This PR makes the following changes to the connection pool of JDBC Catalog
1. Set the maximum connection survival time, the default is 30 minutes
- Moreover, one-half of the maximum survival time is the recyclable time,
- One-tenth is the check interval for recycling connections
2. Keepalive only takes effect on the connection pool on BE, and will be activated based on one-fifth of the maximum survival time.
3. The maximum number of existing connections is changed from 100 to 10
4. Add the connection cache recycling thread on BE, and add a parameter to control the recycling time, the default is 28800 (8 hours)
5. Add CatalogID to the key of the connection pool cache to achieve better isolation, requires refresh catalog to take effect
6. Upgrade druid connection pool to version 1.2.20
7. Added JdbcResource's setting of default parameters when upgrading the FE version to avoid errors due to unset parameters.
This PR proposes mapping external catalog JSON types to String instead of JsonB in Apache Doris. This change is motivated by the realization that JDBC retrieves JSON data as a String JSON string, regardless of its storage format (Json(String) or Json(Binary)). Mapping to String streamlines data retrieval, simplifies write-backs, and ensures compatibility with all JSON(String) and JSON(Binary) functions, despite potentially misleading displays of JSON data as Strings in Doris. This approach avoids the performance overhead and complexity of converting each row of data from JsonB to String, making the process more efficient and elegant.
About Upgrade
To ensure query compatibility with existing Catalogs in the upgraded version,we currently still retain the capability to query external JSON types as JSONB. However, once you upgrade to the new version and either refresh the Catalog or create a new one, all external JSON types will be treated as Strings. To ensure consistent behavior,and possible future removal of support for JSON as JSONB query code, it is highly recommended that you manually refresh your Catalog as soon as possible after upgrading to the new version.
This commit overhauls the JDBC connector logic within our project, transitioning from the previous mechanism of fetching data through JNI calls for individual ResultSet items to a more efficient and unified approach using the VectorTable data structure.
Use the unified jni framework to refactor java udf.
The unified jni framework takes VectorTable as the container to transform data between c++ and java, and hide the details of data format conversion.
In addition, the unified framework supports complex and nested types.
The performance of basic types remains consistent, with a 30% improvement in string types and an order of magnitude improvement in complex types.
First of all, mysql does not have a boolean type, its boolean type is actually tinyint(1), in the previous logic, We force tinyint(1) to be a boolean by passing tinyInt1isBit=true, which causes an error if tinyint(1) is not a 0 or 1, Therefore, we need to match tinyint(1) according to tinyint instead of boolean, and this change will not affect the correctness of where k = 1 or where k = true queries
The java-udf module has become increasingly large and difficult to manage, making it inconvenient to package and use as needed. It needs to be split into multiple sub-modules, such as : java-commom、java-udf、jdbc-scanner、hudi-scanner、 paimon-scanner.
Co-authored-by: lexluo <lexluo@tencent.com>
This PR refactors the old way of writing data to JDBC External Table & JDBC Catalog, mainly including the following tasks
1. Continuing the work of @BePPPower 's PR #18594, changing the logic of splicing Inster sql to operating off-heap memory and using preparedStatement.set to write data logic to complete
2. Supplement the support written by largeint type, mainly to adapt to Java.Math.BigInteger, which uses binary operations
3. Delete the splicing SQL logic in the JDBC External Table & JDBC Catalog related written code
ToDo: Binary type，like bit,binary, blob...
Finally, special thanks to @BePPPower , @AshinGau for his work
Co-authored-by: Tiewei Fang <43782773+BePPPower@users.noreply.github.com>
Refactoring the filtering conditions in the current ExecNode from an expression tree to an array can simplify the process of adding runtime filters. It eliminates the need for complex merge operations and removes the requirement for the frontend to combine expressions into a single entity.
By representing the filtering conditions as an array, each condition can be treated individually, making it easier to add runtime filters without the need for complex merging logic. The array can store the individual conditions, and the runtime filter logic can iterate through the array to apply the filters as needed.
This refactoring simplifies the codebase, improves readability, and reduces the complexity associated with handling filtering conditions and adding runtime filters. It separates the conditions into discrete entities, enabling more straightforward manipulation and management within the execution node.
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
Currently, there are some useless includes in the codebase. We can use a tool named include-what-you-use to optimize these includes. By using a strict include-what-you-use policy, we can get lots of benefits from it.
PR(#17960) has introduced vector table which can map java table to c++ block.
In some cases(java udf & jdbc exector), we should map c++ block to java table. This PR implements this function.
The memory structure of java vector table and c++ block is consistent,
so the implementation doesn't copy the block, just passes the memory address.
jdbc read array type get result from Doris is string, PG is java.sql.array, CK is java.lang.object
it's difficult to maintain and read the code,
so change all database's array result to string, then add a cast function from string to doris array type
In clickhouse's 4.x version of jdbc, some UInt types use special Java types, so I adapted Doris's ClickHouse JDBC External
```
com.clickhouse.data.value.UnsignedByte;
com.clickhouse.data.value.UnsignedInteger;
com.clickhouse.data.value.UnsignedLong;
com.clickhouse.data.value.UnsignedShort;
```
When using JDBC Catalog to query the Doris data, because Doris does not provide the cursor reading method (that is, fetchBatchSize is invalid), Doris will send the data to the client at one time, resulting in client OOM.
The MySQL protocol provides a stream reading method. Doris can use this method to avoid OOM. The requirements of using the stream method are setting fetchbatchsize = Integer.MIN_VALUE and setting ResultSet.TYPE_FORWARD_ONLY, ResultSet.CONCUR_READ_ONLY
This pr does three things:
1. Use Druid instead of HikariCP in JdbcClient
2. when download udf jar, add the name of the jar package after the local file name.
3. refactor some jdbcResource code
when pg table have some unsupported column type like: point, polygon, jsonb......
jdbc catalog will convert it to string type in doris. but get result set in java is org.postgresql.util.PGobject
Some test need this pr: #16442
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;
