direct-redis 0.3.1

Serialize any python datatypes and does redis actions using redis-py

Direct-Redis

• Serialize any python datatypes and executes redis commands using redis-py
• When loading, it auutomatically converts serialized data into original data types

Getting Started

Install via pypi

pip install direct-redis

Instantiate

from direct_redis import DirectRedis
r = DirectRedis(host='localhost', port=6379)

Supporting Data Types

• Built-in
  • string
  • number(int, float)
  • dictionary
  • list
  • tuple
  • etc (all other python built-in types)
• Module Classes
  • pandas
  • numpy

Supporting Redis Commands

Direct-Redis Supports

• Basic Functions
  • KEYS
  • RANDOMKEY
  • TYPE
  • SET
  • GET
• Hash Functions
  • HKEYS
  • HSET
  • HMSET
  • HGET
  • HMGET
  • HGETALL
  • HVALS
• Set Functions
  • SADD
  • SREM
  • SMEMBERS
  • SPOP
  • SDIFF
  • SCARD (Default)
  • SRANDMEMBER
• List Functions
  • LPUSH
  • RPUSH
  • LPUSHX
  • RPUSHX
  • LRANGE
  • LPOP
  • RPOP
  • LINDEX

Examples

String

• Originally redis stores string into bytes.

>>> s = "This is a String. \n스트링입니다."
>>> print(s)
This is a String.
스트링입니다.   

>>> r.set('s', s)   

>>> r.get('s')   
'This is a String. \n스트링입니다.'    

>>> type(r.get('s'))
<class 'str'>

Numbers

>>> mapping = {
...     'a': 29,
...     'b': 0.5335113,
...     'c': np.float64(0.243623466363223),
... }   

>>> r.hmset('nums', mapping)   

>>> r.hmget('nums', *mapping.keys())   
[29, 0.5335113, 0.243623466363223]    

>>> list(mapping.values()) == r.hmget('nums', *mapping.keys())
True

Nested Dictionaries and Lists

>>> l = [1,2,3]
>>> d = {'a': 1, 'b': 2, 'c': 3}   

>>> r.hmset('list and dictionary', {'list': l, 'dict': d})   

>>> r.hgetall("list and dictionary")
{'list': [1, 2, 3], 'dict': {'a': 1, 'b': 2, 'c': 3}}

>>> type(r.hgetall("list and dictionary")['list'])
<class 'list'>   

>>> type(r.hgetall("list and dictionary")['dict'])
<class 'dict'>

Pandas DataFrame

>>> df =  pd.DataFrame([[1,2,3,'235', '@$$#@'], 
                       ['a', 'b', 'c', 'd', 'e']])
>>> print(df)
   0  1  2    3      4
0  1  2  3  235  @$$#@
1  a  b  c    d      e   

>>> r.set('df', df)   

>>> r.get('df')
   0  1  2    3      4
0  1  2  3  235  @$$#@
1  a  b  c    d      e   

>>> type(r.get('df'))
<class 'pandas.core.frame.DataFrame'>

Numpy Array

>>> arr = np.random.rand(10).reshape(5, 2)
>>> print(arr)
[[0.25873887 0.00937433]
 [0.0472811  0.94004351]
 [0.92743943 0.93898677]
 [0.87706341 0.85135288]
 [0.06390652 0.86362001]]   

>>> r.set('a', arr)   

>>> r.get('a')   
array([[0.25873887, 0.00937433],
       [0.0472811 , 0.94004351],
       [0.92743943, 0.93898677],
       [0.87706341, 0.85135288],
       [0.06390652, 0.86362001]])   

>>> type(r.get('a'))
<class 'numpy.ndarray'>

Author

direct-redis is developed and maintained by Yonghee Cheon ([e-mail]).
It can be found here: https://github.com/yonghee12/direct-redis

Special thanks to:

• Andy McCurdy, the author of redis-py.