Showing posts with label cassandra. Show all posts
Showing posts with label cassandra. Show all posts

January 04, 2022

Top 20 Apache Cassandra Interview Questions and Answers

  

                Apache recommends Cassandra as one of the most popular NoSQL distributed database management systems. Cassandra is an open-source database that is meant to store and manage enormous amounts of data without failure. Apache Cassandra is a Java database with flexible schemas that is highly scalable for Big Data models and was originally built by Facebook. There is no single point of failure with Apache Cassandra. Cassandra is a combination of column-oriented and key–value store databases, and it is one of the most popular NoSQL databases. The keyspace entity in Cassandra is the table or column family, which is the application's outermost container.

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Ques. 1): What is the purpose of Cassandra and why should you utilise it?

Answer:

Cassandra was created with the goal of handling large data workloads across several nodes with no single point of failure. Cassandra's use is influenced by a number of things.

  • It's fault-tolerant and reliable.
  • Scalability from gigabytes to petabytes
  • It's a database with columns.
  • There are no singular points of failure.
  • There isn't a requirement for a separate caching layer.
  • Schema design that is adaptable
  • It has a flexible data storage system, simple data distribution, and quick write speeds.
  • The ACID (Atomicity, Consistency, Isolation, and Durability) qualities are supported.
  • Cloud and multi-data centre capabilities
  • Compression of data

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Ques. 2): What are Cassandra's applications?

Answer:

When it comes to app development and data management, Cassandra has become the go-to solution for many businesses. Because of the ease with which an operator can work, even fresh start-ups choose it.

Cassandra is a fantastic application for collecting data from a variety of sources at a rapid rate. Cassandra could be used in an internet of things application. It might also be utilised in product and retail apps, as well as messaging, social media analytics, and even a recommendation engine.

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Ques. 3): What are the advantages of utilising Cassandra?

Answer:

  • Apache Cassandra, unlike any other database, provides near real-time speed, making the work of Developers, Administrators, Data Analysts, and Software Engineers much easier.
  • Cassandra is built on a peer-to-peer architecture rather than a master–slave design, assuring no failure.
  • It also ensures incredible flexibility by allowing numerous nodes to be added to each Cassandra cluster in any data centre. In addition, any client can send a request to any server.
  • Cassandra supports extensible scalability and can be simply scaled up and down depending on the needs. This NoSQL application does not need to be restarted while scaling because it has a high throughput for read and write operations.
  • Cassandra is also known for its powerful data replication on nodes feature, which allows users to store data in numerous locations and recover data from a different location if one node fails. The amount of copies that users want to make can be set by them.
  • When used for large datasets, it performs admirably, making it the NoSQL DB of choice for most businesses.
  • Operates on a column-oriented structure, which speeds up and simplifies the slicing process. With a column-based data model, even data access and retrieval become more efficient.
  • Furthermore, Apache Cassandra features a schema-free/schema-optional data model, which eliminates the need to display all of the columns that your application requires.
  • Learn how Cassandra vs. MongoDB can help you advance your career.

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Ques. 4): In Cassandra, explain the idea of adjustable consistency.

Answer:

Cassandra's tunable consistency is a fantastic feature that makes it a popular database among developers, analysts, and big data architects. Consistency refers to all replicas having up-to-date and synced data rows. Cassandra's adjustable consistency allows users to choose the level of consistency that best suits their needs. It encourages two types of constancy: eventual and strong consistency.

The former ensures consistency when no new updates are made to a data item, i.e., all accesses eventually return the most recently modified value. Replica convergence is a term used to describe systems that achieve eventual consistency.

For strong consistency, Cassandra supports the following condition:

R + W > N where,

N – Number of replicas

W – Number of nodes that need to agree for a successful write

R – Number of nodes that need to agree for a successful read

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Ques. 5): What is Cassandra's data storage method?

Answer:

Bytes are used to store all data.

Cassandra ensures that the bytes are encoded correctly when you specify validator.

The column is then ordered using a comparator depending on the encoding's specific ordering.

While composites are simply byte arrays with a specific encoding, each component holds a two-byte length, a byte encoded component, and a termination bit.

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Ques. 6): What is the definition of memtable?

Answer:

A memTable is a place where data is written and temporarily stored. After the data in the commit log has been completed, it is written to memtable.

In Cassandra, Memtable is a storage engine. Because each column category has its own MemTable, data in MemTable is classified into a key, and data is retrieved using the key. When the write memory is filled, the messages are automatically deleted.

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Ques. 7): Explain the Bloom Filter concept.

Answer:

Bloom filter is an off-heap (off the Java heap to native memory) data structure associated with SSTable that checks whether there is any data accessible in the SSTable before conducting any I/O disc action.

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Ques. 8): What are the functions of the shell commands "Capture" and "Consistency"?

Answer:

Cassandra has a number of Cqlsh shell commands. The command "Capture" saves the result of a command to a file, whereas the command "Consistency" shows the current consistency level or sets a new one.

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Ques. 9): What is the purpose of the read repair request?

Answer:

When the coordinator node sends requests, it checks in with the nodes to see if they have any outdated information. This data is transmitted to be read and repaired in the background before being replaced with the updated data. Read and repair requests are a way to maintain the data current while also ensuring that the requested row is consistent across all replicas.

 

Ques. 10): How does Cassandra write?

Answer:

Cassandra executes the write operation in two steps: first, it writes to a disc commit log, and then it commits to an in-memory structure called memtable. The write is complete after the two commits are successful. SSTables are used to store writes in the table structure (sorted string tables). Cassandra is more efficient when it comes to writing.

 

Ques. 11): What are the best Cassandra monitor tools?

Answer:

Despite the fact that Cassandra has built-in tolerance mechanisms, it still needs to be monitored for optimal outcomes. Cassandra utilises the following tools to keep track of its databases:

  • Solarwind server and application monitor
  • Instana
  • Instaclustr
  • AppDynamics
  • Dynatrace
  • Machine engine applications manager.

 

Ques. 12): What is Cassandra- CQL collections?

Answer:

Cassandra Multiple values can be stored in a single variable using CQL collections. CQL collections can be used in Cassandra in the following ways.

List: It is used when the order of the data needs to be maintained, and a value is to be stored multiple times (holds the list of unique elements)

SET: It is used for group of elements to store and returned in sorted orders (holds repeating elements)

MAP: It is a data type used to store a key-value pair of elements

 

Ques. 13): What is Super Column in Cassandra?

Answer:

Cassandra Super Column is a unique element consisting of similar collections of data. They are actually key–value pairs with values as columns. It is a sorted array of columns, and they follow a hierarchy when in action: keystore > column family > super column > column data structure in JSON.

Similar to the row keys, super column data entries contain no independent values but are used to collect other columns. It is interesting to note that super column keys appearing in different rows do not necessarily match and will not ever.

 

Ques. 14): Describe the CAP Theorem.

Answer:

With a strong necessity to scale systems when new resources are required, the CAP Theorem is critical to the scaling strategy's success. It's a good approach to deal with scaling in distributed systems. The Consistency, Availability, and Partition Tolerance (CAP) theorem asserts that customers can only have two of these three qualities in distributed systems like Cassandra.

It's necessary to sacrifice one of them. Consistency ensures that the client receives the most recent writing; availability ensures a sensible response in the shortest time possible; and partition tolerance ensures that the system continues to operate even if network partitions occur. AP and CP are the two alternatives available.

 

Ques. 15): What is the difference between Column and Super Column?

Answer:

Both elements work on the principle of tuples having name and value. However, the former’s value is a string, while the value of the latter is a map of columns with different data types.

Unlike Columns, Super Columns do not contain the third component of timestamp.

 

Ques. 16): What exactly is a Column Family?

Answer:

A column family, as the name implies, is a structure with an endless number of rows. A key–value pair is used to refer to these, with the key being the column name and the value being the column data. In Java, it's equivalent to a hashmap, while in Python, it's analogous to a dictionary. Remember that the columns in the rows are not confined to a specified list. Furthermore, the column family is extremely adaptable, with one row having 100 columns and the other simply having two.

 

Ques. 17): Define the management tools in Cassandra.

Answer:

DataStax OpsCenter: It is the Internet-based management and monitoring solution for Cassandra cluster and DataStax. It is free to download and includes an additional edition of OpsCenter.

SPM primarily administers Cassandra metrics and various OS and JVM metrics. Besides Cassandra, SPM also monitors Hadoop, Spark, Solr, Storm, ZooKeeper, and other Big Data platforms. The main features of SPM include correlation of events and metrics, distributed transaction tracing, creating real-time graphs with zooming, anomaly detection, and heartbeat alerting.

 

Ques. 18): In Cassandra, explain the distinctions between a node, a cluster, and a data centre.

Answer:

Cassandra is made up of several parts. A cluster is a collection of nodes that have comparable sorts of data organised together, whereas a node is a single machine running Cassandra. When serving consumers from different parts of the world, data centres are essential components. You can divide a cluster's nodes into various data centres.

 

Ques. 19): What is the purpose of the Bloom Filter in Cassandra?

Answer:

A bloom filter is a space-saving data structure for determining if an element belongs to a set. In other words, it's used to see if an SSTable contains data for a specific row. When executing a KEY LOOKUP in Cassandra, it is utilised to save IO.

 

Ques. 20): What exactly is SSTable? What makes it unique among relational tables?

Answer:

SSTable stands for 'Sorted String Table,' and it refers to a crucial Cassandra data file that supports regular written memtables. They exist for each Cassandra table and are kept on disc. Because of their immutability, SSTables do not allow the insertion or removal of data items once they have been written. Cassandra creates three different files for each SSTable: a partition index, a partition summary, and a bloom filter.

 

November 17, 2021

Top 20 Apache Spark Interview Questions & Answers

  

Ques: 1). What is Apache Spark?

Answer:

Apache Spark is an open-source real-time processing cluster computing framework. It has a vibrant open-source community and is now the most active Apache project. Spark is a programming language that allows you to programme large clusters with implicit data parallelism and fault tolerance.

Spark is one of the Apache Software Foundation's most successful projects. Spark has unquestionably established itself as the market leader in Big Data processing. Spark is used by many enterprises on clusters with thousands of nodes. Spark is now used by large companies like as Amazon, eBay, and Yahoo!


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Ques: 2). What advantages does Spark have over MapReduce?

Answer:

Compared to MapReduce, Spark has the following advantages:

Spark implements processing 10 to 100 times quicker than Hadoop MapReduce due to the availability of in-memory processing, whereas MapReduce uses persistence storage for any of the data processing activities.

Unlike Hadoop, Spark has built-in libraries that allow it to do a variety of functions from the same core, including as batch processing, steaming, machine learning, and interactive SQL queries. Hadoop, on the other hand, only supports batch processing.

Hadoop is heavily reliant on discs, but Spark encourages caching and data storage in memory. Spark is capable of performing computations multiple times on the same dataset. This is called iterative computation while there is no iterative computing implemented by Hadoop.

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Ques: 3). What exactly is YARN?

Answer:

YARN is a fundamental element in Spark, similar to Hadoop, in that it provides a central and resource management platform for delivering scalable operations across the cluster. YARN, like Mesos, is a distributed container manager, but Spark is a data processing tool. Spark can be run on YARN in the same way that Hadoop Map Reduce can. Running Spark on YARN needs the use of a Spark binary distribution with YARN support.

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Ques: 4). What's the difference between an RDD, a Dataframe, and a Dataset?

Answer:

Resilient Distributed Dataset (RDD) - RDD stands for Resilient Distributed Dataset. It is the most basic data structure in Spark, consisting of an immutable collection of records partitioned among cluster nodes. It allows us to do fault-tolerant in-memory calculations on massive clusters.

RDD, unlike DF and DS, will not keep the schema. It merely stores data. If a user wants to apply a schema to an RDD, they must first build a case class and then apply the schema to the data.

We will use RDD for the below cases:

-When our data is unstructured, A streams of text or media streams.

-When we don’t want to implement any schema.

-When we don’t care about the column name attributes while processing or accessing.

-When we want to manipulate the data with functional programming constructs than domain specific expressions.

-When we want low-level transformation, actions and control on the dataset.

DataFrame:

-Like RDD DataFrames are immutable collection of data.

-Unlike RDD DataFrame will have schema for their data making user to easily access/process large set of data which is distributed among the nodes of cluster.

-DataFrame provides a domain specific language API to manipulate distributed data and makes Spark accessible to a wider audience, beyond specialized data engineers.

-From Spark 2.x Spark DataFrames are nothing but Dataset[Row] or alias (Untyped API)

consider DataFrame as an alias for a collection of generic objects Dataset[Row], where a Row is a generic untyped JVM object

DataSet:

Dataset, by contrast, is a collection of strongly-typed JVM objects, dictated by a case class you define in Scala or a class in Java. To implement case class on RDD and use as Dataset[T].

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Ques: 5). Can you explain how you can use Apache Spark along with Hadoop?

Answer:

Apache Spark provides the benefit of being Hadoop compatible. They make a powerful tech team when they work together. Using Apache Spark and Hadoop combines the processing capability of Spark with the best capabilities of HDFS and YARN. The following are some examples of how to use Hadoop Components with Apache Spark:

Batch & Real-Time Processing – MapReduce and Spark can work together, where the former handles batch processing, and the latter handles real-time processing.

HDFS – Spark can make use of the HDFS to leverage the distributed replicated storage.

MapReduce – Apache Spark can be used in conjunction with MapReduce in a similar Hadoop cluster or independently as a processing framework.

YARN – You can run Spark applications on YARN.

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Ques: 6). What is the meaning of the term "spark lineage"?

Answer:

• In Spark, regardless of the actual data, all dependencies between RDDs will be logged in a graph. In Spark, this is referred to as a lineage graph.

• RDD stands for Resilient Distributed Dataset, with the term "resilient" referring to fault tolerance. We can re-compute the missing or damaged partition due to node failure using RDD Lineage Graph. When we generate new RDDs based on existing RDDs, we use lineage graph spark to handle the dependencies. Each RDD keeps a pointer to one or more parents, together with metadata describing the type of relationship it has with the parent RDD.

• RDD Lineage Graph in Spark can be obtained using the ToDebugString method.

 

Ques: 7). List the various components of the Spark Ecosystem.

Answer:

These are the five types of components in the Spark Ecosystem:

GraphX: Enables graphs and graph-parallel computation.

MLib: It is used for machine learning.

Spark Core: A powerful parallel and distributed processing platform.

Spark Streaming: Handles real-time streaming data.

Spark SQL: Combines Spark's functional programming API with relational processing.

 

Ques: 8). What is RDD in Spark? Write about it and explain it.

Answer:

Resilient Distributed Dataset (RDD) is an acronym for Resilient Distributed Dataset. RDDs are a fault-tolerant core data structure in Spark that is immutable. They've disseminated partitioned datasets among the cluster nodes.

Parallelizing and referencing a data set are the two methods for constructing RDDS. Lazy evaluation is the responsibility of the RDDS. The faster processing performance in Spark is due to the lazy evaluation of RDDs.

 

Ques: 9). In Spark, how does streaming work?

Answer:

Spark gets data in real time that is separated into batches. The Spark Engine processes these batches of data, and the final stream of results is returned back in batches. DStream, or Discretized Stream, is the most basic stream unit in Spark.

 

Ques: 10). Is it feasible to access and analyse data stored in Cassandra databases using Apache Spark?

Answer: 

Yes, Apache Spark may be used to retrieve and analyse data stored in Cassandra databases. Apache Spark can access and analyse data contained in Cassandra databases using the Spark Cassandra Connector. Spark should have a functionality that allows Spark executors to communicate with local Cassandra nodes and request just local data.

Cassandra and Apache Spark can be connected to speed up queries by lowering network traffic between Spark executors and Cassandra nodes.

 

Ques: 11). What are the advantages of using Spark SQL?

Answer:

Spark SQL carries out the following tasks:

Loads data from a variety of structured datasources, such as a relational database management system (RDBMS).

It may query data using SQL commands within the Spark programme as well as JDBC/ODBC connectors from third-party tools such as Tableau.

It can also provide SQL and Python/Scala code interaction.

 

Ques: 12). What is the purpose of Spark Executor?

Answer:

The Executors are obtained on top of worker nodes in the clusters when a SparkContext is formed. Spark Executors are in charge of performing computations and storing data on the worker node. They are also in charge of returning the results to the driver.

 

Ques: 13). What are the advantages and disadvantages of Spark?

Answer:

Advantages: Spark is known for real-time data processing, which may be employed in applications such as stock market analysis, finance, and telecommunications.

Spark's stream processing allows for real-time data analysis, which can aid in fraud detection, system alarms, and other applications.

Due to its lazy evaluation mechanism and parallel processing, Spark processes data 10 to 100 times quicker.

Disadvantages: When compared to Hadoop, Spark consumes greater storage space.

The task is distributed over numerous clusters rather than taking place on a single node.

Spark's in-memory processing might be costly when dealing with large amounts of data.

When compared to Hadoop, Spark makes better use of data.

 

Ques: 14). What are some of the drawbacks of utilising Apache Spark?

Answer:

The following are some of the drawbacks of utilising Apache Spark:

There is no file management system built-in. To take benefit of a file management system, integration with other platforms such as Hadoop is essential.

Higher latency, but lower throughput as a result

It does not support the processing of real-time data streams. In Apache Spark, live data streams are partitioned into batches, which are then processed and turned back into batches. To put it another way, Spark Streaming is more like micro-batch data processing than true real-time data processing.

There are fewer algorithms available.

Record-based window requirements are not supported by Spark streaming. It is necessary to distribute work across multiple clusters instead of running everything on a single node.

Apache Spark's in-memory ability becomes a bottleneck when used for the cost-efficient processing of big data.

 

Ques: 15). Is Apache Spark compatible with Apache Mesos?

Answer:

Yes. Spark can work on Apache Mesos-managed clusters, just as it works on YARN-managed clusters. Spark may run without a resource manager in standalone mode. If it has to execute on multiple nodes, it can use YARN or Mesos.

 

Ques: 16). What are broadcast variables, and how do they work?

Answer:

Accumulators and broadcast variables are the two types of shared variables in Spark. Instead of shipping back and forth to the driver, the broadcast variables are read-only variables cached in the Executors for local referencing. A broadcast variable preserves a read-only cached version of a variable on each computer instead of delivering a copy of the variable with tasks.

Additionally, broadcast variables are utilised to distribute a copy of a big input dataset to each node. To cut transmission costs, Apache Spark distributes broadcast variables using efficient broadcast algorithms.

There is no need to replicate variables for each task when using broadcast variables. As a result, data can be processed quickly. In contrast to RDD lookup(), broadcast variables assist in storing a lookup table inside the memory, enhancing retrieval efficiency.

 

Ques: 17). In Apache Spark, how does caching work?

Answer:

Caching RDDs in Spark speeds up processing by allowing numerous accesses to the same RDD. The function of Discretized Streams, or DStreams, in Spark streaming is to allow users to cache or retain data in memory.

The functions cache () and persist(level) are used to cache data in memory and cache memory based on the storage level specified, respectively.

The persist () without the level specifier is the same as cache, which means it caches the data in memory. The persist(level) method caches data at the provided storage level, such as on disc, on the server, or in off-heap memory.

 

Ques: 18).  What exactly is Akka? What does Spark do with it?

Answer:

Akka is a Scala and Java framework for reactive, distributed, parallel, and robust concurrent applications. Akka is the foundation for Apache Spark.

When assigning tasks to worker nodes, Spark employs Akka for job scheduling and messaging between the master and the worker node.

 

Ques: 19). What applications do you utilise Spark streaming for?

Answer:

When real-time data must be streamed into the Spark programme, this method is employed. It can be broadcast from a variety of places, such as Kafka, Flume, Amazon Kinesis, and others. For processing, the streamed data is separated into batches.

Spark streaming is used to conduct real-time sentiment analysis of customers on social media sites like as Twitter and Facebook, among others.

Live streaming data processing is critical for detecting outages, detecting fraud in financial institutions, and making stock market predictions, among other things.

 

Ques: 20). What exactly do you mean when you say "lazy evaluation"?

Answer:

The way Spark works with data is intellectual. When you ask Spark to perform a task on a dataset, it follows your instructions and records them so that it doesn't forget them - but it doesn't do anything until you tell it to. When map() is invoked on an RDD, the operation is not done immediately. Transformations aren't evaluated by Spark until you use them. This aids in the overall data processing workflow optimization.