What are the limitations of Apache spark?

What are the limitations of Apache Spark

• No File Management system. Spark has no file management system of its own.
• No Support for Real-Time Processing. Spark does not support complete Real-time Processing.
• Small File Issue.
• Cost-Effective.
• Window Criteria.
• Latency.
• Less number of Algorithms.
• Iterative Processing.

What type of applications are not suitable or efficient with spark RDDs model and why?

Limitations. Spark RDDs are not much suitable for applications that make updates to the state store such as storage systems for a web application. For these applications, it is more efficient to use systems that perform traditional update logging and data checkpointing, such as databases.

Which of the following are uses of Apache spark SQL?

(21)Which of the following are uses of Apache Spark SQL? (i)It executes SQL queries. (ii)When we run SQL within another programming language we will get the result as Dataset/DataFrame. (iv)We can read data from existing Hive installation using SparkSQL.

Why do we use Apache spark?

Apache Spark is an open-source, distributed processing system used for big data workloads. It utilizes in-memory caching and optimized query execution for fast queries against data of any size.

What is an Apache Spark What are the advantages of using Apache spark over Hadoop?

Spark has been found to run 100 times faster in-memory, and 10 times faster on disk. It’s also been used to sort 100 TB of data 3 times faster than Hadoop MapReduce on one-tenth of the machines. Spark has particularly been found to be faster on machine learning applications, such as Naive Bayes and k-means.

What are spark applications?

A Spark application is a self-contained computation that runs user-supplied code to compute a result. Spark applications run as independent sets of processes on a cluster. It always consists of a driver program and at least one executor on the cluster.