People looking for more insight and preparation when attending an Impala interview can access a comprehensive guide dedicated to Impala Interview questions on this blog.

The subject matter covered is extensive and ranges from Impala basics to more intricate capabilities and functions relating to data processing.

Interview questions come in various formats, ranging from conventional interviews to contemporary questionnaires; readers will have many preparation options to gain an in-depth knowledge of Impala platform features that they can leverage effectively during an interview.

1. What is an Impala?

Impala is an efficient real-time execution engine that uses Hadoop’s Impala daemon, state store, and metadata for query processing. It is designed for real-time SQL queries and huge batch workloads.

2. What are Impala’s goals?

To understand Impala’s function in Hadoop, query data with Impala SQL, partition tables, and its advantages.

3. How do you conduct a query in Impala?

Conducting a query in Impala involves writing queries in SQL and executing them using the Impala query engine.

4. What is the difference between Impala and Hive?

Impala is a general-purpose SQL query engine for analytical and transactional applications that enables millisecond to hour question execution. On the other hand, Hive is a data warehousing tool designed for long-running queries.

5. What is the difference between Impala and Hive regarding fault tolerance?

Hive guarantees fault tolerance, whereas Impala cannot.

6. What is the difference between Impala and Hive regarding MapReduce usage?

Hive queries are translated into MapReduce programs, whereas Impala does not use MapReduce.

7. What is the difference between Impala and Hive regarding real-time queries?

Impala is faster for real-time queries than Hive.

8. What is the difference between Impala and Hive regarding batch operations?

Hive is better suited for batch operations than Impala.

9. What is the primary purpose of Impala?

To connect Hive and Apache Hadoop as a general-purpose SQL query engine.

10. What are Impala’s main daemons?

Impala daemon, state store, and megastore.

11. What is the role of the megastore in Impala?

It stores table definitions in MySQL or PostgreSQL using the Hive metastore.

12. How does Impala store table definitions?

It stores table definitions in MySQL or PostgreSQL using the Hive metastore.

13. How does Impala access Hive tables?

If all column data types are available, Impala can access Hive tables.

14. Does Impala guarantee fault tolerance?

No, Impala does not guarantee fault tolerance.

15. How does Impala handle extensive data metadata collection?

All metadata is cached locally on each Impala system, speeding up huge data metadata collection.

16. How does Impala perform queries without checking with the name node?

It gathers metadata such as table definition data types and file block area.

17. How does Impala differ from Hive in terms of startup time?

Hive takes seconds to start up before conducting tiny operations, while Impala enables millisecond to hour-long searches.

18. How does Impala differ from Hive in terms of metadata usage?

Hive takes seconds to start up before conducting tiny operations, while Impala employs the MPP to speed up metadata usage.

19. How does Impala differ from Hive in terms of performance?

Impala is faster than Hive for real-time queries.

20. How does Impala work?

Impala does not employ MapReduce processing but runs on the same Hadoop cluster as MapReduce, Peg, and Hive. It helps query massive amounts of data and runs rapid, interactive SQL queries on HDFS or HBase data, as well as distributed file systems with numerous agent nodes.

21. What are Impala’s limitations?

One limitation of Impala is that it has no fault tolerance. Users must retry searches if one node fails.

22. What data formats does Impala support?

Impala supports uncompressed, LZO-compressed text, sequence, RC with snappy or Gzip, ORC, Evro data, and Parquet files.

23. Does Impala support SQL 92?

No, Impala does not support SQL 92.

24. How does Impala distribute queries in a cluster?

Impala distributes queries in a cluster for easy scaling and cheap commodity hardware.

25. What are the components of Impala?

The fundamental components of Impala are its daemon, state store, and metadata. HDFS is the storage layer, while Impala is the query planner and daemon.

26. How does Impala distribute queries in a cluster?

Impala Ds process the inquiry simultaneously and distribute the query burden among other nodes. The central coordinator node divides the query burden among different nodes and aggregates the results before giving them to the client.

27. How does Impala handle massive data sets?

Impala’s design is more efficient for massive data sets since it doesn’t use MapReduce. Unlike MapReduce, it handles HDFS data and distributes the burden on cluster nodes.

28. Is Impala fault-tolerant?

No, Impala is not fault-tolerant. Therefore, giant data sets take a long time to process.

29. How does Impala handle data in a cluster?

Impala Ds process the inquiry simultaneously and distribute the query burden among other nodes. The Impala state store monitors Impala D’s health and updates other daemons.

30. What is the role of the Impala daemon in the Impala system?

The Impala daemon communicates with Hive, Impala, and HDFS and operates on each data node where Impala is installed. It performs queries

31. What is the purpose of State Store D in Impala?

State Store D updates the health status of all Impala daemon processes.

32. What happens if a node fails?

If a node fails, the state store notifies all other nodes, preventing further queries from being assigned to affected nodes.

33. How does the state store link all enslaved Impala D nodes and send their status to other cluster nodes?

The state store centrally links all enslaved Impala D nodes and sends their status to other cluster nodes.

34. How do Hive and Impala exchange metadata?

Hive and Impala exchange metadata; thus, tables from one may be used in the other.

35. What are the popular setup options for a megastore?

MySQL and PostgreSQL are popular setup options for a megastore.

36. How does Impala outperform Hive?

Impala outperforms Hive due to its state store and meta store.

37. How do you start the Impala D process on each data node in Impala?

The Impala server starts the Impala D process on each data node.

38. What are some Impala Shell commands?

Some Impala Shell commands include connect, describe, explain, help, history, insert, quit, refresh, select, set, shell, display, use, and version.

39. What is the difference between Impala’s shell tool and the Sentry project?

The Impala shell tool assists in creating databases and tables, inserting data, and querying, while the open-source Sentry project controls data access using authorisation, authentication, and auditing.

40. What are the benefits of using Impala in Hadoop?

The benefits of using Impala in Hadoop include flexible integration with Hado.

“Multiple choice questions (MCQs) can effectively assess your comprehension and ensure your grasp of the content presented in this blog post!”​

1. Which of the following is an advantage of Impala over Hive?

a. Impala cannot query data with Hive SQL

b. Impala cannot partition tables

c. Impala is slower than Hive

d. Impala cannot be used for long-running batch processes

2. Which of the following file types can Impala read?

a. JSON

b. Evro

c. Parquet

d. All of the above

3. Which of the following is a disadvantage of using Hive for batch operations?

a. Fault tolerance

b. Scalability

c. Hive does not support uncompressed data

d. Hive is slower than Impala

4. Which of the following is a component of the Impala architecture?

a. State store

b. Coordinator node

c. Planner

d. Execution engine

5. Which of the following is a disadvantage of using Impala for long-running batch processes?

a. Fault tolerance

b. Real-time querying

c. Data file sharing

d. Scalability

6. Which of the following is an open-source SQL engine for Hadoop?

a. Hive

b. Impala

c. Spark

d. HBase

7. What is Impala’s function in Hadoop?

a. To manage and query extensive, complicated data in real-time using SQL and familiar programming languages

b. To process HDFS data using MapReduce

c. To execute batch processing operations

d. To replace traditional relational database management systems

8. Which programming languages are supported by Impala?

a. Java

b. Python

c. C++

d. SQL.

By reading through the Impala Interview Questions blog, readers can equip themselves with all the skills required for any forthcoming interviews related to Impala.

All the best!