Cassandra Interview Questions
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1. What is Cassandra?
Cassandra is a distributed, column-family database management system designed to provide high availability and partition tolerance at the cost of consistency.
It is a NoSQL database used for large-scale, real-time data processing applications.
2. What is the difference between Cassandra and HBase?
The main difference between Cassandra and HBase is their column family structure.
Cassandra provides availability and partition tolerance while sacrificing some consistency, while HBase provides consistency and partition tolerance at the cost of availability.
3. What is the critical challenge in maintaining consistency and scalability in distributed systems?
The key challenge in maintaining consistency and scalability in distributed systems is that most RDBMS systems have only one disk for data writing and the other for recovery when a node fails.
As RDBMS scales, the problem becomes more evident.
4. What is the purpose of partition tolerance?
Partition tolerance is vital because sites are becoming geographically distributed.
It allows the system to continue functioning even if one or more nodes in a cluster go down, ensuring that data remains accessible to clients.
5. What is the difference between offline replication and online replication?
Online replication replicates data as written to the database, while offline replication replicates data at a specified time.
Online replication makes partition tolerance possible by moving data between data centres.
6. What is HBase?
A distributed, column-family database management system based on HDFS, HBase allows real-time read and write access to enormous amounts of data.
In exchange for high availability and partition tolerance, it sacrifices consistency.
7. Is eventual consistency a good enough case?
Whether eventual consistency is a good enough case depends on the application’s needs. If consistency is critical, then eventual consistency may not be sufficient.
However, for some applications, eventual consistency may be acceptable.
8. What is the role of RDBMS in ensuring data moves across data centres?
RDBMS in A and C is the only place where RDBMS supports wild IP applications.
This means that RDBMS in these configurations can be used to ensure that data moves across data centres, making them suitable for partition tolerance.
9. What is the new feature in Facebook’s graph search?
The new feature in Facebook’s graph search is not necessarily done by Cassandra but rather by the graph store.
This shows that while Cassandra may be a popular choice for large-scale, real-time data processing applications, it is not the only option available.
10. What is partition tolerance in a distributed database?
Distributed databases with partition tolerance can continue running if one or more nodes in a cluster fail.
This implies data is replicated across numerous nodes and can be retrieved from another if one fails.
11. What is the purpose of the Cap theorem?
According to the Cap theorem, distribution systems can only optimise consistency, availability, and partition tolerance.
Developing a distributed system requires choosing between consistency, availability, and partition tolerance.
12. What are graph databases used for in support searches?
Graph databases are used for support searches to store and manage data related to relationships and connections between different entities.
13. Why are graph databases not fully implementing Cassandra’s models?
The main state of distributed graph databases prevents them from thoroughly implementing Cassandra’s models. Their graph wrapper has been attempted, but their current support has not been disclosed.
14. How is data migrated from the HBase cluster to Cassandra?
Data from HBase nodes is extracted and loaded onto Cassandra nodes to migrate. Similar data format and structure but different interfaces.
Data must be retrieved from HBase and pushed to Cassandra, requiring column family definitions.
15. What is the challenge of managing a distributed system in RDBMS?
Managing a distributed RDBMS system requires consistency and scalability. Atomicity, consistency, interaction, and durability are hard to maintain, making scale unfeasible.
In RDBMS, replications are only available offline.
16. What are the three trends driving the promotion of NoSQL?
NoSQL grows with data, usage profile changes, and severe distributed systems. Data grows, and queries become more sophisticated.
Thus, RDBMS prioritise partition keys. Distributed transaction management ensures cluster transactions with data replication.
Hold commits and rollbacks until all cluster machines or replicas are copied.
17. What are the critical differences between HBase and Cassandra regarding data storage?
HBase permits MapReduce programs on HDFS, while Cassandra does not share HDFS nodes. HBase stores data in a column-family-based format, but Cassandra does not.
18. What is the choice between Cassandra and HBase?
Application requirements, availability, and partition tolerance determine Cassandra or HBase. The site’s topology determines click stream analysis. Choose Cassandra for global multi-jogrifugal sites.
If merely locally distributed, HBase is best. Cassandra is best for eventual consistency.
19. What is the problem with distributed transaction management when replicating data in RDBMS?
The relational structure and data storage make RDBMS difficult. RDBMS’ high cost per terabyte makes scaling difficult for startups.
The exponential expansion of data makes RDBMS unviable for user-dependent applications.
20. How does data growth affect query complexity in RDBMS?
When replicating data in RDBMS, distributed transaction management is needed to assure cluster transactions.
This necessitates careful commit and rollback management until all cluster nodes or replicas are replicated002E.
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The need for assets to ensure transactions commit at the same time is not very high in certain situations, such as social applications like Facebook. However, missing updates can still be a concern.
22. How does the cost per terabyte affect off-storage issues?
Off-storage issues arise when the cost per terabyte increases, leading to no-squill and other issues.
23. What is the range of volume in which traditional RDBMS has been working well?
Traditional RDBMS has been working well in 10 to 100 million transactions.
24. What is the need for asset properties in a data warehouse system?
Traditional systems may not have asset properties available as volume increases, as banks expand large and the requirement for assets at the transaction level also increases.
25. How can the data warehouse system reduce costs and improve transaction aggregation?
The data warehouse system can reduce costs and improve transaction aggregation by not requiring asset properties.
26. What are the limitations of RDBMS and the need to address the schema aspect?
To handle data changes, RDBMS schemas must be updated. This makes the RDBMS cycle more complicated, requiring a rewrite and application relaunch.
Increasing demand to eliminate schema-bound problem allows organizations to bring, store, and use data.
27. What is the importance of addressing the schema aspect of RDBMS to improve its agility and userexperience?
Addressing the schema aspect of RDBMS can improve its agility and user experience by making it more adaptable to predict user behavior and visit patterns, and maintaining the integrity of the system.
28. What is the need for a flexible schema regarding RDBMS?
A flexible schema can accommodate the changing dimensions of data and keep the entire schema up to date, as well as accommodate the addition of new fields and data for different users.
29. What are the challenges of building a data model in situations where adding a column can waste space foralmost every user?
Existing models may become too complex or forced to fit into generic models, leading to query complexity.
30. What is meant by rigid schema?
A rigid schema requires upfront definition, flexibility, and the ability to store new data types like JSON and XML.
31. What challenges are associated with traditional RTMSs regarding dynamic schema problems?
Traditional RTMSs store data in blobs or strings, making query parsing difficult.
32. How is a system that allows for JSON storage and querying directly more efficient and flexible thantraditional RTMSs?
A system that allows for JSON storage and querying directly is more efficient and flexible because it stores data in a structured way and allows easy querying.
33. What are the changing data structures that are becoming more predominant?
The need for on-demand schema is becoming more predominant.
34. What is the right-once and read-many-type usage pattern?
The right-once and read-many-type usage pattern is a shift in user patterns where users create content and consume it multiple times.
35. What are the issues with the application’s ability to recognise and update attributes in analytics applications?
The application may not know which attributes are present if multiple columns are added. This can cause uncertainty in the application’s ability to store and process data.
36. What is the difference between no-scale databases and RDBMS?
No-scale databases define the table name but not the columns. New rows can only use the new columns, although previous rows can. Old data cannot be updated because it is written once and read numerous times.
All column families and new columns require a modified table. In contrast, RDBMS define table and column names, giving more data storage and querying flexibility.
37. How is the altered schema for the N-Uf attribute not required for new data sets in Cassandra?
In Cassandra, column names are defined as family names, and columns are stored in name and value.
The altered schema for the N-Uf attribute is not required for new data sets.
38. What are some issues with distributed transaction management and replication as more nodes are added to RTMSs?
Distributed transaction management and replication become issues as more nodes are added.
39. What is the difference between traditional RDBMS and IDBMS?
In a traditional RDBMS, column names are defined upfront and stored in the catalogue system catalogue. In contrast, in an IDBMS, column names are defined upfront and stored in the table’s placeholder.
40. What is the main issue with using RDBMS in the new world of data?
The main issues with a no S Q L D B scenario are the lack of a top-level column, the need for a flexible schema, and the challenges in managing documents and schemas due to the increasing volume and usage patterns of new applications.
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“No scale” refers to a type of database that does not store relational structures but instead handles relationships in a relational form, where relationships are not explicitly defined in the database.
42. What are the four types of No-Scale databases?
The four types of No-Scale databases are key values store, column store, document store, and graph store.
43. What is the main issue with using RDBMS in the new world of data?
The main issue with using RDBMS in the new world of data is the lack of a top-level column and the need for a flexible schema.
The challenges in managing documents and schemas due to the increasing volume and usage patterns of new applications are also significant problems.
44. What is a key-value store example of a No-Scale database?
A key-value store example of a No-Scale database is where data is stored in a session and cookie, allowing for personalized interactions and tailoring to the user’s needs.
The responsibility for handling joins is typically on the application developer, not the database.
45. How do caching layers address the problem of dealing with large databases?
Caching layers are added to the database to address the problem of dealing with large databases. These layers allow users to query the cache with a specific user ID and retrieve the entire session data stored as a contiguous byte in the cache.
46. What is a key value store?
A key value store is a database that stores only keys and values, with only three operations: get, put, and delete.
It is not possible to query the value inside the key, as it is a locked-in storage mechanism.
47. How does a key value store work?
In a key value store, data is stored as key-value pairs, where the key is used to access a contiguous array of bytes that contain the value.
Accessing data inside a key value store is limited, as it is only possible to query the key.
48. What is the biggest limitation of a key value store?
The biggest limitation of a key value store is that it cannot query by the data inside the key. Data values are always like a black box, and only the key can be accessed.
49. What is a document store?
A document store is a storage system that stores entire JSON data, allowing for flexibility in querying. It is similar to a locker with a glass inside, with the key being the key and the value being a JSON file.
This allows for easy querying and exporting of JSON or XML data.
50. What is a column store?
A column store expands the key value store by storing the value in a column family, which is grouped into specific sections. Examples of column family databases include Cassandra, Edgebase, and Google’s Big Table.
51. What is a graph database?
A graph database stores a proper graph structure with nodes and properties between them. Examples of available graph databases include Neo4j and Hypergraph.
52. How does a document store differ from a key-value store?
In a document store, the value is stored as a JSON file, allowing for easy querying and exporting of JSON or XML data. Key value stores hold nothing, while columns store values as columns and column families.
53. What is the main difference between document stores and HDFS storage?
The main difference between document stores and HDFS storage is that document stores store data in memory using columnar storage, allowing faster access to data. In contrast, HDFS storage is a raw file that cannot be accessed directly.
54. What are the characteristics of OSKL, specifically HANA?
OSKL, specifically HANA, is a highly available relational database that can be scaled, supports flexible schema and memory storage, is semi-structured with sparse storage and limited query capability, and has limited access control.
55. What are the issues with migrating from SQL databases like Cassandra to Azure?
The key problem with migrating to a no-SQL database like Azure is the need for a hidden tail of the data stored inside it. Migrating from SQL databases like Cassandra to Azure requires careful planning, and portability across different manual scales can be an issue.
56. Which of the following is not a vital characteristic of a no-scale database?
A) A flexible schema.
B) High scalability.
C) Easy configuration.
D) Cost-effectiveness.
57. What is the critical difference between HANA and HDFS storage?
A) HANA stores data in memory, while HDFS is a raw file that cannot be accessed directly.
B) SDFS is distributed, while HANA is relational.
C) Unlike HDFS, HANA enables configurable schema and memory storage.
D) HDFS and HANA are the same.
58. What is the main characteristic of OSKL (specifically HANA)?
A) Highly available and can be scaled, making it more flexible.
B) Semi-structured, with sparse storage and limited query capability.
C) Distributed database that supports flexible schema and memory storage.
D) Relational database that is easy to configure and has consistency.
59. What is the difference between no-scale databases and RDBMS regarding schema changes?
A) No-scale databases do not require altered schema for new data sets.
B) RDBMS requiresan altered schema for new data sets.
C) Old data cannot be updated in no-scale databases.
D) RDBMS allow updating old data.
60. What is the critical challenge in RDBMS systems?
A) The lack of consistency when replicating data is not a problem.
B) Managing the distributed system is not a high stress on RDBMS systems.
C) The critical challenge in RDBMS systems is maintaining consistency and scalability in distributed systems.
D) RDBMS systems can handle unstructured data, and managing distributed systems is not complex.
Answers:
56. D) Cost-effectiveness.
57. A) HANA stores data in memory, while HDFS is a raw file that cannot be accessed directly.
58. A) Highly available and can be scaled, making it more flexible.
59. B) RDBMS requiresan altered schema for new data sets.
60. C) The critical challenge in RDBMS systems is maintaining consistency and scalability in distributed systems.
Cassandra interview questions for experiencedwere thoughtfully designed, while responses provided detailed answers that illustrated their expertise in Cassandra technology in plain terms.
It shows their authors have taken care in explaining complex subject matters clearly to readers of all levels.
If you want to expand your knowledge of interview questions on Cassandraor are interviewing for positions that require understanding the technology, this blog should be your go-to read.
Cassandra was built to handle massive volumes of data, making it ideal for big data applications. Extremely scalable, fault-tolerant and reliable, Cassandra provides an outstanding big-data solution.
Furthermore, its adaptable schema enables it to meet specific business requirements perfectly.
Cassandra is an adaptable NoSQL database that is perfect for big data applications.
Cassandra’s interview questions and answers for experienced PDF could be an excellent option if you need something that can manage vast volumes of information without breaking its consistency.
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