This tool allows you to take data from an MySQL server (only tested on 5.x) and write a PostgresSQL compatable (8.2 or higher) dump file or pipe it directly into your running PostgreSQL server (8.2 or higher).
Attention
Currently there is no support for importing spatial data from MySQL.
If you’re like me you don’t like random stuff polluting your python install. Might I suggest installing this in an virtualenv?
> virtualenv --no-site-packages ~/envs/py-mysql2pgsql
> source ~/envs/py-mysql2pgsql/bin/activate
I have only done limited testing on this platform using Python 2.7. Here are the driver dependencies for windows, install these before attempting to install py-mysql2pgsql or it will fail.
All dependencies should be automatically installed when installing the app the following ways
> pip install py-mysql2pgsql
> git clone git://github.com/philipsoutham/py-mysql2pgsql.git
> cd py-mysql2pgsql
> python setup.py install
Looking for help?
> py-mysql2pgsql -h
usage: py-mysql2pgsql [-h] [-v] [-f FILE]
Tool for migrating/converting data from mysql to postgresql.
optional arguments:
-h, --help show this help message and exit
-v, --verbose Show progress of data migration.
-f FILE, --file FILE Location of configuration file (default:
mysql2pgsql.yml). If none exists at that path,
one will be created for you.
Don’t worry if this is your first time, it’ll be gentle.
> py-mysql2pgsql
No configuration file found.
A new file has been initialized at: mysql2pgsql.yml
Please review the configuration and retry...
As the output suggests, I file was created at mysql2pgsql.yml for you to edit. For the impatient, here is what the file contains.
# if a socket is specified we will use that
# if tcp is chosen you can use compression
mysql:
hostname: localhost
port: 3306
socket: /tmp/mysql.sock
username: mysql2psql
password:
database: mysql2psql_test
compress: false
destination:
# if file is given, output goes to file, else postgres
file:
postgres:
hostname: localhost
port: 5432
username: mysql2psql
password:
database: mysql2psql_test
# if only_tables is given, only the listed tables will be converted. leave empty to convert all tables.
#only_tables:
#- table1
#- table2
# if exclude_tables is given, exclude the listed tables from the conversion.
#exclude_tables:
#- table3
#- table4
# if supress_data is true, only the schema definition will be exported/migrated, and not the data
supress_data: false
# if supress_ddl is true, only the data will be exported/imported, and not the schema
supress_ddl: false
# if force_truncate is true, forces a table truncate before table loading
force_truncate: false
Pretty self explainitory right? A couple things to note, first if destination -> file is populated all outpute will be dumped to the specified location regardless of what is contained in destination -> postgres. So if you want to dump directly to your server make sure the file value is blank.
Say you have a MySQL db with many, many tables, but you’re only interested in exporting a subset of those table, no problem. Add only the tables you want to include in only_tables or tables that you don’t want exported to exclude_tables.
Other items of interest may be to skip moving the data and just create the schema or vice versa. To skip the data and only create the schema set supress_data to true. To migrate only data and not recreate the tables set supress_ddl to true; if there’s existing data that you want to drop before importing set force_truncate to true. force_truncate is not necessary when supress_ddl is set to false.
One last thing, the –verbose flag. Without it the tool will just go on it’s merry way without bothering you with any output until it’s done. With it you’ll get a play-by-play summary of what’s going on. Here’s an example.
> py-mysql2pgsql -v -f mysql2pgsql
START PROCESSING table_one
START - CREATING TABLE table_one
FINISH - CREATING TABLE table_one
START - WRITING DATA TO table_one
24812.02 rows/sec [20000]
FINISH - WRITING DATA TO table_one
START - ADDING INDEXES TO table_one
FINISH - ADDING INDEXES TO table_one
START - ADDING CONSTRAINTS ON table_one
FINISH - ADDING CONSTRAINTS ON table_one
FINISHED PROCESSING table_one
START PROCESSING table_two
START - CREATING TABLE table_two
FINISH - CREATING TABLE table_two
START - WRITING DATA TO table_two
FINISH - WRITING DATA TO table_two
START - ADDING INDEXES TO table_two
FINISH - ADDING INDEXES TO table_two
START - ADDING CONSTRAINTS ON table_two
FINISH - ADDING CONSTRAINTS ON table_two
FINISHED PROCESSING table_two
Since there is not a one-to-one mapping between MySQL and PostgreSQL data types, listed below are the conversions that are applied. I’ve taken some liberties with some, others should come as no surprise.
| MySQL | PostgreSQL |
|---|---|
| char | character |
| varchar | character varying |
| tinytext | text |
| mediumtext | text |
| text | text |
| longtext | text |
| tinyblob | bytea |
| mediumblob | bytea |
| blob | bytea |
| longblob | bytea |
| binary | bytea |
| varbinary | bytea |
| bit | bit varying |
| tinyint | smallint |
| tinyint unsigned | smallint |
| smallint | smallint |
| smallint unsigned | integer |
| mediumint | integer |
| mediumint unsigned | integer |
| int | integer |
| int unsigned | bigint |
| bigint | bigint |
| bigint unsigned | numeric |
| float | real |
| float unsigned | real |
| double | double precision |
| double unsigned | double precision |
| decimal | numeric |
| decimal unsigned | numeric |
| numeric | numeric |
| numeric unsigned | numeric |
| date | date |
| datetime | timestamp without time zone |
| time | time without time zone |
| timestamp | timestamp without time zone |
| year | smallint |
| enum | character varying (with check constraint) |
| set | ARRAY[]::text[] |
I ported much of this from an existing project written in Ruby by Max Lapshin over at https://github.com/maxlapshin/mysql2postgres. I found that it worked fine for most things, but for migrating large tables with millions of rows it started to break down. This motivated me to write py-mysql2pgsql which uses a server side cursor, so there is no “paging” which means there is no slow down while working it’s way through a large dataset.
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