@Data4
To enable parallel read and write operations, the ThreadPool functionality can be leveraged. This process involves specifying a list of tables that need to be read, creating a method for reading these tables from the JDBC source and saving them in DELTA format, and subsequently employing the ThreadPool for parallel processing.
- Prepare the List of Tables for Reading: Before proceeding with parallel read and write operations, you must first assemble a list of tables to be read. The list should be defined as follows:
iterationList = ["table1", "table2", "table3"]
- In this step, a method named "transformation" will be implemented to handle the reading of tables from the JDBC source and saving them in DELTA format. The transformation method will be structured as follows:
def transformation(table):
print(f"Thread for: {table} starts")
spark.read.format("jdbc").load(table).write.saveAsTable(table)
print(f"Thread for: {table} completed")
- To achieve parallel processing, the ThreadPool with an appropriate number of threads can be initialized. In this example, we will create a ThreadPool with 3 threads. The "map" function of the ThreadPool is then employed to execute the "transformation" method in parallel for each table in the "iterationList".
from multiprocessing.pool
import ThreadPool
# Initialize ThreadPool with 3 threads
pool = ThreadPool(3)
# Execute the transformation method in parallel for each table in iterationList
pool.map(transformation, iterationList)
