Hot Storage: How It Works, Use Cases & Key Considerations
8 min read
What Is Hot Storage?
Hot storage refers to a type of data storage optimized for high-speed access. It serves data at a rapid pace, helping meet the demands of applications that require instant data retrieval. This is achieved by utilizing high-performance hardware and infrastructure, typically involving solid-state drives (SSDs), which offer faster read/write speeds compared to traditional hard disk drives (HDDs).
Organizations that rely on real-time data access, such as financial institutions or streaming services, often choose hot storage solutions. These organizations need to quickly access and process data to deliver experiences to their users. Hot storage is crucial for scenarios where data must be available and processed without delay.
This is part of a series of articles about observability.
In this article, you will learn:
How Hot Storage Works
Hot storage relies on high-performance hardware and optimized software configurations to deliver rapid data access. It typically involves on SSDs, which are much faster than HDDs in both read and write operations. These SSDs are often paired with low-latency networking and caching mechanisms, ensuring that data is always available for quick retrieval.
The architecture of hot storage is designed for parallelism and high concurrency. Multiple data requests can be processed simultaneously, allowing systems to maintain speed under heavy load. This is achieved through technologies like non-volatile memory express (NVMe) and controllers that manage data flow between storage and applications.
In addition to hardware, the software layer is also important in optimizing data retrieval. Data is often pre-indexed and cached, reducing the time it takes to locate and serve frequently accessed information.
Characteristics of Hot Storage
Hot storage systems typically have the following capabilities.
Low-Latency Access
Low-latency access ensures that data retrieval happens almost instantly. The architecture prioritizes speed, enabling applications to access required data without noticeable delay. This access is crucial for services needing swift interactions, such as live-streaming platforms or online gaming.
High Read/Write Performance
The storage infrastructure is designed with parallel processing capabilities. Utilizing fast SSDs, hot storage achieves rapid data throughput, ensuring that both reading and writing processes occur at high speeds. This level of performance is required in situations where data is frequently modified, such as in transaction-heavy environments or real-time data editing platforms.
Real-Time or Near-Real-Time Data Retrieval
Hot storage provides real-time or near-real-time data retrieval capabilities, making it useful for time-sensitive operations. This ensures that data can be quickly accessed and processed, meeting the stringent requirements of critical applications. Hot storage systems integrate efficient data indexing and retrieval algorithms to support fast transactions and data updates.
Scalability and Availability for High-Demand Workloads
Scalability and availability are central to hot storage, as they ensure systems can grow and adapt to meet expanding data demands. Hot storage infrastructure is built to handle increased workloads without degrading performance. Availability is maintained through redundant systems and automated processes, with continuous access during infrastructure changes or failures.
Chris Cooney
Head of Developer Advocacy @ Coralogix
Chris Cooney wrote code every day for 10 years as a software engineer. Then, Chris led the technical strategy for a department of 200, for a few years. His role encompassed complex migrations from on-premise to the cloud, PaaS rollouts across the company, centralised provisioning and maintenance of cloud resources, assisting with the creation and execution of a tooling strategy, and more. Now, Chris talks about Observability at conferences, makes videos and still writes as much code as he can.
Tips from the expert:
In my experience, here are tips that can help you better utilize and manage hot storage systems:
Prioritize caching for frequently accessed data: Implement intelligent caching mechanisms to keep the most frequently accessed data in-memory or near-memory layers. This ensures faster retrieval, minimizes load on the storage infrastructure, and reduces latency spikes during peak usage.
Use NVMe over Fabrics (NVMe-oF) for low-latency networking: When designing hot storage, leverage NVMe-oF to extend the speed benefits of NVMe SSDs over high-speed networks. This enables faster communication between storage devices and compute nodes, improving data access performance, especially in distributed environments.
Implement predictive analytics for workload optimization: Analyze data access patterns using predictive analytics to preemptively move frequently used data closer to the compute layer. This anticipates demand and ensures optimal hot storage performance during high traffic or peak processing periods.
Set up automatic tiering between hot, warm, and cold storage: Enable automated tiering strategies that move data between hot, warm, and cold storage tiers based on usage patterns. This dynamic approach optimizes storage costs while maintaining high-speed access to active datasets and offloading infrequently used data to lower-cost storage.
Monitor SSD wear levels and implement wear-leveling algorithms: Track SSD wear metrics (e.g., TBW – Terabytes Written) to prevent unexpected performance degradation or failures due to intensive write cycles. Use wear-leveling algorithms to evenly distribute write operations and extend the lifespan of SSDs.
Key Hot Storage Use Cases
Here are some of the main examples of when to use hot storage infrastructure.
Log Analytics
Systems like cybersecurity monitoring, infrastructure management, and application performance analysis rely on real-time log data to detect anomalies and troubleshoot issues quickly. Hot storage enables immediate retrieval of logs, allowing teams to analyze and respond to operational events without delay.
In environments where log data is generated continuously and must be processed at high volumes, hot storage enables efficient querying and analysis. This infrastructure supports the high read/write performance needed to keep up with data flows from large-scale systems, ensuring that log data can be ingested, indexed, and accessed in near real time.
Real-Time Analytics and Big Data Processing
Real-time analytics and big data processing applications require rapid data access to analyze and interpret information on-the-fly. Hot storage enables organizations to harness insights quickly, enabling timely decision-making in sectors such as finance, marketing, and logistics.
Data-intensive tasks are simplified by using parallel processing and high-throughput storage solutions. This allows businesses to handle large datasets more efficiently, using modern tools like machine learning models to derive insights.
Online Transaction Processing (OLTP) Systems
Online transaction processing systems demand immediate data read/write capabilities. These systems support applications like banking, eCommerce, and reservation platforms, where rapid response times are critical for user satisfaction and operational efficiency. Hot storage ensures transactions are processed swiftly, maintaining data integrity and consistency.
OLTP environments benefit from the high concurrency and low-latency features of hot storage, which enable multiple transactions to occur simultaneously without performance loss. This capability is essential for maintaining system resilience and meeting user expectations in high-traffic scenarios.
Streaming Applications
Streaming applications, such as video services or live broadcasts, rely heavily on hot storage for delivering content to users. These applications demand persistent, high-speed data throughput to ensure buffer-free experiences. Hot storage supports real-time streaming, maintaining quality and consistency during spikes in user activity.
To optimize performance, hot storage systems use efficient data caching and load balancing techniques. These methods distribute data across storage nodes, ensuring content is readily available and reducing latency.
Machine Learning and AI Workloads
Machine learning and AI workloads greatly benefit from hot storage, which supports the need for fast data processing and retrieval. These workloads involve training models with large datasets and making predictions in real time, requiring rapid data access. Hot storage ensures data-intensive operations run smoothly, enabling accurate and timely AI-driven insights.
With its read/write capabilities, hot storage meets the demands of iterative processing and complex algorithms. This infrastructure helps accelerate the training phases of machine learning models and enables the deployment of AI services.
Hot Storage vs Cold Storage: What Is the Difference?
Hot storage and cold storage differ primarily in terms of accessibility and cost.
Accessibility
Hot storage focuses on providing immediate access to data, suitable for scenarios requiring rapid data processing. Data stored in hot storage is “live” and readily available to applications, often used in environments where downtime or delays can lead to significant operational disruptions.
Cold storage is intended for data archiving, offering lower access speeds but reduced costs, suitable for data that is infrequently accessed yet needs to be retained. This data is typically stored for archival purposes or compliance requirements, where speed is not a priority. Access times can be much slower, often requiring minutes or even hours for retrieval.
Cost Model
Hot storage typically incurs higher costs due to the need for advanced infrastructure and maintenance. The use of SSDs, low-latency networking, and high-performance controllers drives up the price. Additionally, the ongoing operational costs, such as energy consumption and cooling, can be substantial
Cold storage is more economical for long-term retention of rarely accessed data. Cold storage solutions use more cost-effective hardware, such as HDDs or tape systems, that provide large capacities at a lower price, making them more suitable for long-term data preservation.
Considerations for Choosing Hot Storage
Organizations should consider the following factors when evaluating hot storage solutions.
Performance Requirements and Data Access Patterns
Applications needing high-speed access, such as real-time analytics or transaction systems, are prime candidates for hot storage. Determining whether a system demands frequent, low-latency access to large datasets will guide the decision towards adopting hot storage solutions.
Data access patterns reveal how often and in what manner data is retrieved, impacting the storage setup. Frequent read/write operations with low latency demands require hot storage. Careful analysis of workload characteristics will ensure that the chosen storage solution meets operational needs without over-provisioning resources.
Cost Implications of High-Performance Storage
High-performance storage, such as hot storage, can be expensive, given the technology and infrastructure required for instant data access. Organizations must consider these financial aspects when deciding on storage solutions, balancing the need for speed and performance against budgetary limitations.
While hot storage offers benefits for high-demand applications, its costs can outweigh benefits for data that doesn’t require rapid access. Evaluating the requirements of data usage patterns and calculating potential returns on investment will aid in making appropriate financial decisions.
Scalability and Durability
Scalability and durability are vital considerations when choosing hot storage. Ensuring the storage solution can grow with the business and maintain performance levels amid increasing data loads is crucial. Hot storage solutions must offer flexible scalability options, allowing integration with existing infrastructure and adaptation to changing demands.
Durability ensures long-term data integrity and reliability, protecting against data loss or corruption. Opting for storage systems with data protection features and high durability ratings guarantees that data remains secure over time.
Data Redundancy and Disaster Recovery Options
Implementing redundancy ensures data is replicated across multiple locations, providing resilience against hardware failures and data corruption. This is especially important for maintaining continuous accessibility and reliability in critical systems.
Disaster recovery plans complement redundancy by defining processes for rapid data restoration after system disruptions. Hot storage solutions should incorporate features that enable quick data recovery and minimize downtime.
Integration with Other Storage Tiers
By combining hot storage with warm or cold storage tiers, organizations can allocate resources more effectively, storing frequently accessed data in high-performance systems and archiving less-used information cost-efficiently. This tiered approach maximizes cost savings while maintaining performance levels.
Implementing a multi-tier storage strategy requires integration capabilities. Hot storage solutions should provide interoperability with lower-cost storage options, enabling easy data transitions between tiers.
Coralogix sets itself apart in observability with its modern architecture, enabling real-time insights into logs, metrics, and traces with built-in cost optimization. Coralogix’s straightforward pricing covers all its platform offerings including APM, RUM, SIEM, infrastructure monitoring and much more. With unparalleled support that features less than 1 minute response times and 1 hour resolution times, Coralogix is a leading choice for thousands of organizations across the globe.
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