read row by row from storage engine.
After the second phase read, Block will contain all the data needed for the query
`date_time_v2` will check scale when constructed datatimev2:
```
LOG(FATAL) << fmt::format("Scale {} is out of bounds", scale);
```
This [PR](https://github.com/apache/doris/pull/15510) has fixed this issue, but parquet does not use constructor to create `TypeDescriptor`, leading the `scale = -1` when reading datetimev2 data.
The origin scan pools are in exec_env.
But after enable new_load_scan_node by default, the scan pool in exec_env is no longer used.
All scan task will be submitted to the scan pool in scanner_scheduler.
BTW, reorganize the scan pool into 3 kinds:
local scan pool
For olap scan node
remote scan pool
For file scan node
limited scan pool
For query which set cpu resource limit or with small limit clause
TODO:
Use bthread to unify all IO task.
Some trivial issues:
fix bug that the memtable flush size printed in log is not right
Add RuntimeProfile param in VScanner
Support new table value function `iceberg_meta("table" = "ctl.db.tbl", "query_type" = "snapshots")`
we can use the sql `select * from iceberg_meta("table" = "ctl.db.tbl", "query_type" = "snapshots")` to get snapshots info of a table. The other iceberg metadata will be supported later when needed.
One of the usage:
Before we use following sql to time travel:
`select * from ice_table FOR TIME AS OF "2022-10-10 11:11:11"`;
`select * from ice_table FOR VERSION AS OF "snapshot_id"`;
we can use the snapshots metadata to get the `committed time` or `snapshot_id`,
and then, we can use it as the time or version in time travel clause
The main purpose of this pr is to import `fileCache` for lakehouse reading remote files.
Use the local disk as the cache for reading remote file, so the next time this file is read,
the data can be obtained directly from the local disk.
In addition, this pr includes a few other minor changes
Import File Cache:
1. The imported `fileCache` is called `block_file_cache`, which uses lru replacement policy.
2. Implement a new FileRereader `CachedRemoteFilereader`, so that the logic of `file cache` is hidden under `CachedRemoteFilereader`.
Other changes:
1. Add a new interface `fs()` for `FileReader`.
2. `IOContext` adds some statistical information to count the situation of `FileCache`
Co-authored-by: Lightman <31928846+Lchangliang@users.noreply.github.com>
For outer join / right outer join / right semi join, when HashJoinNode::pull->process_data_in_hashtable outputs a block, it will output all rows of a key in the hash table into a block, and the output of a key is completed After that, it will check whether the block size exceeds the batch size, and if it exceeds, the output will be terminated.
If a key has 2000w+ rows, memory overflow will occur when the subsequent block operations on the 2000w+ rows are performed.
