spl.js VS spyql

Use spatialite WASM https://github.com/jvail/spl.js Can read geopackage and Shapefiles and GeoJSON and KML and GPX and perform operations

I use SPATIALITE WEBASSEMBLY let's you use CSV or excel or JSON or sqlite or gpkg or shapefiles or KML in the browser https://github.com/jvail/spl.js

I recommend you add spatialite webassembly so you can run spatial functions operations https://github.com/jvail/spl.js Support geopackage vector features and esri mobile geodatabase (sqlite) and shapefiles and kml and output GEOJSON for map view (ExportGeojson2)

https://github.com/jvail/spl.js The geopackage pub.dev package could not work for me reading vector geometry from. Geopackage

Take a look at spatialite web assembly you can do full spatialite functions and operations on GeoJSON data or Shapefile or geopackage and even KNN NEAREST And point in polygon. No server needed run in the browser https://github.com/jvail/spl.js

We've recently been using more client-side (browser based approaches for this type of work) that have more horsepower and functionality than TurfJS) we've successfully used both https://github.com/jvail/spl.js Web Assembly Version

https://github.com/jvail/spl.js Your use case could be good for doing this in spatialite Create a new geopackage Import GeoJSON from featureserver or mapserver layers -f=GeoJSON for each layer or use virtualgeojson You can make the import happen for new records or changes And then create new table and append the records Then you can execute SQL queries Counts or Distinct or where clauses

I prefer a SQL-like format. It’s not as complete but it cover most of the day-to-day use cases. Take a look at https://github.com/dcmoura/spyql (I am the author). Congrats on fq!

SPyQL documentation: spyql.readthedocs.io

spyql – Query data on the command line with SQL-like SELECTs powered by Python expressions

This is great!

I am the author of SPyQL [1]. Combining JC with SPyQL you can easily query the json output and run python commands on top of it from the command-line :-) You can do aggregations and so forth in a much simpler and intuitive way than with jq.

I just wrote a blogpost [2] that illustrates it. It is more focused on CSV, but the commands would be the same if you were working with JSON.

[1] https://github.com/dcmoura/spyql

You can import the data into a PostgreSQL/MySQL/SQLite/... database and then query the database. However, even with the right choice of indexes, it might take a while to run queries on a table with hundreds of millions of records. You can easily import your data to these databases with SpyQL: $ spyql "SELECT * FROM csv TO sql(table=my_table_name) | sqlite3 my.db" (you would need to create the table my_table_name before running the command).

https://github.com/dcmoura/spyql/blob/master/notebooks/json_...

And ClickHouse looks like a normal relational database - there is no need for multiple components for different tiers (like in Druid), no need for manual partitioning into "daily", "hourly" tables (like you do in Spark and Bigquery), no need for lambda architecture... It's refreshing how something can be both simple and fast.

I thought this library was pretty nifty for json. It's also relatively fast compared to most json parsers: https://github.com/dcmoura/spyql