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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: You need to select small dataset from stored data, however, it is not recommended that you use Spark for that, why?
Answer: You should not use Spark for such use cases, because, Spark has to go through the all stored files and then find your result from it. You should consider using RDBMS or some other storage which index particular columns of the data and data retrieval would be faster in that case.
Question: What is structured streaming?
Answer: If you know, previously streaming data is processed using DStream and its API. But with the Structured streaming stream data will be processed using built in Spark SQL engine.
Question: What is the benefits of using structured streaming using Spark SQL?
Answer: If you see previous version of Spark, you need to learn different API for processing streaming data, which makes things difficult/Harder for developer. But with structured streaming
- Computation: You write streaming computation same as batch computation.
- Continuous data handling : Spark SQL engine take care of running streaming data computation, and updating final result as data arrives continuously.
- DataFrame/DataSet API: You can use the same DataFrame/DataSet API for streaming aggregations, stream data and batch data joins.
- Fault-tolerance: System ensures end-to-end exactly once fault-tolerance guarantees through checkpointing and write-ahead-log (WAL).
Question: How structured streaming are handled internally by Spark?
Answer: As we have mentioned previously that structured streaming is processed using Spark SQL engine only, which processes data stream as a series of small batch jobs. Having this micros batch helps in having latencies as low as 100 milliseconds and make sure that data will be processed exactly once (very important).
Question: Is there any option available, so that streaming data can be processed less than 100 milliseconds latencies?
Answer: In Spark 2.3, new feature is introduced for a low-latency data processing by changing the mode in streaming application, this new mode will be called Continuous Processing, by which end-to-end latencies can be as low as 1 millisecond with at-least once guarantees. Good point about this feature is that you dont have to change your Dataset/DataFrame code, just change the mode to get low latency, if you need it in your application.
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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