You are running into some trouble? Look here, or get in touch with the creators of Flowman.

1. Installation

ClassNotFoundException when reading/writing from/to S3

You have probably downloaded and installed Apache Spark directly from its homepage. Unfortunately since version 3.2, some Hadoop libraries are now missing from the official Spark Hadoop distributions. But Flowman contains a small script to install the missing dependencies, which should fix the error.

Wrong jar dependencies used

Some Hadoop/Spark distributions come along with a lot of jars, for example some (outdated) MS SQL JDBC connector. This can cause problems, but you can manually explicitly force Spark to use the correct jar

2. Execution

SQL error while trying to write to a MariaDB database

There seems to be a change in the default behaviour of MariaDB in regard to quoting column names. Spark uses quotation marks (”), which is not supported by MariaDB in default setup. Fortunately you can change the allowed SQL mode on MariaDB side by executing (with admin privileges) to allow ANSI quotes:


You can also set the SQL mode as a MariaDB server startup parameter by adding the following option to the MariaDB daemon:


Java error while trying to write to Azure SQL / SQL Server

Some Hadoop distributions (e.g. Cloudera) come along with an outdated MS SQL JDBC connector. This causes problems with the MS SQL Server Plugin, butyou can manually explicitly force Spark to use the correct JDBC

Database locking error while trying to write into a SQL database

You might run into some database locking issues when writing into a jdbcTable relation. The reason is that Spark (and therefore Flowman) will perform a highly parallelized write process using multiple threads, processes and workers all writing to the same database at the same time. This may be simply too much fo the target database, especially if there are constraints (like a primary key) or indexes. In this case the write operation may fail with some error messages complaining about locks.

In order to mitigate this problem, you can easily instruct Flowman to use a temporary staging table to write to in a first step and then copy its contents to the final table. This will solve locking issues in most cases, and can be simply achieved by specifying the (optional) field stagingTable as in the following example:

    kind: jdbcTable
    # Specify the name of the connection to use
    connection: frontend
    # Specify the table
    table: "users"
    # Specify name of temporary staging table (optional)
    stagingTable: "users_staging"
      kind: avro
      file: "${project.basedir}/schema/users.avsc"
      - user_id
      - name: "users_idx0"
        columns: [user_first_name, user_last_name]

Changing log level

Flowman provides two methods for changing the log level, either via command line arguments or via a log4j configuration file. If you temporarily want to increase the logging verbosity, you can simply use the -X and -XX command line switches for both flowexec and the Flowman Shell.

Another option is to provide either a log4j.properties or log4j2.properties file in the conf directory of your Flowman installation. Note that log4j2.properties uses the Log4j 2.x syntax and is only supported since Spark 3.3. Environments using older Spark versions still have to rely on the Log4j 1.x configuration file log4j.properties. You will find templates for both Log4j versions in the conf directory.