VERA Features and Benefits List
The VERA engine’s advanced features enhance performance, optimize computing resource utilization, and elevate the user experience.
| Name | Description | Capabilities | Benefits |
|---|---|---|---|
| Compute / storage separation | Separating compute and storage in VERA enables each to scale independently. | VERA ensures that stateful applications can scale efficiently, have improved resiliency, and achieve cost optimization, all while ensuring the performance delivery needed for dynamic, cloud-native environments. | This decoupling means you can add more computing power without being constrained by storage limitations, and storage can scale without impacting compute resources. This flexibility is crucial for handling varying workloads, especially in stateful streaming applications where state sizes can grow rapidly. |
| Tiered Storage | VERA significantly reduces job downtime and improves system scalability. | VERA optimizes scaling a job by synchronously trimming redundant state data during rescaling (file granular merging and trimming) and addressing slow state recovery and long job interruption times by downloading state data asynchronously (lazy state loading). | VERA balances state recovery speed with resource efficiency, improving scalability, resilience, and fault tolerance in cloud-native environments. |
| Streaming join optimization | VERA separates keys from values. | Large values are stored separately, with references placed in the log-structured merge-tree (LSM tree). | In scenarios like anomaly detection or recommendation systems, only a small portion of the state contributes to final results. Key-value separation allows VERA to quickly access keys without immediately loading large values, reducing CPU and I/O overhead. . |
| Scheduling functions | Allows faster job restarts after job failures or new job updates. | VERA enablea faster, more scalable job recovery by minimizing downtime through task-level recovery and seamless rescaling. | VERA's optimizations reduce operational overhead, allowing for predictable and stable stream processing under heavy loads, enabling real-time applications to meet SLAs and ensure efficient system operation. |
| Dynamic Complex Event Processing (CEP) | Dynamic CEP allows changing the event processing rules without service interruption. | VERA's dynamic CEP allows updates to rules in real-time and also efficiently serializes and deserializes newly created rules to facilitate smooth integration with exernal databases. | With VERA, teams collaborate more effectively. Business Intelligence (BI) teams can rely on stable data feeds while upstream teams make necessary changes without risking operational continuity. |
| Create Database as Database (CDAS) and enhanced catalogs | CDAS minimizes downtime and data loss across distributed systems, while enhanced catalogs provide real-time metadata for better state and query management. | Strengthens the reliability and efficiency of large-scale stream processing systems by ensuring high availability, improving system performance, and enabling scalability. | Allows updates to event processing rules on the fly and meets the stringent requirements of modern, cloud-native stream processing workloads with enhanced catalogs. |
| Built-in Flink Machine Learning (ML) | VERA includes Apache Flink® library of pre-installed machine learning APIs. | Leverage fully-integrated open-source Apache Flink library ML and AI capabilities using pre-installed and pre-configured machine learning APIs. | For critical applications like fraud detection, recommendation systems, or predictive maintenance the ML models can operate on data in real time. |
Related Topics
- For an overview, see VERA Engine.
- For a deep dive into VERA, see the VERA white paper.