HDFStore: table.select and RAM usage
hdfstore, pandas, pytables, python
Solution
This is a known issue, see the reference here: https://github.com/pydata/pandas/pull/2755
Essentially the query is turned into a `numexpr` expression for evaluation. There is an issue where I can't pass a lot of `or` conditions to numexpr (its dependent on the total length of the generated expression).
So I just limit the expression that we pass to numexpr. If it exceeds a certain number of `or` conditions, then the query is done as a filter, rather than an in-kernel selection. Basically this means the table is read and then reindexed.
This is on my enhancements list: https://github.com/pydata/pandas/issues/2391 (17).
As a workaround, just split your queries up into multiple ones and concat the results. Should be much faster, and use a constant amount of memory
Problem
I am trying to select random rows from a HDFStore table of about 1 GB. RAM usage explodes when I ask for about 50 random rows. I am using pandas `0-11-dev, python 2.7, linux64`. In this first case the RAM usage fits the size of `chunk` ``` with pd.get_store("train.h5",'r') as train: for chunk in train.select('train',chunksize=50): pass ``` In this second case, it seems like the whole table is loaded into RAM ``` r=random.choice(400000,size=40,replace=False) train.select('train',pd.Term("index",r)) ``` In this last case, RAM usage fits the equivalent `chunk` size ``` r=random.choice(400000,size=30,replace=False) train.select('train',pd.Term("index",r)) ``` I am puzzled, why moving from 30 to 40 random rows induces such a dramatic increase in RAM usage. Note the table has been indexed when created such that index=range(nrows(table)) using the following code: ``` def txtfile2hdfstore(infile, storefile, table_name, sep="\t", header=0, chunksize=50000 ): max_len, dtypes0 = txtfile2dtypes(infile, sep, header, chunksize) with pd.get_store( storefile,'w') as store: for i, chunk in enumerate(pd.read_table(infile,header=header,sep=sep,chunksize=chunksize, dtype=dict(dtypes0))): chunk.index= range( chunksize*(i), chunksize*(i+1))[:chunk.shape[0]] store.append(table_name,chunk, min_itemsize={'values':max_len}) ``` Thanks for insight EDIT TO ANSWER Zelazny7 Here's the file I used to write Train.csv to train.h5. I wrote this using elements of Zelazny7's code from How to trouble-shoot HDFStore Exception: cannot find the correct atom type ``` import pandas as pd import numpy as np from sklearn.feature_extraction import DictVectorizer def object_max_len(x): if x.dtype != 'object': return else: return len(max(x.fillna(''), key=lambda x: len(str(x)))) def txtfile2dtypes(infile, sep="\t", header=0, chunksize=50000 ): max_len = pd.read_table(infile,header=header, sep=sep,nrows=5).apply( object_max_len).max() dtypes0 = pd.read_table(infile,header=header, sep=sep,nrows=5).dtypes for chunk in pd.read_table(infile,header=header, sep=sep, chunksize=chunksize): max_len = max((pd.DataFrame(chunk.apply( object_max_len)).max(),max_len)) for i,k in enumerate(zip( dtypes0[:], chunk.dtypes)): if (k[0] != k[1]) and (k[1] == 'object'): dtypes0[i] = k[1] #as of pandas-0.11 nan requires a float64 dtype dtypes0.values[dtypes0 == np.int64] = np.dtype('float64') return max_len, dtypes0 def txtfile2hdfstore(infile, storefile, table_name, sep="\t", header=0, chunksize=50000 ): max_len, dtypes0 = txtfile2dtypes(infile, sep, header, chunksize) with pd.get_store( storefile,'w') as store: for i, chunk in enumerate(pd.read_table(infile,header=header,sep=sep,chunksize=chunksize, dtype=dict(dtypes0))): chunk.index= range( chunksize*(i), chunksize*(i+1))[:chunk.shape[0]] store.append(table_name,chunk, min_itemsize={'values':max_len}) ``` Applied as ``` txtfile2hdfstore('Train.csv','train.h5','train',sep=',') ```