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# What is left to do:

> I would appreciated feedback and help on all these issues and where next to take ruffus.

> Please write to me ( ruffus_lib at llew.org.uk) or join the project.

> Some of these proposals are well-fleshed-out:

> - Clean up
> - (Plug-in) File Dependency Checking via MD5 or Databases

> Others require some more user feedback about semantics:

> - Combining output from multiple jobs automatically
> - Harvesting return values from jobs
> - Linking the output from one task as the input to the next automatically

> Some issues are do-able but difficult and I don’t have the experience:

> - Run jobs on remote (clustered) processes via SGE/Hadoop

## Linking the output from one task as the input to the next automatically

> For each task, we would have to log all the output files from each job, and use that as the input into the next stage.

> This is, after all, what make does.

> Except that we would be creating the dependency tree on the fly.

> Syntactically, it would make things a bit easier for the user, with minor additions to the @file and @file_re syntax.

## Clean up

> The plan is to store the files and directories created via a standard interface.

> The placeholders for this are a function call `register_cleanup`.

> Jobs can specify the files they created and which need to be deleted by returning a list of file names from the job function.

> So:

>     raise Exception = Error

>     return False = halt pipeline now

>     return string / list of strings = cleanup files/directories later

>     return anything else = ignored

> The cleanup file/directory store interface can be connected to a text file or a database.

> The cleanup function would look like this:

>     pipeline_cleanup(cleanup_log("../cleanup.log"), [instance ="october19th" ])
>     pipeline_cleanup(cleanup_msql_db("user", "password", "hash_record_table"))

> The parameters for where and how to store the list of created files could be similarly passed to pipeline_run as an extra parameter:

>     pipeline_run(cleanup_log("../cleanup.log"), [instance ="october19th" ])
>     pipeline_run(cleanup_msql_db("user", "password", "hash_record_table"))

> where cleanup_log and cleanup_msql_db are classes which have functions for

> > 1. storing file
> > 2. retrieving file
> > 3. clearing entries

> - Files would be deleted in reverse order, and directories after files.

> - By default, only empty directories would be removed.

>   But this could be changed with a `--forced_remove_dir` option

> - An `--remove_empty_parent_directories` option would be supported by os.removedirs(path).

## (Plug-in) File Dependency Checking via MD5 or Databases

> So that MD5 / a database can be used instead of coarse-grained file modification times.

> As always, the design is a compromise between flexibility and easy of use.

> The user can already write their own file dependency checking function and supply this:

>     @check_if_uptodate(check_md5_func)
>     @files(io_files)
>     def task_func (input_file, output_file):
>         pass

> The question is can we

> > 1. supply a check_md5() function
> > 2. allow the whole pipeline to use this.

> Most probably we need an extra parameter somewhere:

>     pipeline_run(md5_hash_database = "current/location/files.md5")

> There is prior art on this in `scons`.

> If we use a custom object/function, can we use orthogonal syntax for

> 1. disk modifications times,
> 2. md5 hashes saved to a file,
> 3. md5 hashes saved to a database?

>     pipeline_run(file_up_to_date_lookup = md5_hash_file("current/location/files.md5"))
>     pipeline_run(file_up_to_date_lookup = mysql_hash_store("user", "password", "hash_record_table"))

> where `md5_hash_file` and `mysql_hash_store` are objects which have get/set functions for looking up modification times from file names.

> Of course that allows you to fake the whole process and not even use real files...

## SQL hooks

> See above.

> I have no experience with systems which link to SQL. What would people want from such a feature?

> Ian Holmes?

## Combining files

> A common operation (See intermediate example and complicated example) seems to be combining multiple input files into one or more output files (i.e. a “many->1” operation).

> A the moment, this can be easily done by writing a simple parameter generating function with a glob (see this code.)

> However, we could directly support this in ruffus in two ways, both leveraging `@files_re`.

> The idea is that input files to be grouped together should share similar traits identified by either a regular expression or a function.

> Suppose we had the following files:

>     cows.mammals.animal
>     horses.mammals.animal
>     sheep.mammals.animal

>     snake.reptile.animal
>     lizard.reptile.animal
>     crocodile.reptile.animal

>     pufferfish.fish.animal

> and we wanted to end up with three different resulting output:

>     cow.mammals.animal
>     horse.mammals.animal
>     sheep.mammals.animal
>         -> mammals.results

>     snake.reptile.animal
>     lizard.reptile.animal
>     crocodile.reptile.animal
>         -> reptile.results

>     pufferfish.fish.animal
>         -> fish.results

> We could either add a “combining” regex parameter:

>     # \1 = species [cow, horse]
>     # \2 = phylogenetics group [mammals, reptile, fish]
>     @files_re(  "*.animal",
>                 r"(.+)\.(.+)\.animal",
>                 combining(r"\2"),           # regular expression showing that
>                                             # "mammals/reptile" etc. groups files
>                 r"\1.\2.animal",            # input file
>                 r"\2.results")              # output file(s)
>     def sort_animals_into_groups(species_file, result_file):
>         " ... more code here"

> Or we could allow a grouping function like the key parameter in itertools.groupby:

>     @files_re(  "*.animal",
>                 r"(.+)\.(.+)\.animal",
>                 combining_func(lambda x: x.split(".")[1]),
>                 r"\1.\2.animal",            # input file
>                 r"\2.results")              # output file(s)
>     def sort_animals_into_groups(species_file, result_file):
>         " ... more code here"

> In both cases, the extra parameter would be wrapped by a “tagging” class (`combining` and `combining_func`) for clarity.

> Is this too much extra complexity for ruffus or `@files_re` to support?

> Is the syntactic convenience worthwhile?

## Return values

> Is it a good idea to allow jobs to pass back calculated values?

> This requires trivial modifications to run_pooled_job_without_exceptions

> The user function cannot return False. That is reserved for signalling breaks.

> The most useful thing would be to associate job parameters with results.

> What should be the syntax for getting the results back?

## Run jobs on remote (clustered) processes via SGE/Hadoop

> Can we run jobs on remote processes using SGE / Hadoop?

> Can we abstract all job management using drmaa?

> Python examples at http://gridengine.sunsource.net/howto/drmaa_python.html

### SGE

> Look at Qmake execution model:

#### 1) SGE nodes are taken over completely

> See last example in multiprocessing for creating a distributed queue.

> We would use qrsh instead of ssh. The size of the pool would be the (maximum) number of jobs

> Advantages:

> - Simple to implement
> - Efficient

> Disadvantages:

> - Other users might not appreciate having python jobs taking over the nodes for a protracted length of time
> - We would not be able to use SGE to view / manage jobs

#### 2) Start a qrsh per job

> Advantages:

> - jobs look like any other SGE task

> Disadvantages:

> - Slower. Overheads might be high.
> - We might have to create a new pool per task
> - If we maintain an empty pool, and then dynamically attach processes, the code might be difficult to write (may not fit into the multiprocessing way of doing things / race-conditions etc.)

### Hadoop

Can anyone help me with this / have any experience?

### Navigation

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Ruffus v. ('1.0.5',)
- Home |
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