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How to get your data into the database

In the previous chapter, we have learned how to define the database layout and tell the framework to create the database accordingly. The following describes how to fill this database with data that previously resided in one or many ascii tables.


There are many ways to achieve this and you are certainly free to fill the database in any way you want, if you already know how to do it.

The strategy we adopt is to use the database’s own import mechanisms which are many times faster for large amounts of data than manually inserting data row by row.

The import thus becomes a two-step process:

  1. create one ascii file per data model, each of which has columns that exactly will match the columns in the database.
  2. run one SQL command for each of these files to load it into the matching database table.

Since you might already have step 1 finished or might be able to get it with your own data handling tools, let’s have a look at step 2 first.

Loading ascii data into the database

In the following, we assume that you use MySQL as your database engine (this is also our recommendation when a new database is set up for the first time). Other engines have similar mechanisms for bulk loading data.

The mysql command we use looks like this:

mysql> LOAD DATA INFILE '/path/to/data.file' into table <TAB>;

where <TAB> is the name of the database table corresponding to the file being loaded.


The table names have a prefix node_, i.e. the table for a model called State will be called node_state, unless you specify the table name in the model’s definition. You can see a list of all tables by giving mysql the command SHOW TABLES;.

The LOAD DATA command has several more options and switches for setting the column delimiter, skipping header lines and the like. Mathematical or logical operations can be run on the columns too, before the data get inserted into the database.

You can read all about LOAD DATA at

A more complete example would look like:


Preparing the input files

In the not so unlikely case that the data are not yet in a format exacly matching the database layout, the Node Software ships with a rewrite tool to convert your data into such a format. The output will be ascii files that can be loaded as described in the previous section and will fulfill the following criteria:

  • One file per database table. LOAD DATA cannot update existing rows.
  • Same number of columns in the file as in the table and in the right order. Although LOAD DATA can take a list of columns to circumvent this restriction, it makes sense to get this right.
  • Links between the tables are in place. The key values that link tables (e.g. states and transitions) should be already in the ascii files (even though they can still be generated with LOAD DATA by using some SQL magic).
  • A consistent delimiter between the columns (no fixed record length) and consistent quoting.
  • Empty (NULL) values are written as \N, not 0 or anything else. (Can also be fixed later if this is the only thing missing)

The tool can be used to convert almost any format of file. It’s easiest to convert files with its records stored as lines (one line per record), but the tool also supports blocks of data stretching several lines.

To use the rewrite tool, you need to tell it how your original data files are named and how they are structured. This is done in something called a mapping file. The mapping file describes how the rewriter should extract data from your custom text files. It will then use your data models (which you should have defined by now) to create output files in a format the database can import.

Starting the rewrite

Once you have defined the mapping file as described in the following section, you need to place yourself in the imptools/ directory (this is so the rewriter can find all its dependencies) and then give the mapping file as an argument to the imptools/ program:

$ python ../nodes/MyNode/

The result will be a set of ascii output files on the right form.


For large amounts of raw data, the rewrite operation can be very time consuming. We have found that a speed-up of as much as five times can be achieved by not using standard Python but an alternative implementation of Python called pypy. If installed you run the rewrite program just as above except you replace python with pypy. See for further details on pypy usage.

The mapping file

The mapping file is a standard Python file and describes how the rewriter reads the raw data so it can be converted on the form needed for database import. imptools/ is a minimal mapping file one can build from. A much more extensive example is found in the nodes/ExampleNode directory.

The mapping file must define a variable called mapping which contains a list of definitions that describe how the rewriter should parse each text file and correlate the data to the data models.

Let’s start a sample mapping file. It starts by defining some convenient variables storing input/output filenames (just to make it easier to refer to them further down). We also include imptools/ which holds helper methods for parsing data. The only mandatory part is the mapping list:

from imptools.linefuncs import *

# the names of the input files
basepath = "/path/to/your/raw_data/"
outpath = "/path/to/store/rewritten/files/"
file1 = basepath + 'raw_file1.txt'
file2 = basepath + 'raw_file2.txt'
file3 = basepath + 'raw_file3.txt'
outfile1 = outpath + 'references.dat'
outfile2 = outpath + 'species.dat'

mapping = [ ... ]  # described below

The mapping list

The mapping variable is a list of Python dictionaries. A standard python dictionary is written as {key:value, key2:value2, ... } and is a very efficient means of storing data. Each of the dictionaries in mapping describes how to output data to exactly one output file and thus correspons to one database table (described by a model in node/ It can use any number of raw input data files to get this data.

Each mapping is executed in parallel, using multiple processors if available. This means that you should not have any mapping write to the same output file as any other mapping. For example, one could be tempted to have two mappings both write to an outfile states.dat, writing upper- and lower- states into the file respectively. Due to the parallel operation, this will likely lead to file lock clashes. Output instead to two files (e.g. states_upper.dat and states_lower.dat) and read them separately into the same table later.

Only certain key names are allowed in each mapping dictionary. One of these keys, linemap holds a list with further dictionaries since it details exactly how to read each line/block of data from the input. The structure of the mapping variable looks like this:

mapping = [
   {key : value,
    key : value,
    linemap : [
        {linemap_key : value,
         linemap_key : value},
        {linemap_key : value,
         linemap_key : value}] },

And so on, continuing with more dictionaries. The key s and value s of each dictionary describes all aspects of the parsing, although not all options are mandatory depending on your structure.

key value
outfile The name of the file that should be created. Each such output file will later be read into one database table/ model.
infiles Input file(s). This may be a single file name or a list of multiple file names. More than one file may be relevant if the raw data is stored in multiple files related to each other by line number only.
linemap A list of dictionaries defining how to parse each line/block of the file(s) into its components (see the next table below for the keys relevant when defining the linemap list)
headlines Number of header lines at the top of the input file(s) (default: 0). If more than one infile is used, this must be a list of headlines in the same order, as many as there are input files.
commentchar Which comment symbol is used in the input file(s) to indicate a line to ignore (default is: ‘#’). As above, this must be a list if more than one filename is used.
errlines Whole lines in the input file(s) that should be considered non-valid and ignored (no default). As above, this must be a list if more than one filename is read.
linestep A step length (in number of lines) when reading the input file. Default (0) means stepping one line at a time. A linestep of 1 means skipping every other line. If more than one file is read at a time, this must be a list of the same length as there are files. So a lineoffset of [0,2] would mean that while every line is read in the first file, only every third is used in the second file.
lineoffset A starting offset when reading a file, after headers have been skipped. So a lineoffset of 3 would first skip the header (if any), then another 3 lines. This is most useful in combination with linestep, to make sure the first line of data is read from the right start point. If many files are read, this must be given as a list of offsets, as many as there are files.
startblock This is a string or a list of strings to be interpreted as starting sentinels for data records stretching over more than one line. So if every data block is wrapped in BEGIN ... END clauses, you should put “BEGIN” here. (default is the line break character, making each “block” equivalent to a line). The variables linestep and lineoffset will step through full blocks instead of lines if this is given.
endblock This is a string or list of strings to be interpreted as ending sentinels for data records stretching over more than one line. So if every data block is wrapped in BEGIN ... END clauses, you should put “END” here. (default is the line break character, making each “block” equivalent to a line). If blocks are only separated by a single sentinel (e.g. ... RECORD ... RECORD ... ), simply put the same sentinel (“RECORD” in this example) as both startblock and endblock.

A note about reading multiple files at the same time: The only main use for this is really if your raw data is related to data in other files by record number only (i.e. by counting line number or maybe block number). If you cannot use line numbers since you use, say, an ID string to relate data in one file to that in another, you cannot correlate them to each other this way. You should then instead read the files as separate reads. Exactly how the read will looks depend on your planned database layout and the models you need to populate. /nodes/vald/ contains an advanced example of reading upper and lower atomic States from a file in two passes, using ID hashes to relate them to a second model (Transitions).

The linemap key points to another list with dictionaries. This is the actual operating piece of code and describes exactly how to parse each line or block (or lines/blocks, if more than one input file is read simultaneously). Each dictionary works for a single database field in your current model (that is, the model your output file will be read to down the line) and describes exactly how to parse the current line/block so as to produce a value in that field.

linemap_key value
cname The name of the field in your database model to populate.
cbyte A tuple (linefunction, arguments). This names a function capable of parsing the line(s) to produce the data needed to feed to the field cname. The only provision of a linefunction is that it should take an argument linedata as its first argument. This will contain the current line/block to parse, or a list of lines/blocks if more than one input file were read simultaneously. You can define your own linefunctions directly in the mapping file. A host of commonly needed line functions (such as reading a particular index range or the Nth separated section etc) come with the package and can be used directly by importing from imptools/
filenum This is an integer or a list of integers used only when more than one file is read simultaneously. It allows you to specify the index/indices of the file/files to be parsed. Default is file 0. Note: If you need to somehow merge data from two or more files to produce one value, you need to write a custom line function for this and then use this setting to specify which file(s) should be used.
cnull Indicates what should be interpreted as NULL data. If this string is found, the N symbol will be stored in the output file instead.
debug This will activate verbose error messages for this parsing only. Useful for finding problems with the mapping.

Continuing our example, here’s how this could look in the mapping file (the line breaks are technically not needed, but make things easier to read). Note also that we imported earlier, making the line functions bySepNr and charrange available (among many others):

mapping = [
  # first dictionary, writing into outfile1 (defined above) from an
  # input file file1.
    'outfile': outfile1,
    'infiles': file1,
    'headlines' : 3,
    'commentchar' : '#',
    'linemap' : [
         'cbyte':(bySepNr, 0, '||')}, # get 0th part of record separated by ||
         'cbyte':(bySepNr, 1, '||')}, # get 1st part of record separated by ||
            # ...
  # next model dictionary, writing species.dat
    'outfile' : outfile2,
    'infiles' : (file2, file3), # using more than one file!
    'commentchar' : (';', '#'),
    'headliens' : (1, 3),
    'lineoffset' : (0, 1),
    'linemap' : [
        'cbyte':(charrange, 23, 25)}, # pick a range by index
        'cbyte'(charrange, 45, 45, 1)}, # retrieved from file3!
          # ...
        'filenum':1,     # read from current line of second file!
        'cbyte':(charrange, 0, 10),

The line functions

Since the mapping file is a normal Python module, you are free to code your own line functions to extract the data from each line/block in your file. There are only three requirements for how a line function may look:

  • The function must take at least one argument, which will hold the current line or block being processed, as a string. The import program will automatically send this to the function as it steps through the file. If you read multiple input files and supplied multiple linenum values in the mapping, this first argument will be a list with the corresponding lines/blocks. It’s up to the custom function to handle this list properly.
  • The function must return its extracted piece of data in a format suitable for the field it is to be stored in. So a function parsing data for a CharField should return strings, whereas one parsing for an IntegerField should return integer values.

Below is a simple example of a line function:

def charrange(linedata, start, end):
    Simple extractor that cuts out part of a line
    based on string index.
    return linedata[start:end].strip()

In the mapping dictionary we will call this with e.g. 'cbyte' : (charrange, 12, 17). The first element of the tuple is the function object, everything else will be fed to the function as arguments. The function should return the string to store.

The default line functions coming with the package will handle most common use cases. Just import linefuncs * from your mapping file to make them available. You can find more info in the Linefuncs Documentation.

More advanced line parsing

Sometimes you need more advanced parsing. Say for example that you need to parse two different sections of lines from one or more files and combine them into a unique identifier that you will then use as a key for connecting your model to another via a One-to-Many relationship. Or maybe you want to put a value in different fields depending on if they are bigger/smaller than a certain value. There is no way for the default line functions in to account for all possibilities.

The solution is to write your own line function. You have the full power of Python at your command. Often you can use the default functions as “building blocks”, linking them together to get what you want. Just code your custom line functions directly in the mapping file.

The mapping file will skip lines/blocks starting with the commentchar character or containing data matching the errorline key value. But sometimes you don’t have enough information to know if the line/block should be skipped. You can then analyze this in your custom line function. If there is a problem raise RuntimeError - the import system will then cleanly skip that line/block for you.

Here is an example of a line function that wants to create a unique id by parsing different parts of lines from different files:

from imptools.linefuncs import *

def get_id_from_line(linedata, sepnr, index1, index2):
    extracts id from several lines.
      sepnr - nth separator to pick from file 1
      index1, index2 - indices marking start/end index from file 2

      (file3 is (in this example) always used the same way,
       so we hard-code the indices for that file.)
    l1 = bySepNr(linedata[0], sepnr, ',')
    l2 = charrange(linedata[1], index1, index2)
    l3 = charrange(linedata[2], 0, 3)
    if l3 == '000':
        l3 = 'unknown'
    # create unique id
    return "%s-%s-%s" % (l1, l2, l3)

Here we made use of the default line functions as building blocks to build a complex parsing using three different files. We also do some checking to replace data on the spot. The end result is a string combined from all sources.

This function assumes linedata is a list. It must thus be called from a mapping where at least three files are read (inputfiles is a list of at least three file names) and where filenum is given as a list specifying which files’ lines/blocks are to be sent to the function. The the mapping dictionary could look something like this:

    'infiles': [file1,file2,file3],
    'linemap': [
         filenum = (0,1,2)
         cbyte: (get_id_from_line, 3, 25, 29)},

See nodes/ExampleNode for more examples of mappings and linefuncs.

How to update an existing database

As long as your database schema has not changed, you can use this same rewrite mechanism to append new data to your database. Just run the rewriter on your new raw data, then use the LOAD DATA INFILE (MySQL) or equivalent again to import it into your database.

An important limitation of LOAD DATA INFILE is that it will not change already existing rows. So you cannot update data in-place with this method (it is also not the purpose of this import system).

For altering existing rows in the database, the standard SQL-command UPDATE TABLE will do the trick in most cases.

Adding data in the form of new columns to existing tables, can be done as follows. Add the empty column using SQL ALTER TABLE, fill it with UPDATE TABLE and then add the corresponding field definition in your and to make the NodeSoftware aware of it.

The underlying Django system comes with many third-party tools for helping you manage your database however. We recommend you look into Django-South ( This Django-plugin allows you to write simple “migration” scripts for updating an existing database schema or do data conversions between different versions of a live database.