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. As the bank’s digital interactions grew, it wanted to provide faster and more reliable services to its customers.
Challenge
The primary challenge for Swedbank was the existing backend data layer’s latency and reliability. The bank’s infrastructure, which was responsible for powering its mobile and web applications, was not meeting the expected performance standards.
High latency times of up to 500 milliseconds for data retrieval were common which hindered the user experience and affected service efficiency.
Solution
To improve data access speeds and ensure high service availability, Swedbank implemented Hazelcast for 2 critical functions:
Storing security tokens
Serving as a distributed cache
Results
Swedbank achieved high availability with zero downtime, ensuring consistent and reliable access to its banking services.
The implementation of Hazelcast significantly reduced data retrieval latency from 500 milliseconds down to just 50 milliseconds.
Hazelcast’s straightforward object storage and management features sped up the development process and provided faster and more effective improvements to applications.
II. HUK-COBURG
HUK-COBURG, a leading insurance provider, faced performance bottlenecks because of its legacy infrastructure. The company’s real-time pricing application suffered from slow response times which impacted customer service and operational efficiency.
Challenge
The main challenge was the inefficiency of the existing system, particularly in processing and retrieving data from the mainframe. This slowed down the application and made it difficult to scale as demand increased.
The existing infrastructure’s complexity and lack of scalability were major obstacles to meeting the growing needs of the business and its customers.
Solution
HUK-COBURG chose Hazelcast as a distributed cache solution to overcome these hurdles. It cached mainframe data for faster data access and processing and effectively bypassed the latency issues associated with the legacy system. This streamlined operations and significantly improved application performance.
Results
The system’s availability was boosted to 24/7 to meet continuous customer service requirements.
Hazelcast provided a tenfold improvement in data retrieval speeds and enhanced the overall efficiency of the real-time pricing application.
The success with Hazelcast encouraged HUK-COBURG to further use it as a content cache for other internal applications.
Apache Spark Case Studies
Here’s how different organizations used Apache Spark to address complex data processing challenges and improve their services.
A. Trovit
Trovit, a leading search engine for classified ads, aims to provide users with accurate, timely, and relevant search results. Operating in a highly competitive online marketplace, Trovit needed to constantly innovate to improve user experience and engagement.
Challenge
Trovit’s initial setup relied heavily on Hadoop MapReduce, a framework known for its robustness in processing large data sets. However, as Trovit’s data volume and complexity grew, the limitations of Hadoop MapReduce became apparent.
The framework relied on disk for data processing which caused delays and affected the speed at which search results could be refreshed.
Also, the limited flexibility of Hadoop MapReduce made it difficult for developers to experiment with new algorithms and improve recommendation accuracy. This inflexibility in data processing workflows was starting to slow down innovation and flexibility.
Solution
Spark’s simple APIs, which supported multiple programming languages, made the transition easy. Trovit’s development team quickly adapted to Spark while using their existing programming skills.
After evaluating various data processing frameworks, Trovit decided on Apache Spark for its superior in-memory processing capabilities. Spark could cache data in memory between operations which made processing large datasets much faster.
Trovit used Spark SQL for integration with their existing SQL-based data analysis. It also explored Spark’s machine learning libraries to enhance their recommendation algorithms. This improved data processing speeds and opened up new avenues for data exploration and model development.
Results
With faster data processing and experimentation with new machine learning algorithms, Trovit’s recommendations becoming more accurate and useful. This increased user engagement and satisfaction.
Trovit experienced a huge reduction in data processing time, from hours to minutes. This meant the recommendation engine could be updated more often so users always saw the latest and most relevant classified ads.
With Apache Spark’s distributed data processing capabilities, Trovit scaled its operations efficiently as its data volume and processing needs grew. Spark's flexible processing model allowed Trovit to adjust to market changes easily, without being held back by technology constraints.
B. Big Data Pet-Tracking App
A US company specializing in telecommunications started a project to create a service for pet owners. It was accessible via a mobile app and used wearable trackers to monitor pets’ locations in real-time.
Challenge
The company expected more users and wanted a solution that could handle larger amounts of data. They needed to process over 30,000 events per second from 1 million devices, efficiently and reliably. Also, the solution needed to handle transferring media content like audio, video, and photos. This would let pet owners interact with their pets directly.
Solution
The core of the solution relied on Apache Spark, Apache Kafka, and MongoDB, all deployed in the cloud to ensure scalability.
Apache Spark’s in-memory data processing was used to compile and examine data instantly. This setup allowed for fast processing of location data and events, so they could quickly respond to emergencies, like a pet leaving a predetermined safe zone.
Results
Pet owners could monitor their pets in real-time and communicate through media.
The application achieved its goal of processing more than 30,000 events per second.
The system could handle more users and accommodate the increasing load without compromising on performance.
Streamlining Real-Time Analytics: The Timeplus Advantage
Timeplus is designed as a streaming-first data analytics platform that is uniquely equipped for handling both live streaming and historical data through SQL. This integration provides a seamless blend of real-time and retrospective analysis which makes Timeplus perfect for data-driven insights.
i. Comparative Advantage Over Hazelcast & Spark
Timeplus focuses on real-time analytics, unlike Hazelcast and Spark, which have broader capabilities. When simplicity and direct data processing are important, Timeplus is a better choice because of its streamlined operational model and reduced complexity.
ii. Simplifying Real-Time Data Processing
Timeplus simplifies real-time data tasks by providing direct data pushes and using a straightforward processing model. This means you don't need the complex setups of Kafka, Flink, or Spark for many real-time data applications. This efficiency speeds up data processing and simplifies the architecture to make real-time analytics more accessible.
iii. Development Efficiency & Accessibility
With Timeplus, the development and operational costs of deploying real-time analytics are significantly lower. It enhances accessibility for data analysts and allows them to focus more on deriving insights rather than dealing with the complexities of data processing infrastructure.
iv. Integration & Ease Of Use
Timeplus focuses on making things simple for users. It offers connections to multiple data sources, like Apache Kafka, and supports simple data upload methods like CSV. Its user-friendly approach simplifies the data ingestion and analytics process and makes it easier to obtain valuable insights.
v. Developer & Analyst Empowerment
With Timeplus, developers and analysts can use their SQL expertise to create real-time applications, dashboards, and alerts. This feature helps create dynamic visualizations and monitoring tools for real-time data, making it easier to make fast, well-informed decisions based on data.
Conclusion
In the Hazelcast vs Spark debate, your choice depends on what your project requires. Hazelcast focuses on speed, making it ideal for tasks that need quick responses. On the other hand, Spark is great for handling large datasets and provides a wide range of analytics and machine learning features.
Introducing Timeplus into this equation adds a new dimension to the discussion. It stands out because of its simple approach to streaming data analytics.
Timeplus simplifies the integration and analysis of real-time data which is perfect if you want quick insights without the complexities of traditional data processing setups. It seamlessly merges live and historical data analysis so you can get timely and well-informed insights with minimal effort. Sign up for a free trial or request a live demo now.