What is the local-ssd used for in databricks?

[646901]

What is the use-case for local-ssd's in databricks clusters? I noticed some clusters have many Tb's worth and some have no local ssd's.

What are the pro's and con's of changing the disk size bigger and smaller?

According to the docs:

> The disk cache is configured to use at most half of the space available on the local SSDs provided with the worker nodes. For configuration options, see Configure the disk cache.

But what is the other half used for? Is it swap? Sorting tempfiles written during execution.

[Kaniz_Fatma]

Hi @646901 , Local SSDs in Databricks clusters serve several purposes and can impact performance, cost, and scalability. 

Let’s delve into the details:

Use Cases for Local SSDs:

• Low-Latency Storage: Local SSDs provide fast, low-latency storage that is ideal for workloads requiring quick access to data. These workloads include caching, intermediate data storage, and temporary files.
• Disk Cache: As you mentioned, Databricks uses local SSDs for disk caching. The disk cache stores frequently accessed data to speed up subsequent reads. This improves query performance by reducing the need to fetch data from slower storage (such as DBFS or external storage).
• Temporary Files: During execution, Spark creates temporary files (e.g., during shuffling, sorting, or intermediate computations). Local SSDs are used to store these temporary files, which helps avoid bottlenecks caused by slower network storage.

Pros and Cons of Changing Disk Size:

• Increasing Disk Size:
  • Pros:
    • More Caching: Larger local SSDs allow for more caching, which can improve query performance.
    • Reduced Spill: With more space, Spark can spill less data to slower storage (e.g., DBFS).
  • Cons:
    • Cost: Larger SSDs consume more resources (both in terms of DBUs and actual storage costs).
    • Resource Allocation: Larger SSDs reduce the available resources for other tasks (e.g., memory, CPU).
• Decreasing Disk Size:
  • Pros:
    • Cost Savings: Smaller SSDs are more cost-effective.
    • Resource Efficiency: Less space allocated to SSDs means more resources available for other tasks.
  • Cons:
    • Reduced Caching: Smaller SSDs limit the amount of data that can be cached.
    • Increased Spill: Spark may spill more data to slower storage due to limited local space.

The Other Half of Local SSDs:

• The other half of local SSDs not used for caching is typically reserved for temporary files generated during Spark job execution. These files include intermediate results, shuffle data, and other transient data.
• It’s not used for swap (as swap is typically managed by the operating system and resides in RAM or disk).
• By keeping this space available, Databricks ensures that Spark jobs have sufficient room for temporary storage without causing resource contention.

In summary, local SSDs play a crucial role in improving performance by caching data and storing temporary files. The trade-off lies in balancing cost, performance, and resource allocation based on your workload requirements.