sqlcsv 0.1.0

Import/Export data to/from SQL database with CSV

sqlcsv

Simple command line tool that can be used to:

• SELECT data from database and export the result as CSV
• INSERT data into database from CSV

Installation

Via PyPI:

$ pip3 install sqlcsv

By default it does not install any database drivers. Install them by your own need:

# MySQL
$ pip3 install mysqlclient

# PostgreSQL
$ pip3 install psycopg2

Usage

In the examples below we use following table schema with MySQL:

CREATE TABLE testtable(
  id INT AUTO_INCREMENT PRIMARY KEY,
  int_col INT,
  float_col FLOAT,
  varchar_col VARCHAR(255)
)

Connection

You can specify database connection info by SQLAlchemy URL using --db-url:

$ sqlcsv --db-url 'mysql://testuser:testpassword@127.0.0.1:3306/testdb' <subcommand> ...

Also it can be read from SQLCSV_DB_URL environment variable:

$ export SQLCSV_DB_URL='mysql://testuser:testpassword@127.0.0.1:3306/testdb'
$ sqlcsv <subcommand> ...

From here we assume that connection is specified using environment variable.

SELECT

Assume we already have following records on the table in our database:

+----+---------+-----------+-------------+
| id | int_col | float_col | varchar_col |
+----+---------+-----------+-------------+
|  1 |       1 |         1 | aaa         |
|  2 |       2 |         2 | bbb         |
|  3 |    NULL |      NULL | NULL        |
+----+---------+-----------+-------------+

Use select subcommand and give SELECT query using '--sql':

$ sqlcsv select \
  --sql 'SELECT * FROM testtable'
id,int_col,float_col,varchar_col
1,1,1.0,aaa
2,2,2.0,bbb
3,,,

If you want to save the result to file, use --outfile:

$ sqlcsv select \
  --sql 'SELECT * FROM testtable' \
  --outfile out.csv

INSERT

Assume we have following CSV file:

int_col,float_col,varchar_col
1,1.0,aaa
2,2.0,bbb

Use insert subcommand and give INSERT query with placeholders using '--sql', followed by --types specifying types of each field:

$ sqlcsv insert \
  --sql 'INSERT INTO testtable(int_col, float_col, varchar_col) VALUES (%s, %s, %s) \
  --types int,float,str

The resulted records in the table are to be:

+----+---------+-----------+-------------+
| id | int_col | float_col | varchar_col |
+----+---------+-----------+-------------+
|  1 |       1 |         1 | aaa         |
|  2 |       2 |         2 | bbb         |
+----+---------+-----------+-------------+

Note that type names in --types are the same as Python primitive type function names. Also it can be short form like --types i,f,s

NULLs

You may have CSV file contains empty cell and may want to treat them as NULL in SQL like:

int_col,float_col,varchar_col
1,,aaa
2,2.0,

In such case use --nullable to convert them to None before insertion:

$ sqlcsv insert \
  --sql 'INSERT INTO testtable(int_col, float_col, varchar_col) VALUES (%s, %s, %s) \
  --types int,float,str \
  --nullable false,true,true

The result to be:

+----+---------+-----------+-------------+
| id | int_col | float_col | varchar_col |
+----+---------+-----------+-------------+
|  1 |       1 |      NULL | aaa         |
|  2 |       2 |         2 | NULL        |
+----+---------+-----------+-------------+

Values of --nullable are one of true or false. They can also be written as t or f in short forms.

Read SQL from file

In both select and insert subcommands you can use --sqlfile option to read query from a file intead of using --sql:

$ sqlcsv select --sqlfile query.sql
$ sqlcsv insert --sqlfile query.sql

CSV dialect

If your input or output have to be tab-separated (TSV), use --tab like:

$ sqlcsv --tab select --sql 'SELECT * FROM testtable'
id	int_col	float_col	varchar_col
1	1	1.0	aaa
2	2	2.0	bbb

For other format options, see sqlcsv --help. Basically it supports the same option as csv package in Python's standard libraries does.

Comparison between other tools

LOAD (MySQL) or COPY (PostgreSQL)

Major RDBMSs usually have built-in instructions to import data from files such as LOAD instruction for MySQL or COPY for PostgreSQL. They are obviously the primary options you may consider but there are some limitations for them:

• Few platform support imports across network; others only can do from local files
• Specification for data format or instruction varies for each platform

Sqlcsv works remotely and provides unified interfaces at least for CSV format.

CSVKit

CSVKit is a popular toolkit for manipulating CSV files. It provides sql2csv and csvsql commands for export/import data to/from files. Consider using them before choosing sqlcsv if they just satisfy your needs, as they have much more users and contributers, though there might be a few reasons to prefer sqlcsv to them (and this is why it was created) :

• CSVKit depends on several libraries including agate but not all of them are needed for interoperability between SQL databases and CSV files. Sqlcsv uses [csv package in Python's standard libraries] for I/O with CSV files and SQLAlchemy for querying SQL databases, which leads to less library dependencies.
• CSVKit's csvsql command takes just table name for import target, which make it easy to use. However, it is sometimes inconvenient in such cases where CSV file includes only a part of columns and others are generated dynamically by SQL expressions. Sqlcsv's insert subcommand, by contrast, takes INSERT statement, which might be verbose but provides more flexibility.

Pandas

If you do not care about library dependencies, do not need custom INSERT statement and do not need command line interfaces, then just use pandas' DataFrame.to_sql method or read_sql function. They will help you a lot if used with DataFrame.to_csv method or read_csv function.

Embulk

If your dataset is so large that needs performance optimization such as parallel processing, or you want some sophisticated I/O functionality such as retrying, consider using Embulk. It also supports various data stores and data formats with many plugins.