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...
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...
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 *
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
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...
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 ...