I have a speed/efficiency related question about Python:
I need to extract multiple fields from a nested JSON File (after writing to the .txt
files, they have ~64k lines and the current snippet does it in ~ 9 mins), where each line can contain floats and strings.
Normally, I would just put all my data in numpy
and use np.savetxt()
to save it..
I have resorted to simply assembling the lines as strings, but this is rather slow. So far I'm doing:
- Assemble each line as a string(extract the desired field from JSON)
- Write string to the concerned file
I have several problems with this:
- it's leading to more separate
file.write()
commands, which are very slow as well (around 64k * 8 calls (for 8 files))
So my question is:
- What is a good routine for this kind of problem? One that balances out
speed vs memory-consumption
for most efficient writing to disk. - Should I increase my
DEFAULT_BUFFER_SIZE
? (it's currently 8192)
I have checked this File I/O in Every Programming Language and this python org: IO but didn't help much except(in my understanding after going through it, file io should already be buffered in python 3.6.x) and I found that my default DEFAULT_BUFFER_SIZE
is 8192
.
Here's the part of my snippet -
def read_json_line(line=None):
result = None
try:
result = json.loads(line)
except Exception as e:
# Find the offending character index:
idx_to_replace = int(str(e).split(' ')[-1].replace(')',''))
# Remove the offending character:
new_line = list(line)
new_line[idx_to_replace] = ' '
new_line = ''.join(new_line)
return read_json_line(line=new_line)
return result
def extract_features_and_write(path_to_data, inp_filename, is_train=True):
# It's currently having 8 lines of file.write(), which is probably making it slow as writing to disk is involving a lot of overheads as well
features = ['meta_tags__twitter-data1', 'url', 'meta_tags__article-author', 'domain', 'title', 'published__$date',\
'content', 'meta_tags__twitter-description']
prefix = 'train' if is_train else 'test'
feature_files = [open(os.path.join(path_to_data,'{}_{}.txt'.format(prefix, feat)),'w', encoding='utf-8')
for feat in features]
with open(os.path.join(PATH_TO_RAW_DATA, inp_filename),
encoding='utf-8') as inp_json_file:
for line in tqdm_notebook(inp_json_file):
for idx, features in enumerate(features):
json_data = read_json_line(line)
content = json_data['meta_tags']["twitter:data1"].replace('\n', ' ').replace('\r', ' ').split()[0]
feature_files[0].write(content + '\n')
content = json_data['url'].split('/')[-1].lower()
feature_files[1].write(content + '\n')
content = json_data['meta_tags']['article:author'].split('/')[-1].replace('@','').lower()
feature_files[2].write(content + '\n')
content = json_data['domain']
feature_files[3].write(content + '\n')
content = json_data['title'].replace('\n', ' ').replace('\r', ' ').lower()
feature_files[4].write(content + '\n')
content = json_data['published']['$date']
feature_files[5].write(content + '\n')
content = json_data['content'].replace('\n', ' ').replace('\r', ' ')
content = strip_tags(content).lower()
content = re.sub(r"[^a-zA-Z0-9]", " ", content)
feature_files[6].write(content + '\n')
content = json_data['meta_tags']["twitter:description"].replace('\n', ' ').replace('\r', ' ').lower()
feature_files[7].write(content + '\n')