Compare commits

30 Commits

Author SHA1 Message Date
fe780b96ef Refact: change method to access to schema an tables 2025-01-03 09:53:25 +01:00
c2813e5adb refact: move fs_files to raw_datas 2025-01-03 09:16:22 +01:00
f3036ca40d doc: explain concepts 2025-01-03 09:07:48 +01:00
86912c6d3f clean: remove makefile 2025-01-03 09:00:44 +01:00
646b3cfd92 feat: start datacatalogue 2025-01-03 08:59:54 +01:00
db14b4a49a Feat: activate uv 2025-01-03 08:59:38 +01:00
9d45625a5e Feat: move to models and add consume_flux 2025-01-03 08:46:15 +01:00
07fb92e2fa Feat: create fs_datacatalogue 2025-01-03 08:46:15 +01:00
88795fdad3 Feat: create datacatalogue with fs_datacatalogue 2025-01-03 08:46:15 +01:00
aa1ead5435 refact: move graph libs to own directory 2025-01-03 08:46:15 +01:00
c347deee85 Feat: test on pandas xlsx and ods file reader 2025-01-03 08:46:15 +01:00
5dfc1c9751 Feat: start testing fs_repository 2025-01-03 08:46:15 +01:00
7fc10128da Feat: test consume_flux 2025-01-03 08:46:15 +01:00
fe8f76245b Feat: start flux 2025-01-03 08:46:15 +01:00
d613bf00df Feat: add __init__ and mod function signature 2025-01-03 08:46:15 +01:00
8a03ba8329 refact: rename stage to repository 2025-01-03 08:46:15 +01:00
8774ec11e4 Feat: put table's callback and layout in factory 2025-01-03 08:46:15 +01:00
30913a2cea Feat: callback to toggle editing 2025-01-03 08:46:15 +01:00
159b4a8275 Feat: add navigation 2025-01-03 08:46:15 +01:00
3c1d275634 feat: organise router path 2025-01-03 08:46:15 +01:00
8313323ca1 feat: global design 2025-01-03 08:46:15 +01:00
12e5dce1b4 feat: add tailwindcss 2025-01-03 08:46:15 +01:00
2f25c219af feat: add recursive schema 2025-01-03 08:46:15 +01:00
13f80d8553 feat: add schema and table listing 2025-01-03 08:46:15 +01:00
a533443caf feat: init dashboard 2025-01-03 08:46:15 +01:00
226ce84dce Feat: add is_dag to Graph 2024-10-27 14:10:33 +01:00
9ff68cb285 Feat: add get functions on sources and targets 2024-10-27 13:48:37 +01:00
5c69bb5503 Feat: add pure graph 2024-10-07 06:17:01 +02:00
c90f407cfc Feat: init graphs 2024-10-07 06:09:01 +02:00
867747d748 Fix: filename 2024-10-07 06:08:52 +02:00
56 changed files with 1451 additions and 71 deletions

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@@ -1,66 +0,0 @@
DATA_BASE=./datas
PDF_BASE=$(DATA_BASE)/pdfs
PDF_YEARS=$(wildcard $(PDF_BASE)/*)
RAW_BASE=$(DATA_BASE)/raw
RAW_CRG=$(RAW_BASE)/CRG
RAW_CRG_YEARS=$(subst $(PDF_BASE), $(RAW_CRG), $(PDF_YEARS))
$(RAW_CRG)/%/: $(wildcard $(PDF_BASE)/%/*)
echo $(wildcard $(PDF_BASE)/$*/*)
@echo ----
ls $(PDF_BASE)/$*/
@echo ----
echo $*
@echo ----
echo $^
@echo ----
echo $?
#./datas/raw/CRG/%:
#pdf-oralia extract all --src $$year --dest $$(subst $$PDF_BASE, $$RAW_CRG, $$year)
# $(RAW_CRG_YEARS): $(PDF_PATHS)
# for year in $(PDF_PATHS); do \
# echo $$year; \
# echo $$(subst $$PDF_BASE, $$RAW_CRG, $$year); \
# echo "----"; \
# done;
extract_pdfs:
for year in 2021 2022 2023 2024; do \
mkdir -p $(RAW_CRG)/$$year/extracted;\
pdf-oralia extract all --src $(PDF_BASE)/$$year/ --dest $(RAW_CRG)/$$year/extracted; \
pdf-oralia join --src $(RAW_CRG)/$$year/extracted/ --dest $(RAW_CRG)/$$year/; \
done
clean_raw:
rm -rf ./PLESNA Compta SYSTEM/raw/**/*.csv
clean_built:
rm -rf $(DATA_BASE)/staging/**/*.csv
rm -rf $(DATA_BASE)/gold/**/*.csv
rm -rf $(DATA_BASE)/datamart/**/*.csv
rm -rf $(DATA_BASE)/datamart/**/*.xlsx
run_ingest:
python -m scripts ingest
run_feature:
python -m scripts feature
run_datamart:
python -m scripts datamart
build: clean_built run_ingest run_feature run_datamart
clean_all: clean_built clean_raw
import_nextcloud:
rsync -av ~/Nextcloud/PLESNA\ Compta\ SYSTEM/Histoire/ ./datas/Histoire
push_nextcloud:
rsync -av ./datas/datamart/ ~/Nextcloud/PLESNA\ Compta\ SYSTEM/DataMart

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@@ -1,5 +1,15 @@
# E(T)LT pour Plesna
## Installation
## Concepts
- `dataplatform`: agrégation d'un datacatalogue, de moteur de compute et du dag des transformations.
- `datacatalogue`: gestion du contenu des datastores.
- `datastore`: interface de stockage des données.
- `compute`: moteur de traitement des fluxs.
- `graph/dag`: organisation logique des fluxs et des données.
## Stages
- Raw: fichiers les plus brutes possibles

64
dashboard/app.py Normal file
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import dash
from dash import Dash, dcc, html
from .datalake import stages
from .pages import config, home, repository, schema, table
external_scripts = [{"src": "https://cdn.tailwindcss.com"}]
# external_script = ["https://tailwindcss.com/", {"src": "https://cdn.tailwindcss.com"}]
app = Dash(
__name__,
use_pages=True,
external_scripts=external_scripts,
suppress_callback_exceptions=True,
)
app.scripts.config.serve_locally = True
dash.register_page(
home.__name__,
path="/",
layout=home.layout,
)
dash.register_page(config.__name__, path="/config", layout=config.layout)
dash.register_page(
repository.__name__,
path_template="/repository/<repository_name>",
layout=repository.layout_factory(stages),
)
dash.register_page(
schema.__name__,
path_template="/stg/<repository_name>/schema/<schema_name>",
layout=schema.layout_factory(stages),
)
dash.register_page(
table.__name__,
path_template="/stg/<repository_name>/schm/<schema_name>/table/<table_name>",
layout=table.layout_factory(stages),
)
table.callback_factory(app)
app.layout = html.Div(
[
html.Div(
[
dcc.Link(
html.H1(
"Plesna",
),
href="/",
className="text-4xl p-4 text-center grow align-baseline",
),
dcc.Link(
"Config",
href="/config",
className="flex-none hover:bg-amber-100 p-4 align-middle",
),
],
className="bg-amber-300 flex flex-row shadow",
),
dash.page_container,
]
)
if __name__ == "__main__":
app.run(debug=True)

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from dash import dcc, html
from ..libs.repository.repository import AbstractRepository
def html_list_schema(stage:AbstractRepository, with_tables=True):
""" Build html list of schema in stage """
ul_classes = "ml-2"
schema_baseurl = f"/stg/{stage.name}/schema/"
if with_tables:
return html.Ul(
[
html.Li(
children = [
dcc.Link(
schema,
href=schema_baseurl + schema,
className="text-lg hover:underline"
),
html_list_table(stage, schema)
],
className=""
) for schema in stage.schemas()
],
className=ul_classes
)
return html.Ul(
[
html.Li(
dcc.Link(
schema,
href=schema_baseurl + schema,
className="text-lg hover:underline"
),
) for schema in stage.schemas()
],
className=ul_classes
)
def html_list_table(stage:AbstractRepository, schema:str):
""" Build html list of table in stage """
table_baseurl = f"/stg/{stage.name}/schm/{schema}/table/"
return html.Ul(
[
html.Li(
dcc.Link(
table,
href=table_baseurl + table,
className="hover:underline"
),
) for table in stage.tables(schema=schema)
],
className="ml-4"
)

14
dashboard/datalake.py Normal file
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from dotenv import dotenv_values
from .libs.repository.fs_repository import FSRepository
env = {
**dotenv_values(".env"),
}
stages = {
"raw": FSRepository("raw", f"{env['DATA_PATH']}/{env['RAW_SUBPATH']}"),
"staging": FSRepository("staging", f"{env['DATA_PATH']}/{env['STAGING_SUBPATH']}"),
"gold": FSRepository("gold", f"{env['DATA_PATH']}/{env['GOLD_SUBPATH']}"),
"mart": FSRepository("mart", f"{env['DATA_PATH']}/{env['MART_SUBPATH']}"),
}

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from collections.abc import Callable
from datetime import datetime
import pandas as pd
from pydantic import BaseModel
from ..repository.repository import AbstractRepository
class Schema(BaseModel):
repository: str
schema: str
class Table(BaseModel):
repository: str
schema: str
table: str
class Flux(BaseModel):
sources: list[Table]
destinations: dict[str, Table]
transformation: Callable[[list[pd.DataFrame]], dict[str, pd.DataFrame]]
class State(BaseModel):
statuses: dict[str, dict]
qty_out: int
failed_lines: list[str]
start: datetime
end: datetime
Repositories = dict[str, AbstractRepository]
def open_source(repositories: Repositories, source: Table) -> pd.DataFrame:
return repositories[source.repository].read(source.table, source.schema)
def write_source(
content: pd.DataFrame, repositories: Repositories, destination: Table
) -> str:
return repositories[destination.repository].write(
content, destination.table, destination.schema
)
def consume_flux(flux: Flux, repositories: dict[str, AbstractRepository]) -> State:
start = datetime.now()
src_dfs = [open_source(repositories, source) for source in flux.sources]
built_dfs = flux.transformation(src_dfs)
statuses = {
dest: write_source(df, repositories, flux.destinations[dest])
for dest, df in built_dfs.items()
}
end = datetime.now()
qty_out = 0
failed_lines = []
return State(
statuses=statuses,
qty_out=qty_out,
failed_lines=failed_lines,
start=start,
end=end,
)

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from pathlib import Path
import pandas as pd
from .repository import AbstractRepository
ACCEPTABLE_EXTENTIONS = {
"csv": [".csv"],
"excel": [".xls", ".xlsx"],
}
class FSRepository(AbstractRepository):
def __init__(self, name, basepath, metadata_engine=None):
self.name = name
self.basepath = Path(basepath)
assert self.basepath.exists()
self._metadata_engine = metadata_engine
def ls(
self, dir="", only_files=False, only_directories=False, recursive=False
) -> list[str]:
dirpath = self.basepath / dir
if only_files:
return [
str(f.relative_to(dirpath))
for f in dirpath.iterdir()
if not f.is_dir() and not str(f).startswith(".")
]
if only_directories:
if recursive:
return [
str(f[0].relative_to(dirpath))
for f in dirpath.walk()
if not str(f).startswith(".")
]
return [
str(f.relative_to(dirpath))
for f in dirpath.iterdir()
if f.is_dir() and not str(f).startswith(".")
]
return [
str(f.relative_to(dirpath))
for f in dirpath.iterdir()
if not str(f).startswith(".")
]
def schemas(self, recursive=True) -> list[str]:
return self.ls("", only_directories=True, recursive=True)
def tables(self, schema: str = ".") -> list[str]:
return self.ls(schema, only_files=True)
def build_table_path(self, table: str, schema: str):
table_path = self.basepath
if schema == ".":
return table_path / table
return table_path / schema / table
def infos(self, table: str, schema: str = "."):
table_path = self.build_table_path(table, schema)
pass
def read(self, table: str, schema: str = ".", **read_options):
table_path = self.build_table_path(table, schema)
assert table_path.exists()
extension = table_path.suffix
if extension in ACCEPTABLE_EXTENTIONS["csv"]:
return pd.read_csv(table_path, **read_options)
if extension in ACCEPTABLE_EXTENTIONS["excel"]:
return pd.read_excel(table_path, engine = "openpyxl", **read_options)
raise ValueError("Bad extention. Can't open the table.")
def write(self, content, table: str, schema: str = "."):
table_path = self.build_table_path(table, schema)
pass
def delete_table(self, table: str, schema: str = "."):
table_path = self.build_table_path(table, schema)
pass

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from abc import ABC
class AbstractMetadataEngine(ABC):
pass

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import abc
from .metadata import AbstractMetadataEngine
class AbstractRepository(abc.ABC):
metadata_engine = AbstractMetadataEngine
@abc.abstractmethod
def schemas(self) -> list[str]:
"""List schemas"""
raise NotImplementedError
@abc.abstractmethod
def tables(self, schema) -> list[str]:
"""List table in schema"""
raise NotImplementedError
@abc.abstractmethod
def infos(self, table: str, schema: str) -> dict[str, str]:
"""Get infos about the table"""
raise NotImplementedError
@abc.abstractmethod
def read(self, table: str, schema: str):
"""Get content of the table"""
raise NotImplementedError
@abc.abstractmethod
def write(self, content, table: str, schema: str):
"""Write content into the table"""
raise NotImplementedError
@abc.abstractmethod
def delete_table(self, table: str, schema: str):
"""Delete the table"""
raise NotImplementedError

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14
dashboard/pages/config.py Normal file
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from dash import html
from dotenv import dotenv_values
import os
env = {
**dotenv_values(".env"),
**os.environ,
}
layout = html.Div([
html.H1('This is our Config page'),
html.Ul(children = [html.Li(f"{k} = {v}") for k,v in env.items()]),
])

27
dashboard/pages/home.py Normal file
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from dash import dcc, html
from ..components.lists import html_list_schema
from ..datalake import stages
layout = html.Div([
html.Div(children=[
html.Ul(
children=[
html.Li(
children=[
dcc.Link(
stagename,
href=f"/stage/{stagename}",
className="text-2xl text-center p-2 bg-amber-100 rounded shadow"
),
html_list_schema(stage)
],
className="flex-1 bg-gray-100 rounded flex flex-col shadow"
) for stagename, stage in stages.items()
],
className="flex flex-row space-x-2"
)
],
className="w-full mt-4 px-2"
),
])

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from dash import html
from ..components.lists import html_list_schema
from ..libs.repository.repository import AbstractRepository
def layout_factory(repositories: dict[str, AbstractRepository]):
def layout(repository_name: str = ""):
repository = repositories[repository_name]
return html.Div(
[
html.H2(f"{repository.name}", className="text-2xl p-4 py-2"),
html_list_schema(repository),
],
className="flex flex-col",
)
return layout

28
dashboard/pages/schema.py Normal file
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from dash import dcc, html
from ..libs.repository.repository import AbstractRepository
def layout_factory(repositories: dict[str, AbstractRepository]):
def layout(repository_name: str = "", schema_name: str = ""):
repository = repositories[repository_name]
return html.Div(
[
html.H2(
[
dcc.Link(
f"{repository.name}",
href=f"/repository/{repository.name}",
className="hover:underline",
),
html.Span(" > "),
html.Span(
f"{schema_name}",
),
],
className="text-2xl p-4 py-2",
),
]
)
return layout

130
dashboard/pages/table.py Normal file
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from dash import Input, Output, State, dash_table, dcc, html
from dash.exceptions import PreventUpdate
from ..libs.repository.repository import AbstractRepository
def layout_factory(repositories: dict[str,AbstractRepository]):
def layout(repository_name:str="", schema_name:str="", table_name:str=""):
repository = repositories[repository_name]
df = repository.read(table=table_name, schema=schema_name)
return html.Div([
dcc.Store(id="table_backup"),
html.Div([
html.H2([
dcc.Link(
f"{repository.name}",
href=f"/repository/{repository.name}",
className="hover:underline"
),
html.Span(" > "),
dcc.Link(
f"{schema_name}",
href=f"/stg/{repository.name}/schema/{schema_name}",
className="hover:underline"
),
html.Span(" > "),
html.Span(table_name),
],
className="text-2xl"
),
html.Div([
html.Button(
"Editer",
id="btn_edit",
className="rounded border px-2 py-1",
style={"display": "block"}
),
html.Button(
"Sauver",
id="btn_save",
className="rounded border px-2 py-1 border-green-500 hover:bg-green-500",
style={"display": "none"}
),
html.Button(
"Annuler",
id="btn_cancel",
className="rounded border px-2 py-1 border-red-500 hover:bg-red-500",
style={"display": "none"}
),
],
className="flex flex-row space-x-2",
id="toolbar"
),
],
className="flex flex-row justify-between p-4"
),
html.Div([
html.Div([
dash_table.DataTable(
id="datatable",
data=df.to_dict('records'),
columns=[{"name": i, "id": i} for i in df.columns],
filter_action="native",
sort_action="native",
sort_mode="multi",
editable=False
)
])
],
className="overflow-y-auto"
),
],
className="p-2"
)
return layout
def callback_factory(app):
@app.callback(
Output("datatable", 'editable', allow_duplicate=True),
Output("table_backup", 'data'),
Input("btn_edit", "n_clicks"),
State("datatable", 'data'),
prevent_initial_call=True
)
def activate_editable(n_clicks, df_src):
if n_clicks is None:
raise PreventUpdate
if n_clicks > 0:
df_backup = df_src.copy()
return True, df_backup
raise PreventUpdate
@app.callback(
Output("datatable", 'editable', allow_duplicate=True),
Output("datatable", 'data', allow_duplicate=True),
Input("btn_cancel", "n_clicks"),
State("table_backup", 'data'),
prevent_initial_call=True
)
def cancel_modifications(n_clicks, data):
if n_clicks is None:
raise PreventUpdate
if n_clicks > 0 and data is not None:
return False, data.copy()
raise PreventUpdate
@app.callback(
Output("datatable", 'editable'),
Output("datatable", 'data'),
Input("btn_save", "n_clicks"),
State("datatable", 'editable'),
)
def save_modifications(n_clicks, editable):
if n_clicks is None:
raise PreventUpdate
if n_clicks > 0:
return not editable
return editable
@app.callback(
Output("btn_edit", "style"),
Output("btn_save", "style"),
Output("btn_cancel", "style"),
Input("datatable", "editable"),
)
def toolbar(editable):
if editable:
return {"display": "none"}, {"display": "block"}, {"display": "block"}
return {"display": "block"}, {"display": "none"}, {"display": "none"}

0
plesna/__init__.py Normal file
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from plesna.models.flux import Flux, FluxMetaData
def consume_flux(flux: Flux) -> FluxMetaData:
metadata = flux.transformation.function(
sources=flux.sources, targets=flux.targets, **flux.transformation.extra_kwrds
)
return FluxMetaData(data=metadata)

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plesna/dataplatform.py Normal file
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from plesna.datastore.datacatalogue import DataCatalogue
from plesna.graph.graph_set import GraphSet
class DataPlateformError(Exception):
pass
class DataPlateform:
def __init__(self):
self._graphset = GraphSet()
self._metadata_engine = ""
self._transformations = {}
self._datacatalogues = {}
def add_datacatalague(self, name: str, datacatalogue: DataCatalogue):
if name in self._datacatalogues:
raise DataPlateformError("The datacatalogue {name} already exists")
self._datacatalogues[name] = datacatalogue
@property
def datacatalogues(self):
return list(self._datacatalogues)
def get_datacatalogue(self, name: str):
return self._datacatalogues[name]

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import abc
from plesna.models.storage import Schema, Table
class DataCatalogue:
def __init__(self):
pass
@property
@abc.abstractmethod
def schemas(self) -> list[str]:
"""List schema's names"""
raise NotImplementedError
@abc.abstractmethod
def schema(self, name: str) -> Schema:
"""Get the schema properties"""
raise NotImplementedError
@abc.abstractmethod
def tables(self, schema:str) -> list[str]:
"""List table's name in schema"""
raise NotImplementedError
@abc.abstractmethod
def table(self, schema:str, table:str) -> Table:
"""Get the table properties"""
raise NotImplementedError
@abc.abstractmethod
def infos(self, table: str, schema: str) -> dict[str, str]:
"""Get infos about the table"""
raise NotImplementedError

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class DataStore:
def __init__(self, name):
self._name

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from pathlib import Path
from pydantic import BaseModel, computed_field
from plesna.models.storage import Schema, Table
from .datacatalogue import DataCatalogue
class FakeSchema(BaseModel):
name: str
@computed_field
@property
def ref(self) -> Schema:
return Schema(
id=str(self.name),
value=str(self.name),
)
class FakeTable(BaseModel):
name: str
data: dict[str, list]
@computed_field
@property
def ref(self) -> Table:
return Table(
id=str(self.name),
value=str(self.name),
)
class FakeDataCatalogue(DataCatalogue):
"""DataCatalogue based on dictionnaries"""
def __init__(self, name: str):
self.name = name
def ls(
self, dir="", only_files=False, only_directories=False, recursive=False
) -> list[str]:
dirpath = self._basepath / dir
if only_files:
return [
str(f.relative_to(dirpath))
for f in dirpath.iterdir()
if not f.is_dir() and not str(f).startswith(".")
]
if only_directories:
if recursive:
return [
str(f[0].relative_to(dirpath))
for f in dirpath.walk()
if not str(f).startswith(".")
]
return [
str(f.relative_to(dirpath))
for f in dirpath.iterdir()
if f.is_dir() and not str(f).startswith(".")
]
return [
str(f.relative_to(dirpath))
for f in dirpath.iterdir()
if not str(f).startswith(".")
]
def schemas(self) -> dict[str, FSSchema]:
"""List schemas (sub directories within basepath)"""
subdirectories = self.ls("", only_directories=True, recursive=True)
return {str(path): FSSchema(path=path) for path in subdirectories}
def tables(self, schema_id=".") -> dict[str, FSTable]:
"""List table in schema (which are files in the directory)"""
schema_path = schema_id
return {path: FSTable(path=path) for path in self.ls(schema_path, only_files=True)}

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from pathlib import Path
from pydantic import BaseModel, computed_field
from plesna.models.storage import Schema, Table
from .datacatalogue import DataCatalogue
class FSTable(BaseModel):
path: Path
@computed_field
@property
def ref(self) -> Table:
return Table(
id=str(self.path),
value=str(self.path),
)
class FSSchema(BaseModel):
path: Path
tables: list[str]
@computed_field
@property
def ref(self) -> Schema:
return Schema(
id=str(self.path),
value=str(self.path),
)
class FSDataCatalogue(DataCatalogue):
"""DataCatalogue based on files tree structure"""
def __init__(self, name: str, basepath: str = "."):
self._basepath = Path(basepath)
self.name = name
assert self._basepath.exists()
def ls(
self, dir="", only_files=False, only_directories=False, recursive=False
) -> list[str]:
dirpath = self._basepath / dir
if only_files:
return [
str(f.relative_to(dirpath))
for f in dirpath.iterdir()
if not f.is_dir() and not str(f).startswith(".")
]
if only_directories:
if recursive:
return [
str(f[0].relative_to(dirpath))
for f in dirpath.walk()
if not str(f).startswith(".")
]
return [
str(f.relative_to(dirpath))
for f in dirpath.iterdir()
if f.is_dir() and not str(f).startswith(".")
]
return [
str(f.relative_to(dirpath))
for f in dirpath.iterdir()
if not str(f).startswith(".")
]
@property
def schemas(self) -> list[str]:
"""List schemas (sub directories within basepath)"""
subdirectories = self.ls("", only_directories=True, recursive=True)
return [str(d) for d in subdirectories]
def schema(self, schema: str) -> FSSchema:
"""List schemas (sub directories within basepath)"""
tables = self.ls(schema, only_files=True)
return FSSchema(path=Path(schema), tables=tables)
def table(self, schema: str, table:str) -> FSTable:
"""List table in schema (which are files in the directory)"""
schema_path = schema_id
return {path: FSTable(path=path) for path in self.ls(schema_path, only_files=True)}

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plesna/graph/__init__.py Normal file
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plesna/graph/graph.py Normal file
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from functools import reduce
from typing import Callable
from pydantic import BaseModel
class Node(BaseModel):
name: str
infos: dict = {}
def __hash__(self):
return hash(self.name)
class Edge(BaseModel):
arrow_name: str
source: Node
target: Node
edge_kwrds: dict = {}
class Graph:
def __init__(self, nodes: list[Node] = [], edges: list[Edge] = []):
self._edges = []
self._nodes = set()
self.add_edges(edges)
self.add_nodes(nodes)
def add_node(self, node: Node):
self._nodes.add(node)
def add_nodes(self, nodes: list[Node]):
for node in nodes:
self.add_node(node)
def add_edge(self, edge: Edge):
self._edges.append(edge)
self.add_node(edge.source)
self.add_node(edge.target)
def add_edges(self, edges: list[Edge]):
for edge in edges:
self.add_edge(edge)
@property
def nodes(self):
return self._nodes
@property
def edges(self):
return self._edges
def get_edges_from(self, node: Node) -> list[Edge]:
"""Get all edges which have the node as source"""
return [edge for edge in self._edges if edge.source == node]
def get_edges_to(self, node: Node) -> list[Edge]:
"""Get all edges which have the node as target"""
return [edge for edge in self._edges if edge.target == node]
def get_direct_targets_from(self, node: Node) -> set[Node]:
"""Get direct nodes that are accessible from the node"""
return set(edge.target for edge in self._edges if edge.source == node)
def get_targets_from(self, node: Node) -> set[Node]:
"""Get all nodes that are accessible from the node
If the graph have a loop, the procedure be in an infinite loop!
"""
direct_targets = self.get_direct_targets_from(node)
undirect_targets = [self.get_targets_from(n) for n in direct_targets]
undirect_targets = reduce(lambda x, y: x.union(y), undirect_targets, set())
return direct_targets.union(undirect_targets)
def get_direct_sources_from(self, node: Node) -> set[Node]:
"""Get direct nodes that are targeted the node"""
return set(edge.source for edge in self._edges if edge.target == node)
def get_sources_from(self, node: Node) -> set[Node]:
"""Get all nodes that are targeted the node"""
direct_sources = self.get_direct_sources_from(node)
undirect_sources = [self.get_sources_from(n) for n in direct_sources]
undirect_sources = reduce(lambda x, y: x.union(y), undirect_sources, set())
return direct_sources.union(undirect_sources)
def is_dag(self) -> bool:
visited = set()
for node in self._nodes:
if node not in visited:
try:
targets = self.get_targets_from(node)
except RecursionError:
return False
visited.union(targets)
return True

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from typing import Callable
from pydantic import BaseModel
class Node(BaseModel):
name: str
infos: dict = {}
def __hash__(self):
return hash(self.name)
class EdgeOnSet(BaseModel):
arrow: Callable
sources: dict[str, Node]
targets: dict[str, Node]
edge_kwrds: dict = {}
class GraphSet:
def __init__(self):
self._edges = []
self._node_sets = set()
def append(self, edge: EdgeOnSet):
self._edges.append(edge)
self._node_sets.add(frozenset(edge.sources.values()))
self._node_sets.add(frozenset(edge.targets.values()))
@property
def node_sets(self):
return self._node_sets
def is_valid_dag(self):
pass

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plesna/models/flux.py Normal file
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from pydantic import BaseModel
from plesna.models.storage import Table
from plesna.models.transformation import Transformation
class Flux(BaseModel):
sources: dict[str, Table]
targets: dict[str, Table]
transformation: Transformation
class FluxMetaData(BaseModel):
data: dict

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plesna/models/storage.py Normal file
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from pydantic import BaseModel
class Schema(BaseModel):
"""Logical agregation for Table
id: uniq identifier for the schema
value: string which describe where to find the schema in the storage system
"""
id: str
value: str
class Table(BaseModel):
"""Place where data are stored
id: uniq identifier for the table
value: string which describe where to find the table in the storage system
"""
id: str
value: str

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from collections.abc import Callable
from pydantic import BaseModel
class Transformation(BaseModel):
"""
The function have to have at least 2 arguments: sources and targets
Other arguments will came throught extra_kwrds
The function will have to return metadata as dict
"""
function: Callable
extra_kwrds: dict = {}

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@@ -1,7 +1,6 @@
jupyter==1.0.0
pandas==1.5.0
pdf-oralia==0.3.11
pydantic==2.6.1
pandas==2.2.2
pydantic==2.8.2
click==8.1.7
dlt[duckdb]>=0.4.3a0
openpyxl>=3.0.0
openpyxl==3.1.5
xlrd==2.0.1

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tests/__init__.py Normal file
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from plesna.compute.consume_flux import consume_flux
from plesna.models.flux import Flux
from plesna.models.storage import Table
from plesna.models.transformation import Transformation
def test_consume_flux():
sources = {
"src1": Table(id="src1", value="here"),
"src2": Table(id="src2", value="here"),
}
targets = {
"tgt1": Table(id="tgt1", value="this"),
"tgt2": Table(id="tgt2", value="that"),
}
def func(sources, targets, **kwrds):
return {
"sources": len(sources),
"targets": len(targets),
"kwrds": len(kwrds),
}
flux = Flux(
sources=sources,
targets=targets,
transformation=Transformation(function=func, extra_kwrds={"extra": "super"}),
)
meta = consume_flux(flux)
assert meta.data == {
"sources": 2,
"targets": 2,
"kwrds": 1,
}

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from pathlib import Path
import pytest
from plesna.dataplatform import DataPlateform
from plesna.datastore.fs_datacatalogue import FSDataCatalogue
FIXTURE_DIR = Path(__file__).parent / Path("raw_data")
@pytest.fixture
def raw_catalogue(tmp_path):
raw_path = Path(tmp_path) / "raw"
raw_path.mkdir()
return FSDataCatalogue("raw", raw_path)
@pytest.fixture
def bronze_catalogue(tmp_path):
bronze_path = Path(tmp_path) / "bronze"
bronze_path.mkdir()
return FSDataCatalogue("bronze", bronze_path)
@pytest.fixture
def silver_catalogue(tmp_path):
silver_path = Path(tmp_path) / "silver"
silver_path.mkdir()
return FSDataCatalogue("silver", silver_path)
def test_add_catalogue(
raw_catalogue: FSDataCatalogue,
bronze_catalogue: FSDataCatalogue,
silver_catalogue: FSDataCatalogue,
):
dp = DataPlateform()
dp.add_datacatalague("raw", raw_catalogue)
dp.add_datacatalague("bronze", bronze_catalogue)
dp.add_datacatalague("silver", silver_catalogue)
assert dp.datacatalogues == ["raw", "bronze", "silver"]
assert dp.get_datacatalogue("raw") == raw_catalogue

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import shutil
from pathlib import Path
import pytest
from plesna.datastore.fs_datacatalogue import FSDataCatalogue
from plesna.models.storage import Schema
FIXTURE_DIR = Path(__file__).parent.parent / Path("./raw_datas/")
@pytest.fixture
def location(tmp_path):
loc = tmp_path
username_loc = loc / "username"
username_loc.mkdir()
salary_loc = loc / "salary"
salary_loc.mkdir()
example_src = FIXTURE_DIR
assert example_src.exists()
for f in example_src.glob("*"):
if "username" in str(f):
shutil.copy(f, username_loc)
else:
shutil.copy(f, salary_loc)
return loc
def test_init(location):
repo = FSDataCatalogue("example", location)
assert repo.ls() == [
"username",
"salary",
]
assert repo.ls(recursive=True) == [
"username",
"salary",
]
def test_list_schema(location):
repo = FSDataCatalogue("example", location)
assert repo.schemas == [".", "username", "salary"]
assert repo.schema(".").ref == Schema(id=".", value=".")
assert repo.schema("username").ref == Schema(id="username", value="username")
def test_list_tables_schema(location):
repo = FSDataCatalogue("example", location)
assert repo.schema(".").tables == []
assert repo.schema("username").tables == [
'username.csv',
'username-password-recovery-code.xlsx',
'username-password-recovery-code.xls',
]
assert repo.schema("salary").tables == ["salary.pdf"]

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Username;Identifier;First name;Last name
booker12;9012;Rachel;Booker
grey07;2070;Laura;Grey
johnson81;4081;Craig;Johnson
jenkins46;9346;Mary;Jenkins
smith79;5079;Jamie;Smith
1 Username Identifier First name Last name
2 booker12 9012 Rachel Booker
3 grey07 2070 Laura Grey
4 johnson81 4081 Craig Johnson
5 jenkins46 9346 Mary Jenkins
6 smith79 5079 Jamie Smith

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from pathlib import Path
import pytest
from plesna.dataplatform import DataPlateform
from plesna.datastore.fs_datacatalogue import FSDataCatalogue
FIXTURE_DIR = Path(__file__).parent / Path("raw_data")
@pytest.fixture
def raw_catalogue(tmp_path):
raw_path = Path(tmp_path) / "raw"
return FSDataCatalogue(raw_path)
@pytest.fixture
def bronze_catalogue(tmp_path):
bronze_path = Path(tmp_path) / "bronze"
return FSDataCatalogue(bronze_path)
@pytest.fixture
def silver_catalogue(tmp_path):
silver_path = Path(tmp_path) / "silver"
return FSDataCatalogue(silver_path)
@pytest.fixture
def dataplateform(
raw_catalogue: FSDataCatalogue,
bronze_catalogue: FSDataCatalogue,
silver_catalogue: FSDataCatalogue,
):
dp = DataPlateform()
dp.add_datacatalague("raw", raw_catalogue)
dp.add_datacatalague("bronze", bronze_catalogue)
dp.add_datacatalague("silver", silver_catalogue)
pass

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tests/graphs/test_graph.py Normal file
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import pytest
from plesna.graph.graph import Edge, Graph, Node
def test_append_nodess():
nodeA = Node(name="A")
nodeB = Node(name="B")
graph = Graph()
graph.add_node(nodeA)
graph.add_node(nodeB)
assert graph.nodes == {nodeA, nodeB}
def test_append_edges():
nodeA = Node(name="A")
nodeB = Node(name="B")
nodeC = Node(name="C")
edge1 = Edge(arrow_name="arrow", source=nodeA, target=nodeC)
edge2 = Edge(arrow_name="arrow", source=nodeB, target=nodeC)
graph = Graph()
graph.add_edge(edge1)
graph.add_edge(edge2)
assert graph.nodes == {nodeA, nodeB, nodeC}
def test_init_edges_nodes():
nodeA = Node(name="A")
nodeB = Node(name="B")
nodeC = Node(name="C")
edge1 = Edge(arrow_name="arrow", source=nodeB, target=nodeC)
graph = Graph()
graph.add_node(nodeA)
graph.add_edge(edge1)
assert graph.nodes == {nodeA, nodeB, nodeC}
@pytest.fixture
def nodes():
return {
"A": Node(name="A"),
"B": Node(name="B"),
"C": Node(name="C"),
"D": Node(name="D"),
}
@pytest.fixture
def dag_edges(nodes):
return {
"1": Edge(arrow_name="arrow", source=nodes["A"], target=nodes["C"]),
"2": Edge(arrow_name="arrow", source=nodes["B"], target=nodes["C"]),
"3": Edge(arrow_name="arrow", source=nodes["C"], target=nodes["D"]),
}
@pytest.fixture
def notdag_edges(nodes):
return {
"1": Edge(arrow_name="arrow", source=nodes["A"], target=nodes["C"]),
"2": Edge(arrow_name="arrow", source=nodes["B"], target=nodes["C"]),
"3": Edge(arrow_name="arrow", source=nodes["C"], target=nodes["D"]),
"4": Edge(arrow_name="arrow", source=nodes["D"], target=nodes["B"]),
}
def test_get_edges_from(nodes, dag_edges):
edges = dag_edges
graph = Graph(edges=edges.values())
assert graph.get_edges_from(nodes["A"]) == [edges["1"]]
def test_get_targets_from(nodes, dag_edges):
edges = dag_edges
graph = Graph(edges=edges.values())
assert graph.get_direct_targets_from(nodes["A"]) == set([nodes["C"]])
assert graph.get_direct_targets_from(nodes["C"]) == set([nodes["D"]])
assert graph.get_direct_targets_from(nodes["D"]) == set()
assert graph.get_targets_from(nodes["A"]) == set([nodes["C"], nodes["D"]])
def test_get_sources_from(nodes, dag_edges):
edges = dag_edges
graph = Graph(edges=edges.values())
assert graph.get_direct_sources_from(nodes["A"]) == set()
assert graph.get_direct_sources_from(nodes["C"]) == set([nodes["A"], nodes["B"]])
assert graph.get_direct_sources_from(nodes["D"]) == set([nodes["C"]])
assert graph.get_sources_from(nodes["D"]) == set(
[nodes["A"], nodes["B"], nodes["C"]]
)
def test_valid_dage(dag_edges, notdag_edges):
graph = Graph(edges=dag_edges.values())
assert graph.is_dag()
graph = Graph(edges=notdag_edges.values())
assert not graph.is_dag()

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from plesna.graph.graph_set import EdgeOnSet, GraphSet, Node
def test_init():
nodeA = Node(name="A")
nodeB = Node(name="B")
nodeC = Node(name="C")
def arrow(sources, targets):
targets["C"].infos["res"] = sources["A"].name + sources["B"].name
edge1 = EdgeOnSet(
arrow=arrow, sources={"A": nodeA, "B": nodeB}, targets={"C": nodeC}
)
graph_set = GraphSet()
graph_set.append(edge1)
assert graph_set.node_sets == {frozenset([nodeA, nodeB]), frozenset([nodeC])}

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Username;Identifier;First name;Last name
booker12;9012;Rachel;Booker
grey07;2070;Laura;Grey
johnson81;4081;Craig;Johnson
jenkins46;9346;Mary;Jenkins
smith79;5079;Jamie;Smith
1 Username Identifier First name Last name
2 booker12 9012 Rachel Booker
3 grey07 2070 Laura Grey
4 johnson81 4081 Craig Johnson
5 jenkins46 9346 Mary Jenkins
6 smith79 5079 Jamie Smith

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tests/test_flux.py Normal file
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import pandas as pd
import pytest
from dashboard.libs.flux.flux import Flux, consume_flux
from dashboard.libs.repository.repository import AbstractRepository
FakeTable = pd.DataFrame
FakeSchema = dict[str, pd.DataFrame]
FakeSchemas = dict[str, FakeSchema]
class FakeRepository(AbstractRepository):
def __init__(self, schemas: FakeSchemas):
self._schemas = {}
for schema_name, tables in schemas.items():
schema = {}
for table, df in tables.items():
schema[table] = {
"df": df,
"metadata": {
"status": "new",
"qty_read": 0,
"qty_write": 0,
},
}
self._schemas[schema_name] = schema
def schemas(self):
"""List schemas"""
return list(self._schemas.keys())
def tables(self, schema):
"""List table's name in schema"""
return list(self._schemas[schema].keys())
def infos(self, table: str, schema: str) -> dict[str, str]:
"""Get infos about the table"""
return self._schemas[schema][table]["metadata"]
def read(self, table, schema) -> pd.DataFrame:
"""Get content of the table"""
self._schemas[schema][table]["metadata"]["qty_read"] += 1
return self._schemas[schema][table]["df"]
def write(self, content, table, schema) -> dict[str, str]:
"""Write content into the table"""
try:
self._schemas[schema][table]["df"] = content
except KeyError:
self._schemas[schema][table] = {
"df": content,
"metadata": {
"status": "new",
"qty_read": 0,
"qty_write": 0,
},
}
self._schemas[schema][table]["metadata"]["status"] = "modified"
self._schemas[schema][table]["metadata"]["qty_write"] += 1
return self.infos(table, schema)
def delete_table(self, table, schema):
"""Delete the table"""
raise NotImplementedError
def test_fakerepository():
fakerepository = FakeRepository(
{
"foo": {
"table1": pd.DataFrame({"A": []}),
"table2": pd.DataFrame({"B": []}),
},
"bar": {
"table1": pd.DataFrame({"C": []}),
"table2": pd.DataFrame({"D": []}),
},
}
)
assert fakerepository.schemas() == ["foo", "bar"]
assert fakerepository.tables("foo") == ["table1", "table2"]
assert fakerepository.infos("table1", "foo") == {
"status": "new",
"qty_read": 0,
"qty_write": 0,
}
assert fakerepository.read("table1", "foo").equals(pd.DataFrame({"A": []}))
assert fakerepository.infos("table1", "foo") == {
"status": "new",
"qty_read": 1,
"qty_write": 0,
}
df = pd.DataFrame({"A": [1, 2]})
assert fakerepository.write(df, "table1", "foo") == {
"status": "modified",
"qty_read": 1,
"qty_write": 1,
}
def test_consume_flux():
source_repository = FakeRepository(
{
"source": {
"table1": pd.DataFrame({"A": [1, 2, 3]}),
},
}
)
dest_repository = FakeRepository(
{
"destination": {},
}
)
repositories = {
"source": source_repository,
"dest": dest_repository,
}
transformation = lambda dfs: {"dest": dfs[0] * 2}
flux = Flux(
sources=[{"repository": "source", "schema": "source", "table": "table1"}],
destinations={
"dest": {"repository": "dest", "schema": "destination", "table": "table1"}
},
transformation=transformation,
)
state = consume_flux(flux, repositories)
assert state.statuses["dest"] == {'status': 'modified', 'qty_read': 0, 'qty_write': 1}
assert dest_repository.read("table1", "destination").equals(pd.DataFrame({"A": [2, 4, 6]}))

7
uv.lock generated Normal file
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version = 1
requires-python = ">=3.13"
[[package]]
name = "plesna"
version = "0.1.0"
source = { virtual = "." }