In Spark, the level of parallelism is determined by the number of partitions and the number of executor cores. Each task runs on a single core, so having more executor cores allows more tasks to run in parallel.
To achieve parallelism, you need to explicitly introduce parallel processing. Spark provides several ways to achieve parallelism, such as using map operations on RDDs or DataFrames, or using the concurrent.futures module in Python.
1) If your function can be applied to elements of an RDD or DataFrame, you can use Spark's map operation to run it in parallel across the cluster.
# Define your function
def my_function(x)
return x * x
# Apply the function in parallel
result_rdd = rdd.map(my_function)
# Collect the results
results = result_rdd.collect()
print(results)
2) If you need to run a Python function in parallel and it doesn't fit well with Spark's map operation, you can use the concurrent.futures module.
# Define your function
def my_function(x)
return x * x
# Example data
data = [1, 2, 3, 4, 5]
# Use ThreadPoolExecutor or ProcessPoolExecutor
with concurrent.futures.ThreadPoolExecutor() as executor:
results = list(executor.map(my_function, data))
print(results)
