A room filled with developers, architects, and decision-makers, all deep in discussion, trying to pick the perfect big data processing framework. It is a scene that is pretty common in the tech world. More often than not, these debates center around the choice between Hazelcast vs Spark.
While both frameworks provide powerful solutions for low-latency data processing across clusters, they do have some key differences that affect when and how they should be used.
To help you find the best option, we will compare Hazelcast and Spark and explore their architectures and data processing models. We will also uncover factors like speed, ease of use, and advanced analytics capabilities that matter most in picking the right framework.
What Is Hazelcast?
Hazelcast is a cutting-edge software platform that provides distributed computing and memory management solutions. It is primarily known for its In-Memory Data Grid (IMDG), which is a high-performance, distributed cache that allows for the sharing and processing of data across multiple machines in a cluster.
Hazelcast provides real-time processing and access to large volumes of data with minimal latency. This makes it ideal for quick, efficient operations like web session clustering, caching, and real-time event processing.
Key features of Hazelcast include:
Programming language support: Supports various programming languages which makes it a versatile option for developers.
Dynamic scalability: Allows for the addition of more nodes to the Hazelcast cluster without downtime to enhance performance and fault tolerance.
Efficient data processing: Ideal for handling streaming data, executing complex algorithms across the Hazelcast jet cluster, or serving as a reliable message broker.
What Is Apache Spark?
Apache Spark is a powerful, open-source unified analytics engine designed for large-scale data processing and analytics. It is renowned for handling both batch and real-time data processing at an impressive scale. Spark provides a comprehensive ecosystem that includes:
MLlib: For machine learning
GraphX: For graph processing
Spark SQL: For processing structured data
Structured streaming: For stream processing
This ecosystem makes Spark a complete platform for big data analytics. With it, developers and data scientists can build complex data pipelines and analytical applications efficiently. Spark’s core advantages include:
Multiple language support: Offers programming in Scala, Python, Java, and R to cater to a wide range of developers.
Advanced data processing capabilities: Because of its in-memory computing architecture, tasks can be processed several times faster than with traditional disk-based engines.
Efficiency and reliability: With its robust fault tolerance and resource management system, Apache Spark efficiently performs complex calculations across clusters and handles vast datasets.
Hazelcast vs Spark: Head-To-Head Comparison
Hazelcast and Spark each have their own roles in the big data ecosystem. While they share some common capabilities, there are major differences between them. Let’s compare them head to head.
1. Data Processing Model
Hazelcast features an In-Memory Data Grid (IMDG) as its fundamental data structure. IMDG provides swift data storage and computation – all taking place in memory and distributed across various nodes in a cluster. Data is partitioned and stored in cluster memory for low-latency access. Reads and writes scale linearly as nodes are added.
This makes Hazelcast suitable for applications that need real-time, transactional data processing and millisecond response times. The in-memory access is ideal for fraud detection, trading systems, gaming leaderboards, and online user sessions.
On the other hand, Spark uses Resilient Distributed Datasets (RDDs) which represent immutable collections of objects distributed across nodes. Spark transforms data processing commands into Directed Acyclic Graphs (DAGs) that are executed in parallel across the cluster.
This makes Spark oriented towards scalable batch and micro-batch workloads on large datasets. The DAG model is optimized for ETL, data analytics, machine learning, and other complex data pipelines.
2. Ease Of Use & Deployment
Spark offers advanced APIs in Python, Scala, Java, and R. These APIs hide complex details to simplify the development of distributed data applications. APIs like DataFrames in Spark SQL and machine learning pipelines in Spark MLlib accelerate development. This allows developers and data scientists to quickly create prototypes and deploy Spark workloads.
Hazelcast has a simpler programming model but requires more programming effort from developers. It natively integrates with Java applications for distributed caching, processing, and messaging.
Hazelcast lacks some of the higher-level APIs and machine-learning toolkits that Spark provides out of the box. This gives Spark an edge in terms of ease of development and deployment.
3. Fault Tolerance & High Availability
In Spark, fault tolerance works by using a mechanism known as lineage which records every operation applied to data. If any part of the data is lost because of a failure, Spark uses this record to rebuild the lost data.
This method reduces the risk of data loss by enabling automatic recovery and ensures data integrity through checkpointing where the current state of data is periodically saved for recovery purposes.
Hazelcast provides high availability through its distributed in-memory architecture. Data is partitioned and replicated across nodes to avoid single points of failure. Nodes joining and leaving do not cause data loss. Linear scalability makes it easy to add more nodes.
4. Data Partitioning & Distribution
In Spark, data is split into partitions that are distributed across nodes in the cluster. Computations are executed in parallel on the nodes where the partitions are located to optimize data locality. Spark automatically handles redistributing partitions between nodes as required.
In Hazelcast, data is partitioned using a distributed hash algorithm and scattered in a grid-like fashion across nodes in the in-memory data grid. This provides low-latency parallel processing while retaining data locality. However, Hazelcast lacks native support for cluster resource management.
Spark has more comprehensive cluster coordination capabilities for scheduling, monitoring, and optimizing workloads across large clusters. Hazelcast offers simpler data sharding across nodes in an in-memory grid.
5. Advanced Analytics & Machine Learning
One of Spark’s biggest strengths is its advanced libraries for data analytics and machine learning workloads. This includes:
GraphX for graph processing
MLlib for machine learning pipelines
Spark Streaming for stream analysis
Spark SQL for structured data processing
Spark optimizes in-memory data sharing across these workloads and iteratively executes the steps in ML algorithms. This makes Spark a versatile engine for a wide range of data science use cases.
Hazelcast focuses on low-latency transactional workloads rather than complex analytics or machine learning. It lacks the algorithms, pipelines, and toolkit that Spark provides out of the box for ML workloads.
6. Ecosystem & Integration
Spark has an open-source ecosystem with hundreds of tools for monitoring, optimization, cloud deployment, integrations with data sources, and visualization. This includes options like Kafka, HBase, HDFS, S3, Cassandra, etc.
Hazelcast has native integrations for Java-based infrastructures. While Hazelcast can be deployed on Kubernetes and the cloud, it lacks some of the rich tooling ecosystem Spark provides across the data analytics landscape.
Spark is used in areas like data engineering, machine learning, business intelligence, and developing applications. Meanwhile, Hazelcast works in environments that use Java and require distributed caching and processing capabilities.
7. Memory Management
Hazelcast is designed for purely in-memory storage and computing. Data is directly stored in RAM across the IMDG for fast access. Hazelcast efficiently partitions and rebalances data as nodes scale up and down.
Spark uses optimized in-memory processing to accelerate data sharing and iterative algorithms. But when memory is full, it transfers excess data to disk. Spark in-memory caching is immutable, unlike Hazelcast’s mutable distributed map.
Both offer memory optimization but Hazelcast gives more control over managing in-memory data while Spark auto-manages based on workload needs.
8. Community & Support
Spark and Hazelcast are both open-source projects with large communities. However, Spark has been widely adopted across the industry because of its versatility in areas ranging from data engineering and analytics to machine learning.
Leading vendors like Databricks, IBM, Microsoft, and startups actively contribute to Spark and associated tools like Delta Lake. Hazelcast sees the most usage among Java developers for transactional applications.
Feature | Hazelcast | Spark |
Data Processing Model | IMDG for real-time processing. Ideal for scenarios requiring fast responses. | Uses RDDs and DAGs for batch and micro-batch processing, suited for complex analytics and ML. |
Ease of Use and Deployment | Simpler model but requires more effort for complex tasks. Mainly integrates with Java. | Provides high-level APIs in multiple languages, making development and deployment quicker and easier. |
Fault Tolerance and High Availability | Achieves high availability through distributed in-memory architecture, with data partitioned and replicated across nodes. | Utilizes lineage to track data transformations, allowing for reconstruction of lost data and minimizing data loss with automatic recovery. |
Data Partitioning and Distribution | Uses a distributed hash algorithm for low-latency processing, focusing on data locality. | Splits data into partitions distributed across the cluster, optimizing for data locality and efficient workload distribution. |
Advanced Analytics and Machine Learning | Focused on low-latency transactional workloads, lacks built-in ML algorithms and toolkits. | Excels with built-in libraries for structured data processing, ML, graph processing, and stream analysis. |
Ecosystem and Integration | Limited to Java-based integrations but supports Kubernetes and cloud deployment. | A rich open-source ecosystem with extensive tooling for monitoring, optimization, and integration with data sources. |
Memory Management | Designed for in-memory storage and computing, offering direct control over data management. | Optimizes in-memory processing but spills data to disk when memory is full, using immutable in-memory caching. |
Community and Support | Strong among Java developers for transactional applications, with a focused community. | Widely adopted across various use cases, supported by a large community and major tech companies. |
Hazelcast Case Studies
Let’s take a look at 2 case studies to understand how organizations use Hazelcast’s in-memory computing capabilities to address critical performance and scalability challenges.
I. Swedbank
Swedbank, a prominent name in the banking and financial services sector, recognized the need to enhance its mobile and web application experiences.