title: Apache Ignite inserts extremely slow
body: <p>I'm attempting to load a large matrix into an Apache Ignite master node running in AWS. The EC2 instance has 128GB of memory and 512GB of disk space.</p>
  
  <p>The matrix is a CSV with 50,000 columns and 15,000 rows. </p>
  
  <p>The loading is extremely slow - the first 150 inserts batch together and take over 30 minutes to work. I am using the Python Thin Client</p>
  
  <pre><code>import pandas as pd
  import pyignite
  from pyignite import Client
  
  client = Client()
  
  client.connect('127.0.0.1', 10800)
  print('deleting records...')
  client.sql('DELETE FROM full_test_table')
  df = pd.read_csv('exon.csv')
  
  col = list(df)
  col = col[1:]
  
  names = ', '.join('"' + item + '"' for item in col)
  names = 'name, ' + names
  #print(names)
  
  for index, row in df.iterrows():
      print('inserting for {0}'.format(str(row[0])))
      row[0] = '\"{0}\"'.format(row[0])
  
      row[0] = str(index)
  
      values = ', '.join(str(item) for item in row)
      sql = 'INSERT INTO full_test_table ({0}) VALUES({1})'.format(names, values)
      client.sql(sql)
  </code></pre>
  
  <p>I would like to use Python to load the data, as I'm more familiar with that than Java. This seems unreasonably slow to me - even PostgreSQL can take these inserts in seconds. What's the issue?</p>
  
  <p>I've tried the COPY command from CSV as well - that doesn't seem to work any faster.</p>