In an earlier installment of the elmcity+azure series, I created an event logger for my Azure service based on SQL Data Services (SDS). The general strategy for that exercise was as follows:

  1. Make a thin wrapper around the REST interface to the query service
  2. Use the available query syntax to produce raw results
  3. Capture the results in generic data structures
  4. Refine the raw results using a dynamic language

Now I’ve repeated that exercise for Azure’s native table storage engine, which is more akin to Amazon’s SimpleDB and Google’s BigTable than to SDS. Over on GitHub I’ve posted the C# interface library, the corresponding tests, and the IronPython wrapper which I’m using in the interactive transcript shown below.

As in the SDS example, I’m using the C#-based library in two complementary ways. My Azure service, which currently has to be written in C#, uses it to log events. But when I want to analyze those logs, I use the same library from IronPython.

I haven’t made a CPython version of this library, but it would be straightforward to do so. More generally, I’m hoping this example will help anyone who wants to understand, or create alternate interfaces to, the Azure table store’s RESTful API. 

>>> from tablestorage import *

>>> list_tables()
['test1']

>>> r = create_table('test2')
>>> print r.http_response.status
Created

>>> list_tables()
['test1', 'test2']

>>> nr = nextrow()
>>> nr.next()
'r0'

>>> d = {'name':'jon','age':52,'dt':System.DateTime.Now}
>>> r = insert_entity('test2',pk,nr.next(),d)
>>> print r.http_response.status
Created

>>> for i in range(10):
...   d = {'name':'jon','count':i}
...   r = insert_entity('test2',pk,nr.next(),d)
...   print r.http_response.status
...
Created
...etc...
Created

>>> r = query_entities('test2','count gt 5')
>>> len(r.response)
4

>>> for dict in r.response:
...   print dict
Dictionary[str, object]({'PartitionKey' : 'partkey1',
  'RowKey' : 'r10', 'Timestamp' :
  <System.DateTime object at 0x000000000000002E
  [2/17/2009 12:37:54 PM]>, 'count' : 6, 'name' : 'jon'})
Dictionary[str, object]({'PartitionKey' : 'partkey1',
  'RowKey' : 'r11', 'Timestamp' :
  <System.DateTime object at 0x000000000000002F
  [2/17/2009 12:37:55 PM]>, 'count' : 7, 'name' : 'jon'})
...etc...

>>> for dict in r.response:
...   print dict['count']
6
7
8
9

>>> for entity in sort_entities(r.response,'count','desc'):
...   print entity['count']
9
8
7
6

>>> r = update_entity('test2',pk,'r13',{'name':'doug','age':17})
>>> r = query_entities('test2','age eq 17')
>>> print r.response[0]['name']
doug

>>> r = merge_entity('test2',pk,'r13',{'sex':'M'})
>>> r = query_entities('test2','age eq 17')
>>> print r.response[0]['name']
doug
>>> print r.response[0]['sex']
M

>>> r = query_entities('test2', "sex eq 'M'")
>>> r.response.Count
1
>>> r.response[0]['RowKey']
'r13'

>>> r = delete_entity('test2',pk,'r13')
>>> r = query_entities('test2', "sex eq 'M'")
>>> r.response.Count
0

>>> r = query_entities('test2',"dt ge datetime'2009-02-16'")
>>> r.response.Count
1
>>> r.response[0]
Dictionary[str, object]({'PartitionKey' : 'partkey1',
 'RowKey' : 'r2', 'Timestamp' :
  <System.DateTime object at 0x000000000000005F
  [2/17/2009 12:34:29 PM]>, 'age' : 52, 'name' : 'jon',
  'dt' : <System.DateTimeobject at 0x0000000000000060
  [2/17/2009 7:33:53 AM]>})

>>> r = query_entities('test2',"dt ge datetime'2010'")
>>> r.response.Count
0