Getting StartedΒΆ

Obviously first, install data-migrator.

Now create a new directory with your migration scripts. Your milage may very, but we assume you have client access to source data. Some like we discussed in the example. A client like mysql spitting out csv’s in some form and also expect you have client access to the target database. To automate and make it repetitive, just use make. We add some Makefile-foo here but do not worry. Basically what we want to execute is something like this:

mysql [SOURCE] -e "SELECT * FROM table" -B" | python | mysql [TARGET]

In a more generic way and running the clients directly on the host, we will get:

TARGETS = table
OPTIONS ?=-p 2 --debug
OUT_DIR ?= results


default: clean install all

all: $(TARGETS)

      pip install data-migrator

      @rm -rf $(OUT_DIR)
      @find . -name *.pyc -delete

$(OUT_DIR)/%.sql: | $(OUT_DIR)
      ssh [SOURCE_HOST] "sudo mysql connect -e $($(@F)) -B" | python transform_$*.py  $(OPTIONS) -o $(OUT_DIR)


      mkdir -p $@

      ssh [TARGET_HOST] "sudo mysql [TARGET_DB]" < $(OUT_DIR)/table.sql

See that we use a simple query and extract the first 100 lines. The rest of the magic of the Makefile is to separate the extraction from the loading, and allow to easily extend the script with more tables and source. Note that in this case, we are defining the extract query in the makefile, and we are using sudo rights to extract and upload the data. You might want to have an opinion about that.

We now have the ground work for extracting a table, transforming it and loading it. Next step is to build the filter and transform the data into something the target database can accept. Like in the example we can build a simple transformer:

from data_migrator import models, transform
from data_migrator.emitters import MySQLEmitter

def parse_b(v):
  if v == 'B':
    return 'transformed_B'
   return v.lower()

class Result(models.Model):
  id   = models.IntField(pos=0) # keep id
  uuid = models.UUIDField()     # generate new uuid4 field
  # replace NULLs and trim
  a    = models.StringField(pos=1, default='NO_NULL', max_length=5, null='NULL', replace=lambda x:x.upper())
  # parse this field
  b    = models.StringField(pos=2, parse=parse_b, name='my_b')

class Meta:
table_name = 'new_table_name'

# django-esc like creating and saving (to a manager)
Result(a='my a', b='my b').save()

if __name__ == "__main__":
  transform.Transformer(models=[Result], emitter=MySQLEmitter).process()

  # prove we have objects
  assert(len(Result.objects) > 1)

And we now have a nice self explaining transformer, which will generate something like:

-- transformation for Result to table new_table_name
-- input headers: id,a,b
-- stats: in=10,dropped=0,out=10

SET SQL_SAFE_UPDATES = 0; -- you need this to delete without WHERE clause
DELETE FROM `new_table_name`;
ALTER TABLE `new_table_name` AUTO_INCREMENT = 1;

INSERT INTO `new_table_name` (`id`, `uuid`, `a`, `my_b`) VALUES (0, "ac7100b9-c9ad-4069-8ca5-8db1ebd36fa3", "MY A", "my b");
INSERT INTO `new_table_name` (`id`, `uuid`, `a`, `my_b`) VALUES (1, "38211712-0eb2-4433-b28f-e3fe33492e7a", "NO_NULL", "some value");
INSERT INTO `new_table_name` (`id`, `uuid`, `a`, `my_b`) VALUES (2, "a3478903-aed9-462c-8f47-7a89013bc6ea", "CHOPP", "transformed_B");

There you are, you have setup your first pipeline. Execute this by running:

$ make table  # extract the data from the database, transform it
$ make upload # load it into the database

You can lookup the intermediate result by viewing the generated sql results/new_table_name.sql. data-migrator does not focus on the database schema (yet!) so the table is expected to exist in the target system. But by default the system (or actually the emitter) is wiping the data, not recreating the table. If you have issues with the python libraries, run make install do install the library from this makefile.

Now go ahead and add more fields. See fields reference for more details about the options of the fields.