Processing 10 TB of Data in Databricks!!

Interviewer: Let’s assume you’re processing 10 TB of data in Databricks. How would you configure the cluster to optimize performance?

Candidate: To process 10 TB of data efficiently, I would recommend a cluster configuration with a large number of nodes and sufficient memory.

First, I would estimate the number of partitions required to process the data in parallel. Assuming a partition size of 128 MB, we would need:

10 TB = 10 x 1024 GB = 10,240 GB
Number of partitions = 10,240 GB / 128 MB = 80,000 partitions

To process these partitions in parallel, I would recommend a cluster with a large number of nodes. The number of nodes required can be calculated using the following formula:

Number of nodes = Total number of partitions / Number of partitions per node

Assuming we want to process 100-200 partitions per node (a reasonable number to ensure efficient processing), we would need:

Number of nodes = 80,000 partitions / 100-200 partitions/node = 400-160 nodes

However, this calculation assumes that each node can process all the partitions assigned to it simultaneously, which may not be the case in reality. To account for this, we can introduce a factor called the “node utilization factor” (NUF), which represents the percentage of node resources utilized for processing.

A common value for NUF is 0.5-0.8, indicating that 50-80% of node resources are utilized for processing. Taking this into account, we can adjust the number of nodes required as follows:

Number of nodes = (80,000 partitions / 100-200 partitions/node) / NUF
= (400-160 nodes) / 0.5-0.8
= 100-200 nodes

Therefore, I would recommend a cluster with 100-200 nodes to process 10 TB of data efficiently.

Interviewer: How would you decide the number of executors and executor cores required?

Candidate: To decide the number of executors and executor cores, I would consider the following factors:

– Number of partitions: 80,000 partitions
– Desired level of parallelism: 100-200 nodes
– Memory requirements: 10-20 GB per node

Assuming 5-10 executor cores per node, we would need:

Number of executor cores = 100-200 nodes x 5-10 cores/node = 500-2000 cores

Number of executors = Number of executor cores / 5-10 cores/executor = 50-200 executors

Interviewer: What about memory requirements? How would you estimate the total memory required?

Candidate: To estimate the total memory required, I would consider the following factors:

– Number of executors: 50-200 executors
– Memory per executor: 10-20 GB

Total memory required = Number of executors x Memory per executor = 500-4000 GB

Therefore, we would need a cluster with at least 500-4000 GB of memory to process 10 TB of data efficiently.

#Databricks