Using Stetl

This section explains how to use Stetl for your ETL. It assumes Stetl is installed and you are able to run the examples. It may be useful to study some of the examples, especially the core ones found in the examples/basics directory. These examples start numbering from 1, building up more complex ETL cases like (INSPIRE) transformation using Jinja2 Templating.

In addition there are example cases like the Dutch Topo map (Top10NL) ETL in the examples/top10nl directory .

The core concepts of Stetl remain pretty simple: an input resource like a file or a database table is mapped to an output resource (also a file, a database, a remote HTTP server etc) via one or more filters. The input, filters and output are connected in a pipeline called a processing chain or Chain. This is a bit similar to a current in electrical engineering: an input flows through several filters, that each modify the current. In our case the current is (geospatial) data. Stetl design follows the so-called Pipes and Filters Architectural Pattern.

Stetl Config

Stetl components (Inputs, Filters, Outputs) and their interconnection (the Pipeline/Chain) are specified in a Stetl config file. The file format follows the Python .ini file-format.

To illustrate, let’s look at the example 2_xslt. This example takes the input file input/cities.xml and transforms this file to a valid GML file called output/gmlcities.gml. The Stetl config file looks as follows.

[etl]
chains = input_xml_file|transformer_xslt|output_file

[input_xml_file]
class = inputs.fileinput.XmlFileInput
file_path = input/cities.xml

[transformer_xslt]
class = filters.xsltfilter.XsltFilter
script = cities2gml.xsl

[output_file]
class = outputs.fileoutput.FileOutput
file_path = output/gmlcities.gml

Most of the sections in this ini-file specify a Stetl component: an Input, Filter or Output component. Each component is specified by its (Python) class and per-component specific parameters. For example [input_xml_file] uses the class inputs.fileinput.XmlFileInput reading and parsing the file input/cities.xml specified by the file_path property. [transformer_xslt] is a Filter that applies XSLT with the script file cities2gml.xsl that is in the same directory. The [output_file] component specifies the output, in this case a file.

These components are coupled in a Stetl Chain using the special .ini section [etl]. That section specifies one or more processing chains. Each Chain is specified by the names of the component sections, their interconnection using a the Unix pipe symbol “|”.

So the above Chain is input_xml_file|transformer_xslt|output_file. The names of the component sections like [input_xml_file] are arbitrary.

Note: since v1.1.0 a datastream can be split (see below) to multiple Outputs using () like :

[etl]
chains = input_xml_file|transformer_xslt|(output_gml_file)(output_wfs)

In later versions also combining Inputs and Filter-splitting will be provided.

Configuring Components

Most Stetl Components, i.e. inputs, filters, outputs, have properties that can be configured within their respective [section] in the config file. But what are the possible properties, values and defaults? This is documented within each Component class using the @Config decorator much similar to the standard Python @property, only with some more intelligence for type conversions, defaults, required presence and documentation. It is loosely based on https://wiki.python.org/moin/PythonDecoratorLibrary#Cached_Properties and Bruce Eckel’s http://www.artima.com/weblogs/viewpost.jsp?thread=240845 with a fix/hack for Sphinx documentation.

See for example the stetl.inputs.fileinput.FileInput documentation.

For class authors: this information is added via the Python Decorators much similar to @property. The stetl.component.Config is used to define read-only properties for each Component instance. For example,

class FileInput(Input):
    """
    Abstract base class for specific FileInputs, use derived classes.
    """

    # Start attribute config meta
    # Applying Decorator pattern with the Config class to provide
    # read-only config values from the configured properties.

    @Config(ptype=str, default=None, required=False)
    def file_path(self):
        """
        Path to file or files or URLs: can be a dir or files or URLs
        or even multiple, comma separated. For URLs only JSON is supported now.

        Required: True

        Default: None
        """
        pass

    @Config(ptype=str, default='*.[gxGX][mM][lL]', required=False)
    def filename_pattern(self):
        """
        Filename pattern according to Python glob.glob for example:
        '\*.[gxGX][mM][lL]'

        Required: False

        Default: '\*.[gxGX][mM][lL]'
        """
        pass

    # End attribute config meta

    def __init__(self, configdict, section, produces):
        Input.__init__(self, configdict, section, produces)

        # Create the list of files to be used as input
        self.file_list = Util.make_file_list(self.file_path, None, self.filename_pattern, self.depth_search)

This defines two configurable properties for the class FileInput. Each @Config has three parameters: p_type, the Python type (str, list, dict, bool, int), default (default value if not present) and required (if property in mandatory or optional).

Within the config one can set specific config values like,

[input_xml_file]
class = inputs.fileinput.XmlFileInput
file_path = input/cities.xml

This automagically assigns file_path to self.file_path without any custom code and assigns the default value to filename_pattern. Automatic checks are performed: if file_path (required=True) is present, if its type is string. In some cases type conversions may be applied e.g. when type is dict or list. It is guarded that the value is not overwritten and the docstrings will appear in the auto-generated documentation, each entry prepended with a CONFIG tag.

Running Stetl

The above ETL spec can be found in the file etl.cfg. Now Stetl can be run, simply by typing

stetl -c etl.cfg

Stetl will parse etl.cfg, create all Components by their class name and link them in a Chain and execute that Chain. Of course this example is very trivial, as we could just call XSLT without Stetl. But it becomes interesting with more complex transformations.

Suppose we want to convert the resulting GML to an ESRI Shapefile. As we cannot use GDAL ogr2ogr on the input file, we need to combine XSLT and ogr2ogr. See example 3_shape. Now we replace the output by using outputs.ogroutput.Ogr2OgrOutput, which can execute any ogr2ogr command, converting whatever it gets as input from the previous Filter in the Chain.

[etl]
chains = input_xml_file|transformer_xslt|output_ogr_shape

[input_xml_file]
class = inputs.fileinput.XmlFileInput
file_path = input/cities.xml

[transformer_xslt]
class = filters.xsltfilter.XsltFilter
script = cities2gml.xsl

# The ogr2ogr command-line. May be split over multiple lines for readability.
# Backslashes not required in that case.
[output_ogr_shape]
class = outputs.ogroutput.Ogr2OgrOutput
temp_file = temp/gmlcities.gml
ogr2ogr_cmd = ogr2ogr
        -overwrite
        -f "ESRI Shapefile"
        -a_srs epsg:4326
        output/gmlcities.shp
        temp/gmlcities.gml

Using Docker

A convenient way to run Stetl is via a Docker image. See the installation instructions at Install with Docker. A full example can be viewed in the Smart Emission project: https://github.com/Geonovum/smartemission/tree/master/etl.

In the simplest case you run a Stetl Docker container from your own built image or the Dockerhub-provided one, justb4/stetl:latest basically as follows:

sudo docker run -v <host dir>:<container dir> -w <work dir> justb4/stetl:latest <any Stetl arguments>

For example within the current directory you may have an etl.cfg Stetl file:

WORK_DIR=`pwd`
sudo docker run -v ${WORK_DIR}:${WORK_DIR} -w ${WORK_DIR} justb4/stetl:latest -c etl.cfg

A more advanced setup would be (network-)linking to a PostGIS Docker image like kartoza/postgis:

# First run Postgis, remains running,
sudo docker run --name postgis -d -t kartoza/postgis:9.4-2.1

# Then later run Stetl
STETL_ARGS="-c etl.cfg -a local.args"
WORK_DIR="`pwd`"

sudo docker run --name stetl --link postgis:postgis -v ${WORK_DIR}:${WORK_DIR} -w ${WORK_DIR} justb4/stetl:latest  ${STETL_ARGS}

The last example is used within the SmartEmission project. Also with more detail and keeping all dynamic data (like PostGIS DB), your Stetl config and results, and logs within the host. For PostGIS see: https://github.com/Geonovum/smartemission/tree/master/services/postgis and Stetl see: https://github.com/Geonovum/smartemission/tree/master/etl.

Stetl Integration

Note: one can also run Stetl via its main ETL class: stetl.etl.ETL. This may be useful for integrations in for example Python programs or even OGC WPS servers (planned).

Reusable Stetl Configs

What we saw in the last example is that it is hard to reuse this etl.cfg when we have for example a different input file or want to map to different output files. For this Stetl supports parameter substitution. Here command line parameters are substituted for variables in etl.cfg. A variable is declared between curly brackets like {out_xml}. See example 6_cmdargs.

[etl]
chains = input_xml_file|transformer_xslt|output_file

[input_xml_file]
class = inputs.fileinput.XmlFileInput
file_path = {in_xml}

[transformer_xslt]
class = filters.xsltfilter.XsltFilter
script = {in_xsl}

[output_file]
class = outputs.fileoutput.FileOutput
file_path = {out_xml}

Note the symbolic input, xsl and output files. We can now perform this ETL using the stetl -a option in two ways. One, passing the arguments on the commandline, like

stetl -c etl.cfg -a "in_xml=input/cities.xml in_xsl=cities2gml.xsl out_xml=output/gmlcities.gml"

Two, passing the arguments in a properties file, here called etl.args (the name of the suffix .args is not significant).

stetl -c etl.cfg -a etl.args

Where the content of the etl.args properties file is:

# Arguments in properties file
in_xml=input/cities.xml
in_xsl=cities2gml.xsl
out_xml=output/gmlcities.gml

This makes an ETL chain highly reusable. A very elaborate Stetl config with parameter substitution can be seen in the Top10NL ETL.

Connection Compatibility

During ETL Chain processing Components typically pass data to a next stetl.component.Component . A stetl.filter.Filter Component both consumes and produces data, Inputs produce data and Outputs only consume data.

Data and status flows as stetl.packet.Packet objects between the Components. The type of the data in these Packets needs to be compatible only between two coupled Components. Stetl does not define one unifying data structure, but leaves this to the Components themselves.

Each Component provides the type of data it consumes (Filters, Outputs) and/or produces (Inputs, Filters). This is indicated in its class definition using the consumes and produces object constructor parameters. Some Components can produce and/or consume multiple data types, like a single stream of records or a record array. In those cases the produces or consumes parameter can be a list (array) of data types.

During Chain construction Stetl will check for compatible formats when connecting Components. If one of the formats is a list of formats, the actual format is determined by:

  1. explicit setting: the actual input_format and/or output_format is set in the Component .ini configuration
  2. no setting provided: the first format in the list is taken as default

Stetl will only check if these input and output-formats for connecting Components are compatible when constructing a Chain.

The following data types are currently symbolically defined in the stetl.packet.FORMAT class:

  • any - ‘catch-all’ type, may be any of the types below.
  • etree_doc - a complete in-memory XML DOM structure using the lxml etree
  • etree_element - each Packet contains a single DOM Element (usually a Feature) in lxml etree format
  • etree_feature_array - each Packet contains an array of DOM Elements (usually Features) in lxml etree format
  • geojson_feature - as struct but following naming conventions for a single Feature according to the GeoJSON spec: http://geojson.org
  • geojson_collection - as struct but following naming conventions for a FeatureCollection according to the GeoJSON spec: http://geojson.org
  • ogr_feature - a single Feature object from an OGR source (via Python SWIG wrapper)
  • ogr_feature_array - a Python list (array) of a single Feature objects from an OGR source
  • record - a Python dict (hashmap)
  • record_array - a Python list (array) of dict
  • string- a general string
  • struct - a JSON-like generic tree structure
  • xml_doc_as_string - a string representation of a complete XML document
  • xml_line_stream - each Packet contains a line (string) from an XML file or string representation (DEPRECATED)

Many components, in particular Filters, are able to transform data formats. For example the XmlElementStreamerFileInput can produce an etree_element, a subsequent XmlAssembler can create small in-memory etree_doc s that can be fed into an XsltFilter, which outputs a transformed etree_doc. The type any is a catch-all, for example used for printing any object to standard output in the stetl.packet.Component. An etree_element may also be interesting to be able to process single features.

Starting with Stetl 1.0.7 a new stetl.filters.formatconverter.FormatConverterFilter class provides a Stetl Filter to allow almost any conversion between otherwise incompatible Components.

TODO: the Packet typing system is still under constant review and extension. Soon it will be possible to add new data types and converters. We have deliberately chosen not to define a single internal datatype like a “Feature”, both for flexibility and performance reasons.

Multiple Chains

Usually a complete ETL will require multiple steps/commands. For example we need to create a database, maybe tables and/or making tables empty. Also we may need to do postprocessing, like removing duplicates in a table etc. In order to have repeatable/reusable ETL without any manual steps, we can specify multiple Chains within a single Stetl config. The syntax: chains are separated by commas (steps are sill separated by pipe symbols).

Chains are executed in order. We can even reuse the specified components from within the same file. Each will have a separate instance within a Chain.

For example in the Top10NL example we see three Chains:

[etl]
chains = input_sql_pre|schema_name_filter|output_postgres,
                input_big_gml_files|xml_assembler|transformer_xslt|output_ogr2ogr,
                input_sql_post|schema_name_filter|output_postgres

Here the Chain input_sql_pre|schema_name_filter|output_postgres sets up a PostgreSQL schema and creates tables. input_big_gml_files|xml_assembler|transformer_xslt|output_ogr2ogr does the actual ETL and input_sql_post|schema_name_filter|output_postgres does some PostgreSQL postprocessing.

Chain Splitting

In some cases we may want to split processed data to multiple Filters or Outputs. For example to produce output files in multiple formats like GML, GeoJSON etc or to publish converted (Filtered) data to multiple remote services (SOS, SensorThings API) or just for simple debugging to a target Output and StandardOutput.

See issue https://github.com/geopython/stetl/issues/35 and the Chain Split example.

Here the Chains are split by using () in the ETL Chain definition:

# Transform input xml to valid GML file using an XSLT filter and pass to multiple outputs.
# Below are two Chains: simple Output splitting and splitting to 3 sub-Chains at Filter level.

[etl]
chains = input_xml_file | transformer_xslt |(output_file)(output_std),
         input_xml_file | (transformer_xslt|output_file) (output_std) (transformer_xslt|output_std)


[input_xml_file]
class = inputs.fileinput.XmlFileInput
file_path = input/cities.xml

[transformer_xslt]
class = filters.xsltfilter.XsltFilter
script = cities2gml.xsl

[output_file]
class = outputs.fileoutput.FileOutput
file_path = output/gmlcities.gml

[output_std]
class = outputs.standardoutput.StandardOutput