# 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 (yonghee.cheon@gmail.com).
It can be found here: https://github.com/yonghee12/direct-redis
Special thanks to:
* Andy McCurdy, the author of redis-py.
