How to have parallelization in Key-Value Databases?

Question

My intent is to version large csv files and hence, I am using key-value databases where key will be the column(s) from a complete row and value will be the row itself. For eg:

Name, Age, Roll.No
Aviral, 22, 1
Apoorv, 19, 2

If I put Roll no as the key, my intent is to have key in the DB as rollno(probably its hash) and value as the complete row: Aviral, 22, 1

I have completed the above implementation but in order to work on large csv files(even 20gb with 534M rows), speed is too slow. I am implementing dask but its slower than normal pandas sequential streaming. My doubt is, how can I have parallel inserts in a key-value database?

import json
import sys
from datetime import datetime
from hashlib import md5

import dask.dataframe as dd
import dask.multiprocessing
import pandas as pd

from kyotocabinet import *


class IndexInKyoto:

    def hash_string(self, string):
        return md5(string.encode('utf-8')).hexdigest()

    def dbproc(self, db):
        db[self.hash_string(self.key)] = self.row

    def index_row(self, key, row):
        self.row = row
        self.key = key
        DB.process(self.dbproc, "index.kch")

# destination = "/Users/aviralsrivastava/dev/levelsdb-learning/10gb.csv"
destination = "10M_rows.csv"
df = dd.read_csv(destination)
df_for_file_attributes = pd.read_csv(destination, nrows=2)
column_list = list(df_for_file_attributes)

# df = df.compute(scheduler='processes')     # convert to pandas

start_time = datetime.utcnow()
row_counter = 0
ob = IndexInKyoto()

# function to apply to each sub-dataframe
@dask.delayed
def print_a_block(d):
    #for row in d.itertuples(index=False):
    # print(row)
    print("a block called!")
    d = d.to_dict(orient='records')
    for row in d:
        key = str(row["0"])
        row = json.dumps(row, default=str)
        ob.index_row(key, row)

print("Calling compute!")
dask.compute(*[print_a_block(d) for d in df.to_delayed()])
print(datetime.utcnow() - start_time)

Answer 1

Kyotocabinet doesn't allow you to parallelize inserts (https://fallabs.com/kyotocabinet/spex.html), Each Writer will block till another Writer completes so you can't parallelize inserts in kyotocabinet, but Redis will allow such insert, to optimize further use Redis pipelining (https://redis.io/topics/pipelining) which will batch your data and reduce the RTT to a great extent while loading huge data.

The Reason why your task is running slower than sequential processing is overhead of managing multiprocess writing a DB sequentially.