I have trouble querying a table of > 5 million records from MS SQL Server database. I want to select all of the records, but my code seems to fail when selecting to much data into memory.
This works:
import pandas.io.sql as psql
sql = "SELECT TOP 1000000 * FROM MyTable"
data = psql.read_frame(sql, cnxn)
...but this does not work:
sql = "SELECT TOP 2000000 * FROM MyTable"
data = psql.read_frame(sql, cnxn)
It returns this error:
File "inference.pyx", line 931, in pandas.lib.to_object_array_tuples
(pandas\lib.c:42733) Memory Error
I have read here that a similar problem exists when creating a dataframe from a csv file, and that the work-around is to use the 'iterator' and 'chunksize' parameters like this:
read_csv('exp4326.csv', iterator=True, chunksize=1000)
Is there a similar solution for querying from an SQL database? If not, what is the preferred work-around? Should I use some other methods to read the records in chunks? I read a bit of discussion here about working with large datasets in pandas, but it seems like a lot of work to execute a SELECT * query. Surely there is a simpler approach.
Update: Make sure to check out the answer below, as Pandas now has built-in support for chunked loading.
You could simply try to read the input table chunk-wise and assemble your full dataframe from the individual pieces afterwards, like this:
import pandas as pd
import pandas.io.sql as psql
chunk_size = 10000
offset = 0
dfs = []
while True:
sql = "SELECT * FROM MyTable limit %d offset %d order by ID" % (chunk_size,offset)
dfs.append(psql.read_frame(sql, cnxn))
offset += chunk_size
if len(dfs[-1]) < chunk_size:
break
full_df = pd.concat(dfs)
It might also be possible that the whole dataframe is simply too large to fit in memory, in that case you will have no other option than to restrict the number of rows or columns you're selecting.