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Project Description
# datapackage-pipelines

A modular framework for a stream-processing ETL based on Data Packages

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## QUICK START

```
# Install from PyPi
$ pip install datapackage-pipelines

# The pipeline definition
$ cat > pipeline-spec.yaml
albanian-treasury:
schedule:
crontab: '0 * * * *'
pipeline:
-
run: simple_remote_source
parameters:
resources:
-
url: "https://raw.githubusercontent.com/openspending/fiscal-data-package-demos/master/al-treasury-spending/data/treasury.csv"
schema:
fields:
-
name: "Date executed"
type: date
osType: date:generic
-
name: "Value"
type: number
osType: value
-
name: "Supplier"
type: string
osType: supplier:generic:name
-
run: model
-
run: metadata
parameters:
metadata:
name: 'al-treasury-spending'
title: 'Albania Treasury Service'
granularity: transactional
countryCode: AL
homepage: 'http://spending.data.al/en/treasuryservice/list/year/2014/inst_code/1005001'

-
run: downloader
-
run: dump
parameters:
out-file: al-treasury-spending.zip
^D

# List Available Pipelines
$ dpp
Available Pipelines:
- ./albanian-treasury

# Invoke the pipeline manually
$ dpp run ./albanian-treasury
INFO :Main :RUNNING ./albanian-treasury:
INFO :Main :- /Users/adam/code/os/datapackage-pipelines/datapackage_pipelines/manager/../lib/simple_remote_source.py
INFO :Main :- /Users/adam/code/os/datapackage-pipelines/datapackage_pipelines/manager/../lib/model.py
INFO :Main :- /Users/adam/code/os/datapackage-pipelines/datapackage_pipelines/manager/../lib/metadata.py
INFO :Main :- /Users/adam/code/os/datapackage-pipelines/datapackage_pipelines/manager/../lib/downloader.py
INFO :Main :- /Users/adam/code/os/datapackage-pipelines/datapackage_pipelines/manager/../lib/dump.py
INFO :Simple_Remote_Source :Processed 0 rows
INFO :Model :Processed 0 rows
INFO :Metadata :Processed 0 rows
INFO :Downloader :Starting new HTTPS connection (1): raw.githubusercontent.com
DEBUG :Downloader :"GET /openspending/fiscal-data-package-demos/master/al-treasury-spending/data/treasury.csv HTTP/1.1" 200 3784
INFO :Downloader :TOTAL 40 rows
INFO :Downloader :Processed 40 rows
INFO :Dump :Processed 40 rows
INFO :Main :WAITING FOR ./albanian-treasury:/Users/adam/code/os/datapackage-pipelines/datapackage_pipelines/manager/../lib/simple_remote_source.py
INFO :Main :WAITING FOR ./albanian-treasury:/Users/adam/code/os/datapackage-pipelines/datapackage_pipelines/manager/../lib/model.py
INFO :Main :WAITING FOR ./albanian-treasury:/Users/adam/code/os/datapackage-pipelines/datapackage_pipelines/manager/../lib/metadata.py
INFO :Main :WAITING FOR ./albanian-treasury:/Users/adam/code/os/datapackage-pipelines/datapackage_pipelines/manager/../lib/downloader.py
INFO :Main :WAITING FOR ./albanian-treasury:/Users/adam/code/os/datapackage-pipelines/datapackage_pipelines/manager/../lib/dump.py
INFO :Main :DONE ./albanian-treasury: [0, 0, 0, 0, 0]

# Examine Results
$ unzip -t al-treasury-spending.zip
Archive: al-treasury-spending.zip
testing: datapackage.json OK
testing: data/treasury.csv OK
No errors detected in compressed data of al-treasury-spending.zip.

$ unzip -p al-treasury-spending.zip datapackage.json | json_pp
{
"name" : "al-treasury-spending",
"granularity" : "transactional",
"homepage" : "http://spending.data.al/en/treasuryservice/list/year/2014/inst_code/1005001",
"countryCode" : "AL",
"resources" : [
{
"schema" : {
"fields" : [
{
"slug" : "Date_executed",
"title" : "Date executed",
"type" : "date",
"format" : "fmt:%Y-%m-%d",
"osType" : "date:generic",
"conceptType" : "date",
"name" : "Date executed"
},
{
"type" : "number",
"decimalChar" : ".",
"slug" : "Value",
"conceptType" : "value",
"format" : "default",
"osType" : "value",
"name" : "Value",
"title" : "Value",
"groupChar" : ","
},
{
"title" : "Supplier",
"slug" : "Supplier",
"name" : "Supplier",
"format" : "default",
"osType" : "supplier:generic:name",
"type" : "string",
"conceptType" : "supplier"
}
],
"primaryKey" : [
"Date executed"
]
},
"path" : "data/treasury.csv"
}
],
"title" : "Albania Treasury Service",
"model" : {
"measures" : {
"Value" : {
"source" : "Value",
"title" : "Value"
}
},
"dimensions" : {
"supplier" : {
"attributes" : {
"Supplier" : {
"title" : "Supplier",
"source" : "Supplier"
}
},
"primaryKey" : [
"Supplier"
],
"dimensionType" : "entity"
},
"date" : {
"dimensionType" : "datetime",
"primaryKey" : [
"Date_executed"
],
"attributes" : {
"Date_executed" : {
"title" : "Date executed",
"source" : "Date executed"
}
}
}
}
}
}
```

## Documentation

This framework is intended for running predefined processing steps on sources of tabular data.
These steps (or 'processors') can be extraction steps (e.g. web scrapers), transformation steps or loading steps (e.g. dump to file).

A combination of these processors in a specific order is called a 'pipeline'.
Each pipeline has a separate schedule, which dictates when it should be run.

Pipelines are independent (i.e. a pipeline does not depend on another pipeline),
but the processors that compose the pipeline can be shared among pipelines, as part of a common library.

All processing in this framework is done by processing the streams of data, row by row. At no point the entire data-set
is loaded into memory. This allows efficient processing in terms of memory usage as well as truly parallel execution
of all processing steps, making use of your machine's CPU effectively.

## Running Instructions

Running instructions are stored in files named `pipeline-spec.yaml`.

Each one of these files is a YAML file which contains instructions for fetching one or more FDPs. For example, such a
file might look like this:

```
albonian-spending:
schedule:
cron: '3 0 * * *'
pipeline:
-
run: fetch-albonian-fiscal-data
parameters:
kind: 'expenditures'
-
run: translate-codelists
-
run: normalize-dates
albonian-budget:
schedule:
cron: '0 0 7 1 *'
pipeline:
-
run: fetch-albonian-fiscal-data
parameters:
kind: 'budget'
-
run: translate-codelists
```

**What do we have here?**

Two running instructions for two separate data packages - one fetching the Albonian spending data and another fetching
its budget data. You can see that the pipelines are very similar, and are based on the same building blocks:
`fetch-albonian-fiscal-data`, `translate-codelists` and `normalize-dates`. The differences between the two are
- their schedules: spending data is fetched on a daily basis, whilst budgets are fetched on January 7th every year
(Albonian government officials adhere to very precise publishing dates)
- the running parameters for the `fetch-albonian-fiscal-data` executor are different -
so that code is reused and controlled via running parameters
- the pipeline for spending data has an extra step (`normalize-dates`)

**Spec:**

This YAML file is basically a mapping between *Pipeline IDs* to their specs. Task IDs are the way we reference the
pipeline in various places so choose wisely.

A pipeline spec has two keys:
- `schedule`: can have one sub-key, which can currently be only `crontab`. The value for the former is a standard
`crontab` schedule row.
- `pipeline`: a list of steps, each is an object with the following properties:
- `run`: the name of the executor - a Python script which will perform the step's actions.
This script is searched in:
- the current directory (read: where the running instructions file is located),
- in paths specified in the `DATAPIPELINES_PROCESSOR_PATH` environment variable,
- in extensions (see below),
- or in the common lib of executors.
(in this order)
Relative paths can be specified with the 'dot-notation': `a.b` is referring to script `b` in directory `a`;
`...c.d.e` will look for `../../c/d/e.py`.
- `parameters`: running parameters which the executor will receive when invoked.
- `validate`: should data be validated prior to entering this executor. Data validation is done using the JSON table
schema which is embedded in the resource definition.

## Processors

Processors are Python scripts with a simple API, based on their standard input & standard output streams (as well as
command line parameters).

All processors output a tabular data package to the standard output. This is done in the following way:
- The first line printed to `stdout` must be the contents of the `datapackage.json` - that is, a JSON object without
any newlines.
- After that first line, tabular data files can be appended (we don't support any other kind of files ATM).
Each tabular data file must be printed out in the following way:
- First line must always be an empty line (that is, just a single newline character).
- Subsequent lines contain the contents of the data rows of the file (i.e. no header row or other chaff)
- Each row in the file must be printed as a single-line JSON encoded object, which maps the header names to values

Processors will receive an tabular data package in the exact same format in their stdin
(Except the first processor in the pipeline, which will receive nothing in its stdin).

Parameters are passed as a JSON encoded string in the first command line argument of the executor.

Files should appear in the same order as the resources defined in the FDP. Only data for local files is expected -
remote resources can just be ignored.

### Why JSON and not CSV?

Well, for a multitude of reasons:
- JSON encoding is not dependent on locale settings of the executing machine
- JSON has better type indication: strings vs. numbers vs. booleans vs. missing values (with time and date values as
the only exception)
- JSON is easier to work with in Python

*What about time and dates, then?*
Just use their string representation and make sure that the JSON Table Schema contains the correct format definition
for that field.

The framework will take these JSONs and convert them to proper CSV files before uploading - with a correct dialect,
encoding and locale info.

## Developing Executors

To avoid boilerplate, the `ingest` and `spew` utility functions for executors can come in handy:

```python

from executor_util import ingest, spew

if __name__=="__main__":
params, fdp, resource_iterator = ingest()

# do something with fdp
# ...

def resource_processor(row_iterator):
resource_spec = row_iterator.spec
# you can modify the resource if needed here
for row in row_iterator:
# do something with row
# ...
yield row

spew(fdp, (process_resource(r) for r in resource_iterator))

```

## Extensions

You can define extensions to this framework, by creating Python packages names `datapackage_pipelines_<extension-name>`.

Extensions provide two things:
- Processor packs: processors defined under the `processors` directory in the extension package can be accessed as
`<extension-name>.<processor-name>`.
- Source templates, which provide a method for creating standard pipelines based on common parameters.

The directory structure of a sample extension package would be:
```
datapackage_pipelines_ml/
processors/
svm.py
neural_network.py
__init__.py
generator.py
schema.json
```

In this case, after installing `datapackage_pipelines_ml` package,
the `ml.svm` will be available to use in a pipeline.

## Source Templates

Source templates is a way to generate processing pipelines based on user provided parameters.

When `dpp` is used, it scans for `pipeline-spec.yml` files for existing pipelines.
Along with them, it also tries to find files calles `<extension-name>.source-spec.yml`.
In our example above, we will try to find files named `ml.source-spec.yml`.

Once found, it will try to validate them. If validated, they will be
used as input to a generator logic that produces the details of one or more pipelines.

Both the validation and generation are done using the `Generator` class, which must be available
in the module when imported (i.e. `datapackage_pipelines_ml.Generator`).
This class must inherit from `datapackage_pipelines.generators.GeneratorBase`, and implement
two methods:
- `get_schema()` - should return a valid JSON schema file for validating source templates.
- `generate_pipeline(source)` - should return a list of tuples: `(pipeline_id, schedule, steps)`

Sample `generator.py` file:
```python
import os
import json

from datapackage_pipelines.generators import \
GeneratorBase, SCHEDULE_DAILY, slugify, steps

SCHEMA_FILE = os.path.join(os.path.dirname(__file__), 'schema.json')


class Generator(GeneratorBase):

@classmethod
def get_schema(cls):
return json.load(open(SCHEMA_FILE))

@classmethod
def generate_pipeline(cls, source):
schedule = SCHEDULE_DAILY
pipeline_id = slugify(source['title']).lower()

if source['kind'] == 'svm':
yield pipeline_id, schedule, steps(
('svm', source['svm-parameters'])
)
elif source['kind'] == 'neural-network':
yield pipeline_id, schedule, steps(
('neural_network', source['nn-parameters'])
)
```

## Running the Datapackage-Pipeline Deamon

```
$ python -m celery worker -B -A datapackage_pipelines.app
```

Will run all pipelines based on their defined schedule using `celery`.


## Contributing

Please read the contribution guideline:

[How to Contribute](CONTRIBUTING.md)

Thanks!
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