20 Apache Ignite Interview Questions and Answers

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speed at petabyte scale. If you are interviewing for a position that involves Apache Ignite, you can expect to be asked questions about your knowledge and experience with the platform. In this article, we will review some of the most common Apache Ignite interview questions and provide tips on how to answer them.

Apache Ignite Interview Questions and Answers

Here are 20 commonly asked Apache Ignite interview questions and answers to prepare you for your interview:

1. What is Apache Ignite?

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

2. Can you explain what an in-memory data grid (IMDG) is? How does it differ from a database?

An in-memory data grid is a type of data storage that keeps data in RAM instead of on disk. This allows for much faster access to data, but it also means that the data is not persisted and will be lost if the system goes down. A database, on the other hand, will keep data on disk, which means it will be slower to access but the data will be persisted.

3. What are the main components of Apache Ignite?

The main components of Apache Ignite are the Ignite Kernel, the Ignite Client, and the Ignite UI. The Ignite Kernel is the core of the Ignite platform and is responsible for managing the Ignite cluster and providing the APIs that Ignite clients and applications can use to access Ignite functionality. The Ignite Client is a Java library that applications can use to connect to an Ignite cluster and access Ignite functionality. The Ignite UI is a web-based interface that administrators can use to manage an Ignite cluster.

4. Why should we use Apache Ignite instead of other solutions like Redis or Hazelcast?

Apache Ignite is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time. It is designed to deliver high-performance, scalability, and availability for demanding in-memory applications.

5. What are some advantages and disadvantages of using Apache Ignite over Hadoop?

Some advantages of using Apache Ignite over Hadoop include Ignite’s ability to handle streaming data and its support for real-time analytics. Additionally, Ignite is able to scale horizontally more easily than Hadoop. However, some disadvantages of using Ignite include its lack of built-in security features and its lack of support for certain Hadoop file formats.

6. Can you give me a brief introduction to ACID transactions? How do they work?

ACID transactions are a set of properties that guarantee that a transaction will be processed reliably. The acronym stands for Atomicity, Consistency, Isolation, and Durability. Atomicity means that all of the steps in a transaction must be completed, or none of them will be completed. Consistency means that the transaction must leave the database in a consistent state. Isolation means that the transaction must be isolated from other transactions. Durability means that the transaction must be durable, meaning that it will not be lost if there is a power outage or other failure.

7. What are the different types of cache stores supported by Apache Ignite?

There are four types of cache stores supported by Apache Ignite:

1. File store
2. JDBC store
3. Hibernate store
4. Memcached store

8. What is object affinity?

Object affinity is a feature of Apache Ignite that allows for better performance by allowing the user to specify which nodes should store which objects. This way, when an operation is performed on an object, the request can be routed to the node that is already storing that object, rather than having to send the request to all nodes and waiting for the correct one to respond. This can be a big performance boost, especially for large distributed systems.

9. Can you explain how client nodes connect to server nodes in Apache Ignite?

Client nodes in Apache Ignite can connect to server nodes in a few different ways. The most common way is for the client node to connect to the server node via TCP/IP. The client node will need to know the IP address and port number of the server node in order to make the connection. Another way for a client node to connect to a server node is by using HTTP. In this case, the client node will need to know the URL of the server node.

10. Can you explain what lazy loading is in the context of Apache Ignite?

Lazy loading is a feature of Apache Ignite that allows you to defer the loading of certain data until it is actually needed. This can be useful if you have a lot of data that you know will not be used right away, as it can help to improve startup time and reduce memory usage.

11. Explain how Apache Ignite handles cluster failure.

Apache Ignite is designed to handle failures in a cluster gracefully. If a node in the cluster fails, Ignite will automatically re-balance the cluster and redistribute the data and tasks amongst the remaining nodes. This ensures that the cluster remains operational even in the event of node failure.

12. What’s your understanding of memory settings in Apache Ignite?

There are a few different types of memory that can be configured in Apache Ignite: public memory, system memory, and off-heap memory. Public memory is the memory that is used by Ignite to store data structures and objects that are accessible to all nodes in the cluster. System memory is used to store internal data structures and objects that are not accessible to other nodes. Off-heap memory is used to store data structures and objects that are not stored in the JVM heap, which can help to reduce GC overhead.

13. Can you explain what distributed persistence is?

Apache Ignite’s distributed persistence feature allows you to store your Ignite data across multiple nodes in a cluster. This way, if one node goes down, your data will still be available on the other nodes.

14. What is the Data Grid API?

The Data Grid API is a set of Java interfaces that provide a standard way of interacting with a data grid. The API defines how data is stored in the grid, how it is accessed, and how it can be manipulated. The Data Grid API is implemented by various data grid products, including Apache Ignite.

15. What are some examples of applications that can be built on top of Apache Ignite?

Apache Ignite can be used as an in-memory database, a distributed cache, a messaging system, or a compute grid.

16. When would you recommend using partitioned caches in Apache Ignite?

Partitioned caches are best used when your data is too large to fit on a single node, or when you need to scale out your Ignite cluster to handle more traffic. Partitioning your data across multiple nodes will help improve performance and ensure that your data is highly available.

17. What are the differences between partitioned and replicated caches?

Partitioned caches are those in which each node in the cluster is responsible for a portion of the data, while replicated caches are those in which each node in the cluster contains a complete copy of the data. The main difference between the two is in terms of performance and scalability. Partitioned caches are generally more scalable, since each node only needs to handle a portion of the data, but replicated caches can be faster since each node has a complete copy of the data and can therefore access it more quickly.

18. What happens when there are concurrent updates to the same key in a partitioned cache?

When there are concurrent updates to the same key in a partitioned cache, the updates will be applied in the order in which they were received. However, if the updates are received from different nodes, then they may be applied out of order.

19. What is the difference between optimistic and pessimistic locking? Which one would you prefer in certain situations?

Optimistic locking is when a lock is acquired on a resource, but the lock is not immediately released. The lock is only released when the transaction is complete. Pessimistic locking is when a lock is acquired on a resource and immediately released when the transaction is complete.

In general, optimistic locking is preferred because it allows for better concurrency. However, in some situations, such as when there is a high likelihood of contention for the resource, pessimistic locking may be a better choice.

20. What is near caching? Can you explain its purpose and usage?

Near caching is a type of caching that is used in order to improve the performance of an application by storing frequently accessed data in memory. This data is typically stored on the client side, which means that it can be accessed more quickly than if it were stored on the server side. Near caching can be used in order to improve the performance of an application that makes use of a remote database.