dirslashls

Joined 4 years ago

Karma
2
Submissions
13
Member since
Jan 24
https://sqlframes.com/
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Recent submissions

Comment · 4 years ago
Would love to see any factual references about the trend. It's in my vested interested as a founder of sqlframes.com
Comment · 4 years ago
Nice one. How do you decide the difficulty of each problem? One hard problem seemed easy using analytic functions while a medium problem felt hard as it required a self-join the realization of which is the harder part.
Comment · 4 years ago
Creative!
Comment · 4 years ago
Nice. It would be good to have a way to reset to get back to the initial state after zooming/panning.
Comment · 4 years ago
Great!

How does this compare to react-virtualized (https://github.com/bvaughn/react-virtualized)? Does it handle tables (rendered with table elements and not div)? Does it support column virtual scrolling?

Comment · 4 years ago
Nice work and great use of React Flow.

What level of sophisticated queries can be created with it? For example, can it do "Top N and Other"? Which BTW, was recently added for SQL Frames ( https://sqlframes.com/docs/data_sets/top_n_other_sets )

Most of these visual SQL builders I have seen only tackle the basic level of SQL. I am interested in an advanced SQL builder myself to plug as a no-code SQL builder to front the SQL Frames API.

Comment · 4 years ago
Sorry, the correct link is https://sqlframes.com/docs/sql_frames/intro

I didn't find a way to edit my initial comment.

Comment · 4 years ago
The morphing of the charts looks great. I don't know how that works, but currently I am using eCharts to achieve similar effects.
Comment · 4 years ago
Comment · 4 years ago
Go to https://sqlframes.com/demo and in the code editor enter the following and execute (this example is taken from the first example on PRQL github page). It generates SQL, but it also computes and displays the results within the browser (though the data set below gives no results).

const employees = SQL.values([{ title: 'Developer', country: 'USA', salary: 120, payroll_tax: 20, healthcare_cost: 6 }]); employees.schemaName = 'employees'; const { groupBy, where: { gt, eq, and }, agg: { count, sum, avg } } = SQL; return employees.pdf(SQL.script('[salary]+[payroll_tax]').as('gross_salary'),SQL.script('[gross_salary]+[healthcare_cost]').as('gross_cost')) .fdf(and(gt('gross_cost',0),eq('country','USA'))) .gdf(groupBy('title','country') ,avg('salary').as('average_salary') ,sum('salary').as('sum_salary') ,avg('gross_salary').as('average_gross_salary') ,sum('gross_salary').as('sum_gross_salary') ,avg('gross_cost').as('average_gross_cost') ,sum('gross_cost').as('sum_gross_cost') ,count().as('count')) .having(gt('count',200)) .orderBy('sum_gross_cost');

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