With the explosive growth of time-series data in recent years, led by increasing adoption of IoT technologies, the processing of this data has become a challenging task for enterprises across many industries. As traditional general-purpose databases and data historians are seldom able to handle the scale of modern time-series datasets, there is a trend toward deploying a purpose-built time-series database (TSDB) as a core part of the enterprise data infrastructure. And considering the requirements of time-series data processing, cloud native has become an essential component for all modern time-series databases.

Introduction

A cloud native time-series database takes full advantage of cloud technology and distributed systems in processing time-series data. With a cloud native time-series database, you can quickly spin up infrastructure to prototype, develop, test, and deliver new applications and features, shortening the time to market while reducing costs through flexible payment models such as pay-as-you-go. By leveraging the benefits of cloud native, your systems can handle the demands of modern computing and provide reliable, high-quality services to customers around the world.

There are six interrelated elements that a time-series database must have to be cloud native: a distributed design, scalability, elasticity, resiliency, observability, and automation. This article will discuss each of these elements as it relates to time-series data processing.

Distributed Design

In a distributed architecture, the components of a system are spread across multiple nodes instead of being centralized in a single location. The decoupling of compute and storage resources is particularly important in a cloud native time-series database context because it enables these key components to be scaled independently and more quickly than a tightly coupled system.

Furthermore, by replicating data and services across multiple nodes, distributed systems can continue to function even if some of the nodes fail. This is essential for fault tolerance and disaster recovery. And by distributing processing tasks across multiple nodes, distributed systems can achieve better performance than centralized systems. This is because the workload is spread out, allowing each node to focus on a smaller subset of tasks, which can be completed more quickly. As time-series data platforms are often ingesting and processing large amounts of data 24 hours a day, they benefit greatly from the fault tolerance and enhanced performance provided by a distributed design.

Finally, distributed systems do not require custom, ultra-high-end servers or expensive and restrictive software licenses. Instead, they can make use of commodity hardware and open-source software, and for that reason can be built more cost-effectively than centralized systems.

Scalability

A high level of scalability is also necessary for a cloud native time-series database as it ensures that systems and processes can accommodate increasing demand. This is facilitated by the distributed design mentioned above: because workloads are processed by multiple decentralized nodes, it is easy to add more nodes to handle larger amounts of data without overloading any single node, and likewise to remove nodes in the event that requirements change and resources need to be reallocated.

Scalability is particularly important at this stage because time-series datasets are rapidly increasing in scale. As a business grows, the amount of data passing through its pipelines can only become larger, meaning that existing data infrastructure must be expanded to meet new business requirements. Cloud native scalability also helps to reduce costs associated with expanding or upgrading data systems, adding resources incrementally as needed rather than in large and expensive blocks.

Elasticity

Elasticity refers to the ability of a system to dynamically provision and deprovision resources based on changes in demand. Automating the process of scaling resources up or down on an as-needed basis enables data systems to handle sudden spikes in workload and to accommodate growth over time while maintaining optimal performance and avoiding downtime. This builds on the scalability mentioned previously and takes it one step further into the cloud.

By providing elasticity, a cloud native time-series database allows you to respond quickly to changing business needs, opportunities, or challenges. You can launch new services, products, and applications quickly and efficiently without worrying about resource constraints - additional nodes are deployed on demand to ensure adequate performance. You can also match resource consumption to actual demand in real time: using only the resources that are required at a particular moment prevents overprovisioning and unnecessary costs.

Resilience

Cloud native design understands that faults will occur and provides resilience to recover quickly from faults and ensure business continuity. High availability and high reliability are key components of resilience.

For a cloud native time-series database, high availability is achieved by replicating data across multiple nodes; if one node fails, another can take its place and the database can continue to provide services. The database system must ensure appropriate data consistency and have a mechanism for establishing consensus. To implement high reliability, a traditional write-ahead log (WAL) is still an excellent option for cloud native systems.

With a highly available and highly reliable time-series data platform, you can be sure that your data is accurate and that it can be used by your applications when you need it. In addition, this kind of resilience can help to reduce costs associated with disruptions by minimizing downtime and reducing the need for recovery efforts.

Observability

Observability provides a comprehensive view of system performance and behavior that lets you detect problems quickly and address them before they cause significant downtime or service disruptions. Given the critical nature of the time-series database in the overall data infrastructure, observability is an indispensable characteristic used to identify and address bottlenecks, optimize resource utilization, and improve system performance and reliability.

A cloud native time-series database must integrate with observability systems to enable real-time visibility into system behavior. This integration lets enterprises not only optimize system performance, but also maintain compliance and improve customer satisfaction due to decreased downtime.

Automation

For a time-series database to be truly cloud native, its deployment, management, and scaling must be automated processes. Automation is a critical component of cloud native infrastructure and application management.

In a cloud native application, automation ties into resilience and scalability: automated failover and disaster recovery mechanisms enhance the resilience of systems, while automated infrastructure resource provisioning and deprovisioning enhances their scalability.

Going further, automation enables consistency across cloud-native environments by enforcing unified policies and configurations across all components of the system. With an automated cloud native time-series database, you can be sure that your nodes and infrastructure are configured correctly and consistently, reducing the risk of errors or security vulnerabilities.

Containerization, and Kubernetes in particular, simplify the deployment and management of applications while delivering increased agility for DevOps teams. At the same time, containerization greatly enhances portability, enabling deployment across various cloud platforms in addition to on-premises and hybrid environments. These technologies are an excellent fit for cloud-based time-series data platforms.

Conclusion

Considering the growing importance of the cloud in all data applications, especially time-series data processing, modern time-series databases must be cloud native to meet the business requirements of tomorrow. The distributed design of cloud native data platforms is a powerful tool for building the scalable, fault-tolerant, and high-performance systems that are required for handling time-series datasets. And by leveraging cloud-native technologies and principles in their data infrastructure, enterprises can achieve better business outcomes, faster time-to-market, higher customer satisfaction, and increased revenue.

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To learn more about the transformative nature of cloud native applications and open source software, join us at KubeCon + CloudNativeCon Europe 2023, hosted by the Cloud Native Computing Foundation, which takes place from April 18-21.          

ABOUT THE AUTHOR

Jeff Tao Founder and CEO, TDengine

 

Jeff Tao is the founder and CEO of TDengine. He has a background as a technologist and serial entrepreneur, having previously conducted research and development on mobile Internet at Motorola and 3Com and established two successful tech startups. Foreseeing the explosive growth of time-series data generated by machines and sensors now taking place, he founded TDengine in May 2017 to develop a high-performance time-series database purpose-built for modern IoT and IIoT businesses.