About littlekoi

littlekoi · 02-06-2020

Background EXAjload is a standard tool for bulk data loading. Its documentation is brief, but the tool is more powerful than you might think. It is possible to: import data from STDOUT of other processes without creating a tmp file on disk; import da...

littlekoi · 09-12-2020

Generally speaking, you have to know all columns and types before execution of run(). Exasol sever must be able to plan query ahead, and in order to plan it all output columns are required. If you run query with UDF from remote Python script, you may...

littlekoi · 09-12-2020

Try this UDF (adjust path to JAR): CREATE OR REPLACE JAVA SCALAR SCRIPT json_to_geo (json VARCHAR(2000000)) RETURNS VARCHAR(2000000) AS /* * Convert the geometry to a GeoJSON representation * https://javadoc.io/doc/org.orbisgis/h2gis/latest/org/h2gis...

littlekoi · 19-10-2020

We have an alternative implementation in Java: CREATE OR REPLACE JAVA SCALAR SCRIPT geo_to_json (geo VARCHAR(2000000), srid DECIMAL(9,0)) RETURNS VARCHAR(2000000) AS /* * Convert the geometry to a GeoJSON representation * https://javadoc.io/doc/org.o...

littlekoi · 02-10-2020

Yep, the current disk space usage available in stat tables is not useful. Interestingly enough, the reporting in ExaOperation is usually correct.This is the formula / function in PyEXASOL which gets as close as possible to disk space usage reported b...

littlekoi · 14-09-2020

Unlike classic row-store databases (MySQL, PostgreSQL), analytical column-store databases cannot serve hundreds and thousands of simultaneous connections efficiently. The main goal for analytical databases is to serve small number of complex queries ...

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EXAjload: advanced techniques

Re: Dynamic output parameters in Python UDF

Re: EXASOL v7 and GEOJSON. Need help :)

Re: Exasol GEOMETRY to GeoJSON

Re: Exasol Storage Reporting

Re: Parallel connections/connection pools in Python?

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