On 01/04/2017 06:54 AM, Pavel Stehule wrote:
Hi
2017-01-04 14:00 GMT+01:00 vod vos <vodvos@xxxxxxxx
<mailto:vodvos@xxxxxxxx>>:
__
Now I am confused about I can create 1100 columns in a table in
postgresql, but I can't copy 1100 values into the table. And I
really dont want to split the csv file to pieces to avoid mistakes
after this action.
The PostgreSQL limit is "Maximum Columns per Table250 - 1600 depending
on column types" - this limit is related to placing values or pointers
to values to one page (8KB).
You can hit this limit not in CREATE TABLE time, but in INSERT time.
I create a table with 1100 columns with data type of varchar, and
hope the COPY command will auto transfer the csv data that contains
some character and date, most of which are numeric.
Numeric is expensive type - try to use float instead, maybe double.
If I am following the OP correctly the table itself has all the columns
declared as varchar. The data in the CSV file is a mix of text, date and
numeric, presumably cast to text on entry into the table.
Regards
Pavel
I use the command: COPY rius FROM "/var/www/test/test.csv" WITH
DELIMITER ';' ;
Then it shows:
ERROR: row is too big: size 11808, maximum size 8160
---- On 星期二, 03 一月 2017 05:24:18 -0800 *John McKown
<john.archie.mckown@xxxxxxxxx
<mailto:john.archie.mckown@xxxxxxxxx>>* wrote ----
On Mon, Jan 2, 2017 at 2:57 PM, Rob Sargent
<robjsargent@xxxxxxxxx <mailto:robjsargent@xxxxxxxxx>>wrote:
Perhaps this is your opportunity to correct someone else's
mistake. You need to show the table definition to convince
us that it cannot be improved. That it may be hard work
really doesn't mean it's not the right path.
This may not be possible. The data might be coming in from an
external source. I imagine you've run into the old "well, _we_
don't have any problems, so it must be on your end!" scenario.
Example: we receive CSV files from an external source. These
files are _supposed_ to be validated. But we have often received
files where NOT NULL fields have "nothing" in them them. E.g. a
customer bill which has _everything_ in it _except_ the customer
number (or an invalid one such as "123{"); or missing some other
vital piece of information.
In this particular case, the OP might want to do what we did in
a similar case. We had way too many columns in a table. The
performance was horrible. We did an analysis and, as usual, the
majority of the selects were for a subset of the columns, about
15% of the total. We "split" the table into the "high use"
columns table & the "low use" columns table. We then used
triggers to make sure that if we added a new / deleted an old
row from one table, the corresponding row in the other was
created / deleted.
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Maranatha! <><
John McKown
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Adrian Klaver
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