JDBCUtil | 玩转JDBC打造数据库操作万能工具类JDBCUtil，加入了高效的数据库连接池，利用了参数绑定有效防止SQL注入

As the exception message says:

Convert HashMap data into json string something like below.

We had similar issues in the Quarkus CI and the fix was to update the MySQL container as the version used was too old and used ciphers that were not supported anymore by the JDK.

No common ciphers between the JDK and the MySQL instance can be found so things are not working properly.

Could you try to use a more recent version of the MySQL (or MariaDB) image? Typically the default one from Testcontainers is too old.

In general, databases only allow parameters in DML, not in DDL. And in DML, it is only allowed for values. In the case of CREATE USER and CREATE ROLE, you aren't dealing with values (at least not for the user or role name), so parameterization is not possible for those. This is similar to not allowing parameters for the table name or column name in a select statement.

In theory, something like the password in CREATE PASSWORD is a value and could be parameterized, but in practice this isn't possible (at least, not that I'm aware of), as all DDL is handled as non-parameterizable.

As a minor form of protection against SQL injection, since JDBC 4.3 (introduced in Java 9), you can use Statement.enquoteIdentifier to quote identifiers, and Statement.enquoteLiteral for literals like passwords. These methods have a default implementation, but if you're using a platform with non-standard identifier quotes (e.g. like MySQL), then you must make sure it is actually overridden (which isn't the case in current versions of MySQL Connector/J AFAIK).

The first line of the stack trace is

org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'com.vaadin.tutorial.crm.ui.views.ClientListView': Bean instantiation via constructor failed; nested exception is org.springframework.beans.BeanInstantiationException: Failed to instantiate [com.vaadin.tutorial.crm.ui.views.ClientListView]: Constructor threw exception; nested exception is org.springframework.dao.InvalidDataAccessResourceUsageException: could not prepare statement; SQL [select bank0_.id as id1_0_, bank0_.Name as name2_0_ from BANK bank0_]; nested exception is org.hibernate.exception.SQLGrammarException: could not prepare statement`

What happens if you run this against your database outside of spring (i.e. directly from a db client)?

Flyway had some breaking changes between version 7.5.0 and 7.5.1. The maintainer of zonkyio/embedded-database-spring-test just implemented a fix/workaround in his library, which is included in version 1.6.3. :-)

I was able to solve this issue by passing the certificate location as job parameter --extra-files and referring in the code as below:

Spark on EMR create the driver in one of of the CORE node (by default) not on master code.

A driver (On CORE node) can't access files in master node.

So what option do you have -

1. While spinning up EMR cluster write a bootstrap script to copy the rootca_ca.jks file (s3 cp) to every worker (CORE & TASK) node and you don't to change anything in the program
2. As you are using s3-dist-cp to copy the file, it puts your file in HDFS not in linux file system.
   To access the file you need to add file system perfix. hdfs:///tmp/rootca_ca.jks.
   You don't need to put name-node address & port because its configures by EMR in core-site.xml
3. As your files are already in S3, you can use EMRFS (which is nothing but S3 as a hadoop file system). To access a file using EMRFS just put S3 url s3://test/rootca_ca.jks.
   But make sure your EC2 EMR IAM role has access to get the object from S3
   Also it comes with its own cost but as you file is small you can leverage this

• Option 1: is very hard to maintain over time, if your file needs to be changed on running cluster you will have to do it manually on every worker node.

• Option 2: as HDFS is shared file system, you can need to maintain the file in one place.

• Option 3: there is not much difference as option 2, just that your file stays and read from S3. You don't have to copy the file in HDFS.

1. I guess this: