Metadata-Version: 2.1
Name: ClickSQL
Version: 0.1.8.8
Summary: SQL programming
Home-page: http://www.github.com/sn0wfree/ClickSQL
Author: sn0wfree
Author-email: snowfreedom0815@gmail.com
License: MIT Licence
Description: # ClickSQL: ClickHouse client for Humans 
         
        
         
        Package information:
        
         
        ClickSQL is a smart client for ClickHouse database, which may help users to use ClickHouse more easier and smoother. 
        
        
        more information for ClickHouse can be found at [here](http://clickhouse.tech)
        
        
        
        ## Installation
        
        `pip install ClickSQL`
        
        ## Usage
        ### Initial connection
        to setup a database connection and send a heartbeat-check signal
        
        ```python
        from ClickSQL import BaseSingleFactorTableNode
        
        conn_str = "clickhouse://default:test121231@99.99.9.9:8123/system"
        Node = BaseSingleFactorTableNode(conn_str)
        
        >>> connection test:  Ok.
        
        ``` 
        
        ### Query
        #### execute a SQL Query
        ```python
        from ClickSQL import BaseSingleFactorTableNode
        
        conn_str = "clickhouse://default:test121231@99.99.9.9:8123/system"
        Node = BaseSingleFactorTableNode(conn_str)
        
        Node('show tables from system limit 1')
        
        >>> connection test:  Ok.
        >>>                             name
        >>> 0  aggregate_function_combinators
        ```
        
        #### execute a Query without SQL
        ```python
        from ClickSQL import BaseSingleFactorTableNode
        
        factor = BaseSingleFactorTableNode(
                'clickhouse://default:default@127.0.0.1:8123/sample.sample',
                cols=['cust_no', 'product_id', 'money'],
                order_by_cols=['money asc'],
                money='money >= 100000'
            )
        
        
        factor['money'].head(10)
        
        >>> connection test:  Ok.
        >>>        money
        >>> 0  1000000.0
        >>> 1  1000000.0
        >>> 2  1000000.0
        >>> 3  1000000.0
        >>> 4  1000000.0
        >>> 5  1000000.0
        >>> 6  1000000.0
        >>> 7  1000000.0
        >>> 8  1000000.0
        >>> 9  1000000.0
        
        
        ```
        
        
        ## Insert data
        insert data into database by various ways
        ### Insert data via DataFrame
        ```python
        from ClickSQL import BaseSingleFactorTableNode as factortable
        import numpy as np
        import pandas as pd
        factor = factortable( 'clickhouse://default:default@127.0.0.1:8123/sample.sample'  )
        db = 'sample'
        table = 'sample'
        df  = pd.DataFrame(np.random.random(size=(10000,3)),columns=['cust_no', 'product_id', 'money'])
        factor.insert_df(df, db, table, chunksize=100000)
            
        
        ```
        
        ### Insert data via SQL(Inner)
        ```python
        from ClickSQL import BaseSingleFactorTableNode as factortable
        
        factor = factortable( 'clickhouse://default:default@127.0.0.1:8123/sample.sample'  )
        
        factor("insert into sample.sample select * from other_db.other_table")
            
        
        ```
        
        ### Create table
        
        #### Create table by SQL
        ```python
        from ClickSQL import BaseSingleFactorTableNode
        
        conn_str = "clickhouse://default:test121231@99.99.9.9:8123/system"
        Node = BaseSingleFactorTableNode(conn_str)
        
        Node('create table test.test2 (v1 String, v2 Int64, v3 Float64,v4 DataTime) Engine=MergeTree() order by v4')
        ```
        
        #### Create table by DataFrame
        ```python
        from ClickSQL import BaseSingleFactorTableNode
        import numpy as np
        import pandas as pd
        
        conn_str = "clickhouse://default:test121231@99.99.9.9:8123/system"
        Node = BaseSingleFactorTableNode(conn_str)
        db = 'test'
        table = 'test2'
        
        
        df_or_sql_or_dict  = pd.DataFrame(np.random.random(size=(10000,2)),columns=['v1', 'v3'])
        df_or_sql_or_dict['v2'] =1
        df_or_sql_or_dict['v4'] =pd.to_datetime('2020-01-01 00:00:00')
        
        Node.create( db,  table,  df_or_sql_or_dict,    key_cols=['v4'],)
        ```
        
        
        ### Contribution
        there is welcome to do more work to improve this package more convenient
        
        ## Author
        sn0wfree
        
        ## functions
        1. get data from clickhouse
        2. insert data into clickhouse
        3. create 
        4. alter
        
        
        # Plan
        ## Available function 
        1. access clickhouse service
        2. execute standard SQL and transform into dataframe
        3. able to execute select query 
        4. able to execute insert query 
        5. no require clickhouse-client
        6. auto create table sql
        7. can execute explain query
        
        ## schedule
        1. ORM
        2. create a pandas_liked executable function, which can compatible with pandas 
        3. alter function & drop function
        4. can execute user role query
        5. create analysis component
        6. auto report system
        7. table register system
        8. data manager system
        8. meta data manager
        9. distributed query（query+insert）
        
        
        
        
Keywords: ClickSQL,Databases
Platform: UNKNOWN
Description-Content-Type: text/markdown
