Calculating appropriate Spark configurations, particularly memory configurations, is a blend of understanding your workload, resources available, and the specifics of how Spark utilizes memory. Let’s walk through an example calculation for some of these settings, mainly focusing on memory.

 

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Scenario: Suppose you have a cluster node with 64 GB of RAM and 16 cores. You want to run Spark on this node, taking into account other processes and the overhead of the system.

1. Executor Memory (spark.executor.memory):

 

• • System Memory: Assume you reserve 20% of the total memory (64 GB) for the Operating System and other processes. That’s 0.20×64 GB=12.8 GB0.20×64 GB=12.8 GB.

• • So, for Spark and other tasks, you have 64 GB−12.8 GB=51.2 GB64 GB−12.8 GB=51.2 GB available.

• • Spark Overhead: Spark typically needs a bit of overhead memory, which by default is the maximum of 384 MB or 10% of the executor memory, whichever is larger. Let’s reserve 10% for now.

• • Assuming you want to use most of the available memory for Spark (let’s use 80% of 51.2 GB), the executor memory would be 0.80×51.2 GB=40.96 GB0.80×51.2 GB=40.96 GB.

• • Subtracting the 10% overhead (roughly 4 GB), we get approximately 40.96 GB−4 GB=36.96 GB40.96 GB−4 GB=36.96 GB for the actual spark.executor.memory.

2. Driver Memory (spark.driver.memory):

 

• • You want to reserve some memory for the driver. Typically, for most applications, the driver doesn’t require as much memory as the executor unless you’re collecting large RDDs or DataFrames to the driver.

• • Let’s reserve 10% of the available memory after OS reservation (i.e., 10% of 51.2 GB) for the driver: 0.10×51.2 GB=5.12 GB0.10×51.2 GB=5.12 GB.

Setting in spark.conf:

Based on the above calculations, you might set:

spark.executor.memory 36g
spark.driver.memory 5g

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This is a simplistic calculation, and in real-world scenarios, several other factors come into play. It’s essential to consider the nature of your tasks, the amount of parallelism, data shuffling, garbage collection overhead, and more.

Furthermore, the cores available also play a role in how you’d set spark.executor.cores and spark.driver.cores.

After setting configurations, always monitor application performance and iterate on settings as needed.