A Database and an ETL Tool

Hacker News

GS-Base is a database designed to keep things as simple as possible and the same time to make the most of the (even old) hardware. With the generic set of UI commands and functions you can perform virtually any data operations/transformations without programming (though scripting is available as well to automate most of the tasks). The "native" file format is just a zipped collection of CSV tables with LongText, File/Image, Code content saved as zip substreams so it can be browsed simply in the File Manager. GS-Base can perform intense multicore field value and pivot table calculations so it loads and keeps all the data in RAM. For example, a table with 150 million rows/records with four text fields containing up to several characters each with calculated fields and formulas can use around 15GB of RAM. Like in classic SQL databases, binary objects can be stored as links to the original disk files, but even if they are stored as zip64 substreams in the database, e.g. hundreds of thousands of "Memo" fields can be loaded almost instantly. Recent updates: The maximum number of fields is now 16,384 (the max. number of records is still 256 millions). You can apply up to 16,384 field filters at the same time. Users can predefine how individual columns in the loaded text/CSV files are to be treated. New field validations and conversions (when entering the data) based on the regex. New UI commands to perform permanent and dynamic table joining easier. Easy splitting and normalization of data tables (that is, the reverse of the above) with a few clicks. Added support for the SQLite *.db files.

Visit product View on Hacker NewsOct 27, 2023

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, tasks · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% 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

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