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Previous | Next | Audio Book for Spark Interview Questions is available here | Top 150 Latest Spark Interview Questions | Quickly go through Spark Training Python & Scala
Question: What all types of plans can be created by the Catalyst optimizer?
Answer: Catalyst optimizer can create following two query plans
1.Logical plan: It defines the computations on the DataSets, without defining how to carry out the specific computations.
2.Physical plan: It defines computation of the datasets, which can be executed to get expected result. Generally multiple physical plans are generated by the optimizer and then later on using Cost-based optimizer, less costly plan will be selected to execute the query.
Question: How do you print the all the plans created by Catalyst optimizer for running a query?
Answer: We have to use explain(Boolean) method. Something similar to below
Question: What is Project Tungsten?
Answer: You can say that, this is one of the largest execution engine for Spark. It has more focus on observing CPU and Memory, rather than I/O and network. In Spark CPU and Memory was the major bottleneck for performance.
Before Spark 2.0, a most of the CPU cycles were wasted, rather than using for computation, they were used for read/write of intermediate data to CPU cache.
Project tungsten helped in improving efficiency of memory and CPU, so that maximum hardware limits can be used.
Question: Do you see any pain point with regards to Spark DStream?
Answer: There are some common issues with the Spark DStream
- Timestamp: It consider timestamp, when event entered into the Spark system, rather than attached timestamp of the event.
- API: You have to write different code for both batch and steam processing.
- Failure condition: Developer has to manage various failure conditions.
Question: Please list down some advantages of the Structured Streaming compare to DStream?
Answer: As we have seen in previous question DStream has various issues, thats the reason Structured Streaming introduced in the Apache Spark
- It is Fast
- Fault-tolerant
- Exactly-once stream processing approach
- Input data can be thought as a append only table (grows continuously)
- Trigger: It can specify a trigger, which can check for input data in a defined time interval.
- API: The high level API is built on the SparkSQL engine and is tightly integrated with SQL queries, DataFrame and DataSet APIs.
Previous | Next | Audio Book for Spark Interview Questions is available here | Top 150 Latest Spark Interview Questions | Quickly go through Spark Training Python & Scala
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