Databricks cell-level code parallel execution through the Python threading library

[Phani1]

Hi Team,

We are currently planning to  implement Databricks cell-level code parallel execution through the Python threading library. We are interested in comprehending the resource consumption and allocation process from the cluster. Are there any potential implications or challenges regarding resources if we proceed with this method?

Below is the  code snippet for your reference.

import threading

def table_creation(sql_statement):

    spark.sql(sql_statement)

s1 = """CREATE TABLE a1(time timestamp)"""

s2 = """CREATE TABLE b1(time timestamp)"""

try:

    notebook_a_thread = threading.Thread(target=table_creation, args=(s1,))

    notebook_b_thread = threading.Thread(target=table_creation, args=(s2,))

    notebook_a_thread.start()

    notebook_b_thread.start()

    notebook_a_thread.join()

    notebook_b_thread.join()

except Exception as e:

    print(e)

Regards,

Janga

[Kaniz_Fatma]

Hi @Phani1, Implementing Databricks cell-level code parallel execution through the Python threading library can be beneficial for performance, but there are some considerations to keep in mind.

Let’s break it down:

1. Resource Consumption and Allocation:

   • When you execute code in parallel using threads, each thread consumes resources (CPU, memory, etc.). In your case, the table_creation function is executed concurrently for tables a1 and b1.
   • The resource consumption depends on the complexity of the SQL statements and the data being processed. Make sure your cluster has sufficient resources to handle multiple threads simultaneously.

2. Implications and Challenges:

   • Concurrency: Parallel execution introduces concurrency. While this can speed up processing, it can also lead to contention for resources. For example, if both threads try to create tables simultaneously, there might be conflicts.
   • Thread Safety: Ensure that your SQL statements and data access are thread-safe. Databricks provides a thread-safe Spark session, but custom code within threads should be carefully designed.
   • Cluster Configuration: Consider the cluster configuration (e.g., number of worker nodes, instance types) to handle concurrent requests efficiently.
   • Overhead: Thread creation and management have overhead. If the SQL statements are simple, the overhead might outweigh the benefits of parallel execution.
   • Debugging: Debugging parallel code can be challenging. If an exception occurs in one thread, it won’t necessarily halt the entire process.

3. Alternatives:

   • concurrent.futures: Instead of using raw threads, consider using the concurrent.futures library, which provides a higher-level interface for parallel execution. It allows you to submit tasks to a thread pool or process pool.
   • Databricks Jobs: If your use case involves more complex workflows, consider using Databricks Jobs to manage parallel execution. Jobs allow you to schedule and orchestrate tasks across notebooks.

4. Best Practices:

   • Profile your workload to understand resource usage.
   • Test with a smaller dataset before deploying to production.
   • Monitor resource utilization during parallel execution.
   • Handle exceptions gracefully within threads.

Remember that parallel execution can significantly improve performance, but it requires careful planning and testing. If done correctly, it can lead to better resource utilization and faster processing times¹². Good luck with your implementation! 😊

import threading

def table_creation(sql_statement):
    spark.sql(sql_statement)

s1 = """CREATE TABLE a1(time timestamp)"""
s2 = """CREATE TABLE b1(time timestamp)"""

try:
    notebook_a_thread = threading.Thread(target=table_creation, args=(s1,))
    notebook_b_thread = threading.Thread(target=table_creation, args=(s2,))

    notebook_a_thread.start()
    notebook_b_thread.start()

    notebook_a_thread.join()
    notebook_b_thread.join()
except Exception as e:
    print(e)

¹: Databricks Community: Cell-Level Code Parallel Execution ²: Bandit Tracker: How To Run Databricks Notebooks In Parallel