

In [128]:

    
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sqlite3
import data_access

%matplotlib inline



In [121]:

    
conn = sqlite3.connect(data_access.get_database())

Examine Instances



In [122]:

    
query = 'SELECT DISTINCT instance_name FROM check_results'

instances = pd.read_sql(query, conn)
instances









    Out[122]:






  
    
      
      instance_name
    
  
  
    
      0
      had-data-001



In [130]:

    
query = ('SELECT strftime("%Y-%m-%d", run_start_timestamp) as yr_mon_day, SUM(check_violation_cnt) as tot '
            'FROM check_results '
            'WHERE instance_name="{}" '
            'AND check_type NOT LIKE "setup_%" '
            'GROUP BY yr_mon_day '
            'ORDER BY yr_mon_day')
instance_dfs = []
for iname in instances['instance_name']:
    res = pd.read_sql(query.format(iname), conn)
    instance_dfs.append(res)



In [131]:

    
for df in instance_dfs:
    df.plot()

Examine Databases



In [114]:

    
query = 'SELECT DISTINCT database_name FROM check_results'

databases = pd.read_sql(query, conn)
databases









    Out[114]:






  
    
      
      database_name
    
  
  
    
      0
      rumprod



In [142]:

    
query = ('SELECT strftime("%Y-%m-%d", run_start_timestamp) as yr_mon_day, SUM(check_violation_cnt) as tot '
            'FROM check_results '
            'WHERE instance_name="{}" '
            'AND database_name="{}" '
            'AND check_type NOT LIKE "setup_%" '
            'GROUP BY yr_mon_day '
            'ORDER BY yr_mon_day')
database_dfs = []
for dname in databases['database_name']:
    res = pd.read_sql(query.format('had-data-001', dname), conn)
    database_dfs.append(res)
database_dfs









    Out[142]:





[   yr_mon_day      tot
 0  2015-09-23     1086
 1  2015-09-24      357
 2  2015-09-25     5219
 3  2015-09-26  1952340]



In [143]:

    
for df in database_dfs:
    df.plot()



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