[2023/05/15] Real-Time Database Release & Query Auto Debugging with ChatGPT

We are excited to announce the release of two prominent features in ZettaBlock: Toggle Between Data Lake and Database for Real-Time API Calls, and Failed Query Auto Debugging with ChatGPT.

Toggle Between Data Lake and Database for Real-Time API Calls

This new toggle feature empowers our users to switch between Data Lake and Database functionalities, enabling real-time API calls. With this feature, you now have the flexibility to choose the data storage and processing approach that best suits your application's requirements.

The key differences between Data Lake and Database are summarized below:

Main Database - Data Lake (Presto SQL)Realtime Database (PostgreSQL)
Query PerformanceRun historical analytical queries really fast, but latency is quite high (30 min to 2 hours).Allows for real-time data fetching. Lowest latency.
Data FilteringNo inherent requirement to filter recent data.Requires filtering for the latest data (last 1-5 days).
ScalabilityHighly scalable and can handle massive amounts of data.Scalable but has practical limits.
Table schemaExample: ethereum_mainnet.transactionsExample: ethereum.transactions

Failed Query Auto Debugging with ChatGPT

ZettaBlock is releasing the first of a series of updates related to language model enhancements - Failed Query Auto Debugging with ChatGPT. This feature leverages the power of ChatGPT to help users understand and debug errors that arise in queries.

Key Features:

  • Enhanced Error Explanations: When a query encounters an error, ChatGPT will now provide detailed explanations to help users understand the issue at hand. It will pinpoint the specific error in the query and offer insights into the potential causes.
  • Step-by-Step Debugging Assistance: ChatGPT goes beyond merely explaining the errors; it also guides users through the process of debugging their queries. It provides step-by-step instructions on how to identify and rectify common errors, enabling users to troubleshoot more effectively.
  • Personalized Error Handling: ChatGPT takes into account the user's query history and individual patterns of error-making. By leveraging this knowledge, it tailors its explanations and suggestions to match the user's specific context and query patterns, providing a more personalized debugging experience.

If you have any questions or feedback, feel free to let us know at!