Enabling decommissioning in Spark is valuable, especially when dealing with cloud environments and transient instances like SPOT.
Let’s delve into the reasons behind its default state and potential downsides:
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Why Not Enabled by Default?
- Databricks, as a managed Spark platform, makes certain design choices based on a balance of flexibility, performance, and ease of use.
- By default, decommissioning is turned off in Databricks clusters. The following considerations likely influence this decision:
- Stability: Enabling decommissioning introduces additional complexity. Ensuring stability and predictable behaviour across various scenarios is crucial.
- Data Consistency: When a worker is decommissioned, any cached data or shuffle files stored on that worker are lost. This can impact performance and data consistency.
- Resource Management: Databricks aims to maintain a stable cluster size. Decommissioning could lead to frequent node replacements, affecting resource availability.
- User Expectations: Default settings prioritize simplicity and reliability for a broad user base. Not all users may be aware of or require decommissioning.
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Potential Downsides of Enabling Decommissioning:
- Data Loss: As mentioned, cached data and shuffle files on a decommissioned worker are lost. If your workload relies heavily on caching, this could impact performance.
- Increased Instability: Frequent node replacements due to decommissioning might lead to instability, especially if autoscaling is enabled.
- Complexity: Managing decommissioned nodes requires additional logic and coordination. Ensuring proper data migration and task re-computation can be challenging.
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Databricks Improvements:
In summary, while enabling decommissioning can be beneficial, weighing the trade-offs based on your specific use case is essential. Databricks allows users to turn this feature on or off as needed, making informed decisions.