The data engineer is using Spark's MEMORY_ONLY storage level.
Which indicators should the data engineer look for in the Spark UI's Storage tab to signal that a cached table is not performing optimally?
The data engineer is using Spark's MEMORY_ONLY storage level.
Which indicators should the data engineer look for in the Spark UI's Storage tab to signal that a cached table is not performing optimally?
If the Spark MEMORY_ONLY storage level is being used, any data that is spilled to disk indicates that there is insufficient memory to store all the data in memory, which directly contradicts the purpose of using MEMORY_ONLY. Therefore, if the Size on Disk is greater than 0, it signals that the cached table is not performing optimally.
I would say C
C is correct here
C is correct
B. This annotation says that some partitions of the cached data have been spilled to disk because there wasn't enough memory to keep them.
Correct Answer: B Option B, is the most correct and relevant choice for an indicator that a cached table is not performing optimally in a MEMORY_ONLY scenario. If an RDD block includes a "?" annotation, it strongly suggests issues with caching, which would directly impact the performance and expected behavior of MEMORY_ONLY caching. This indication points to a failure to cache the data entirely in memory, which is what MEMORY_ONLY intends to do. Option C, could also be a relevant indicator in general caching scenarios (e.g., MEMORY_AND_DISK), but it contradicts the MEMORY_ONLY setting directly. Therefore, Option B is chosen based on the specific storage level described.
*THE CORRECT ANSWER IS: C* PLEASE IGNORE MY PREVIOUS ANSWER. Long story short, B is correct in the context of non-functional requirement, but the question is based in functional requirement, and sorry for the confusion.
It's simple, if MEMORY_ONLY is used, anything spilled to disk would indicate a problem.
The RDD answer is incorrect for this question due to the fact that while this indicates a failure to cache, it is more specific to identifying individual blocks that failed to cache rather than providing a general signal of a suboptimal performance for the entire cached table.
is MEMORY_ONLY
Correct Answer: B In the Spark UI's Storage tab, an indicator that a cached table is not performing optimally would be the presence of the _disk annotation in the RDD Block Name. This annotation indicates that some partitions of the cached data have been spilled to disk because there wasn't enough memory to hold them. This is suboptimal because accessing data from disk is much slower than from memory. The goal of caching is to keep data in memory for fast access, and a spill to disk means that this goal is not fully achieved.
sorry it's C