Dynamic Resource allocation : Spark Interview Series.

3 min readJan 6, 2024

One challenge with growth in Big Data is, rising cloud costs. Every second, we are generating Terabytes of data; this leads to the need of optimizing cloud spends.

One of the best ways to achieve this is, Using Dynamic resource Allocation in spark.

Dynamic resource allocation in Apache Spark is a feature that allows the Spark application to dynamically adjust the resources allocated to it based on the workload. It helps in optimizing resource utilization and improving the overall efficiency of Spark applications. Here’s a brief explanation:

Dynamic Resource Allocation: 🔄

Definition: Dynamic resource allocation allows Spark applications to request and release resources (memory and CPU) from the cluster manager dynamically.

How It Works:

When enabled, Spark applications can acquire additional resources when the workload increases and release resources when the workload decreases.
The cluster manager monitors the resource usage and makes decisions about allocating or deallocating resources based on the application’s needs.

Benefits:

Efficient Resource Utilization: Ensures that resources are allocated based on the actual demand of the application, avoiding over-provisioning or under-provisioning.
Small cluster, bigger impact : Enables sharing resources among multiple applications running on the same cluster.

How to enable ?

Dynamic resource allocation can be configured using properties in the Spark configuration. (Suggested : spark-defaults.conf)
Key configuration properties include :

spark.dynamicAllocation.enabled true
spark.dynamicAllocation.minExecutors 1
spark.dynamicAllocation.maxExecutors 4
spark.dynamicAllocation.executorIdleTimeout 60s
spark.dynamicAllocation.schedulerBacklogTimeout 60s

Integration with Cluster Managers:

Dynamic resource allocation works with various cluster managers like Apache YARN, Apache Mesos, and Kubernetes.

Use Cases:

Well-suited for scenarios where the workload varies over time, and the demand for resources is unpredictable.
Commonly used in shared environments where multiple Spark applications coexist on the same cluster.

To use Dynamic Resource allocation, External shuffle service needs to be enabled as well.

spark.shuffle.service.enabled true

….Just restart the workers, Anddddd… we’re done !!

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About me :

I’m working as a Lead Data Engineer. Within 3 years of my career, I was promoted to SDE-3 @ Walmart, and then joined the biggest hiring platform in India as a Lead Data Engnieer, in a team which works on next-gen AI use-cases : like recommendations, generative AI, ML based classification.
I have attended and cleared 20+ interviews including Big Tech cos like Amazon, Walmart, UnitedHealth Group(Fortune top 5), Flipkart(Owned by Walmart), Media.net

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