Hi Kerry,
all these JDBC drivers are integrated in our commercial reconciliation
solution EasyRec:
http://www.finbox-solutions.com/solutions/reconciliation-easyrec/
These drivers are not easily portable as h2 (single jar file) as we have
multiple dependencies.
The purpose of my first post was to see if there is interest in these
JDBC drivers to see if we should provide them as standalone package (not
part of our full solution).
Regards,
Guillaume
Le 17/01/2019 à 15:18, Kerry Sainsbury a écrit :
You should mention where your jdbc driver can be found...
On Fri, 18 Jan 2019, 02:46 Guillaume de GENTILE
<[email protected] <mailto:[email protected]>
wrote:
Hi all,
First of all I am an h2 fan since years.
Its embedded CSVREAD function is fantastic for reading CSV or flat
files, however it has some limitations:
* Bad performance when dealing with large CSV files (more than 1Go)
o I have already played with the optimizations parameters,
but the problem remains.
* Bad performance when ordering large data using multiple
columns in “ORDER BY”
o Yes, it is possible to create some indexes to improve the
performances.
* No support for wildcard expression in filename pattern (in
case we need to load all CSV files from an existing folder)
* No cache management(do not re-evaluate the CSVREAD if the
underlying csv file is not amended)
Based on the above statements, I have developed my own CSV JDBC
driver using an in-memory Column Store database in the background.
It uses the same syntax as for h2 (select * from CSVREAD(...)).
_Benefits are:_
* Outstanding performance when dealing with large data (more
than 1Go)
* Outstanding performance when ordering multiple columns on
large files (more than 1Go)
* Support for wildcard expression in the CSVREAD (it is possible
to read all files contained in a specific folder in a row)
* Embedded cache management, the system will use the cache if
the underlying file is not amended
I had some cases where I was not able to use H2 to read large CSV
files due to very bad performances.
This driver is using an In-Memory Column Store database in the
background which is much efficient for storing large data and also
for manipulating data (ex: ordering multiple columns has minimum
impact on performance)
Regards,
Guillaume
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