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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 is Parquet file?
Answer: An open source, column-oriented binary file format for Hadoop that supports very efficient compression and encoding schemes. Parquet allows compression schemes to be specified on a per-column level, and allows adding more encodings as they are invented and implemented. Encoding and compression are separated, allowing Parquet consumers to implement operators that work directly on encoded data without paying a decompression and decoding penalty, when possible.
Question: What is Beeswax application?
Answer: Beeswax is a Hue application, using that you can perform queries on Hive, even you can create tables, load data, and run and manager Hive queries.
Question: Describe LZO compression?
Answer: A free, open source compression library. LZO compression provides a good balance between data size and speed of compression. The LZO compression algorithm is the most efficient of the codecs, using very little CPU. Its compression ratios are not as good as others, but its compression is still significant compared to the uncompressed file sizes. Unlike some other formats, LZO compressed files are splittable, enabling MapReduce to process splits in parallel.
Question: Define MapReduce algorithm?
Answer: A distributed processing framework for processing and generating large data sets and an implementation that runs on large clusters of industry-standard machines.
The processing model defines two types of functions: a map function that processes a key-value pair to generate a set of intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key.
A MapReduce job partitions the input data set into independent chunks that are processed by the map functions in a parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce functions. Typically both the input and the output of the job are stored in a distributed filesystem.
The implementation provides an API for configuring and submitting jobs and job scheduling and management services; a library of search, sort, index, inverted index, and word co-occurrence algorithms; and the runtime. The runtime system partitions the input data, schedules the program's execution across a set of machines, handles machine failures, and manages the required inter-machine communication.
Question: Define a stage in Spark?
Answer: In Spark, a collection of tasks that all execute the same code, each on a different partition. Each stage contains a sequence of transformations that can be completed without shuffling the data.
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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