Distributed Computing
Writing applications that are distributed and parallelized across a cluster of nodes is difficult without the right framework. Hazelcast takes care of the low-level plumbing for you.
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Introduction
In data-driven initiatives, many businesses are moving beyond traditional technologies like relational databases. Distributed computing architectures offer scalable deployments adaptable to changing workloads, benefiting from cost-effective hardware or cloud servers.
However, current distributed systems often lack streamlined application distribution and coordination. While container orchestration suits microservices, overall workflow coordination still demands application-specific coding.
Hazelcast platform simplifies this by submitting server-side code as processing jobs divided into subtasks, distributing them across the cluster for collective computing. Its declarative API models workflows as directed acyclic graphs (DAGs), eliminating coordination concerns. This accelerates development and eases deployment.
Business Requirements
The business requirements of distributed computing deployments are often aligned with large-scale processing tasks, as described below.
- Ability to run large-scale computations (like Monte Carlo simulations) across multiple servers in a cluster that take advantage of massive parallelization to enable the shortest completion time.
- Promote efficiency to get the most out of the hardware, especially since many distributed applications run heavy workloads that can quickly consume many computing resources.
- Ability to add and remove hardware resources as necessary to not only adjust to varying workloads, but also free up resources when they are not needed like when major processing jobs are not being run.
Technical Challenges
Building and running distributed applications is an important pursuit, but requires the right technologies. Without the right systems, you will face significant technical challenges as listed below.
- Complexity of writing code that coordinates the workflows across the many parts of a distributed application. Hazelcast Platform is built for running distributed applications and abstracts away this level of coordination.
- Extensive time and effort to write custom code to handle the interprocess communication between the distributed application processes. Hazelcast applications are written in a simple, declarative way, so developers don’t have to worry about coordinating the individual subtasks.
- Inefficiency of underutilized hardware resources, which then requires wasteful overprovisioning of hardware. Hazelcast Platform handles resource allocation for you to optimize hardware utilization so you don’t end up provisioning more resources than necessary.
- Significant effort in performance tuning, distracting from the main goal of fine tuning the business logic. Our platformwas designed with high performance in mind, so you get the performance advantages while also focusing on business logic.
Why Hazelcast
Hazelcast emphasizes simplicity when it comes to otherwise complex application development. By handling the underlying coordination of subtasks within large distributed applications, Hazelcast lets busines focus on business logic instead of infrastructural code. Hazelcast Platform is a unified real-time data platform that uniquely combines a distributed, fast data store with a high-speed stream processing engine, to run the fastest applications in any type of data-intensive environment. Consider some of the technology advantages that let Hazelcast customers run a wide variety of distributed applications.
Easy to Develop and Deploy
Hazelcast Platform was designed to simplify the application development process by providing a familiar API that abstracts away the complexity of running a distributed application across multiple nodes in a cluster. This allows developers to spend more time on business logic and not on writing custom integration and orchestration code. Our platform can seamlessly integrate with your IT architecture to add new capabilities without having to rip and replace your existing stack. The Hazelcast cloud-native architecture requires no special coding expertise to get the elasticity to scale up or down to meet highly fluctuating workload demands.
Performance at Scale
Whether you process a large volume of transactions, enhance online experiences with faster responsiveness, run large-scale transformations on data, or cut costs with a mainframe integration deployment, Hazelcast Platform is designed for the ultra-performance that today’s banking workloads require. The proven performance advantage is especially valuable for data-focused experimentation that enables ongoing business optimization, especially in data science initiatives including machine learning inference for fraud detection.
Mission-Critical Reliability
With built-in redundancy to protect against node failures, and efficient WAN Replication to support disaster recovery strategies that safeguard against total site failures, Hazelcast Platform was built to provide the resilience to run mission-critical systems. The extensive built-in security framework protects data from unauthorized viewers, and security APIs allow custom security controls for sensitive environments.
Customer Success Story
A global investment bank was operating a latest exposure (“LEX”) deployment responsible for calculating their risk exposure in capital markets. This significant calculation had to be performed after the market closed, giving them a limited time frame to execute the billions of calculations involved in the risk assessment.
This posed a challenge as their existing system was struggling to handle the growing transaction volume, risking delayed reporting to auditors and regulators. To avoid penalties and liquidity requirements, they replaced their legacy system with Hazelcast Platform as the core processing engine.
This unified solution enabled fast, in-memory data access and distributed stream processing, allowing the bank to perform highly parallelized calculations and meet SLAs. With Hazelcast's cloud-native scalability, they could effortlessly accommodate rising transaction volumes by adding nodes incrementally to maintain SLA compliance.
Use Cases
Distributed computing use cases can range in size and performance requirements, but all are about getting the most utilization out of the available hardware resources. Some example use cases include the following.
- Banking stress tests and large-scale risk assessment models
- Monte Carlo simulations
- Scientific calculations
- Stream processing deployments
- Event-driven microservices deployments
- Artificial intelligence and machine learning inference deployments