The Effective Way of Doing Api Ingestion using Databricks

“Coding is like trying to juggle 10 balls at once. Threading helps you keep them all in the air.” In the world of Python programming and PySpark data processing, threading stands as a pivotal technique for unlocking concurrent execution. This blog explores the power of threading in enhancing performance, optimizing resource utilization, and tackling real-world challenges, offering insights and best practices for modern software development.

Threading in Python

In the realm of Python programming, threading represents a gateway to concurrent execution within a single process. This concurrent model enables different sections of code to run simultaneously, a boon, particularly in I/O-bound scenarios where waiting for external resources, dominates processing time.

Python’s threading capabilities are facilitated by the threading module, offering a high-level interface for managing threads. Threads in Python share memory space within a process, simplifying communication and data exchange between them.

Program Example:

from datetime import datetime

import concurrent.futures

import time

def get_data():



with concurrent.futures.ThreadPoolExecutor() as executor:, range(10))

Threading in PySpark

Extending the paradigm of threading to the distributed computing landscape, PySpark equips developers with tools to leverage parallelism across clusters. This is particularly advantageous for handling massive datasets where processing efficiency is paramount.

Threading in PySpark becomes invaluable in scenarios involving I/O operations or blocking calls, such as interfacing with databases or file systems. By interleaving these operations with computations, PySpark optimizes resource utilization and minimizes idle time.

While threading in PySpark aligns with the underlying Spark architecture, including concepts like RDDs and the Spark execution model, its effectiveness hinges on understanding the nuances of your job and cluster configuration.

Why Threading in PySpark?

Asynchronous Operations: Despite Spark’s distributed nature, certain tasks within an application may be I/O-bound. Threading enables asynchronous operations like API calls or file I/O without impeding Spark’s execution context.

Fine-Grained Control: Threading offers granular control over parallelism, allowing optimization for specific tasks or operations within a PySpark application.

Custom Processing Logic: Tailored processing logic can exploit multi-threading within a single machine, complementing Spark’s distributed processing capabilities for specific tasks.

Resource Management: Threading aids in efficient resource management within a Spark application, especially in handling connections to external databases or services.

In essence, while Spark orchestrates distributed parallelism, threading augments PySpark applications by addressing specific needs such as asynchrony, integration, control, custom logic, and resource optimization. It’s imperative to exercise caution and ensure thread safety, particularly when dealing with shared resources or mutable states.

A real-world example: Normal Processing vs. multi-threading in Pyspark

Imagine a scenario where we’re fetching data from an API, and each request takes around a minute to return a single row of data. To replicate this situation, we conducted tests using PySpark on Databricks. We compared the performance of normal processing with multi-threading across different dataset sizes—10 rows, 100 rows, and 1000 rows. Here are the results of our investigation:

  • Normal Processing: We evaluated the standard processing approach within PySpark on Databricks.
  • Multi-threading: We explored the performance gains achieved by employing multi-threading techniques within PySpark on Databricks.

Our aim was to discern how these two methods handle the retrieval and processing of data under the given circumstances. This analysis provides valuable insights into optimizing data processing workflows, especially in scenarios where fetching data from external sources incurs significant latency. Let’s delve into the outcomes of our tests to uncover the most efficient approach for handling such data retrieval tasks.

No. Messages Processed1101001000
Normal (In Min’s)1101001000
Multi-Threading (In Min’s)11535

GitHub link for the example: click here.

The best place to use Threading

Utilizing multi-threading is highly advisable when retrieving data from an API to our bronze location, particularly in scenarios where the volume of API requests exceeds 100.

This approach enhances efficiency by allowing simultaneous data retrieval processes, thereby reducing latency and optimizing resource utilization.

By leveraging multiple threads, we can significantly enhance the throughput of data fetching operations, ensuring timely and responsive data acquisition even under heavy request loads. This proactive utilization of multi-threading aligns with best practices in modern software development, enabling smoother and more scalable data integration processes.


Improved Parallelism: Multi-threading allows for concurrent execution of tasks, enabling PySpark to process multiple operations simultaneously, thereby increasing overall throughput and performance.

Enhanced Resource Utilization: By leveraging multiple threads within a single PySpark application, it’s possible to efficiently utilize available CPU resources, leading to better resource management and reduced idle time.

Reduced Latency: Multi-threading can help decrease the latency of certain operations in PySpark by executing them concurrently, resulting in faster data processing and response times.

Simplified Code Structure: In some cases, multi-threading can simplify the implementation of complex parallel processing logic within PySpark applications, leading to more concise and maintainable code.

Scalability: With multi-threading, PySpark applications can scale more effectively to handle larger datasets and accommodate growing workloads, thereby supporting the scalability requirements of big data processing tasks.


Increased Complexity: Implementing multi-threading in PySpark introduces additional complexity to the application code, as developers need to manage thread synchronization, communication, and potential race conditions.

Risk of Deadlocks: In multi-threaded PySpark applications, deadlocks may occur if threads become blocked waiting for resources that are held by other threads, potentially leading to application instability or crashes.

Limited Scalability on Single Node: While multi-threading can improve performance on a single node, it may not necessarily scale efficiently across multiple nodes in a distributed PySpark cluster, as it primarily leverages CPU resources on a single machine.

Debugging Challenges: Troubleshooting issues in multi-threaded PySpark applications can be more challenging compared to single-threaded ones, as debugging concurrent code requires careful analysis of thread interactions and potential race conditions.


In conclusion, threading in Python, whether in standard programming or within PySpark, presents a powerful tool for enhancing performance and scalability in data processing tasks. Leveraging multi-threading allows for concurrent execution of tasks, reducing latency and improving resource utilization. However, it’s crucial to weigh the advantages against the increased complexity and potential challenges, such as managing thread synchronization and debugging issues like deadlocks. Ultimately, judicious use of threading, particularly in scenarios like API ingestion and data retrieval, can lead to significant improvements in efficiency and throughput, aligning with modern software development best practices.

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Author: Xavier Don Bosco

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