You have an Azure stream analytics environment.
You have identified a large amount of data skew.
Which of the following cannot be used to resolve this skew?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: B.
When the data is distributed unevenly to the resources which should process it, there arises the data skew situation.
Based on the resource caps, the data for each process may be less or more, thus reducing the total processing efficiency.
We can try different methods to identify and resolve the skew in cases where there is data skew.
All the options except, reducing the number of partition keys will be helping reduce the data skew.
But if we reduce the number of partition keys, it may actually increase the data skew.
Option A is incorrect: in cases where we don't have an appropriate key for partition and distribution, it is better to use a round robin.
Option B is correct: This will not resolve the data skew.
In fact, it will increase data skew.
Option C is incorrect: Usually, by default, there will be non-recursive mode; enable recursive reducer where it is applicable.
Option D is incorrect: Combiner mode tries to distribute very big skewed-key value sets to different vertices.
To know more, please refer to the docs below:
In an Azure Stream Analytics environment, data skew can occur when some partitions contain a disproportionate amount of data compared to others. This can lead to performance issues and potentially cause the system to fail. To resolve data skew, there are several options available, but one of the options mentioned in the question cannot be used to resolve it.
A. Use round Robin Distribution: Round Robin Distribution is a method of distributing data evenly across partitions. In this method, data is assigned to partitions in a circular pattern, with each partition receiving a portion of the data. This approach can help to alleviate data skew by ensuring that data is distributed evenly across all partitions.
B. Reduce the partition keys: Reducing the number of partition keys can also help to reduce data skew. By reducing the number of keys used for partitioning, data is distributed more evenly across partitions. This approach can be particularly effective when there are too many partition keys, which can lead to some partitions receiving an excessive amount of data.
C. Use recursive reducer: A recursive reducer is a technique used to aggregate data in a hierarchical manner. This approach can be useful for reducing data skew by dividing the data into smaller, more manageable subsets that can be processed independently. However, this technique may not always be applicable or practical for resolving data skew issues in an Azure Stream Analytics environment.
D. Use row-level combiner mode: Row-level combiner mode is a feature that allows multiple rows of data to be combined into a single row. This approach can help to reduce data skew by consolidating data and reducing the number of records that need to be processed. However, this technique may not always be applicable or practical for resolving data skew issues in an Azure Stream Analytics environment.
Answer C, "Use recursive reducer," is the option that cannot be used to resolve data skew. While a recursive reducer can be useful for aggregating data in a hierarchical manner, it may not always be the most effective approach for resolving data skew in an Azure Stream Analytics environment. The other options, including round robin distribution, reducing the partition keys, and using row-level combiner mode, are all viable solutions for addressing data skew.