Excluence- Query blockchain data using SQL

Hacker News

Hi there, We've built this project as an experiment to address a common challenge faced by dapps in the blockchain realm. Often, dapps require access to event indexers to extract past data such as NFT sales data and NFT metadata. While there are reliable event data API providers available, they might not always cater to unconventional dapp requirements. In such cases, developers have to write their own indexers. During hackathons, we encountered the same issue, and that's why we developed an event indexer and an API system that allows developers to query event data using SQL. Although providing direct access to the public may seem unrealistic due to security concerns, we found a roundabout solution. Instead of taking raw SQL as input, we use ParcelQL, a SQL written as JSON, which is later transpiled into SQL. ParcelQL allows us to control what can be translated into SQL and enables us to utilize pre-built, robust SQL tools to avoid SQL Injections. Our system is based on Postgresql, which provides deep query capabilities, even on JSON data types (event payload). Postgresql-JSON functions have proven to be performant and competitive with other solutions like MongoDB. The API we've designed only grants read access to the table, and we've implemented automatic query cancellation in case of timeouts to prevent system overloading and SQL Injections. We've meticulously prepared documentation about the API and ParcelQL to make it easier for developers to understand and use. We'd love to hear your thoughts on the project and any suggestions or improvements you might have. Your feedback is valuable to us as we continue to enhance and refine our system. And we really want to understand whether this project can be converted into a product or not? Thank you for taking the time to review our project!

Visit product View on Hacker NewsAug 6, 2023

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Query protobufs using SQL56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Query protobufs using SQL

Hacker News2

cq – Query CSVs using SQL56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

cq – Query CSVs using SQL

Hacker News80

Enriching events with MySQL data using SQL Query Enrichment [Snowplow]54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Enriching events with MySQL data using SQL Query Enrichment [Snowplow]

Hacker News2

SQLify77%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Query all your apps using sql

Product Hunt+121Data & Analytics

FireSQL – Query Cloud Firestore using SQL syntax56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

FireSQL – Query Cloud Firestore using SQL syntax

Hacker News1

Text-to-SQL for Blockchain Data Opensourced65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Text-to-SQL for Blockchain Data Opensourced

Hacker News4

An SQL data mapper using Go generics61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An SQL data mapper using Go generics

Hacker News3

Query CrunchBase data directly with SQL61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Query CrunchBase data directly with SQL

Hacker News2

QueryCal33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Query your calendars using SQL

Indie Hackerscommitment-side-project

FireQL: Query Google Firestore database using SQL syntax68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

FireQL: Query Google Firestore database using SQL syntax

Hacker News3

Browse all products Analyze your launch