Laszlo Nagy wrote:
Hi,
I'm working on a pivot table.
Hmm... I wrote an browser based analysis tool and used the working name
pyvot...
I found Numeric to provide the best balance of memory footprint and
speed. I also segregated data prep into a separate process to avoid
excessive memory use at run time. Turns out python
For the site I'm at, I've got 10 years sales history recapped from
4327846 detail records into 458197 item by customer by month records and
top shows a 240Mb memory footprint. I've got 21 cross indexed selection
fields, and can display up to six data types (qty, price, sqft, cost,
gp%, avg). At another site I've got approx 8.5M records recapped into
1M records with 15 indexes and 5 years monthly history living in a 540Mb
memory footprint.
It's reasonably quick: a query like 'select san mateo, foster city and
san carlos accounts, sort by customer and product category and display
this year's sales by month' selects 260 records and renders in the
browser in about 2 seconds. Or on the larger installation 'Show sales
for the past five years for product group 12 sorted by city within
route' selects 160 records and renders in about 3 seconds.
My objective was to keep the info in memory for fast response times.
I played a lot of games getting this all to work well, including some c
extensions, but Numeric's take, sum, tostring and fromstring ended up
with 'pivotal' roles. :)
Regards,
Emile
I would like to write it in Python. I
know, I should be doing that in C, but I would like to create a cross
platform version which can deal with smaller databases (not more than a
million facts).
The data is first imported from a csv file: the user selects which
columns contain dimension and measure data (and which columns to
ignore). In the next step I would like to build up a database that is
efficient enough to be used for making pivot tables. Here is my idea for
the database:
Original CSV file with column header and values:
"Color","Year","Make","Price","VMax"
Yellow,2000,Ferrari,100000,254
Blue,2003,Volvo,50000,210
Using the GUI, it is converted to this:
dimensions = [
{ 'name':'Color', 'colindex:0, 'values':[ 'Red', 'Blue', 'Green',
'Yellow' ], },
{ 'name':'Year', colindex:1, 'values':[
1995,1999,2000,2001,2002,2003,2007 ], },
{ 'name':'Make', colindex:2, 'value':[ 'Ferrari', 'Volvo', 'Ford',
'Lamborgini' ], },
]
measures = [
{ 'name', 'Price', 'colindex':3 },
{ 'name', 'Vmax', 'colindex':4 },
]
facts = [
( (3,2,0),(100000.0,254.0) ), # ( dimension_value_indexes,
measure_values )
( (1,5,1),(50000.0,210.0) ),
.... # Some million rows or less
]
The core of the idea is that, when using a relatively small number of
possible values for each dimension, the facts table becomes
significantly smaller and easier to process. (Processing the facts would
be: iterate over facts, filter out some of them, create statistical
values of the measures, grouped by dimensions.)
The facts table cannot be kept in memory because it is too big. I need
to store it on disk, be able to read incrementally, and make statistics.
In most cases, the "statistic" will be simple sum of the measures, and
counting the number of facts affected. To be effective, reading the
facts from disk should not involve complex conversions. For this reason,
storing in CSV or XML or any textual format would be bad. I'm thinking
about a binary format, but how can I interface that with Python?
I already looked at:
- xdrlib, which throws me DeprecationWarning when I store some integers
- struct which uses format string for each read operation, I'm concerned
about its speed
What else can I use?
Thanks,
Laszlo
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