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Getting Your Code into Kive: A Guide for Software Application Developers

Getting Your Code into Kive: A Guide for Software Application Developers


If you’re considering using Kive, chances are you have a lot of scripts hanging around on your computer to perform different analyses and processing steps on biological datasets. Some of these may be entirely your own code, or they may call external programs you have installed. Maybe they are in a version control system like Git or SVN, or perhaps you rename them whenever you make a significant change. Or, you may have no versioning at all, and simply edit the scripts in place. To use them, you may have to pass in the directory name as an argument, copy the script to a specific place, or edit hardcoded paths within the script itself. When changes are made to the code that affect its behaviour, output from the old version may become unreproducible. Making matters worse, you might not know which version of a given script was used to generate a particular dataset.

This situation can be problematic in a number of situations, for example:

  1. When computation on clinical patient data is performed, a calculation result becomes part of the patient’s medical record which has to be archived because medical treatment decisions can be based on these results. The input data, output data and exact version of the processing software all have to be archived for later reference.

  2. When computational results are published in a scientific journal, they should be reproducible. This is important for comparison to other results, and in order to resolve any questions about the reported results that might arise after publication.

Kive aims to streamline your data processing by handling the versioning and running of your code, and recording every detail of every run. This way, you can focus on developing new and better ways to process and analyse your data, with the knowledge that everything you do will be recorded and reproducible.

In summary, Kive will help you keep track of the program and data input files in your projects; it will help you maintain the inter dependencies between these different files, and it will help you keep a record of the changes you make to any of these files. In addition, you can execute a specific version of a given program on a specific data set to reproduce an earlier result, even if any program involved has been modified since the data was last produced.

We will assume that you have an existing project consisting of a number of different program files. Some of these will be executable, that is they are to be run as a standalone program. We assume that the overall computation is performed by a number of these executables in sequence.

Kive Nomenclature

Migrating to Kive: the BIIIG Picture

The overall process of migrating an existing project into Kive follows a ‘bottom-up’ approach. We suggest you work in the following order:

  1. Readying your Code for Kive

Before you start uploading your code into Kive, it makes sense to make sure that your code is in a form that can best benefit from Kive’s functionality. If this step is done carefully, the later steps will be straightforward. There are two main issues to address:

  1. How computations are performed (The Methods)

    The executables must conform to a simple Kive convention with regard to how they read their input and produce their output. This is required so that Kive can assemble the Method building blocks into an overall pipeline. See the next section for more details on how to achieve this.

  2. How data between computations is transferred (Datatypes)

Kive can perform 'sanity checks' on the data produced by a Method when 
running a pipeline, before passing this data on to other Methods. In order to benefit from
this functionality, Methods must produce data in one of a number of recognised
data types, the most common one in Kive being comma-separated value (CSV) format.
(Kive does have a 'raw' data format, files of which type it simply passes from one 
Method to another without checks, but this data type is discouraged in a production

CSV files are straightforward to produce, for example by using the csv module in
a python program.

**Note** As you are deciding at this stage what file format any intermediate data 
sent between Methods will have, it could make sense to write some additional small code
snippets that check the correct format of these files. See the section 
on [constraints](#constraints) for an example of this.
These additional snippets should be loaded into Kive as code resources with the rest of
your code and can then be bound to a Datatype (see 'custom constraint').
Kive will then call this code to check data integrity at run-time.

This step is described in more detail in the next section.

  1. Upload your Code into Kive

Every separate file containing code, whether executable or dependency, must be uploaded to Kive as a Code resource. Later, if the source code in a Code resource changes, it will be possible to add a CodeResourceRevision to an existing Code resource. Each code resource is granted certain permissions, defining who (which kive users) can run it. This process is described here.

  1. Define Datatypes

    Before you define Methods to run your programs, it makes sense to define Kive Datatypes and Compound Datatypes for your project (Note, however, that you might opt to create Methods at this stage already, if you want to define Datatypes with custom constraints). Essentially, you are telling Kive what kind of data is expected to be in each column of a particular kind of CSV file that your program produces or consumes. This is information you will have determined in step 1. More detailed information about Datatypes can be found here.

  2. Define Methods

    Now Methods are defined using existing Code Resources and existing Compound Datatypes as inputs and outputs. Each Method will have one Code Resource as a main program, and can have a number of additional Code Resources as dependencies. Each Method can be defined to have a number of inputs and outputs, each of a certain Compound Datatype. Methods also have permission settings that define who can invoke them. This step is further described here.

  3. ** Build Pipelines **

    Finally, Methods can be assembled into pipelines. A pipeline will take a certain number of input files of specified types, perform the selected Methods and produce a certain number of output files of specified types. This process is further described here.

  4. ** Run your Analysis **

    Your work as a Developer is done. As a User, you can now upload Datasets (input data) and, depending on their permissions, run existing pipelines with selected input data. Multiple runs can be set to run at the same time. You inspect the status of runs previously started, and stop or rerun any runs. This is also where you can see calculated results, and extract them from Kive. See here for more information about this step.

All of these steps except the first and last can be performed from the web interface under the Kive ‘Developers’ main menu. The first step must be done by you in a text editor of your choice. The last step is performed in Kive under the ‘Users’ main menu.

Step 1: Readying your Code for Kive

As mentioned previously, because Kive handles running of your code automatically, executables you provide must have a specific command line interface. If you are writing new executables for Kive, you can implement this interface from the get-go; otherwise, you will need to modify your code. There are two important changes you’ll need to make:

  1. your program’s command line interface, discussed in the next section; and
  2. the format of the data your program will read and write. This is discussed here.

Executable I/O Conventions

Executables must be called from the command line with the syntax

    ./program_name [input_1] ... [input_n] [output_1] ... [output_k]

Executables can in principle be any file that can be executed under the rules of the underlying operating system (e.g. under linux, these could be a binary executable that has been separately compiled and linked, but are more typically a python or shell script starting with a shebang). The above expects n input files and produces k output files, whose file names are passed in in that order on the command line. Your script may additionally print messages to standard output and standard error, and set its return code to a descriptive value, if you like, and all this information will be recorded in Kive. However, your actual data must be read in from, and output to, the files named on the command line.

For example, suppose you have written a program which takes two inputs and produces one output. The names of the two inputs, and then the names of the output, will be contained in the “argv” array in most programming languages. For example, in C:

    int main (int argc, const char* argv[]) {
        const char *infile1 = argv[1];
        const char *infile2 = argv[2];
        const char *outfile = argv[3];
        # fopen infile1 and infile 2
        # process data
        # fopen outfile, write results there
        printf("Reading from %s and %s, and writing to %s.",
            infile1, infile2, outfile);
        return 0;

Notice that we print some information to standard output, and then return 0 to indicate success. Your program may return any number between 0 and 255, and this will be recorded for the user to view at run-time, as will the contents of standard out and standard error (see the ExecLog documentation for more details). Of course, you do not have to provide a return code, nor do you need to output anything to either of the standard streams.

I/O Convention Restrictions

Argument Name Conventions

Kive puts no restrictions on what characters can appear in your dataset names, except those imposed by your file system. You must take care that your programs can handle all possible characters in file names. For example, on Unix, spaces are allowed. When using scripting languages such as bash, be careful to enclose your variables in quotes, (ie. “$2” instead of just $2) to avoid problems caused by spaces.

Additional Command-line Arguments

This required interface means that you cannot pass additional command line arguments to your program. If you want to do that, write a wrapper for your script, perhaps in a shell scripting language like Bash, which calls the program with the necessary arguments. In the next example, we want to call my_script, which takes 2 inputs and produces 1 output, with the additional argument “–verbose”. We write the following bash wrapper into a file called

    my_program --verbose $1 $2 $3

Now my_program is getting the correct command line arguments for file names, plus “–verbose”. Of course, my_program needs to be careful not to treat “–verbose” as the first input file, perhaps by using an argument parsing library. For this to work, you will need to add my_program as a dependency of when you make a Method out of it.

Variable Number of Arguments

In addition, the above restrictions mean that a program to be run in Kive cannot accept a variable number of arguments. To get around this, you would again define a shell script for each fixed situation that you want to call your program with, and create a shell script that calls your program in each case.

Data Format Conventions

The second modification you’ll need to make is the format of the data fed into, and output by, your code. Kive passes data around mostly in RFC 4018-compliant comma-separated value (CSV) format. That means, for example, if your program outputs data in the form of DNA sequences with accompanying headers, you will need to output a file which looks something like the below.


Of course, not every file you might want to handle can be reasonably coerced into CSV format, such as a configuration or settings file. External programs will probably output results in formats other than CSV - bioinformatics programs often produce files in NEXUS or FASTA format. For these use cases, Kive also allows you to declare data as being “raw”. Raw data will simply be passed around as-is from one step in a pipeline to another. However, whenever possible, we /strongly encourage/ you to write code which inputs and outputs CSV. One of Kive’s strengths is its ability to check the integrity of your data against any constraints you define, which can help catch bugs and unexpected behaviour in your code (see the section on Datatypes, below). With raw data, this functionality is lost. In general, raw data should be reserved for output directly from external programs. If you are writing the program yourself, output to CSV.

Datatypes and CompoundDatatypes


Just as important as uploading your code to Kive, you need to describe to Kive what kinds of data you will be working with. Kive is a tool for manipulating structured data. In fact, from the point of view of the system, the structure of the data is just as important as the contents, if not more so. Kive wants as much information as possible about what your data is supposed to look like, so that it can catch more errors at all steps of Pipeline execution.

Kive passes most data around as CSV files with headers. In Kive terms, each column of a CSV file has a Datatype, indicated by its header, and the entire CSV file has a CompoundDatatype, which is simply composed of the sequence of Datatypes of the individual columns. These concepts are elaborated below.


The most basic information about a piece of data is its type. You are probably familiar with datatypes from your favourite programming language. C, for example, has only a few datatypes - integer, character, double, and variations on these. Languages like Python have many datatypes, including lists, dictionaries, and files.

The datatype system of Kive more closely resembles that of Ada or Haskell (but don’t let that scare you off :) ). Datatypes can be extremely restrictive, and we encourage you to define types as narrowly as possible for the range of data you expect to work with. For example, suppose you are working with DNA coming off a sequencing platform which produces reads between 50 and 300 base pairs long. You could simply declare these as “strings”, but it would be better to define them as “strings of between 50 and 300 characters from the alphabet {A, T, C, G}”. That way, you can be confident you are always passing around DNA within your pipelines. We show how to define these sorts of datatypes below.

Restrictions <a=name=”restrictions”></a>

When one datatype is a special case of another, we say that the former /restricts/ the latter. For example, positive integers are a special case of integers, so positive integers restrict integers. If you are familiar with object oriented programming, restriction in Datatypes is analogous to inheritance in classes. Restrictions can be nested arbitrarily deeply, but cannot be circular (you can’t define Datatypes A, B, and C, such that A restricts B, B restricts C, and C restricts A). You can also have multiple Datatypes restricting a single one (A and B both restrict C), or one Datatype which restricts several others (A restricts both B and C).

Because Kive operates primarily on CSV files, all datatypes in Kive restrict strings. When you create a new Datatype, you must select one or more Datatypes for it to restrict (in addition to any other restrictions you want to define). If you do not want to impose any restrictions on your Datatype, declare it to restrict “string” only. For convenience, you may also declare your Datatype to restrict one of the other predefined types “int”, “float”, or “bool”. Internally, these three are treated as restrictions of “str”; they are there for convenience, so that you do not have to define these common types on your own.


Restrictions only encapsulate “is-a” type relationships among Datatypes. While these can be useful, they don’t allow you to specify exactly what format you expect your data to have. That’s where constraints come in. Constraints hold specific rules that data of a particular Datatype must adhere to. These can be very basic, such as having a particular length, or arbitrarily complex, defined by code that you write.

There are two types of constraints on Datatypes in Kive:

  1. /Basic constraints/ are simple checks built in to Kive, which should cover a good number of data checking cases. These include minimum and maximum length (for strings), minimum and maximum values (for integers and floats), matching a regular expression (for strings), and being formatted as a timestamp (for strings). To define a datatype for strings of DNA between 50 and 300 base pairs long, I could use the three constraints “minlen=50”, “maxlen=300”, and “regexp=’^[ATCG]+$’”. More concisely, I could simply use the one constraint “regexp=’^[ATCG]{50,300}$’”.

Note that both regular expressions begin with ‘^’ and end with ‘$’, which indicates that the pattern should match the whole string. If you omit these, values will be matched if they /begin/ with a string matching your pattern. For example, the string “ATTA123” is allowed under the constraint “regexp=’[ATCG]+’”, but not under the constraint “regexp=’^[ATCG]+$’”.

  1. /Custom constraint/ allows you to define arbitrary checks on your data based on code that you write yourself. Datatypes may have several basic constraints, but only one custom constraint. To understand custom constraints, you need to be familiar with CodeResources, CodeResourceRevisions, and Methods; if not, go read the documentation and come back.

A custom constraint is defined by a special Method which will be used to check the data. This Method must take as input a column of strings called “to_test”, and return a column of positive integers called “failed_row”. For each string in “to_test”, the Method should process the string and check if it matches the constraint. If it does not match, the row of the non-matching string should be appended to the output. Note that rows in Kive are indexed from 1, so if the first input string fails the check, the Method should append a “1” to the output.

Custom Constraint Example

Suppose you are processing phylogenetic trees in Newick format. You could try to write a regular expression to filter these, but it would be complicated and might not work as expected (some programs leave annotations in Newick trees, for example). Instead, you could use the BioPython Phylo module to parse the tree in an external script, and fail the string if it does not parse correctly. A complete Python script to do this is the following (check out the documentation for Python’s csv module, and BioPython’s Phylo module, if you don’t understand what’s going on).

    import sys
    import csv
    from Bio import Phylo
    # StringIO lets us do file operations on strings.
    if sys.version_info[0] == 3:
      from io import StringIO # python3
      from cStringIO import StringIO

    # The Method's driver is called with the input file as its first
    # argument, and the output file as its second argument.
    infile = open(sys.argv[1], "r")
    outfile = open(sys.argv[2], "w")

    # Define a reader for getting the input data, and a writer for
    # writing the output data.
    reader = csv.DictReader(infile)
    writer = csv.DictWriter(outfile, fieldnames="failed_row")

    # Loop through each input string.
    for i, row in enumerate(reader):
        row_num = i+1 # Kive uses 1-indexing for rows
        # Try to parse the tree.
        io = StringIO(row["to_test"]) 
  , "newick")
        except Phylo.NewickIO.NewickError:
            writer.writerow({"failed_row": row_num})

To create your Newick tree Datatype, you would first create a Method with a CodeResourceRevision containing this code as its driver (again, check the documentation for Methods and CodeResourceRevisions). You would then add a CustomConstraint to your Datatype, with the Method as its verification method.

Note that, more than most Methods, the onus is on you to provide code that works correctly - if your code doesn’t work as expected, the checks on your data will be meaningless. To assist you with writing working constraint checking methods, we encourage you to define a prototype Dataset for your Datatypes, which gives examples of valid and invalid values. A prototype is a Dataset with two columns: “example”, and “valid”. “example” contains arbitrary strings, and “valid” is a boolean field, true if the example is a valid instance of the Datatype, or false otherwise.

For example, a prototype for a “positive integer” datatype might be something like this (remember, all types, even integers, are restrictions of strings).


When you provide a prototype, Kive will test your data checking method by running it against the values in “example”, and ensuring that only the rows in “valid” which are false are output. If the Method does not work as expected, nothing will be run on your data until the problem is fixed. Although prototypes are not required, we highly recommend you supply them. Checking the integrity of data at all steps of execution is one of Kive’s core functions, and bugs in data checking methods can be difficult to chase down. Moreover, prototypes provide a helpful reminder for yourself, and an aid for fellow users, of what your Datatype should look like.

Step 2: Upload your code as Code Resources

From the Developers/Code resources menu, individual files can be uploaded into Kive. At this stage, no dependencies are defined between code resources (This only happens when Methods are defined).

Step 3: Define Data types

Data that flows between Methods in Kive will typically take the form of CSV files. An individual CSV file will contain a number of data records of a certain compound type, each record on a separate line. It is this compound type of a CSV file that has to be defined in this step.

This process is divided into two separate menu items:

  1. First, simple data types must be defined.

    This occurs under Developers/Datatypes. This section has commonly-used types such as int, float and string predefined, and it might well be that you will not need to define your own atomic data type, unless you want to create types with constraints or restrictions.

  2. Secondly, Compound data types can be defined.

    This occurs under Developers/Compund datatypes. The Compound data type is simply a named ordered set of simple data types that describes the composition of an individual line on a CSV file.

Step 4: Define Methods

Now that you have defined the code resources and compound data types, you are ready to define the Methods, representing the individual computational units of your project. This occurs under the Developers/Methods menu. Each Method will have a code resource as its ‘main program’, and it could have further code resources as dependencies that it needs in order to run. It will have a number of named inputs and outputs, each of a certain compound data type. It will have permissions that determine which Kive users can access the Method.

Methods can be collected into Method Families of a certain kind for convenience. The ‘main program’ code resource must be an executable, which under unix-like operating systems means that the file should begin with a ‘shebang’ (a #! on the first line) if the executable is a script file. If the chosen code resource does not, then Kive will prompt the user who may overrride this requirement.

Step 5: Create Pipelines

Pipeline Inputs

When creating a new pipeline in the Kive pipeline editor, the pipeline inputs are a good place to start. Inputs can be created with the ‘Add Input’ menu item on the Pipeline editing page. Inputs must be named and of a Compound type that has been previously defined in Step 3.

Creating Steps

The computational nodes of the pipeline are the methods you created in Step 4. These can be added with the ‘Add Method’ menu item on the Pipeline editing page.

Connecting Steps

In order to do work, all inputs of a method have to be connected. A method’s input can come from a pipeline input or from the output of a preceding method. To make a connection, drag the producing node’s output to a consuming method’s input. The connection will only be made if the data types agree.

To create a pipeline output, start a drag on a method’s output marker. In the top-right of the screen, a region will appear with the text ‘Drag here to create an output’. Dragging and dropping the method output into this region will create a new pipeline output that can then be named.

Finishing up

As an editing aid, the ‘View’ menu can be used to automatically display the pipeline in a variety of pleasing ways. Kive performs a rule check on the pipeline during editing. For example, all inputs of all methods must be connected. If a pipeline passes the rule check, the ‘Submit’ button will display a green dot. When it is in this state, click on it to save your changes.

Step 6: Run your Pipeline

Your job as a pipeline developer is done. Now its time to run a pipeline as a user. For this, go into the ‘Users’ menu from the Kive top menu. Your aim is to upload some data and feed it through your newly crafted pipeline to produce output data.

  1. Upload Datasets to be Analysed

    From the Users/Datasets menu item, you can upload Datasets which will be used as inputs for a pipeline.

  2. Select a Pipeline

    From the Users/Analysis menu item, choose a pipeline to run, and then select the Dataset for each of the pipeline’s inputs. This combination will be called a ‘Run’ which you have to name. Once you are ready, click on ‘Start Run’ in the bottom right had corner. Kive will now start the calculation of the pipeline steps.

  3. View Pipeline Run Status

    From the Users/Runs menu item, the process of a run can be monitored. The first page shows a list of all runs; clicking on the name of a run will show its computational progress. If a run has completed, its output files can be viewed and downloaded.