Flexibility and Scalability
QRY is designed to work with any type of API and support on-demand processing jobs, from blockchain RPC and historical data to AI processing jobs. This broad applicability allows the ecosystem to adapt and scale to accommodate various use cases and industries, making it an attractive option for developers with the most diverse needs.
Past. Present. Future.
Building upon QRY's versatility and ability to accommodate a wide range of use cases, the types of data and processing jobs supported by the platform were categorized into three distinct temporal classes. These classes – Past, Present, and Future – highlight the platform's capability to handle data retrieval and processing tasks of varying complexity and requirements.
| Temporal Class | Description | Expected Response Time | Example Use Cases |
|---|---|---|---|
| Past | Data that is timestamped and stored, ready for retrieval | Average to Fast | Transaction history, historical stock values, financial databases, social media archives, historical data |
| Present | Real-time, non-timestamped data that is available for retrieval | Fast to Very Fast | Real-time stock quotes, blockchain state, current account balance, geolocation, real-time sensor data |
| Future | Data that requires processing before it can be retrieved | Job-dependent, varies | Push transactions, AI processing, natural language processing, video compression, 3D rendering, scientific simulations, weather models |
This table also provides insight into the expected response times for each class. However, the actual response for any specific use case will depend on various factors like the underlying infrastructure, network latency, and server load.
This concise overview of each temporal class illustrates QRY's commitment to provide a comprehensive and adaptable solution for diverse data needs.