Top 40 Hazelcast Interview Questions and Answers-(2024)

1 .

Hazelcast is an open-source in-memory data grid (IMDG) platform. It provides a distributed computing solution for caching and processing large volumes of data across multiple nodes in a cluster. With its distributed architecture, Hazelcast enables seamless scalability and high availability for applications requiring fast and efficient access to data.

At its core, Hazelcast offers an in-memory data storage mechanism, allowing applications to store and retrieve data from RAM, which significantly accelerates data access compared to traditional disk-based storage. This makes it particularly suitable for use cases where low-latency access to data is crucial, such as caching frequently accessed data, session management, real-time analytics, and distributed computing.

2 .

Explain the key features of Hazelcast.

Key features of Hazelcast include :

Distributed Data Structures : Hazelcast provides a rich set of distributed data structures such as maps, queues, sets, lists, multimaps, topics, and more, allowing applications to work with distributed data in a familiar programming paradigm.

High Availability and Fault Tolerance : Hazelcast ensures data availability and reliability by replicating data across multiple nodes in the cluster and automatically handling node failures and network partitions.

Scalability : Hazelcast clusters can easily scale horizontally by adding or removing nodes dynamically, allowing applications to handle increasing workloads without downtime.

Distributed Computing : Hazelcast supports distributed computing paradigms such as MapReduce, ExecutorService, and EntryProcessor, enabling parallel processing of data across the cluster.

Near Caching : Hazelcast supports near caching, allowing applications to cache data closer to the client, reducing network latency and improving performance.

Integration : Hazelcast integrates seamlessly with various programming languages and frameworks, including Java, .NET, Node.js, Python, and Spring Framework, making it easy to incorporate into existing applications.

3 .

How does Hazelcast achieve distributed caching?

Hazelcast achieves distributed caching through its in-memory data grid (IMDG) architecture, which is designed to store and manage cached data across a cluster of multiple nodes. Here's how Hazelcast accomplishes distributed caching:

Partitioning : Hazelcast divides the cached data into partitions and distributes these partitions across the nodes in the cluster. Each node is responsible for storing and managing a subset of the data partitions. Partitioning allows data to be distributed evenly across the cluster, ensuring load balancing and scalability.

Replication : Hazelcast provides built-in support for data replication to ensure high availability and fault tolerance. Each partition is replicated to one or more backup nodes in the cluster, depending on the configured replication factor. If a node fails or becomes unreachable, the data stored on that node's partitions can be seamlessly retrieved from the backup nodes, maintaining data consistency and availability.

Client-Server Architecture : Hazelcast follows a client-server architecture, where client applications interact with the Hazelcast cluster through client nodes. Client nodes are lightweight instances that connect to the cluster and provide APIs for caching and retrieving data. Client nodes handle tasks such as data distribution, load balancing, and failover transparently to the client applications.

Consistent Hashing : Hazelcast uses consistent hashing to determine the mapping of data to partitions and nodes in the cluster. Consistent hashing ensures that the distribution of data remains stable even when nodes are added or removed from the cluster. This allows Hazelcast to efficiently route requests to the appropriate nodes without requiring a centralized lookup mechanism.

Automatic Data Rebalancing : When nodes are added or removed from the cluster, Hazelcast automatically rebalances the data partitions to ensure that each node maintains an approximately equal share of the cached data. This dynamic data rebalancing process helps maintain optimal performance and resource utilization across the cluster.

Cluster Membership Management : Hazelcast includes mechanisms for cluster membership management, node discovery, and communication. Nodes in the cluster communicate with each other to share information about the cluster topology, membership changes, and data distribution. This enables Hazelcast to adapt to changes in the cluster environment and ensure seamless operation in dynamic and distributed environments.

4 .

How does Hazelcast cache works?

Hazelcast IMDG stores frequently accessed data in memory over an elastically scalable data grid for database caching.It helps any machine network to dynamically cluster and pool all memory and processors to improve performance of applications.

For read-through persistence, Hazelcast asks the loader implementation to load the entry from the data store if an application asks the cache for data but the data is not there.

Through its write-through and write-behind features, Hazelcast can either synchronously or asynchronously propagate any changes in the cached data back into the original store.

5 .

Can you explain the concept of distributed caching?

Distributed caching is a technique where data is cached across multiple servers in a distributed system. This allows for faster access to data and reduces the load on individual servers.

6 .

How does Hazelcast ensure high availability?

Hazelcast ensures high availability through features such as

* Data replication
* Failover mechanisms
* Automatic cluster rebalancing

7 .

What is the role of Hazelcast in-memory data grid (IMDG)?

The role of Hazelcast as an in-memory data grid (IMDG) is multifaceted and central to its functionality within distributed systems. Here's an overview of the key roles and functionalities of Hazelcast IMDG:

Data Storage and Management : At its core, Hazelcast IMDG provides a distributed, in-memory storage mechanism for storing and managing large volumes of data across multiple nodes in a cluster. It serves as a highly scalable and fault-tolerant data storage layer, enabling applications to cache frequently accessed data, perform fast data lookups, and store temporary or session-related data entirely in memory.

Distributed Computing : Hazelcast IMDG supports distributed computing paradigms, allowing applications to perform parallel processing and computations across the cluster. It provides APIs and abstractions for distributed data processing tasks such as MapReduce, distributed execution of tasks (ExecutorService), and distributed querying (Predicate and SQL).

Caching and Accelerating Access : One of the primary roles of Hazelcast IMDG is to serve as a distributed caching solution. By storing data in memory across multiple nodes, Hazelcast accelerates data access and retrieval, significantly reducing latency compared to traditional disk-based storage solutions. Cached data can include frequently accessed objects, query results, session data, and more.

High Availability and Fault Tolerance : Hazelcast IMDG ensures high availability and fault tolerance through data replication and automatic failover mechanisms. It replicates data across multiple nodes in the cluster, ensuring that data remains accessible even in the event of node failures or network partitions. Automatic failover mechanisms seamlessly redirect client requests to available nodes, maintaining uninterrupted service.

Scalability and Elasticity : Hazelcast IMDG clusters can scale horizontally by adding or removing nodes dynamically. This elastic scalability allows applications to handle increasing workloads and data volumes without downtime or performance degradation. Hazelcast automatically rebalances data and distributes computing tasks across the cluster, ensuring optimal resource utilization.

Integration and Compatibility : Hazelcast IMDG integrates with a wide range of programming languages, frameworks, and technologies, making it suitable for various application environments. It provides client libraries and APIs for languages such as Java, .NET, Python, Node.js, and more. Additionally, Hazelcast supports integration with popular frameworks like Spring, Hibernate, and Jet.

Real-time Data Processing : With its low-latency, in-memory data storage and processing capabilities, Hazelcast IMDG is well-suited for real-time data processing tasks such as streaming analytics, event processing, and complex event processing (CEP). It enables applications to analyze and respond to data in real-time, making it ideal for use cases such as fraud detection, IoT data processing, and financial analytics.

8 .

Explain the concept of distributed data structures in Hazelcast.

The concept of distributed data structures in Hazelcast refers to the ability to work with familiar data structures such as maps, queues, sets, lists, multimaps, topics, and more in a distributed and scalable manner across a cluster of nodes.

These distributed data structures provided by Hazelcast IMDG offer developers a way to store and manipulate data in a distributed environment without needing to manage the complexities of data partitioning, replication, and synchronization manually.

Here's an overview of some key distributed data structures in Hazelcast and how they work:

* Distributed Map (IMap)

* Distributed Queue (IQueue)

* Distributed Set (ISet)

* Distributed List (IList)

* Distributed Multimap (MultiMap)

* Distributed Topic (ITopic)

9 .

How does Hazelcast ensure data consistency in a distributed environment?

Hazelcast ensures data consistency in a distributed environment through a combination of techniques and mechanisms designed to maintain data integrity and coherence across the cluster.

Here are several key ways Hazelcast achieves data consistency :

* Replication

* Quorum-based Consistency

* Synchronous and Asynchronous Replication

* Versioning and Conflict Resolution

* Partitioning and Data Distribution

* Atomicity and Durability Guarantees

* Conflict-free Replicated Data Types (CRDTs)

10 .

What are the different data structures supported by Hazelcast?

Hazelcast supports various data structures such:

* Maps
* Lists
* Sets
* Queues
* Topics

11 .

Can you explain the concept of distributed computing?

Distributed computing is a technique where a task is divided into smaller sub-tasks and executed across multiple servers in a distributed system.

12 .

What are the different deployment options for Hazelcast?

Hazelcast provides various deployment options to suit different use cases, infrastructure configurations, and operational requirements.

Here are the different deployment options for Hazelcast :

* Embedded Deployment

* Client-Server Deployment

* Cloud Deployment

* On-Premises Deployment

* Hybrid Deployment

* Edge Deployment

* Managed Service Deployment

These deployment options offer flexibility and scalability for deploying Hazelcast clusters in various environments, enabling organizations to choose the deployment model that best fits their requirements, infrastructure, and operational preferences.

13 .

How does Hazelcast handle failover and recovery?

Hazelcast employs several strategies and mechanisms to handle failover and recovery in distributed environments, ensuring high availability and fault tolerance.

Here's how Hazelcast handles failover and recovery :

* Node Failure Detection

* Automatic Cluster Rebalancing

* Backup Replication

* Client Failover

* Hot Restart Persistence

* Split-Brain Protection

* Cluster Merge

By employing these strategies and mechanisms, Hazelcast ensures robust failover and recovery capabilities, enabling distributed systems to maintain high availability, data consistency, and fault tolerance in dynamic and challenging environments.

14 .

What is the significance of Hazelcast clustering?

The significance of Hazelcast clustering lies in its ability to provide scalability, fault tolerance, high availability, and distributed data processing capabilities for applications running in distributed environments.

Here are some key aspects of the significance of Hazelcast clustering :

* Scalability

* Fault Tolerance

* High Availability

* Distributed Data Processing

* In-Memory Computing

* Dynamic Cluster Management

* Consistency and Coherence

15 .

Explain the architecture of Hazelcast.

The architecture of Hazelcast is designed to provide a distributed, scalable, and fault-tolerant platform for storing and processing data across a cluster of nodes. Hazelcast follows a peer-to-peer architecture, where each node in the cluster is capable of both storing data and executing application logic.

Here's an overview of the key components and layers of the Hazelcast architecture :

* Cluster Manager

* Node

* Data Grid

* Client

* Network Layer

* Integration Layer

* Management Center

16 .

Can you explain the concept of Hazelcast's Near Cache?

Hazelcast's Near Cache is a caching mechanism that stores frequently accessed data locally on each client node, reducing the need to access data from the remote cache.

17 .

What is the role of Hazelcast's WAN replication?

The role of Hazelcast's WAN (Wide Area Network) replication is to enable data synchronization and replication between geographically distributed Hazelcast clusters across different data centers or regions.

WAN replication plays a crucial role in supporting use cases that require data replication and synchronization across multiple geographical locations, such as disaster recovery, multi-region deployments, and global data distribution.

Here's an overview of the key aspects and benefits of Hazelcast's WAN replication :

* Cross-Datacenter Data Replication

* Active-Active Replication

* Latency Optimization

* Conflict Resolution

* Topology Management

* Reliability and Fault Tolerance

* Integration with External Systems

18 .

How does Hazelcast ensure data security?

Hazelcast provides features such as.

* SSL/TLS encryption
* Role-based access control
* Data encryption

19 .

What are the different types of queries supported by Hazelcast?

Hazelcast supports various types of queries such as.

* SQL queries
* Predicate queries
* Aggregation queries

20 .

What is the role of Hazelcast client and member nodes?

In Hazelcast, client and member nodes play distinct roles within a cluster. Understanding these roles is essential for comprehending how Hazelcast functions and how applications interact with it.

Here's an overview of the roles of Hazelcast client and member nodes :

Member Nodes :

* Storage and Processing

* Data Storage

* Processing

* Cluster Membership

* Networking

Client Nodes :

* Application Interface

* Client Connection

* Request Routing

* Data Access

* Fault Tolerance

21 .

How does Hazelcast handle network partitioning?

Hazelcast employs various strategies and mechanisms to handle network partitioning, ensuring data consistency and cluster stability even in the presence of network partitions.

Network partitioning occurs when communication between nodes in a distributed system is disrupted, leading to the formation of disjointed sub-networks (partitions).

Here's how Hazelcast handles network partitioning :

* Split-Brain Protection

* Quorum-based Consistency

* Cluster Merge

* Node Health Monitoring

* Network Configuration and Tuning

* Dynamic Cluster Management

By employing these strategies and mechanisms, Hazelcast ensures robust handling of network partitioning, enabling distributed systems to maintain data consistency, availability, and stability in dynamic and challenging network environments.

22 .

What are the different programming languages supported by Hazelcast?

Hazelcast provides support for several programming languages, allowing developers to interact with Hazelcast clusters and leverage its features from their preferred programming environments.

Here are the programming languages supported by Hazelcast :

* Java

* C#

* C++

* Python

* Node.js

* Go

* Scala

23 .

How does Hazelcast handle network partitions?

Handling network partitions is a crucial aspect of ensuring data consistency and cluster stability in distributed systems like Hazelcast. Hazelcast employs several strategies and mechanisms to handle network partitions effectively. Here's how Hazelcast handles network partitions:

* Split-Brain Protection

* Quorum-based Consistency

* Cluster Merge

* Node Health Monitoring

* Network Configuration and Tuning

* Dynamic Cluster Management

By employing these strategies and mechanisms, Hazelcast ensures robust handling of network partitions, enabling distributed systems to maintain data consistency, availability, and stability in dynamic and challenging network environments.

24 .

What is the role of the Hazelcast Management Center?

The Hazelcast Management Center is a web-based management and monitoring tool designed to provide administrators and operators with visibility, control, and insights into Hazelcast clusters.

It serves as a centralized platform for managing and monitoring Hazelcast clusters, enabling users to perform various administrative tasks, monitor cluster health and performance, troubleshoot issues, and optimize cluster configurations.

Here's an overview of the role and key features of the Hazelcast Management Center :

* Cluster Monitoring

* Alerting and Notifications

* Cluster Management

* Distributed Data Management

* Security and Access Control

* Logging and Diagnostics

* Integration with Monitoring Systems

25 .

What is the role of Hazelcast's eventing mechanism?

Hazelcast's eventing mechanism enables real-time communication, notification, and event-driven processing within distributed applications built on Hazelcast IMDG.

It provides a flexible and scalable mechanism for reacting to changes in data, cluster topology, or system events, enabling applications to achieve real-time responsiveness, data consistency, and distributed coordination.

26 .

How does Hazelcast handle data backup and recovery?

Hazelcast handles data backup and recovery through various mechanisms and strategies to ensure data durability, fault tolerance, and high availability.

Here's how Hazelcast handles data backup and recovery :

* Replication

* Hot Restart Persistence

* Asynchronous Backup

* Quorum-based Replication

* Incremental Backup

* Split-Brain Protection

27 .

Can you explain the concept of Hazelcast's distributed locking?

Hazelcast provides a distributed locking mechanism that allows applications to coordinate access to shared resources across multiple nodes in a distributed environment.

Distributed locking ensures that only one node at a time can acquire and hold a lock on a particular resource, preventing concurrent access and potential conflicts.

Here's an overview of the concept of Hazelcast's distributed locking :

* Lock Interface

* Lock Acquisition

* Lock Release

* Scoped Locking

* Reentrant Locking

* Failure Handling

* Integration with Distributed Computing

28 .

How does Hazelcast handle data serialization?

Hazelcast provides a flexible and efficient data serialization mechanism to serialize Java objects and data structures for storage, distribution, and processing within a Hazelcast cluster.

Serialization is the process of converting Java objects into a byte stream that can be transmitted over the network, stored in distributed data structures, or persisted to disk.

* Java Serialization

* Custom Serialization

* Portable Serialization

* Data Compression

* Serialization Configuration

* Class Definitions

* Versioning and Compatibility

29 .

What is Hazelcast’s support for NoSQL databases?

Hazelcast provides native support for NoSQL databases such as MongoDB and Apache Cassandra.

Hazelcast provides integration capabilities with various NoSQL databases, allowing developers to leverage Hazelcast's distributed caching, data processing, and distributed computing capabilities in conjunction with NoSQL databases.

While Hazelcast itself is an in-memory data grid (IMDG) rather than a traditional NoSQL database, it complements NoSQL databases by providing high-performance caching, data distribution, and processing capabilities.

30 .

How does Hazelcast handle big data processing?

Hazelcast provides a comprehensive set of features and capabilities for handling big data processing, allowing developers to leverage distributed computing, parallel processing, distributed querying, and integration with external systems to process and analyze large datasets efficiently within a distributed environment.

By combining Hazelcast's IMDG with big data processing frameworks and tools, developers can build scalable, high-performance big data processing solutions that meet the demands of modern data-driven applications.

31 .

Can you explain the concept of Hazelcast's smart routing?

Hazelcast's smart routing mechanism plays a crucial role in optimizing network communication, load balancing, fault tolerance, and resource utilization within a Hazelcast cluster.

By intelligently routing client requests based on partition ownership, load balancing, and topology awareness, smart routing ensures efficient data access, high availability, and scalability for distributed applications built on Hazelcast IMDG.

32 .

What is Hazelcast's support for Apache Kafka?

Hazelcast provides native support for Apache Kafka through its Hazelcast Jet product, which enables processing of real-time data streams from Kafka.

33 .

What is the role of Hazelcast's Jet product?

Hazelcast Jet is a distributed stream processing and batch processing engine designed to perform real-time data processing and analytics on large volumes of data across distributed environments.

It complements Hazelcast IMDG (In-Memory Data Grid) by providing a specialized platform for high-performance, low-latency data processing tasks.

Here's an overview of the role and key features of Hazelcast Jet :

* Stream Processing

* Batch Processing

* Distributed Computing

* Low-Latency Processing

* Integration with Hazelcast IMDG

* Fault Tolerance and High Availability

* Integration with External Systems

34 .

How does Hazelcast support distributed computing?

Hazelcast supports distributed computing by providing a comprehensive set of features, APIs, and primitives for executing distributed computing tasks across a cluster of nodes.

Leveraging Hazelcast's distributed computing capabilities, applications can parallelize data processing, execute computations in a distributed manner, and scale out to handle large volumes of data and processing tasks efficiently.

Here's how Hazelcast supports distributed computing :

* Distributed Data Structures

* Parallel Processing Primitives

* MapReduce Processing Model

* Distributed Messaging

* Event-Driven Processing

* Integration with Distributed Data Processing Frameworks

* Fault Tolerance and Resilience

35 .

What is Hazelcast's support for Apache Flink?

Hazelcast's support for Apache Flink enables users to build scalable, high-performance data processing and analytics solutions by combining the strengths of both platforms.

By integrating Hazelcast IMDG with Apache Flink, users can leverage Hazelcast's distributed data storage and computing capabilities within Flink's stream processing and batch processing pipelines, enabling real-time and batch data processing tasks with high throughput, low latency, and fault tolerance.

36 .

Explain the use of partitioning and data distribution in Hazelcast.

Partitioning and data distribution are fundamental concepts in Hazelcast that enable efficient and scalable storage and processing of data across a distributed cluster of nodes.

These mechanisms allow Hazelcast to divide data into partitions and distribute those partitions across multiple nodes, enabling parallel processing, fault tolerance, and high availability.

Here's an explanation of their use in Hazelcast :

Partitioning :

* Partitioning involves dividing the dataset into smaller subsets called partitions or shards based on a partitioning strategy. Each partition represents a distinct subset of the data.

* Hazelcast employs a hash-based partitioning strategy by default, where each data record is assigned to a partition based on its key's hash value. This ensures an even distribution of data across partitions.

* Partitioning enables parallel processing of data by allowing different partitions to be processed concurrently by different nodes in the cluster. Each node is responsible for storing and processing a subset of the partitions, distributing the processing load across multiple nodes.

Data Distribution :

* Data distribution involves distributing partitions across multiple nodes in the cluster to achieve fault tolerance, load balancing, and scalability.

* Hazelcast employs a distributed data storage model, where each partition is replicated across multiple nodes in the cluster. This replication ensures data redundancy and fault tolerance, allowing data to remain accessible even in the event of node failures.

* Data distribution also facilitates load balancing by evenly distributing partitions across nodes, ensuring that the processing load is evenly distributed and no single node becomes a bottleneck.

* Hazelcast dynamically rebalances data distribution across nodes as the cluster topology changes, such as nodes joining or leaving the cluster, to maintain balanced data distribution and optimal resource utilization.

37 .

What are the best practices for deploying and managing Hazelcast clusters?

Deploying and managing Hazelcast clusters effectively requires careful planning, configuration, monitoring, and maintenance to ensure optimal performance, reliability, and scalability.

Here are some best practices for deploying and managing Hazelcast clusters :

* Cluster Sizing and Capacity Planning

* Topology Design

* Node Configuration

* Networking Configuration

* Partitioning and Data Distribution

* Monitoring and Management

* Backup and Disaster Recovery

* Security Configuration

* Scaling and Elasticity

* Regular Maintenance and Upgrades

38 .

What is the role of Hazelcast's Jet DAG API?

The Hazelcast Jet DAG API plays a crucial role in modeling and executing data processing jobs within the Hazelcast Jet platform.

Let me break it down for you:

Pipeline Modeling :

* The Pipeline API in Hazelcast Jet represents a data processing job as a pipeline. This pipeline consists of interconnected stages.

* Each stage accepts events from the upstream stages, processes them, and passes the results downstream.

* The pipeline expresses the computation steps in a clear and structured manner.

Directed Acyclic Graph (DAG) :

* Hazelcast models your pipeline code into a directed acyclic graph (DAG).

* The DAG consists of stages, where each stage corresponds to a specific computation step.

* The stages are connected in a cascade, forming the overall data processing flow.

Transformation and Parallelization :

* To run a job, Hazelcast transforms the pipeline DAG into the core DAG.

* The top-level component responsible for this transformation is called the Planner.

* The Planner sets up several concurrent tasks, each receiving data from the previous task and emitting results to the next one.

* Lambda functions specified in the pipeline are applied as plug-ins to these tasks.

* The computation becomes amenable to auto-parallelization, allowing Hazelcast Jet to start multiple parallel tasks for a given step.

Example: Word Count Task :

* Let’s consider the Word Count problem, where we analyze input text lines and derive a histogram of word frequencies.

* The pipeline expression for Word Count involves several steps: reading from a text source, flat-mapping lines into words, filtering out empty words, grouping by word, aggregating by counting, and writing to a sink.

* Hazelcast Jet transforms this pipeline into a DAG, enabling parallel execution of these steps.

39 .

How does Hazelcast handle concurrent access to shared resources?

Hazelcast employs several strategies to handle concurrent access to shared resources, ensuring data consistency and avoiding race conditions.

Let’s delve into some of these techniques :

Distributed Locks :

* Hazelcast provides a distributed lock mechanism that allows multiple nodes in a cluster to coordinate access to a shared resource.

* When a node acquires a lock, other nodes are blocked from accessing the same resource until the lock is released.

* This ensures mutual exclusion and prevents concurrent modifications.

Distributed Data Structures :

* Hazelcast offers various distributed data structures like maps, queues, and semaphores.

* These structures are designed to be thread-safe and can be accessed concurrently by multiple nodes.

* For example, a distributed map can be used to store shared data, and Hazelcast ensures proper synchronization.

Eventual Consistency :

* Hazelcast follows the eventual consistency model.

* When data is updated, it is eventually propagated to all nodes in the cluster.

* This approach balances performance and consistency, allowing for high throughput while maintaining data integrity.

In-Memory Computing :

* Hazelcast’s core strength lies in its in-memory computing capabilities.

* By keeping data in memory, Hazelcast avoids disk I/O bottlenecks and provides fast access to shared resources.

* In-memory storage also enhances parallelism and reduces contention.

Partitioning and Replication :

* Hazelcast partitions data across nodes.

* Each partition is owned by a single node, ensuring that concurrent access within a partition is well-defined.

* Replication provides fault tolerance by maintaining backup copies of data on other nodes.

Custom Locking Mechanisms :

* Developers can implement custom locking mechanisms using Hazelcast’s building blocks.

* For more complex scenarios, you can create your own distributed locks or synchronization primitives.

40 .

What are near-cache and how do they work in Hazelcast?

Near caches, also known as near-cache maps, are a feature in Hazelcast that improve read performance by caching frequently accessed data in-memory on client-side nodes.

They work by storing a copy of the most frequently accessed or recently accessed data from a distributed Hazelcast map in-memory on the client node itself, reducing the need for frequent remote access to the distributed cluster for read operations.

Here's how near caches work in Hazelcast :

Cache Coherence :

* When a client node accesses data from a distributed Hazelcast map, the data is retrieved from the nearest member node in the cluster where the data resides.

* The retrieved data is then stored in the near cache on the client node, along with a timestamp indicating when the data was fetched from the cluster.

Read Operations :

* Subsequent read operations for the same data are first checked against the near cache on the client node. If the data is found in the near cache and is not stale (i.e., not expired), it is returned directly from the near cache without the need to access the distributed cluster.

* This reduces the latency and overhead associated with remote network calls to the cluster, resulting in faster read operations and improved application performance.

Invalidation and Eviction :

* Near caches support invalidation and eviction mechanisms to ensure data coherence and cache consistency.

* When data is updated or invalidated in the distributed Hazelcast map, corresponding entries in the near cache are marked as invalid or evicted to ensure that stale data is not served from the cache.

Configuration Options :

* Hazelcast provides various configuration options for near caches, allowing developers to customize cache behavior and eviction policies based on application requirements.

* Configuration options include cache size limits, time-to-live (TTL) and time-to-idle (TTI) expiration policies, eviction policies (such as LRU or LFU), and invalidation settings.

Synchronous and Asynchronous Updates :

* Near caches support both synchronous and asynchronous update modes, allowing developers to control whether updates to the near cache are performed synchronously (immediately) or asynchronously (deferred).

* Synchronous updates ensure that the near cache is always consistent with the distributed map but may introduce additional latency for write operations. Asynchronous updates reduce latency but may temporarily result in stale data in the near cache until updates are propagated.

Client-Side Caching :

* Near caches operate on the client-side nodes in a Hazelcast cluster, allowing each client node to maintain its own independent cache of frequently accessed data.

* This client-side caching approach reduces the load on the server-side nodes in the cluster and improves scalability by distributing caching responsibilities across client nodes.