Apache Spark has gained popularity recently, in comparison to other Big Data tools. This is due to its faster in-memory computing that processes data much more efficiently. Many industry tycoons prefer Spark because it can perform Machine Learning tasks, SQL integration and streaming on large Data Sets, saving them huge operational costs.
Companies such as IBM, Huawei invested heavily in Spark because they recognized the importance of Spark in expanding their Big Data products to include Machine Learning and interactive querying. Many start-ups are quickly building their base of business on Spark.
The IT industry is quickly recruiting programmers with Spark knowledge, as well as other programming languages like Java, Python, Scala and R.
We have compiled the best 10 Spark interview questions and answers, both for newbies and experienced professionals, to help you win any Apache Spark Interview.
1. What is Apache Spark?
Apache Spark is an open source, unified analytics engine that processes large amounts of data. It’s known for its lightning fast processing speed. Programs run at 100 times the rate of Apache Spark, and it comes with built-in libraries that allow for graph processing and machine learning. It can be run on a cluster or standalone mode to access different data sources such as HBase and HDFS.
The execution engine supports cyclic data flows and works with in-memory computing.
2. Spark is so popular because of its processing speed.
Spark is a popular choice for data processing and management because of its attractive features. These are some of the unique features:
a. In-memory Computing: The DAG execution engine supports in-memory computation and fetching data from external drives to speed up the process. Data caching is also supported by the feature.
b. Reusability. Spark codes can be used in multiple ways, including data streaming and ad-hoc queries.
c. Supports multiple programming language: Spark can be integrated with many programming languages, such as Scala, Java, Scala, and R. This allows the user to program in a variety of ways.
Multi-lingual flexibility overcomes an older Hadoop limitation that allowed users to only code in Java.
d. Cost-Efficient: Apache Spark is an open-source processing unit that does not require large storage data centres to process.
e. Flexible Integration: Spark supports Hadoop and YARN.
3. What is Lazy Evaluation?
Spark is programmed so that it can recall and remember previously used commands. The execution of a Spark transformation does not start until an action is taken to trigger it.
4. What is DAG?
DAG stands for Directed Acrylic Graph. There are no direct cycles. It only has finite edges and vertices. Each edge that connects to a vertex points to the next vertex. The vertex is the Spark RDDs, while the edges are the functions performed by the RDDs.
5. What’s the purpose of Spark Core?
The Apache Spark framework supports functional programming using programming languages like Java, Python, Scala and is controlled and managed by the Spark Core.
6. What is shuffling? What command causes it?
Shuffling is the process of moving data from one partition to another, leading data movement towards executors.
Shuffling happens when two tables are connected or if the user performs the ByKey functions – GroupByKey and ReduceByKey.
There are two compression domains.
1. Spark.shuffle.compress which checks the engine for compressed shuffle outputs and
2. Spark.shuffle.spill.compress which decides whether or not to compress transitioning shuffle spill files.
7. What is RDD in Apache Spark and how does it work? What is its significance?
Resilient Distribution Datasets (RDD) is a data structure that supports a fault-tolerant, immutable collection parallel functioning elements.
Spark uses RDDs to implement efficient MapReduce functions. The data is divided into two logical parts, which create RDDs in two different ways.
* Parallelized datasets: Libraries/ Collections meant to work in parallel with other operations.
* Hadoop datasets: Datasets perform functions on file records on storage systems such as the HDFS or HBase.
8. What are the limitations to Apache Spark?
Spark is a popular data processing unit that can process 100x data at high speed. It also has many other relevant and attractive features for big data and machine-learning. However, there are still some limitations.
These include:
- File Management system: Spark does not have an independent file management system. It mostly depends on other file systems, such as HDFS, cloud-based or otherwise.
- Additional memory requirements: Although Apache Spark is economical in large-scale data processing, it is expensive to store the data in memory. Spark runs on a lot of storage, which means that users will need to buy additional RAM. This is a very expensive investment.
- Buffer: Data transferring and receiving: It is not possible to transfer data in Spark until the buffer is full. Also, the buffer stops receiving data when it is full. This means that handling the data pressure manually must be done.
9. Name the data formats that Spark supports.
It can read and process both structured and raw files. Spark files can be saved in JSON, XML and CSV.
10. What are the benefits of Parquet files?
Parquet is a columnar format that can be used to quickly analyse workloads for various data processing systems.
This reduces the amount of data that is downloaded from the disk. It allows access to columns relevant to a specific query. It supports input-output functions as well as type-specific encoding.
CONCLUSION
Apache Spark is a highly-profitable technology in the field Machine Learning and analysis Big Data. It supports many libraries and large tools.
SP has revolutionized data analysis and is opening up new career opportunities for programmers in a variety of fields, including security, health, and E-commerce.
