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Expand governed Dataflow editor and node library

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This commit is contained in:
Albrecht Degering
2026-07-28 11:14:01 +02:00
parent df468a2bd8
commit dee8380631
22 changed files with 3564 additions and 291 deletions
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38
README.md
View File

@@ -17,10 +17,40 @@ the previewed row contents.
- **Risk Compliance:** sanctions matching policy, review, dispositions, and
legal evidence.
The first implementation supports bounded inline tabular sources and the
`filter`, `select`, `aggregate`, `sort`, `limit`, and `output` transforms. The
connector-backed source contract is tracked separately so credentials never
become part of a pipeline definition.
## Node Library
The canonical backend catalogue is exposed to the WebUI and groups executable
nodes by purpose:
| Group | Nodes |
| --- | --- |
| Load | Inline data, connector source |
| Combine | Append rows, join tables |
| Filter | Filter rows, remove duplicates |
| Transform | Select columns, derive column, aggregate, sort, limit |
| Output | Preview output |
Join nodes have explicit left and right ports. Append nodes accept two or more
inputs. Derived columns use a constrained operation catalogue rather than
arbitrary code.
Connector sources are resolved through the versioned Core capability
`connectors.tabular_sources`; Dataflow does not import Connectors or store its
credentials. Source references pin an expected fingerprint so schema/content
drift fails visibly instead of silently changing a run.
## SQL And Preview Safety
The SQL workbench parses one `SELECT` statement into the canonical graph. The
dialect supports projection, aliases, filters, grouping, aggregate functions,
sorting, limits, `DISTINCT`, and one two-source equi-join. It rejects DDL, DML,
subqueries, arbitrary functions, file access, and unchecked pass-through
execution.
Preview reads at most 250 rows per source and enforces time, intermediate-row,
result-byte, graph-node, and response-row bounds. Saved previews record the
pipeline revision, executor version, source fingerprints, node diagnostics,
and output summary, but not source or result rows.
## Development

360
src/govoplan_dataflow/backend/executor.py
View File

@@ -6,26 +6,42 @@ import time
from collections import defaultdict
from dataclasses import dataclass
from decimal import Decimal
from typing import Any
from typing import Any, Callable
from govoplan_dataflow.backend.graph import graph_input_map, topological_order, validate_graph
from govoplan_dataflow.backend.graph import graph_inputs_by_port, topological_order, validate_graph
from govoplan_dataflow.backend.schemas import (
DataflowDiagnostic,
GraphNode,
NodePreviewDiagnostic,
PipelineGraph,
PreviewColumn,
)
EXECUTOR_VERSION = "dataflow-preview-v1"
EXECUTOR_VERSION = "dataflow-preview-v2"
MAX_EXECUTION_SECONDS = 2.0
MAX_RESULT_BYTES = 1_000_000
MAX_SOURCE_ROWS = 250
MAX_INTERMEDIATE_ROWS = 10_000
class PipelineExecutionError(RuntimeError):
def __init__(self, message: str, *, node_id: str | None = None) -> None:
def __init__(
self,
message: str,
*,
node_id: str | None = None,
node_diagnostics: tuple[NodePreviewDiagnostic, ...] = (),
source_fingerprints: tuple[dict[str, Any], ...] = (),
input_row_count: int = 0,
diagnostics: tuple[DataflowDiagnostic, ...] = (),
) -> None:
super().__init__(message)
self.node_id = node_id
self.node_diagnostics = node_diagnostics
self.source_fingerprints = source_fingerprints
self.input_row_count = input_row_count
self.diagnostics = diagnostics
@dataclass(frozen=True)
@@ -40,14 +56,32 @@ class PipelineExecutionResult:
input_row_count: int
def execute_preview(graph: PipelineGraph, *, row_limit: int) -> PipelineExecutionResult:
@dataclass(frozen=True)
class ResolvedSource:
rows: tuple[dict[str, Any], ...]
source_ref: str
provider: str
fingerprint: str
total_rows: int
truncated: bool = False
SourceResolver = Callable[[GraphNode, int], ResolvedSource]
def execute_preview(
graph: PipelineGraph,
*,
row_limit: int,
source_resolver: SourceResolver | None = None,
) -> PipelineExecutionResult:
validation = validate_graph(graph)
errors = [item for item in validation if item.severity == "error"]
if errors:
raise PipelineExecutionError(errors[0].message, node_id=errors[0].node_id)
node_by_id = {node.id: node for node in graph.nodes}
inputs = graph_input_map(graph)
inputs = graph_inputs_by_port(graph)
ordered, cyclic = topological_order(graph)
if cyclic:
raise PipelineExecutionError("Pipeline graph contains a cycle")
@@ -59,11 +93,34 @@ def execute_preview(graph: PipelineGraph, *, row_limit: int) -> PipelineExecutio
input_row_count = 0
for node_id in ordered:
if time.monotonic() - started > MAX_EXECUTION_SECONDS:
raise PipelineExecutionError("Preview exceeded the two-second execution limit", node_id=node_id)
node = node_by_id[node_id]
node_started = time.monotonic()
input_rows = [] if node.type.startswith("source.") else outputs[inputs[node_id]]
node_inputs = inputs.get(node_id, {})
input_sets = [
outputs[source_id]
for port_sources in node_inputs.values()
for source_id in port_sources
]
input_rows = input_sets[0] if len(input_sets) == 1 else []
node_messages: list[str] = []
if time.monotonic() - started > MAX_EXECUTION_SECONDS:
failed = NodePreviewDiagnostic(
node_id=node.id,
status="failed",
input_rows=sum(len(rows) for rows in input_sets),
output_rows=0,
duration_ms=0,
columns=[],
messages=["Preview exceeded the two-second execution limit"],
)
raise PipelineExecutionError(
"Preview exceeded the two-second execution limit",
node_id=node.id,
node_diagnostics=tuple([*node_diagnostics, failed]),
source_fingerprints=tuple(source_fingerprints),
input_row_count=input_row_count,
diagnostics=tuple(item for item in validation if item.severity != "error"),
)
try:
if node.type == "source.inline":
output_rows = [dict(row) for row in node.config.get("rows", [])]
@@ -78,14 +135,64 @@ def execute_preview(graph: PipelineGraph, *, row_limit: int) -> PipelineExecutio
}
)
elif node.type == "source.reference":
if source_resolver is None:
raise PipelineExecutionError(
"This connector-backed source is not available to the local preview executor.",
"Connector-backed preview requires the Connectors tabular-source capability.",
node_id=node.id,
)
resolved = source_resolver(node, MAX_SOURCE_ROWS)
output_rows = [dict(row) for row in resolved.rows]
input_row_count += len(output_rows)
source_fingerprints.append(
{
"node_id": node.id,
"source_ref": resolved.source_ref,
"source_name": node.config.get("source_name"),
"kind": "connector",
"provider": resolved.provider,
"fingerprint": resolved.fingerprint,
"row_count": resolved.total_rows,
"preview_rows": len(output_rows),
"truncated": resolved.truncated,
}
)
if resolved.truncated:
message = (
f"Source preview used {len(output_rows):,} of "
f"{resolved.total_rows:,} rows."
)
node_messages.append(message)
validation.append(
DataflowDiagnostic(
severity="warning",
code="source.preview_truncated",
message=message,
node_id=node.id,
)
)
elif node.type == "combine.union":
ordered_inputs = [
outputs[source_id]
for source_id in node_inputs.get("input", [])
]
output_rows = _union_rows(ordered_inputs, node.config)
elif node.type == "combine.join":
left_rows = outputs[node_inputs["left"][0]]
right_rows = outputs[node_inputs["right"][0]]
output_rows = _join_rows(
left_rows,
right_rows,
node.config,
node_id=node.id,
)
elif node.type == "filter":
output_rows = _filter_rows(input_rows, node.config, node_id=node.id)
elif node.type == "distinct":
output_rows = _distinct_rows(input_rows, node.config)
elif node.type == "select":
output_rows = _select_rows(input_rows, node.config)
elif node.type == "derive":
output_rows = _derive_rows(input_rows, node.config, node_id=node.id)
elif node.type == "aggregate":
output_rows = _aggregate_rows(input_rows, node.config, node_id=node.id)
elif node.type == "sort":
@@ -96,25 +203,45 @@ def execute_preview(graph: PipelineGraph, *, row_limit: int) -> PipelineExecutio
output_rows = [dict(row) for row in input_rows]
else:
raise PipelineExecutionError(f"Unsupported node type: {node.type}", node_id=node.id)
except PipelineExecutionError:
raise
except (ArithmeticError, TypeError, ValueError) as exc:
raise PipelineExecutionError(str(exc), node_id=node.id) from exc
if len(json.dumps(output_rows, default=str).encode("utf-8")) > MAX_RESULT_BYTES:
raise PipelineExecutionError(
"A preview node exceeded the one-megabyte result limit.",
node_id=node.id,
)
except (PipelineExecutionError, ArithmeticError, KeyError, TypeError, ValueError) as exc:
execution_error = (
exc
if isinstance(exc, PipelineExecutionError)
else PipelineExecutionError(str(exc), node_id=node.id)
)
failed = NodePreviewDiagnostic(
node_id=execution_error.node_id or node.id,
status="failed",
input_rows=sum(len(rows) for rows in input_sets),
output_rows=0,
duration_ms=round((time.monotonic() - node_started) * 1000, 3),
columns=[],
messages=[str(execution_error)],
)
raise PipelineExecutionError(
str(execution_error),
node_id=execution_error.node_id or node.id,
node_diagnostics=tuple([*node_diagnostics, failed]),
source_fingerprints=tuple(source_fingerprints),
input_row_count=input_row_count,
diagnostics=tuple(item for item in validation if item.severity != "error"),
) from exc
outputs[node.id] = output_rows
node_diagnostics.append(
NodePreviewDiagnostic(
node_id=node.id,
status="succeeded",
input_rows=len(input_rows),
input_rows=sum(len(rows) for rows in input_sets),
output_rows=len(output_rows),
duration_ms=round((time.monotonic() - node_started) * 1000, 3),
columns=infer_columns(output_rows),
messages=node_messages,
)
)
@@ -126,7 +253,7 @@ def execute_preview(graph: PipelineGraph, *, row_limit: int) -> PipelineExecutio
total_rows=len(all_rows),
truncated=len(rows) < len(all_rows),
columns=infer_columns(all_rows),
diagnostics=[],
diagnostics=[item for item in validation if item.severity != "error"],
node_diagnostics=node_diagnostics,
source_fingerprints=source_fingerprints,
input_row_count=input_row_count,
@@ -156,6 +283,202 @@ def infer_columns(rows: list[dict[str, Any]]) -> list[PreviewColumn]:
return columns
def _union_rows(
inputs: list[list[dict[str, Any]]],
config: dict[str, Any],
) -> list[dict[str, Any]]:
rows = [dict(row) for input_rows in inputs for row in input_rows]
if config.get("mode", "all") == "distinct":
return _distinct_rows(rows, {})
return rows
def _join_rows(
left_rows: list[dict[str, Any]],
right_rows: list[dict[str, Any]],
config: dict[str, Any],
*,
node_id: str,
) -> list[dict[str, Any]]:
left_keys = [str(item) for item in config["left_keys"]]
right_keys = [str(item) for item in config["right_keys"]]
join_type = str(config.get("join_type", "inner"))
right_prefix = str(config.get("right_prefix", "right_"))
left_columns = _ordered_columns(left_rows)
right_columns = _ordered_columns(right_rows)
right_index: dict[tuple[Any, ...], list[tuple[int, dict[str, Any]]]] = defaultdict(list)
for index, row in enumerate(right_rows):
key = _join_key(row, right_keys)
if key is not None:
right_index[key].append((index, row))
output: list[dict[str, Any]] = []
matched_right: set[int] = set()
for left_row in left_rows:
key = _join_key(left_row, left_keys)
matches = right_index.get(key, ()) if key is not None else ()
if matches:
for right_index_value, right_row in matches:
matched_right.add(right_index_value)
output.append(
_merge_join_rows(
left_row,
right_row,
left_columns=left_columns,
right_columns=right_columns,
right_prefix=right_prefix,
)
)
_guard_intermediate_size(output, node_id=node_id)
elif join_type in {"left", "full"}:
output.append(
_merge_join_rows(
left_row,
None,
left_columns=left_columns,
right_columns=right_columns,
right_prefix=right_prefix,
)
)
if join_type in {"right", "full"}:
for index, right_row in enumerate(right_rows):
if index in matched_right:
continue
output.append(
_merge_join_rows(
None,
right_row,
left_columns=left_columns,
right_columns=right_columns,
right_prefix=right_prefix,
)
)
_guard_intermediate_size(output, node_id=node_id)
return output
def _distinct_rows(
rows: list[dict[str, Any]],
config: dict[str, Any],
) -> list[dict[str, Any]]:
columns = [str(item) for item in config.get("columns", [])]
seen: set[str] = set()
output: list[dict[str, Any]] = []
for row in rows:
value = {column: row.get(column) for column in columns} if columns else row
identity = json.dumps(value, sort_keys=True, separators=(",", ":"), default=str)
if identity in seen:
continue
seen.add(identity)
output.append(dict(row))
return output
def _derive_rows(
rows: list[dict[str, Any]],
config: dict[str, Any],
*,
node_id: str,
) -> list[dict[str, Any]]:
target = str(config["target_column"])
operation = str(config["operation"])
columns = [str(item) for item in config["source_columns"]]
separator = str(config.get("separator", " "))
output: list[dict[str, Any]] = []
for row in rows:
values = [row.get(column) for column in columns]
try:
derived = _derive_value(operation, values, separator=separator)
except (ArithmeticError, TypeError, ValueError) as exc:
raise PipelineExecutionError(
f"Cannot derive {target!r} with {operation!r}: {exc}",
node_id=node_id,
) from exc
result = dict(row)
result[target] = derived
output.append(result)
return output
def _derive_value(operation: str, values: list[Any], *, separator: str) -> Any:
if operation == "copy":
return values[0]
if operation == "upper":
return None if values[0] is None else str(values[0]).upper()
if operation == "lower":
return None if values[0] is None else str(values[0]).lower()
if operation == "trim":
return None if values[0] is None else str(values[0]).strip()
if operation == "concat":
return separator.join(str(value) for value in values if value is not None)
if operation == "coalesce":
return next((value for value in values if value not in (None, "")), None)
if any(value is None for value in values):
return None
left, right = values
if isinstance(left, bool) or isinstance(right, bool):
raise TypeError("boolean values are not numeric inputs")
if operation == "add":
return left + right
if operation == "subtract":
return left - right
if operation == "multiply":
return left * right
if operation == "divide":
return left / right
raise ValueError(f"unknown derive operation {operation!r}")
def _join_key(row: dict[str, Any], columns: list[str]) -> tuple[Any, ...] | None:
values = tuple(row.get(column) for column in columns)
if any(value is None for value in values):
return None
try:
hash(values)
except TypeError:
return tuple(
json.dumps(value, sort_keys=True, separators=(",", ":"), default=str)
for value in values
)
return values
def _ordered_columns(rows: list[dict[str, Any]]) -> list[str]:
columns: list[str] = []
for row in rows:
for column in row:
if column not in columns:
columns.append(column)
return columns
def _merge_join_rows(
left_row: dict[str, Any] | None,
right_row: dict[str, Any] | None,
*,
left_columns: list[str],
right_columns: list[str],
right_prefix: str,
) -> dict[str, Any]:
output = {
column: left_row.get(column) if left_row is not None else None
for column in left_columns
}
for column in right_columns:
output_column = f"{right_prefix}{column}"
output[output_column] = right_row.get(column) if right_row is not None else None
return output
def _guard_intermediate_size(rows: list[dict[str, Any]], *, node_id: str) -> None:
if len(rows) > MAX_INTERMEDIATE_ROWS:
raise PipelineExecutionError(
f"Preview join exceeded the {MAX_INTERMEDIATE_ROWS:,}-row intermediate limit.",
node_id=node_id,
)
def _filter_rows(
rows: list[dict[str, Any]],
config: dict[str, Any],
@@ -305,8 +628,11 @@ def _type_name(value: Any) -> str:
__all__ = [
"EXECUTOR_VERSION",
"MAX_SOURCE_ROWS",
"PipelineExecutionError",
"PipelineExecutionResult",
"ResolvedSource",
"SourceResolver",
"execute_preview",
"infer_columns",
]

603
src/govoplan_dataflow/backend/graph.py
View File

@@ -2,28 +2,35 @@ from __future__ import annotations
import hashlib
import json
import re
from collections import deque
from dataclasses import dataclass
from typing import Any
from govoplan_dataflow.backend.schemas import DataflowDiagnostic, GraphNode, PipelineGraph
from govoplan_dataflow.backend.node_library import NODE_TYPES, node_definition
from govoplan_dataflow.backend.schemas import DataflowDiagnostic, GraphEdge, GraphNode, PipelineGraph
SUPPORTED_NODE_TYPES = frozenset(
{
"source.inline",
"source.reference",
"filter",
"select",
"aggregate",
"sort",
"limit",
"output",
}
)
SUPPORTED_NODE_TYPES = frozenset(NODE_TYPES)
FILTER_OPERATORS = frozenset(
{"eq", "ne", "gt", "gte", "lt", "lte", "contains", "is_null", "not_null"}
)
AGGREGATE_FUNCTIONS = frozenset({"count", "sum", "avg", "min", "max"})
DERIVE_OPERATIONS = frozenset(
{
"copy",
"upper",
"lower",
"trim",
"concat",
"coalesce",
"add",
"subtract",
"multiply",
"divide",
}
)
JOIN_TYPES = frozenset({"inner", "left", "right", "full"})
def canonical_graph_payload(graph: PipelineGraph) -> dict[str, Any]:
@@ -49,7 +56,7 @@ def validate_graph(graph: PipelineGraph) -> list[DataflowDiagnostic]:
if len(edge_ids) != len(graph.edges):
diagnostics.append(_error("graph.duplicate_edge", "Edge identifiers must be unique."))
incoming: dict[str, list[str]] = {node_id: [] for node_id in nodes}
incoming: dict[str, list[GraphEdge]] = {node_id: [] for node_id in nodes}
outgoing: dict[str, list[str]] = {node_id: [] for node_id in nodes}
for edge in graph.edges:
if edge.source not in nodes:
@@ -67,8 +74,32 @@ def validate_graph(graph: PipelineGraph) -> list[DataflowDiagnostic]:
_error("edge.self_reference", "A node cannot connect to itself.", node_id=edge.source)
)
continue
source_definition = node_definition(nodes[edge.source].type)
target_definition = node_definition(nodes[edge.target].type)
if source_definition and edge.source_port not in {
port.id for port in source_definition.output_ports
}:
diagnostics.append(
_error(
"edge.unknown_source_port",
f"Node {edge.source!r} has no output port {edge.source_port!r}.",
node_id=edge.source,
)
)
continue
if target_definition and edge.target_port not in {
port.id for port in target_definition.input_ports
}:
diagnostics.append(
_error(
"edge.unknown_target_port",
f"Node {edge.target!r} has no input port {edge.target_port!r}.",
node_id=edge.target,
)
)
continue
outgoing[edge.source].append(edge.target)
incoming[edge.target].append(edge.source)
incoming[edge.target].append(edge)
if not graph.nodes:
diagnostics.append(_error("graph.empty", "Add a source and an output before saving the pipeline."))
@@ -76,11 +107,34 @@ def validate_graph(graph: PipelineGraph) -> list[DataflowDiagnostic]:
source_nodes = [node for node in graph.nodes if node.type.startswith("source.")]
output_nodes = [node for node in graph.nodes if node.type == "output"]
if len(source_nodes) != 1:
if not source_nodes:
diagnostics.append(
_error(
"graph.source_count",
"The first release supports exactly one source node per pipeline.",
"A pipeline needs at least one source node.",
)
)
elif len(source_nodes) > 10:
diagnostics.append(_error("graph.source_limit", "Pipelines are limited to ten sources."))
source_names = [
str(node.config.get("source_name", "")).strip()
for node in source_nodes
if _non_empty_text(node.config.get("source_name"))
]
duplicate_source_names = sorted(
{
name
for name in source_names
if sum(candidate.casefold() == name.casefold() for candidate in source_names) > 1
},
key=str.casefold,
)
if duplicate_source_names:
diagnostics.append(
_error(
"source.duplicate_name",
"Logical source names must be unique: "
f"{', '.join(duplicate_source_names)}.",
)
)
if len(output_nodes) != 1:
@@ -99,16 +153,29 @@ def validate_graph(graph: PipelineGraph) -> list[DataflowDiagnostic]:
)
)
continue
if node.type.startswith("source."):
if incoming.get(node.id):
diagnostics.append(
_error("node.source_has_input", "Source nodes cannot have incoming edges.", node_id=node.id)
)
elif len(incoming.get(node.id, [])) != 1:
definition = node_definition(node.type)
node_edges = incoming.get(node.id, [])
if definition is not None:
for port in definition.input_ports:
connections = [
edge
for edge in node_edges
if edge.target_port == port.id
]
minimum = port.minimum_connections if port.required else 0
if len(connections) < minimum:
diagnostics.append(
_error(
"node.input_count",
"This transform requires exactly one incoming edge.",
"node.input_required",
f"{definition.label} requires {port.label.lower()} input.",
node_id=node.id,
)
)
if not port.multiple and len(connections) > 1:
diagnostics.append(
_error(
"node.input_multiple",
f"{port.label} accepts only one connection.",
node_id=node.id,
)
)
@@ -118,12 +185,18 @@ def validate_graph(graph: PipelineGraph) -> list[DataflowDiagnostic]:
if cyclic:
diagnostics.append(_error("graph.cycle", "Pipeline edges must form an acyclic graph."))
elif source_nodes and output_nodes:
reachable = _reachable_from(source_nodes[0].id, outgoing)
reachable: set[str] = set()
for source in source_nodes:
reachable.update(_reachable_from(source.id, outgoing))
if len(reachable) != len(nodes):
diagnostics.append(
_error("graph.disconnected", "Every node must be connected to the pipeline source.")
_error("graph.disconnected", "Every node must be connected to a pipeline source.")
)
reaches_output = _reachable_from(output_nodes[0].id, incoming)
reverse_adjacency = {
node_id: [edge.source for edge in edges]
for node_id, edges in incoming.items()
}
reaches_output = _reachable_from(output_nodes[0].id, reverse_adjacency)
if len(reaches_output) != len(nodes):
diagnostics.append(
_error("graph.dead_end", "Every node must lead to the pipeline output.")
@@ -132,6 +205,7 @@ def validate_graph(graph: PipelineGraph) -> list[DataflowDiagnostic]:
diagnostics.append(
_error("graph.output_not_terminal", "The output node must be the terminal transform.")
)
diagnostics.extend(_validate_graph_schemas(graph, ordered=ordered))
return diagnostics
@@ -156,8 +230,11 @@ def topological_order(graph: PipelineGraph) -> tuple[list[str], bool]:
return ordered, len(ordered) != len(node_ids)
def graph_input_map(graph: PipelineGraph) -> dict[str, str]:
return {edge.target: edge.source for edge in graph.edges}
def graph_inputs_by_port(graph: PipelineGraph) -> dict[str, dict[str, list[str]]]:
result: dict[str, dict[str, list[str]]] = {}
for edge in graph.edges:
result.setdefault(edge.target, {}).setdefault(edge.target_port, []).append(edge.source)
return result
def _reachable_from(start: str, adjacency: dict[str, list[str]]) -> set[str]:
@@ -172,6 +249,306 @@ def _reachable_from(start: str, adjacency: dict[str, list[str]]) -> set[str]:
return seen
@dataclass(frozen=True)
class _SchemaState:
columns: frozenset[str]
open: bool = False
def knows(self, column: str) -> bool:
return self.open or column in self.columns
def _validate_graph_schemas(
graph: PipelineGraph,
*,
ordered: list[str],
) -> list[DataflowDiagnostic]:
diagnostics: list[DataflowDiagnostic] = []
node_by_id = {node.id: node for node in graph.nodes}
inputs = graph_inputs_by_port(graph)
schemas: dict[str, _SchemaState] = {}
for node_id in ordered:
node = node_by_id[node_id]
node_inputs = inputs.get(node.id, {})
input_states = [
schemas[source_id]
for port_sources in node_inputs.values()
for source_id in port_sources
if source_id in schemas
]
input_state = input_states[0] if input_states else _SchemaState(frozenset(), open=True)
if node.type == "source.inline":
rows = node.config.get("rows")
columns = {
str(column)
for row in rows if isinstance(rows, list) and isinstance(row, dict)
for column in row
} if isinstance(rows, list) else set()
schemas[node.id] = _SchemaState(
frozenset(columns),
open=not columns,
)
continue
if node.type == "source.reference":
columns = _configured_source_columns(node.config.get("source_columns"))
schemas[node.id] = _SchemaState(
frozenset(columns),
open=not columns,
)
continue
if node.type == "combine.union":
if len(input_states) > 1:
closed_shapes = {
state.columns
for state in input_states
if not state.open
}
if len(closed_shapes) > 1:
diagnostics.append(
_warning(
"union.schema_mismatch",
"Appended inputs use different columns; missing values will be null.",
node_id=node.id,
)
)
schemas[node.id] = _SchemaState(
frozenset().union(*(state.columns for state in input_states)),
open=any(state.open for state in input_states),
)
continue
if node.type == "combine.join":
left_state = _port_schema(node_inputs, schemas, "left")
right_state = _port_schema(node_inputs, schemas, "right")
_validate_columns(
diagnostics,
node=node,
state=left_state,
columns=node.config.get("left_keys"),
field="left_keys",
)
_validate_columns(
diagnostics,
node=node,
state=right_state,
columns=node.config.get("right_keys"),
field="right_keys",
)
prefix = str(node.config.get("right_prefix", "right_"))
prefixed_right = {f"{prefix}{column}" for column in right_state.columns}
collisions = left_state.columns & prefixed_right
if collisions:
diagnostics.append(
_error(
"join.output_collision",
f"Join output columns collide: {', '.join(sorted(collisions))}.",
node_id=node.id,
field="right_prefix",
)
)
schemas[node.id] = _SchemaState(
frozenset(left_state.columns | prefixed_right),
open=left_state.open or right_state.open,
)
continue
if node.type == "filter":
_validate_columns(
diagnostics,
node=node,
state=input_state,
columns=[node.config.get("column")],
field="column",
)
schemas[node.id] = input_state
continue
if node.type == "distinct":
_validate_columns(
diagnostics,
node=node,
state=input_state,
columns=node.config.get("columns"),
field="columns",
)
schemas[node.id] = input_state
continue
if node.type == "select":
fields = node.config.get("fields")
selected_columns: list[str] = []
output_columns: list[str] = []
if isinstance(fields, list):
for field in fields:
if isinstance(field, str):
selected_columns.append(field)
output_columns.append(field)
elif isinstance(field, dict):
column = field.get("column")
alias = field.get("alias") or column
if isinstance(column, str):
selected_columns.append(column)
if isinstance(alias, str):
output_columns.append(alias)
_validate_columns(
diagnostics,
node=node,
state=input_state,
columns=selected_columns,
field="fields",
)
_validate_output_names(
diagnostics,
node=node,
columns=output_columns,
field="fields",
)
schemas[node.id] = _SchemaState(frozenset(output_columns))
continue
if node.type == "derive":
_validate_columns(
diagnostics,
node=node,
state=input_state,
columns=node.config.get("source_columns"),
field="source_columns",
)
target = node.config.get("target_column")
if isinstance(target, str) and target:
if target in input_state.columns:
diagnostics.append(
_warning(
"derive.overwrites_column",
f"Derived column {target!r} replaces an existing value.",
node_id=node.id,
field="target_column",
)
)
schemas[node.id] = _SchemaState(
input_state.columns | frozenset((target,)),
open=input_state.open,
)
else:
schemas[node.id] = input_state
continue
if node.type == "aggregate":
group_by = _text_items(node.config.get("group_by"))
aggregates = node.config.get("aggregates")
aggregate_columns = [
str(item.get("column"))
for item in aggregates
if isinstance(aggregates, list)
and isinstance(item, dict)
and item.get("column") not in (None, "", "*")
] if isinstance(aggregates, list) else []
_validate_columns(
diagnostics,
node=node,
state=input_state,
columns=[*group_by, *aggregate_columns],
field="aggregates",
)
aliases = [
str(item.get("alias"))
for item in aggregates
if isinstance(aggregates, list)
and isinstance(item, dict)
and item.get("alias")
] if isinstance(aggregates, list) else []
output_columns = [*group_by, *aliases]
_validate_output_names(
diagnostics,
node=node,
columns=output_columns,
field="aggregates",
)
schemas[node.id] = _SchemaState(frozenset(output_columns))
continue
if node.type == "sort":
fields = node.config.get("fields")
columns = [
str(item.get("column"))
for item in fields
if isinstance(fields, list)
and isinstance(item, dict)
and item.get("column")
] if isinstance(fields, list) else []
_validate_columns(
diagnostics,
node=node,
state=input_state,
columns=columns,
field="fields",
)
schemas[node.id] = input_state
continue
schemas[node.id] = input_state
return diagnostics
def _configured_source_columns(value: object) -> set[str]:
if not isinstance(value, list):
return set()
return {
item if isinstance(item, str) else str(item.get("name"))
for item in value
if (
isinstance(item, str) and item
or isinstance(item, dict) and item.get("name")
)
}
def _port_schema(
inputs: dict[str, list[str]],
schemas: dict[str, _SchemaState],
port: str,
) -> _SchemaState:
source_ids = inputs.get(port, [])
return schemas.get(source_ids[0], _SchemaState(frozenset(), open=True)) if source_ids else _SchemaState(frozenset(), open=True)
def _validate_columns(
diagnostics: list[DataflowDiagnostic],
*,
node: GraphNode,
state: _SchemaState,
columns: object,
field: str,
) -> None:
for column in _text_items(columns):
if not state.knows(column):
diagnostics.append(
_error(
"schema.unknown_column",
f"Column {column!r} is not available at this node.",
node_id=node.id,
field=field,
)
)
def _validate_output_names(
diagnostics: list[DataflowDiagnostic],
*,
node: GraphNode,
columns: list[str],
field: str,
) -> None:
duplicates = sorted({column for column in columns if columns.count(column) > 1})
if duplicates:
diagnostics.append(
_error(
"schema.duplicate_output",
f"Output column names must be unique: {', '.join(duplicates)}.",
node_id=node.id,
field=field,
)
)
def _text_items(value: object) -> list[str]:
if not isinstance(value, list):
return []
return [item for item in value if isinstance(item, str) and item]
def _validate_node_config(node: GraphNode) -> list[DataflowDiagnostic]:
config = node.config
diagnostics: list[DataflowDiagnostic] = []
@@ -186,7 +563,35 @@ def _validate_node_config(node: GraphNode) -> list[DataflowDiagnostic]:
field="source_name",
)
)
elif re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", source_name.strip()) is None:
diagnostics.append(
_error(
"source.name_invalid",
"Logical source names must be SQL identifiers, such as monthly_cases.",
node_id=node.id,
field="source_name",
)
)
if node.type == "source.reference":
if not _non_empty_text(config.get("source_ref")):
diagnostics.append(
_error(
"source.reference_required",
"Choose a connector source.",
node_id=node.id,
field="source_ref",
)
)
expected_fingerprint = config.get("expected_fingerprint")
if expected_fingerprint is not None and not isinstance(expected_fingerprint, str):
diagnostics.append(
_error(
"source.fingerprint",
"The expected source fingerprint must be text.",
node_id=node.id,
field="expected_fingerprint",
)
)
return diagnostics
rows = config.get("rows")
if not isinstance(rows, list):
@@ -215,6 +620,72 @@ def _validate_node_config(node: GraphNode) -> list[DataflowDiagnostic]:
)
if operator not in {"is_null", "not_null"} and "value" not in config:
diagnostics.append(_node_field_error(node, "filter.value", "Enter a comparison value.", "value"))
elif node.type == "distinct":
columns = config.get("columns", [])
if not isinstance(columns, list) or any(not _non_empty_text(item) for item in columns):
diagnostics.append(
_node_field_error(
node,
"distinct.columns",
"Distinct key columns must be named.",
"columns",
)
)
elif node.type == "combine.union":
if config.get("mode", "all") not in {"all", "distinct"}:
diagnostics.append(
_node_field_error(
node,
"union.mode",
"Choose whether duplicate rows are kept or removed.",
"mode",
)
)
elif node.type == "combine.join":
if config.get("join_type", "inner") not in JOIN_TYPES:
diagnostics.append(
_node_field_error(node, "join.type", "Choose a supported join type.", "join_type")
)
left_keys = config.get("left_keys")
right_keys = config.get("right_keys")
if (
not isinstance(left_keys, list)
or not left_keys
or any(not _non_empty_text(item) for item in left_keys)
):
diagnostics.append(
_node_field_error(node, "join.left_keys", "Add at least one left key.", "left_keys")
)
if (
not isinstance(right_keys, list)
or not right_keys
or any(not _non_empty_text(item) for item in right_keys)
):
diagnostics.append(
_node_field_error(node, "join.right_keys", "Add at least one right key.", "right_keys")
)
if isinstance(left_keys, list) and isinstance(right_keys, list) and len(left_keys) != len(right_keys):
diagnostics.append(
_node_field_error(
node,
"join.key_count",
"Left and right joins need the same number of keys.",
"right_keys",
)
)
right_prefix = config.get("right_prefix", "right_")
if (
not _non_empty_text(right_prefix)
or re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*_", str(right_prefix)) is None
):
diagnostics.append(
_node_field_error(
node,
"join.right_prefix",
"Enter an identifier prefix ending in an underscore, such as right_.",
"right_prefix",
)
)
elif node.type == "select":
fields = config.get("fields")
if not isinstance(fields, list) or not fields:
@@ -267,6 +738,58 @@ def _validate_node_config(node: GraphNode) -> list[DataflowDiagnostic]:
_node_field_error(node, "aggregate.alias", "Every aggregate needs an alias.", "aggregates")
)
break
elif node.type == "derive":
if not _non_empty_text(config.get("target_column")):
diagnostics.append(
_node_field_error(
node,
"derive.target_column",
"Choose an output column.",
"target_column",
)
)
operation = config.get("operation")
if operation not in DERIVE_OPERATIONS:
diagnostics.append(
_node_field_error(
node,
"derive.operation",
"Choose a supported derive operation.",
"operation",
)
)
source_columns = config.get("source_columns")
if (
not isinstance(source_columns, list)
or not source_columns
or any(not _non_empty_text(item) for item in source_columns)
):
diagnostics.append(
_node_field_error(
node,
"derive.source_columns",
"Choose at least one source column.",
"source_columns",
)
)
if operation in {"copy", "upper", "lower", "trim"} and isinstance(source_columns, list) and len(source_columns) != 1:
diagnostics.append(
_node_field_error(
node,
"derive.source_count",
"This operation requires exactly one source column.",
"source_columns",
)
)
if operation in {"add", "subtract", "multiply", "divide"} and isinstance(source_columns, list) and len(source_columns) != 2:
diagnostics.append(
_node_field_error(
node,
"derive.numeric_source_count",
"Numeric operations require exactly two source columns.",
"source_columns",
)
)
elif node.type == "sort":
fields = config.get("fields")
if not isinstance(fields, list) or not fields:
@@ -313,13 +836,31 @@ def _error(
)
def _warning(
code: str,
message: str,
*,
node_id: str | None = None,
field: str | None = None,
) -> DataflowDiagnostic:
return DataflowDiagnostic(
severity="warning",
code=code,
message=message,
node_id=node_id,
field=field,
)
__all__ = [
"AGGREGATE_FUNCTIONS",
"DERIVE_OPERATIONS",
"FILTER_OPERATORS",
"JOIN_TYPES",
"SUPPORTED_NODE_TYPES",
"canonical_graph_payload",
"definition_hash",
"graph_input_map",
"graph_inputs_by_port",
"topological_order",
"validate_graph",
]

34
src/govoplan_dataflow/backend/manifest.py
View File

@@ -11,11 +11,17 @@ from govoplan_core.core.modules import (
FrontendModule,
MigrationSpec,
ModuleInterfaceProvider,
ModuleInterfaceRequirement,
ModuleContext,
ModuleManifest,
NavItem,
PermissionDefinition,
RoleTemplate,
)
from govoplan_core.core.tabular_sources import (
CAPABILITY_CONNECTORS_TABULAR_SNAPSHOT_WRITER,
CAPABILITY_CONNECTORS_TABULAR_SOURCES,
)
from govoplan_core.db.base import Base
from govoplan_dataflow.backend.db import models as dataflow_models
@@ -116,14 +122,20 @@ DOCUMENTATION = (
"audit",
),
metadata={
"first_slice": "Inline source, filter, select, aggregate, sort, limit, output, revisioning, and bounded preview.",
"first_slice": (
"Inline and connector sources, union, join, filter, deduplication, select, "
"derived columns, aggregate, sort, limit, output, revisioning, and bounded preview."
),
"sql_safety": "Constrained AST compilation only; no pass-through execution.",
},
),
)
def _dataflow_router(_context):
def _dataflow_router(context: ModuleContext):
from govoplan_dataflow.backend.runtime import configure_runtime
configure_runtime(registry=context.registry, settings=context.settings)
from govoplan_dataflow.backend.router import router
return router
@@ -163,12 +175,30 @@ manifest = ModuleManifest(
"risk_compliance",
"workflow",
),
optional_capabilities=(
CAPABILITY_CONNECTORS_TABULAR_SOURCES,
CAPABILITY_CONNECTORS_TABULAR_SNAPSHOT_WRITER,
),
provides_interfaces=(
ModuleInterfaceProvider(name="dataflow.pipeline_catalog", version=MODULE_VERSION),
ModuleInterfaceProvider(name="dataflow.pipeline_preview", version=MODULE_VERSION),
ModuleInterfaceProvider(name="dataflow.run_lifecycle", version=MODULE_VERSION),
ModuleInterfaceProvider(name="dataflow.dataset_output", version=MODULE_VERSION),
),
requires_interfaces=(
ModuleInterfaceRequirement(
name="connectors.tabular_sources",
version_min="0.1.0",
version_max_exclusive="1.0.0",
optional=True,
),
ModuleInterfaceRequirement(
name="connectors.tabular_snapshot_writer",
version_min="0.1.0",
version_max_exclusive="1.0.0",
optional=True,
),
),
permissions=PERMISSIONS,
role_templates=ROLE_TEMPLATES,
nav_items=(

305
src/govoplan_dataflow/backend/node_library.py Normal file
View File

@@ -0,0 +1,305 @@
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Literal
NodeCategory = Literal["load", "combine", "filter", "transform", "output"]
SqlSupport = Literal["full", "partial", "none"]
@dataclass(frozen=True, slots=True)
class NodePortDefinition:
id: str
label: str
required: bool = True
multiple: bool = False
minimum_connections: int = 1
@dataclass(frozen=True, slots=True)
class NodeConfigField:
id: str
label: str
kind: str
required: bool = False
description: str | None = None
options: tuple[tuple[str, str], ...] = ()
@dataclass(frozen=True, slots=True)
class NodeTypeDefinition:
type: str
category: NodeCategory
label: str
description: str
icon: str
input_ports: tuple[NodePortDefinition, ...] = ()
output_ports: tuple[NodePortDefinition, ...] = (
NodePortDefinition(id="output", label="Output"),
)
config_fields: tuple[NodeConfigField, ...] = ()
default_config: dict[str, Any] = field(default_factory=dict)
sql_support: SqlSupport = "full"
NODE_LIBRARY = (
NodeTypeDefinition(
type="source.inline",
category="load",
label="Inline data",
description="Enter a small JSON table directly in the pipeline.",
icon="braces",
config_fields=(
NodeConfigField(id="source_name", label="SQL source name", kind="text", required=True),
NodeConfigField(id="rows", label="Rows", kind="json_rows", required=True),
),
default_config={"source_name": "inline_source", "rows": []},
),
NodeTypeDefinition(
type="source.reference",
category="load",
label="Connector source",
description="Load a bounded, fingerprinted table exposed by Connectors.",
icon="database",
config_fields=(
NodeConfigField(id="source_ref", label="Source", kind="tabular_source", required=True),
NodeConfigField(id="source_name", label="SQL source name", kind="text", required=True),
NodeConfigField(id="expected_fingerprint", label="Expected fingerprint", kind="readonly"),
),
default_config={
"source_ref": "",
"source_name": "connector_source",
"expected_fingerprint": "",
},
),
NodeTypeDefinition(
type="combine.union",
category="combine",
label="Append rows",
description="Append two or more inputs by column name.",
icon="combine",
input_ports=(
NodePortDefinition(
id="input",
label="Inputs",
multiple=True,
minimum_connections=2,
),
),
config_fields=(
NodeConfigField(
id="mode",
label="Duplicates",
kind="select",
options=(("all", "Keep all"), ("distinct", "Remove duplicates")),
),
),
default_config={"mode": "all"},
sql_support="partial",
),
NodeTypeDefinition(
type="combine.join",
category="combine",
label="Join tables",
description="Match two inputs using one or more key columns.",
icon="git-merge",
input_ports=(
NodePortDefinition(id="left", label="Left"),
NodePortDefinition(id="right", label="Right"),
),
config_fields=(
NodeConfigField(
id="join_type",
label="Join type",
kind="select",
options=(
("inner", "Matching rows"),
("left", "All left rows"),
("right", "All right rows"),
("full", "All rows"),
),
),
NodeConfigField(id="left_keys", label="Left keys", kind="column_list", required=True),
NodeConfigField(id="right_keys", label="Right keys", kind="column_list", required=True),
NodeConfigField(id="right_prefix", label="Right-column prefix", kind="text", required=True),
),
default_config={
"join_type": "inner",
"left_keys": [""],
"right_keys": [""],
"right_prefix": "right_",
},
sql_support="partial",
),
NodeTypeDefinition(
type="filter",
category="filter",
label="Filter rows",
description="Keep rows that satisfy a comparison.",
icon="filter",
input_ports=(NodePortDefinition(id="input", label="Input"),),
config_fields=(
NodeConfigField(id="column", label="Column", kind="column", required=True),
NodeConfigField(
id="operator",
label="Operator",
kind="select",
required=True,
options=(
("eq", "Equals"),
("ne", "Does not equal"),
("gt", "Greater than"),
("gte", "Greater than or equal"),
("lt", "Less than"),
("lte", "Less than or equal"),
("contains", "Contains"),
("is_null", "Is empty"),
("not_null", "Is not empty"),
),
),
NodeConfigField(id="value", label="Value", kind="value"),
),
default_config={"column": "", "operator": "eq", "value": ""},
),
NodeTypeDefinition(
type="distinct",
category="filter",
label="Remove duplicates",
description="Keep the first row for each selected key.",
icon="list-filter",
input_ports=(NodePortDefinition(id="input", label="Input"),),
config_fields=(
NodeConfigField(
id="columns",
label="Key columns",
kind="column_list",
description="Leave empty to compare complete rows.",
),
),
default_config={"columns": []},
sql_support="partial",
),
NodeTypeDefinition(
type="select",
category="transform",
label="Select columns",
description="Choose, order, and rename output columns.",
icon="columns-3",
input_ports=(NodePortDefinition(id="input", label="Input"),),
config_fields=(
NodeConfigField(id="fields", label="Fields", kind="field_mapping", required=True),
),
default_config={"fields": [{"column": "", "alias": ""}]},
),
NodeTypeDefinition(
type="derive",
category="transform",
label="Derive column",
description="Create a column through a constrained reusable operation.",
icon="variable",
input_ports=(NodePortDefinition(id="input", label="Input"),),
config_fields=(
NodeConfigField(id="target_column", label="Output column", kind="text", required=True),
NodeConfigField(
id="operation",
label="Operation",
kind="select",
required=True,
options=(
("copy", "Copy"),
("upper", "Uppercase"),
("lower", "Lowercase"),
("trim", "Trim whitespace"),
("concat", "Concatenate"),
("coalesce", "First non-empty"),
("add", "Add"),
("subtract", "Subtract"),
("multiply", "Multiply"),
("divide", "Divide"),
),
),
NodeConfigField(id="source_columns", label="Source columns", kind="column_list", required=True),
NodeConfigField(id="separator", label="Separator", kind="text"),
),
default_config={
"target_column": "",
"operation": "copy",
"source_columns": [""],
"separator": " ",
},
sql_support="partial",
),
NodeTypeDefinition(
type="aggregate",
category="transform",
label="Aggregate",
description="Group rows and calculate counts or numeric summaries.",
icon="sigma",
input_ports=(NodePortDefinition(id="input", label="Input"),),
config_fields=(
NodeConfigField(id="group_by", label="Group by", kind="column_list"),
NodeConfigField(id="aggregates", label="Calculations", kind="aggregates", required=True),
),
default_config={
"group_by": [],
"aggregates": [{"function": "count", "column": "*", "alias": "row_count"}],
},
),
NodeTypeDefinition(
type="sort",
category="transform",
label="Sort rows",
description="Order rows by one or more columns.",
icon="arrow-up-down",
input_ports=(NodePortDefinition(id="input", label="Input"),),
config_fields=(NodeConfigField(id="fields", label="Sort fields", kind="sort_fields", required=True),),
default_config={"fields": [{"column": "", "direction": "asc"}]},
),
NodeTypeDefinition(
type="limit",
category="transform",
label="Limit rows",
description="Keep only the first number of rows.",
icon="list-end",
input_ports=(NodePortDefinition(id="input", label="Input"),),
config_fields=(NodeConfigField(id="count", label="Rows", kind="number", required=True),),
default_config={"count": 100},
),
NodeTypeDefinition(
type="output",
category="output",
label="Preview output",
description="Expose the terminal table for preview or publication.",
icon="panel-top-open",
input_ports=(NodePortDefinition(id="input", label="Input"),),
output_ports=(),
default_config={},
),
)
NODE_TYPES = {definition.type: definition for definition in NODE_LIBRARY}
CATEGORY_LABELS = {
"load": "Load",
"combine": "Combine",
"filter": "Filter",
"transform": "Transform",
"output": "Output",
}
def node_definition(node_type: str) -> NodeTypeDefinition | None:
return NODE_TYPES.get(node_type)
__all__ = [
"CATEGORY_LABELS",
"NODE_LIBRARY",
"NODE_TYPES",
"NodeCategory",
"NodeConfigField",
"NodePortDefinition",
"NodeTypeDefinition",
"SqlSupport",
"node_definition",
]

206
src/govoplan_dataflow/backend/router.py
View File

@@ -5,9 +5,24 @@ from sqlalchemy.orm import Session
from govoplan_core.audit.logging import audit_event
from govoplan_core.auth import ApiPrincipal, get_api_principal, has_scope
from govoplan_core.core.tabular_sources import (
TabularSnapshotInput,
TabularSource,
TabularSourceAccessError,
TabularSourceError,
TabularSourceNotFoundError,
TabularSourceValidationError,
parse_tabular_csv,
tabular_snapshot_writer,
tabular_source_provider,
)
from govoplan_core.db.session import get_session
from govoplan_dataflow.backend.manifest import ADMIN_SCOPE, READ_SCOPE, RUN_SCOPE, WRITE_SCOPE
from govoplan_dataflow.backend.node_library import CATEGORY_LABELS, NODE_LIBRARY
from govoplan_dataflow.backend.runtime import get_registry
from govoplan_dataflow.backend.schemas import (
NodeLibraryResponse,
NodeTypeDefinitionResponse,
PipelineCreateRequest,
PipelineDeleteResponse,
PipelineDraftRequest,
@@ -18,6 +33,10 @@ from govoplan_dataflow.backend.schemas import (
PipelineSqlResponse,
PipelineUpdateRequest,
PipelineValidationResponse,
TabularSnapshotCreateRequest,
TabularSourceColumnResponse,
TabularSourceListResponse,
TabularSourceResponse,
)
from govoplan_dataflow.backend.service import (
DataflowConflictError,
@@ -74,6 +93,191 @@ def _http_error(exc: DataflowError) -> HTTPException:
return HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(exc))
def _source_http_error(exc: TabularSourceError) -> HTTPException:
if isinstance(exc, TabularSourceAccessError):
return HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail=str(exc))
if isinstance(exc, TabularSourceNotFoundError):
return HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc))
if isinstance(exc, TabularSourceValidationError):
return HTTPException(
status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
detail=str(exc),
)
return HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(exc))
def _node_library_response() -> NodeLibraryResponse:
return NodeLibraryResponse(
nodes=[
NodeTypeDefinitionResponse(
type=definition.type,
category=definition.category,
category_label=CATEGORY_LABELS[definition.category],
label=definition.label,
description=definition.description,
icon=definition.icon,
input_ports=[
{
"id": port.id,
"label": port.label,
"required": port.required,
"multiple": port.multiple,
"minimum_connections": port.minimum_connections,
}
for port in definition.input_ports
],
output_ports=[
{
"id": port.id,
"label": port.label,
"required": port.required,
"multiple": port.multiple,
"minimum_connections": port.minimum_connections,
}
for port in definition.output_ports
],
config_fields=[
{
"id": field.id,
"label": field.label,
"kind": field.kind,
"required": field.required,
"description": field.description,
"options": list(field.options),
}
for field in definition.config_fields
],
default_config=dict(definition.default_config),
sql_support=definition.sql_support,
)
for definition in NODE_LIBRARY
]
)
def _source_response(source: TabularSource) -> TabularSourceResponse:
return TabularSourceResponse(
ref=source.ref,
provider=source.provider,
source_name=source.source_name,
name=source.name,
description=source.description,
columns=[
TabularSourceColumnResponse(
name=column.name,
data_type=column.data_type,
nullable=column.nullable,
)
for column in source.schema
],
schema_version=source.schema_version,
fingerprint=source.fingerprint,
row_count=source.row_count,
byte_count=source.byte_count,
updated_at=source.updated_at,
capabilities=list(source.capabilities),
)
@router.get("/node-types", response_model=NodeLibraryResponse)
def api_node_types(
principal: ApiPrincipal = Depends(get_api_principal),
) -> NodeLibraryResponse:
_require_any_scope(principal, READ_SCOPE, WRITE_SCOPE, RUN_SCOPE, ADMIN_SCOPE)
return _node_library_response()
@router.get("/sources", response_model=TabularSourceListResponse)
def api_list_sources(
query: str = "",
session: Session = Depends(get_session),
principal: ApiPrincipal = Depends(get_api_principal),
) -> TabularSourceListResponse:
_require_any_scope(principal, READ_SCOPE, WRITE_SCOPE, RUN_SCOPE, ADMIN_SCOPE)
registry = get_registry()
provider = tabular_source_provider(registry)
writer = tabular_snapshot_writer(registry)
if provider is None:
return TabularSourceListResponse(available=False, writable=False, sources=[])
try:
sources = provider.list_sources(
session,
principal,
query=query,
limit=100,
)
except TabularSourceError as exc:
raise _source_http_error(exc) from exc
return TabularSourceListResponse(
available=True,
writable=writer is not None,
sources=[_source_response(source) for source in sources],
)
@router.post(
"/sources/snapshots",
response_model=TabularSourceResponse,
status_code=status.HTTP_201_CREATED,
)
def api_create_source_snapshot(
payload: TabularSnapshotCreateRequest,
session: Session = Depends(get_session),
principal: ApiPrincipal = Depends(get_api_principal),
) -> TabularSourceResponse:
_require_any_scope(principal, WRITE_SCOPE, ADMIN_SCOPE)
writer = tabular_snapshot_writer(get_registry())
if writer is None:
raise HTTPException(
status_code=status.HTTP_409_CONFLICT,
detail="No tabular snapshot writer is available.",
)
try:
rows = (
parse_tabular_csv(
payload.csv_text or "",
delimiter=payload.delimiter,
max_rows=10_000,
)
if payload.format == "csv"
else tuple(payload.rows or ())
)
source = writer.create_snapshot(
session,
principal,
snapshot=TabularSnapshotInput(
name=payload.name,
source_name=payload.source_name,
description=payload.description,
rows=rows,
metadata={
"created_via": "dataflow",
"source_format": payload.format,
},
),
)
except TabularSourceError as exc:
raise _source_http_error(exc) from exc
audit_event(
session,
tenant_id=principal.tenant_id,
user_id=getattr(principal.user, "id", None),
api_key_id=principal.api_key_id,
action="dataflow.source_snapshot.created",
object_type="tabular_source",
object_id=source.ref,
details={
"provider": source.provider,
"source_name": source.source_name,
"row_count": source.row_count,
"fingerprint": source.fingerprint,
},
)
response = _source_response(source)
session.commit()
return response
@router.get("/pipelines", response_model=PipelineListResponse)
def api_list_pipelines(
session: Session = Depends(get_session),
@@ -237,6 +441,8 @@ def api_preview_pipeline(
session,
tenant_id=principal.tenant_id,
actor_id=_actor_id(principal),
principal=principal,
registry=get_registry(),
payload=payload,
)
except DataflowError as exc:

11
src/govoplan_dataflow/backend/runtime.py Normal file
View File

@@ -0,0 +1,11 @@
from __future__ import annotations
from govoplan_core.core.runtime import ModuleRuntimeState
_runtime = ModuleRuntimeState("Dataflow")
configure_runtime = _runtime.configure_runtime
get_registry = _runtime.get_registry
get_settings = _runtime.get_settings
settings = _runtime.settings

94
src/govoplan_dataflow/backend/schemas.py
View File

@@ -4,7 +4,7 @@ import math
from datetime import datetime
from typing import Any, Literal
from pydantic import BaseModel, ConfigDict, Field, field_validator
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
PipelineStatus = Literal["draft", "active", "archived"]
@@ -152,6 +152,8 @@ class PipelinePreviewResponse(BaseModel):
truncated: bool
diagnostics: list[DataflowDiagnostic]
node_diagnostics: list[NodePreviewDiagnostic]
source_fingerprints: list[dict[str, Any]]
input_row_count: int
definition_hash: str
executor_version: str
@@ -159,3 +161,93 @@ class PipelinePreviewResponse(BaseModel):
class PipelineDeleteResponse(BaseModel):
deleted: bool
pipeline_id: str
class NodePortDefinitionResponse(BaseModel):
id: str
label: str
required: bool
multiple: bool
minimum_connections: int
class NodeConfigFieldResponse(BaseModel):
id: str
label: str
kind: str
required: bool
description: str | None
options: list[tuple[str, str]]
class NodeTypeDefinitionResponse(BaseModel):
type: str
category: str
category_label: str
label: str
description: str
icon: str
input_ports: list[NodePortDefinitionResponse]
output_ports: list[NodePortDefinitionResponse]
config_fields: list[NodeConfigFieldResponse]
default_config: dict[str, Any]
sql_support: str
class NodeLibraryResponse(BaseModel):
nodes: list[NodeTypeDefinitionResponse]
class TabularSourceColumnResponse(BaseModel):
name: str
data_type: str
nullable: bool
class TabularSourceResponse(BaseModel):
ref: str
provider: str
source_name: str
name: str
description: str | None
columns: list[TabularSourceColumnResponse]
schema_version: str
fingerprint: str
row_count: int | None
byte_count: int | None
updated_at: datetime | None
capabilities: list[str]
class TabularSourceListResponse(BaseModel):
available: bool
writable: bool
sources: list[TabularSourceResponse]
class TabularSnapshotCreateRequest(BaseModel):
name: str = Field(min_length=1, max_length=300)
source_name: str = Field(
min_length=1,
max_length=120,
pattern=r"^[A-Za-z_][A-Za-z0-9_]*$",
)
description: str | None = Field(default=None, max_length=4000)
format: Literal["json", "csv"] = "json"
rows: list[dict[str, Any]] | None = Field(default=None, max_length=10_000)
csv_text: str | None = Field(default=None, max_length=5_000_000)
delimiter: str = Field(default=",", min_length=1, max_length=1)
@model_validator(mode="after")
def validate_format_payload(self) -> TabularSnapshotCreateRequest:
if self.format == "json":
if self.rows is None:
raise ValueError("JSON snapshots require rows.")
if self.csv_text is not None:
raise ValueError("JSON snapshots cannot include CSV text.")
else:
if not self.csv_text:
raise ValueError("CSV snapshots require CSV text.")
if self.rows is not None:
raise ValueError("CSV snapshots cannot include JSON rows.")
return self

62
src/govoplan_dataflow/backend/service.py
View File

@@ -5,6 +5,12 @@ from dataclasses import dataclass
from sqlalchemy import select
from sqlalchemy.orm import Session
from govoplan_core.auth import ApiPrincipal
from govoplan_core.core.tabular_sources import (
TabularReadRequest,
TabularSourceError,
tabular_source_provider,
)
from govoplan_core.db.base import utcnow
from govoplan_dataflow.backend.db.models import (
DataflowPipeline,
@@ -14,6 +20,7 @@ from govoplan_dataflow.backend.db.models import (
from govoplan_dataflow.backend.executor import (
EXECUTOR_VERSION,
PipelineExecutionError,
ResolvedSource,
execute_preview,
)
from govoplan_dataflow.backend.graph import canonical_graph_payload, definition_hash, validate_graph
@@ -333,6 +340,8 @@ def preview_pipeline(
tenant_id: str,
actor_id: str | None,
payload: PipelinePreviewRequest,
principal: ApiPrincipal | None = None,
registry: object | None = None,
) -> PipelinePreviewResponse:
pipeline: DataflowPipeline | None = None
revision: DataflowPipelineRevision | None = None
@@ -360,6 +369,8 @@ def preview_pipeline(
truncated=False,
diagnostics=validated.diagnostics,
node_diagnostics=[],
source_fingerprints=[],
input_row_count=0,
definition_hash="",
executor_version=EXECUTOR_VERSION,
)
@@ -370,7 +381,47 @@ def preview_pipeline(
started_at = utcnow()
run: DataflowRun | None = None
try:
result = execute_preview(graph, row_limit=payload.row_limit)
provider = tabular_source_provider(registry)
def resolve_source(node: GraphNode, limit: int) -> ResolvedSource:
if provider is None:
raise PipelineExecutionError(
"Connector-backed preview requires the Connectors tabular-source capability.",
node_id=node.id,
)
if principal is None:
raise PipelineExecutionError(
"Connector-backed preview requires a tenant API principal.",
node_id=node.id,
)
try:
resolved = provider.read_source(
session,
principal,
request=TabularReadRequest(
source_ref=str(node.config["source_ref"]),
limit=limit,
expected_fingerprint=_clean_optional(
node.config.get("expected_fingerprint")
),
),
)
except TabularSourceError as exc:
raise PipelineExecutionError(str(exc), node_id=node.id) from exc
return ResolvedSource(
rows=tuple(dict(row) for row in resolved.rows),
source_ref=resolved.source.ref,
provider=resolved.source.provider,
fingerprint=resolved.source.fingerprint,
total_rows=resolved.total_rows,
truncated=resolved.truncated,
)
result = execute_preview(
graph,
row_limit=payload.row_limit,
source_resolver=resolve_source,
)
status = "succeeded"
error = None
diagnostics = result.diagnostics
@@ -385,6 +436,7 @@ def preview_pipeline(
status = "failed"
error = str(exc)
diagnostics = [
*exc.diagnostics,
DataflowDiagnostic(
severity="error",
code="preview.execution",
@@ -396,9 +448,9 @@ def preview_pipeline(
rows = []
total_rows = 0
truncated = False
node_diagnostics = []
source_fingerprints = []
input_row_count = 0
node_diagnostics = list(exc.node_diagnostics)
source_fingerprints = list(exc.source_fingerprints)
input_row_count = exc.input_row_count
if pipeline is not None and revision is not None:
run = DataflowRun(
@@ -433,6 +485,8 @@ def preview_pipeline(
truncated=truncated,
diagnostics=diagnostics,
node_diagnostics=node_diagnostics,
source_fingerprints=source_fingerprints,
input_row_count=input_row_count,
definition_hash=graph_hash,
executor_version=EXECUTOR_VERSION,
)

527
src/govoplan_dataflow/backend/sql_compiler.py
View File

@@ -1,12 +1,12 @@
from __future__ import annotations
from typing import Any, Iterable
from typing import Any, Callable, Iterable
import sqlglot
from sqlglot import exp
from sqlglot.errors import ParseError
from govoplan_dataflow.backend.graph import topological_order, validate_graph
from govoplan_dataflow.backend.graph import graph_inputs_by_port, topological_order, validate_graph
from govoplan_dataflow.backend.schemas import (
DataflowDiagnostic,
GraphEdge,
@@ -47,49 +47,121 @@ def compile_sql(
query = statements[0]
_reject_unsupported_query_shape(query)
tables = list(query.find_all(exp.Table))
if len(tables) != 1:
from_clause = query.args.get("from_")
if from_clause is None or not isinstance(from_clause.this, exp.Table):
raise SqlCompilationError(
[_sql_error("sql.source_count", "The first release supports exactly one logical source.")]
[_sql_error("sql.source_required", "SELECT requires a logical tabular source.")]
)
table = tables[0]
joins = list(query.args.get("joins") or [])
if len(joins) > 1:
raise SqlCompilationError(
[_sql_error("sql.join_count", "Dataflow SQL currently supports one two-source join.")]
)
left_table = from_clause.this
right_table = joins[0].this if joins else None
if right_table is not None and not isinstance(right_table, exp.Table):
raise SqlCompilationError(
[_sql_error("sql.join_source", "JOIN requires a logical tabular source.")]
)
tables = [left_table, *([right_table] if isinstance(right_table, exp.Table) else [])]
for table in tables:
if table.catalog or table.db:
raise SqlCompilationError(
[_sql_error("sql.qualified_source", "Use the logical source name without a catalog or schema.")]
)
source_name = table.name
preserved_source = next(
(
node.model_copy(deep=True)
for node in source_nodes
if node.type.startswith("source.")
and str(node.config.get("source_name", "")).casefold() == source_name.casefold()
),
None,
)
source_node = preserved_source or GraphNode(
id="source",
type="source.reference",
label=source_name,
position=GraphPosition(x=80, y=180),
config={"source_name": source_name},
[_sql_error("sql.qualified_source", "Use logical source names without a catalog or schema.")]
)
nodes = [source_node]
source_node_list = list(source_nodes)
left_source = _source_node(
left_table,
source_node_list,
fallback_id="source-left" if right_table is not None else "source",
position=GraphPosition(x=60, y=120 if right_table is not None else 180),
)
nodes = [left_source]
edges: list[GraphEdge] = []
previous_node_id = left_source.id
qualifier_prefixes: dict[str, str] | None = None
if isinstance(right_table, exp.Table):
right_source = _source_node(
right_table,
source_node_list,
fallback_id="source-right",
position=GraphPosition(x=60, y=280),
)
if right_source.id == left_source.id:
raise SqlCompilationError(
[_sql_error("sql.source_identity", "Joined sources must use different graph nodes.")]
)
right_prefix = f"{right_table.alias_or_name}_"
join_config = _join_config(joins[0], left_table=left_table, right_table=right_table)
join_config["right_prefix"] = right_prefix
join_node = GraphNode(
id="join",
type="combine.join",
label=f"Join {left_table.name} and {right_table.name}",
position=GraphPosition(x=300, y=200),
config=join_config,
)
nodes.extend((right_source, join_node))
edges.extend(
(
GraphEdge(
id=f"edge-{left_source.id}-{join_node.id}-left",
source=left_source.id,
target=join_node.id,
target_port="left",
),
GraphEdge(
id=f"edge-{right_source.id}-{join_node.id}-right",
source=right_source.id,
target=join_node.id,
target_port="right",
),
)
)
previous_node_id = join_node.id
qualifier_prefixes = _join_qualifier_prefixes(
left_table,
right_table,
right_prefix=right_prefix,
)
def append_transform(node: GraphNode) -> None:
nonlocal previous_node_id
nodes.append(node)
edges.append(
GraphEdge(
id=f"edge-{previous_node_id}-{node.id}",
source=previous_node_id,
target=node.id,
)
)
previous_node_id = node.id
conditions = _flatten_and(query.args.get("where").this) if query.args.get("where") else []
for index, condition in enumerate(conditions, start=1):
nodes.append(
append_transform(
GraphNode(
id=f"filter-{index}",
type="filter",
label=f"Filter {index}",
position=_position(len(nodes)),
config=_condition_config(condition),
config=_condition_config(
condition,
qualifier_prefixes=qualifier_prefixes,
),
)
)
group = query.args.get("group")
group_by = [_column_name(item, context="GROUP BY") for item in group.expressions] if group else []
group_by = [
_column_name(
item,
context="GROUP BY",
qualifier_prefixes=qualifier_prefixes,
)
for item in group.expressions
] if group else []
aggregate_specs: list[dict[str, Any]] = []
projection_fields: list[dict[str, str]] = []
saw_star = False
@@ -97,7 +169,11 @@ def compile_sql(
for item in query.expressions:
inner = item.this if isinstance(item, exp.Alias) else item
alias = item.alias if isinstance(item, exp.Alias) else ""
aggregate = _aggregate_config(inner, alias=alias)
aggregate = _aggregate_config(
inner,
alias=alias,
qualifier_prefixes=qualifier_prefixes,
)
if aggregate is not None:
saw_aggregate = True
aggregate_specs.append(aggregate)
@@ -109,7 +185,11 @@ def compile_sql(
raise SqlCompilationError(
[_sql_error("sql.select_expression", "SELECT supports columns and COUNT/SUM/AVG/MIN/MAX only.")]
)
column = _column_name(inner, context="SELECT")
column = _column_name(
inner,
context="SELECT",
qualifier_prefixes=qualifier_prefixes,
)
projection_fields.append({"column": column, "alias": alias or column})
if saw_star and len(query.expressions) != 1:
@@ -132,7 +212,7 @@ def compile_sql(
raise SqlCompilationError(
[_sql_error("sql.aggregate_required", "GROUP BY requires at least one aggregate in this dialect.")]
)
nodes.append(
append_transform(
GraphNode(
id="aggregate",
type="aggregate",
@@ -142,7 +222,7 @@ def compile_sql(
)
)
elif not saw_star:
nodes.append(
append_transform(
GraphNode(
id="select",
type="select",
@@ -152,6 +232,21 @@ def compile_sql(
)
)
if query.args.get("distinct"):
if saw_aggregate or group_by:
raise SqlCompilationError(
[_sql_error("sql.distinct_group", "DISTINCT cannot be combined with aggregation yet.")]
)
append_transform(
GraphNode(
id="distinct",
type="distinct",
label="Remove duplicates",
position=_position(len(nodes)),
config={"columns": []},
)
)
order = query.args.get("order")
if order:
fields: list[dict[str, str]] = []
@@ -160,11 +255,15 @@ def compile_sql(
raise SqlCompilationError([_sql_error("sql.order", "Unsupported ORDER BY expression.")])
fields.append(
{
"column": _column_name(item.this, context="ORDER BY"),
"column": _column_name(
item.this,
context="ORDER BY",
qualifier_prefixes=qualifier_prefixes,
),
"direction": "desc" if item.args.get("desc") else "asc",
}
)
nodes.append(
append_transform(
GraphNode(
id="sort",
type="sort",
@@ -187,7 +286,7 @@ def compile_sql(
raise SqlCompilationError(
[_sql_error("sql.limit_range", "LIMIT must be between 1 and 100,000.")]
)
nodes.append(
append_transform(
GraphNode(
id="limit",
type="limit",
@@ -197,7 +296,7 @@ def compile_sql(
)
)
nodes.append(
append_transform(
GraphNode(
id="output",
type="output",
@@ -206,14 +305,6 @@ def compile_sql(
config={},
)
)
edges = [
GraphEdge(
id=f"edge-{source.id}-{target.id}",
source=source.id,
target=target.id,
)
for source, target in zip(nodes, nodes[1:])
]
graph = PipelineGraph(nodes=nodes, edges=edges)
diagnostics = validate_graph(graph)
if any(item.severity == "error" for item in diagnostics):
@@ -229,10 +320,57 @@ def render_sql(graph: PipelineGraph) -> tuple[str, list[DataflowDiagnostic]]:
if cyclic:
raise SqlCompilationError([_sql_error("graph.cycle", "A cyclic graph cannot be rendered as SQL.")])
node_by_id = {node.id: node for node in graph.nodes}
source = node_by_id[ordered[0]]
source_name = str(source.config.get("source_name", "")).strip()
if not source_name:
raise SqlCompilationError([_sql_error("source.name_required", "The source needs a logical SQL name.")])
source_nodes = [node for node in graph.nodes if node.type.startswith("source.")]
join_nodes = [node for node in graph.nodes if node.type == "combine.join"]
if len(join_nodes) > 1:
raise SqlCompilationError(
[_sql_error("sql.join_count", "Only one two-source join can be rendered as SQL.")]
)
left_source: GraphNode
right_source: GraphNode | None = None
join_node = join_nodes[0] if join_nodes else None
right_prefix: str | None = None
right_alias: str | None = None
if join_node is not None:
inputs = graph_inputs_by_port(graph).get(join_node.id, {})
left_source = node_by_id[inputs["left"][0]]
right_source = node_by_id[inputs["right"][0]]
right_prefix = str(join_node.config["right_prefix"])
right_alias = right_prefix[:-1]
elif len(source_nodes) == 1:
left_source = source_nodes[0]
else:
raise SqlCompilationError(
[_sql_error("sql.source_count", "SQL rendering needs one source or one two-source join.")]
)
left_source_name = str(left_source.config.get("source_name", "")).strip()
right_source_name = (
str(right_source.config.get("source_name", "")).strip()
if right_source is not None
else None
)
if not left_source_name or (right_source is not None and not right_source_name):
raise SqlCompilationError(
[_sql_error("source.name_required", "Every source needs a logical SQL name.")]
)
if right_alias and right_alias.casefold() == left_source_name.casefold():
raise SqlCompilationError(
[_sql_error("sql.join_alias", "The right-column prefix conflicts with the left source name.")]
)
def column_expression(name: str) -> exp.Column:
if right_prefix and right_alias and name.startswith(right_prefix):
right_name = name[len(right_prefix) :]
if not right_name:
raise SqlCompilationError(
[_sql_error("sql.column", "A right-side column name is missing.")]
)
return exp.column(right_name, table=right_alias)
if right_source is not None:
return exp.column(name, table=left_source_name)
return exp.column(name)
where_conditions: list[exp.Expression] = []
select_expressions: list[exp.Expression] = [exp.Star()]
@@ -240,25 +378,40 @@ def render_sql(graph: PipelineGraph) -> tuple[str, list[DataflowDiagnostic]]:
order_by: list[exp.Expression] = []
limit: int | None = None
selected = False
distinct = False
for node_id in ordered[1:]:
for node_id in ordered:
node = node_by_id[node_id]
if node.type.startswith("source.") or node.type == "combine.join":
continue
if node.type == "filter":
if selected:
raise SqlCompilationError(
[_node_sql_error(node.id, "sql.filter_order", "Filters after projection or aggregation are not representable yet.")]
)
where_conditions.append(_condition_expression(node.config))
where_conditions.append(
_condition_expression(node.config, column_expression=column_expression)
)
elif node.type == "select":
if selected:
raise SqlCompilationError(
[_node_sql_error(node.id, "sql.multiple_select", "Only one select or aggregate transform is supported.")]
)
if distinct:
raise SqlCompilationError(
[
_node_sql_error(
node.id,
"sql.distinct_order",
"Projection after deduplication is not representable as SELECT DISTINCT.",
)
]
)
select_expressions = []
for field in node.config["fields"]:
column = field if isinstance(field, str) else str(field["column"])
alias = column if isinstance(field, str) else str(field.get("alias") or column)
expression: exp.Expression = exp.column(column)
expression: exp.Expression = column_expression(column)
if alias != column:
expression = expression.as_(alias)
select_expressions.append(expression)
@@ -268,19 +421,55 @@ def render_sql(graph: PipelineGraph) -> tuple[str, list[DataflowDiagnostic]]:
raise SqlCompilationError(
[_node_sql_error(node.id, "sql.multiple_select", "Only one select or aggregate transform is supported.")]
)
select_expressions = [exp.column(column) for column in node.config.get("group_by", [])]
group_by = [exp.column(column) for column in node.config.get("group_by", [])]
if distinct:
raise SqlCompilationError(
[
_node_sql_error(
node.id,
"sql.distinct_order",
"Aggregation after deduplication is not representable in the constrained dialect.",
)
]
)
select_expressions = [
column_expression(str(column))
for column in node.config.get("group_by", [])
]
group_by = [
column_expression(str(column))
for column in node.config.get("group_by", [])
]
for aggregate in node.config["aggregates"]:
function = str(aggregate["function"])
column = aggregate.get("column")
argument: exp.Expression = exp.Star() if function == "count" and column in (None, "", "*") else exp.column(str(column))
argument: exp.Expression = (
exp.Star()
if function == "count" and column in (None, "", "*")
else column_expression(str(column))
)
aggregate_expression = _aggregate_expression(function, argument)
select_expressions.append(aggregate_expression.as_(str(aggregate["alias"])))
selected = True
elif node.type == "distinct":
if node.config.get("columns"):
raise SqlCompilationError(
[
_node_sql_error(
node.id,
"sql.distinct_keys",
"Key-based deduplication is not representable as SELECT DISTINCT.",
)
]
)
if distinct:
raise SqlCompilationError(
[_node_sql_error(node.id, "sql.multiple_distinct", "Only one DISTINCT transform is supported.")]
)
distinct = True
elif node.type == "sort":
order_by = [
exp.Ordered(
this=exp.column(str(field["column"])),
this=column_expression(str(field["column"])),
desc=field.get("direction", "asc") == "desc",
nulls_first=False,
)
@@ -293,13 +482,32 @@ def render_sql(graph: PipelineGraph) -> tuple[str, list[DataflowDiagnostic]]:
[_node_sql_error(node.id, "sql.node_not_representable", f"{node.type!r} cannot be rendered as SQL.")]
)
query = exp.select(*select_expressions).from_(exp.to_table(source_name))
query = exp.select(*select_expressions).from_(exp.to_table(left_source_name))
if join_node is not None and right_source_name and right_alias:
join_conditions = [
exp.EQ(
this=exp.column(str(left_key), table=left_source_name),
expression=exp.column(str(right_key), table=right_alias),
)
for left_key, right_key in zip(
join_node.config["left_keys"],
join_node.config["right_keys"],
strict=True,
)
]
query = query.join(
exp.to_table(right_source_name).as_(right_alias),
on=_combine_and(join_conditions),
join_type=str(join_node.config.get("join_type", "inner")),
)
if where_conditions:
query = query.where(_combine_and(where_conditions))
if group_by:
query = query.group_by(*group_by)
if order_by:
query = query.order_by(*order_by)
if distinct:
query = query.distinct()
if limit is not None:
query = query.limit(limit)
return query.sql(dialect="duckdb", pretty=True), diagnostics
@@ -308,7 +516,6 @@ def render_sql(graph: PipelineGraph) -> tuple[str, list[DataflowDiagnostic]]:
def _reject_unsupported_query_shape(query: exp.Select) -> None:
unsupported_args = {
"with_": "WITH queries",
"distinct": "DISTINCT",
"having": "HAVING",
"qualify": "QUALIFY",
"offset": "OFFSET",
@@ -320,12 +527,126 @@ def _reject_unsupported_query_shape(query: exp.Select) -> None:
raise SqlCompilationError(
[_sql_error("sql.unsupported_clause", f"{label} are not supported by the first Dataflow dialect.")]
)
if any(True for _ in query.find_all(exp.Join)):
raise SqlCompilationError([_sql_error("sql.join", "JOIN support belongs to the comparison/reconciliation slice.")])
if any(True for _ in query.find_all(exp.Subquery)):
raise SqlCompilationError([_sql_error("sql.subquery", "Subqueries are not supported by the first Dataflow dialect.")])
def _source_node(
table: exp.Table,
source_nodes: list[GraphNode],
*,
fallback_id: str,
position: GraphPosition,
) -> GraphNode:
source_name = table.name
preserved = next(
(
node.model_copy(deep=True)
for node in source_nodes
if node.type.startswith("source.")
and str(node.config.get("source_name", "")).casefold() == source_name.casefold()
),
None,
)
return preserved or GraphNode(
id=fallback_id,
type="source.reference",
label=source_name,
position=position,
config={
"source_ref": "",
"source_name": source_name,
"expected_fingerprint": "",
},
)
def _join_config(
join: exp.Join,
*,
left_table: exp.Table,
right_table: exp.Table,
) -> dict[str, Any]:
kind = str(join.args.get("kind") or "").casefold()
side = str(join.args.get("side") or "").casefold()
if kind not in {"", "inner", "outer"} or side not in {"", "left", "right", "full"}:
raise SqlCompilationError(
[_sql_error("sql.join_type", "JOIN supports INNER, LEFT, RIGHT, or FULL joins only.")]
)
join_type = side or ("inner" if kind in {"", "inner"} else "")
if not join_type:
raise SqlCompilationError(
[_sql_error("sql.join_type", "OUTER JOIN requires LEFT, RIGHT, or FULL.")]
)
on_expression = join.args.get("on")
if on_expression is None:
raise SqlCompilationError(
[_sql_error("sql.join_condition", "JOIN requires an ON key comparison.")]
)
left_qualifiers = _table_qualifiers(left_table)
right_qualifiers = _table_qualifiers(right_table)
if left_qualifiers & right_qualifiers:
raise SqlCompilationError(
[_sql_error("sql.join_alias", "Joined sources need distinct names or aliases.")]
)
left_keys: list[str] = []
right_keys: list[str] = []
for condition in _flatten_and(on_expression):
if (
not isinstance(condition, exp.EQ)
or not isinstance(condition.this, exp.Column)
or not isinstance(condition.expression, exp.Column)
):
raise SqlCompilationError(
[_sql_error("sql.join_condition", "JOIN ON accepts equality comparisons between source columns.")]
)
first = condition.this
second = condition.expression
first_qualifier = first.table.casefold()
second_qualifier = second.table.casefold()
if first_qualifier in left_qualifiers and second_qualifier in right_qualifiers:
left_keys.append(first.name)
right_keys.append(second.name)
elif first_qualifier in right_qualifiers and second_qualifier in left_qualifiers:
left_keys.append(second.name)
right_keys.append(first.name)
else:
raise SqlCompilationError(
[
_sql_error(
"sql.join_qualification",
"Every JOIN key must qualify one left and one right source column.",
)
]
)
return {
"join_type": join_type,
"left_keys": left_keys,
"right_keys": right_keys,
}
def _join_qualifier_prefixes(
left_table: exp.Table,
right_table: exp.Table,
*,
right_prefix: str,
) -> dict[str, str]:
return {
**{qualifier: "" for qualifier in _table_qualifiers(left_table)},
**{qualifier: right_prefix for qualifier in _table_qualifiers(right_table)},
}
def _table_qualifiers(table: exp.Table) -> set[str]:
return {
qualifier.casefold()
for qualifier in (table.name, table.alias_or_name)
if qualifier
}
def _flatten_and(expression: exp.Expression) -> list[exp.Expression]:
if isinstance(expression, exp.And):
return [*_flatten_and(expression.this), *_flatten_and(expression.expression)]
@@ -334,14 +655,32 @@ def _flatten_and(expression: exp.Expression) -> list[exp.Expression]:
return [expression]
def _condition_config(expression: exp.Expression) -> dict[str, Any]:
def _condition_config(
expression: exp.Expression,
*,
qualifier_prefixes: dict[str, str] | None = None,
) -> dict[str, Any]:
if isinstance(expression, exp.Not) and isinstance(expression.this, exp.Is):
inner = expression.this
if isinstance(inner.this, exp.Column) and isinstance(inner.expression, exp.Null):
return {"column": _column_name(inner.this, context="WHERE"), "operator": "not_null"}
return {
"column": _column_name(
inner.this,
context="WHERE",
qualifier_prefixes=qualifier_prefixes,
),
"operator": "not_null",
}
if isinstance(expression, exp.Is):
if isinstance(expression.this, exp.Column) and isinstance(expression.expression, exp.Null):
return {"column": _column_name(expression.this, context="WHERE"), "operator": "is_null"}
return {
"column": _column_name(
expression.this,
context="WHERE",
qualifier_prefixes=qualifier_prefixes,
),
"operator": "is_null",
}
mapping: tuple[tuple[type[exp.Expression], str], ...] = (
(exp.EQ, "eq"),
(exp.NEQ, "ne"),
@@ -355,7 +694,11 @@ def _condition_config(expression: exp.Expression) -> dict[str, Any]:
if not isinstance(expression.this, exp.Column):
break
return {
"column": _column_name(expression.this, context="WHERE"),
"column": _column_name(
expression.this,
context="WHERE",
qualifier_prefixes=qualifier_prefixes,
),
"operator": operator,
"value": _literal_value(expression.expression),
}
@@ -366,7 +709,11 @@ def _condition_config(expression: exp.Expression) -> dict[str, Any]:
[_sql_error("sql.like", "LIKE is supported only as a contains pattern: LIKE '%value%'.")]
)
return {
"column": _column_name(expression.this, context="WHERE"),
"column": _column_name(
expression.this,
context="WHERE",
qualifier_prefixes=qualifier_prefixes,
),
"operator": "contains",
"value": value[1:-1],
}
@@ -375,8 +722,12 @@ def _condition_config(expression: exp.Expression) -> dict[str, Any]:
)
def _condition_expression(config: dict[str, Any]) -> exp.Expression:
column = exp.column(str(config["column"]))
def _condition_expression(
config: dict[str, Any],
*,
column_expression: Callable[[str], exp.Column] = exp.column,
) -> exp.Expression:
column = column_expression(str(config["column"]))
operator = str(config["operator"])
if operator == "is_null":
return exp.Is(this=column, expression=exp.Null())
@@ -417,7 +768,12 @@ def _literal_value(expression: exp.Expression) -> Any:
raise SqlCompilationError([_sql_error("sql.literal", f"Unsupported literal {text!r}.")]) from exc
def _aggregate_config(expression: exp.Expression, *, alias: str) -> dict[str, Any] | None:
def _aggregate_config(
expression: exp.Expression,
*,
alias: str,
qualifier_prefixes: dict[str, str] | None = None,
) -> dict[str, Any] | None:
mapping: tuple[tuple[type[exp.Expression], str], ...] = (
(exp.Count, "count"),
(exp.Sum, "sum"),
@@ -432,7 +788,11 @@ def _aggregate_config(expression: exp.Expression, *, alias: str) -> dict[str, An
if isinstance(argument, exp.Star):
column = "*"
elif isinstance(argument, exp.Column):
column = _column_name(argument, context=function.upper())
column = _column_name(
argument,
context=function.upper(),
qualifier_prefixes=qualifier_prefixes,
)
else:
raise SqlCompilationError(
[_sql_error("sql.aggregate_argument", f"{function.upper()} requires a column or * argument.")]
@@ -453,12 +813,37 @@ def _aggregate_expression(function: str, argument: exp.Expression) -> exp.Expres
return mapping[function](this=argument)
def _column_name(expression: exp.Expression, *, context: str) -> str:
if not isinstance(expression, exp.Column) or expression.table:
def _column_name(
expression: exp.Expression,
*,
context: str,
qualifier_prefixes: dict[str, str] | None = None,
) -> str:
if not isinstance(expression, exp.Column):
raise SqlCompilationError(
[_sql_error("sql.column", f"{context} accepts column names only.")]
)
if not expression.table and qualifier_prefixes is not None:
raise SqlCompilationError(
[
_sql_error(
"sql.join_column_qualification",
f"{context} columns must be source-qualified when a JOIN is present.",
)
]
)
if not expression.table:
return expression.name
if qualifier_prefixes is None:
raise SqlCompilationError(
[_sql_error("sql.column", f"{context} accepts unqualified column names only.")]
)
return expression.name
prefix = qualifier_prefixes.get(expression.table.casefold())
if prefix is None:
raise SqlCompilationError(
[_sql_error("sql.column_source", f"{context} references an unknown source qualifier.")]
)
return f"{prefix}{expression.name}"
def _position(index: int) -> GraphPosition:

350
tests/test_graph_and_sql.py
View File

@@ -2,7 +2,13 @@ from __future__ import annotations
import unittest
from govoplan_dataflow.backend.executor import PipelineExecutionError, execute_preview
from govoplan_dataflow.backend.executor import (
MAX_SOURCE_ROWS,
PipelineExecutionError,
ResolvedSource,
execute_preview,
)
from govoplan_dataflow.backend.graph import validate_graph
from govoplan_dataflow.backend.schemas import (
GraphEdge,
GraphNode,
@@ -29,7 +35,66 @@ def inline_source() -> GraphNode:
)
def lookup_source() -> GraphNode:
return GraphNode(
id="lookup",
type="source.inline",
label="Department lookup",
position=GraphPosition(x=40, y=300),
config={
"source_name": "department_lookup",
"rows": [
{"department": "A", "label": "Administration"},
{"department": "B", "label": "Building services"},
],
},
)
class DataflowGraphAndSqlTests(unittest.TestCase):
def test_rejects_invalid_or_duplicate_logical_source_names(self) -> None:
invalid_source = inline_source().model_copy(deep=True)
invalid_source.config["source_name"] = "monthly cases"
duplicate_source = lookup_source().model_copy(deep=True)
duplicate_source.config["source_name"] = "MONTHLY_FILES"
graph = PipelineGraph(
nodes=[
invalid_source,
duplicate_source,
GraphNode(
id="union",
type="combine.union",
label="Append rows",
position=GraphPosition(x=250, y=160),
config={"mode": "all"},
),
GraphNode(
id="output",
type="output",
label="Output",
position=GraphPosition(x=450, y=160),
config={},
),
],
edges=[
GraphEdge(id="edge-1", source="source", target="union"),
GraphEdge(id="edge-2", source="lookup", target="union"),
GraphEdge(id="edge-3", source="union", target="output"),
],
)
diagnostics = validate_graph(graph)
self.assertTrue(any(item.code == "source.name_invalid" for item in diagnostics))
invalid_source.config["source_name"] = "monthly_files"
diagnostics = validate_graph(
graph.model_copy(
update={"nodes": [invalid_source, *graph.nodes[1:]]},
deep=True,
)
)
self.assertTrue(any(item.code == "source.duplicate_name" for item in diagnostics))
def test_compiles_renders_and_executes_grouped_query(self) -> None:
sql = """
SELECT department, COUNT(*) AS records, SUM(amount) AS total
@@ -71,13 +136,207 @@ class DataflowGraphAndSqlTests(unittest.TestCase):
with self.subTest(sql=sql), self.assertRaises(SqlCompilationError):
compile_sql(sql, source_nodes=[inline_source()])
def test_rejects_join_until_comparison_slice(self) -> None:
with self.assertRaisesRegex(SqlCompilationError, "JOIN support"):
compile_sql(
"SELECT * FROM monthly_files JOIN other ON monthly_files.id = other.id",
source_nodes=[inline_source()],
def test_compiles_renders_and_executes_two_source_join(self) -> None:
sql = """
SELECT monthly_files.department, lookup.label
FROM monthly_files
LEFT JOIN department_lookup AS lookup
ON monthly_files.department = lookup.department
ORDER BY monthly_files.department
"""
graph, _, diagnostics = compile_sql(
sql,
source_nodes=[inline_source(), lookup_source()],
)
self.assertEqual([], diagnostics)
self.assertEqual(
[
"source.inline",
"source.inline",
"combine.join",
"select",
"sort",
"output",
],
[node.type for node in graph.nodes],
)
self.assertEqual("lookup_", graph.nodes[2].config["right_prefix"])
rendered, render_diagnostics = render_sql(graph)
self.assertEqual([], render_diagnostics)
roundtrip, _, _ = compile_sql(
rendered,
source_nodes=[inline_source(), lookup_source()],
)
self.assertEqual(
graph.model_dump(mode="json"),
roundtrip.model_dump(mode="json"),
)
result = execute_preview(graph, row_limit=100)
self.assertEqual(
[
{"department": "A", "lookup_label": "Administration"},
{"department": "A", "lookup_label": "Administration"},
{"department": "B", "lookup_label": "Building services"},
],
result.rows,
)
with self.assertRaisesRegex(SqlCompilationError, "source-qualified"):
compile_sql(
"""
SELECT label
FROM monthly_files
JOIN department_lookup AS lookup
ON monthly_files.department = lookup.department
""",
source_nodes=[inline_source(), lookup_source()],
)
def test_distinct_and_derived_columns_are_deterministic(self) -> None:
source = inline_source().model_copy(
update={
"config": {
"source_name": "monthly_files",
"rows": [
{"first": " Ada ", "last": "Lovelace"},
{"first": " Ada ", "last": "Lovelace"},
],
}
}
)
derive = GraphNode(
id="derive",
type="derive",
label="Display name",
position=GraphPosition(x=260, y=160),
config={
"target_column": "display_name",
"operation": "concat",
"source_columns": ["first", "last"],
"separator": " ",
},
)
distinct = GraphNode(
id="distinct",
type="distinct",
label="Unique people",
position=GraphPosition(x=480, y=160),
config={"columns": ["display_name"]},
)
output = GraphNode(
id="output",
type="output",
label="Output",
position=GraphPosition(x=700, y=160),
config={},
)
graph = PipelineGraph(
nodes=[source, derive, distinct, output],
edges=[
GraphEdge(id="source-derive", source=source.id, target=derive.id),
GraphEdge(id="derive-distinct", source=derive.id, target=distinct.id),
GraphEdge(id="distinct-output", source=distinct.id, target=output.id),
],
)
result = execute_preview(graph, row_limit=100)
self.assertEqual(
[{"first": " Ada ", "last": "Lovelace", "display_name": " Ada Lovelace"}],
result.rows,
)
def test_union_can_keep_or_remove_duplicate_rows(self) -> None:
first = inline_source().model_copy(
update={
"id": "first",
"config": {"source_name": "first", "rows": [{"id": 1}, {"id": 2}]},
}
)
second = inline_source().model_copy(
update={
"id": "second",
"config": {"source_name": "second", "rows": [{"id": 2}, {"id": 3}]},
}
)
union = GraphNode(
id="union",
type="combine.union",
label="Append",
position=GraphPosition(x=300, y=200),
config={"mode": "distinct"},
)
output = GraphNode(
id="output",
type="output",
label="Output",
position=GraphPosition(x=520, y=200),
config={},
)
graph = PipelineGraph(
nodes=[first, second, union, output],
edges=[
GraphEdge(id="first-union", source=first.id, target=union.id),
GraphEdge(id="second-union", source=second.id, target=union.id),
GraphEdge(id="union-output", source=union.id, target=output.id),
],
)
result = execute_preview(graph, row_limit=100)
self.assertEqual([{"id": 1}, {"id": 2}, {"id": 3}], result.rows)
def test_connector_source_uses_bounded_resolver_and_records_lineage(self) -> None:
source = GraphNode(
id="connector",
type="source.reference",
label="Monthly import",
position=GraphPosition(x=40, y=160),
config={
"source_ref": "snapshot:source-1",
"source_name": "monthly_import",
"expected_fingerprint": "sha256:expected",
},
)
output = GraphNode(
id="output",
type="output",
label="Output",
position=GraphPosition(x=280, y=160),
config={},
)
graph = PipelineGraph(
nodes=[source, output],
edges=[GraphEdge(id="source-output", source=source.id, target=output.id)],
)
calls: list[tuple[str, int]] = []
def resolve(node: GraphNode, limit: int) -> ResolvedSource:
calls.append((node.id, limit))
return ResolvedSource(
rows=({"id": 1}, {"id": 2}),
source_ref="snapshot:source-1",
provider="snapshot",
fingerprint="sha256:expected",
total_rows=20,
truncated=True,
)
result = execute_preview(
graph,
row_limit=100,
source_resolver=resolve,
)
self.assertEqual([("connector", MAX_SOURCE_ROWS)], calls)
self.assertEqual([{"id": 1}, {"id": 2}], result.rows)
self.assertEqual(20, result.source_fingerprints[0]["row_count"])
self.assertTrue(result.source_fingerprints[0]["truncated"])
self.assertEqual("source.preview_truncated", result.diagnostics[0].code)
def test_graph_validation_rejects_cycle(self) -> None:
source = inline_source()
output = GraphNode(
@@ -95,11 +354,78 @@ class DataflowGraphAndSqlTests(unittest.TestCase):
],
)
from govoplan_dataflow.backend.graph import validate_graph
codes = {item.code for item in validate_graph(graph)}
self.assertIn("graph.cycle", codes)
def test_schema_validation_reports_unknown_and_colliding_columns(self) -> None:
source = inline_source().model_copy(
update={
"config": {
"source_name": "monthly_files",
"rows": [{"id": 1, "right_id": "reserved"}],
}
}
)
lookup = lookup_source().model_copy(
update={
"config": {
"source_name": "department_lookup",
"rows": [{"id": 1}],
}
}
)
join = GraphNode(
id="join",
type="combine.join",
label="Join",
position=GraphPosition(x=280, y=200),
config={
"join_type": "inner",
"left_keys": ["missing_id"],
"right_keys": ["id"],
"right_prefix": "right_",
},
)
output = GraphNode(
id="output",
type="output",
label="Output",
position=GraphPosition(x=500, y=200),
config={},
)
graph = PipelineGraph(
nodes=[source, lookup, join, output],
edges=[
GraphEdge(
id="source-join",
source=source.id,
target=join.id,
target_port="left",
),
GraphEdge(
id="lookup-join",
source=lookup.id,
target=join.id,
target_port="right",
),
GraphEdge(id="join-output", source=join.id, target=output.id),
],
)
diagnostics = validate_graph(graph)
self.assertTrue(
any(
item.code == "schema.unknown_column"
and item.node_id == join.id
and item.field == "left_keys"
for item in diagnostics
)
)
self.assertTrue(
any(item.code == "join.output_collision" for item in diagnostics)
)
def test_preview_truncates_response_without_changing_pipeline_limit(self) -> None:
source = inline_source()
output = GraphNode(
@@ -202,6 +528,14 @@ class DataflowGraphAndSqlTests(unittest.TestCase):
self.assertEqual("filter-1", raised.exception.node_id)
self.assertIn("Cannot apply", str(raised.exception))
self.assertEqual(
[("source", "succeeded"), ("filter-1", "failed")],
[
(diagnostic.node_id, diagnostic.status)
for diagnostic in raised.exception.node_diagnostics
],
)
self.assertEqual(1, len(raised.exception.source_fingerprints))
if __name__ == "__main__":

41
tests/test_node_library.py Normal file
View File

@@ -0,0 +1,41 @@
from __future__ import annotations
import unittest
from govoplan_dataflow.backend.node_library import CATEGORY_LABELS, NODE_LIBRARY, NODE_TYPES
class DataflowNodeLibraryTests(unittest.TestCase):
def test_library_has_unique_executable_nodes_in_every_category(self) -> None:
self.assertEqual(len(NODE_LIBRARY), len(NODE_TYPES))
self.assertEqual(set(CATEGORY_LABELS), {item.category for item in NODE_LIBRARY})
self.assertEqual(
{
"source.inline",
"source.reference",
"combine.union",
"combine.join",
"filter",
"distinct",
"select",
"derive",
"aggregate",
"sort",
"limit",
"output",
},
set(NODE_TYPES),
)
def test_join_and_union_publish_their_connection_contract(self) -> None:
join = NODE_TYPES["combine.join"]
union = NODE_TYPES["combine.union"]
self.assertEqual(["left", "right"], [port.id for port in join.input_ports])
self.assertTrue(union.input_ports[0].multiple)
self.assertEqual(2, union.input_ports[0].minimum_connections)
self.assertEqual([], list(NODE_TYPES["output"].output_ports))
if __name__ == "__main__":
unittest.main()

43
tests/test_schemas.py Normal file
View File

@@ -0,0 +1,43 @@
from __future__ import annotations
import unittest
from govoplan_dataflow.backend.schemas import TabularSnapshotCreateRequest
class DataflowSchemaTests(unittest.TestCase):
def test_snapshot_request_requires_the_selected_format_payload(self) -> None:
json_request = TabularSnapshotCreateRequest(
name="Monthly cases",
source_name="monthly_cases",
format="json",
rows=[{"case_id": "0012"}],
)
csv_request = TabularSnapshotCreateRequest(
name="Monthly cases",
source_name="monthly_cases_csv",
format="csv",
csv_text="case_id;amount\n0012;7.5\n",
delimiter=";",
)
self.assertEqual([{"case_id": "0012"}], json_request.rows)
self.assertEqual(";", csv_request.delimiter)
with self.assertRaises(ValueError):
TabularSnapshotCreateRequest(
name="Missing rows",
source_name="missing_rows",
format="json",
)
with self.assertRaises(ValueError):
TabularSnapshotCreateRequest(
name="Ambiguous",
source_name="ambiguous",
format="csv",
rows=[],
csv_text="id\n1\n",
)
if __name__ == "__main__":
unittest.main()

37
tests/test_service.py
View File

@@ -220,12 +220,49 @@ class DataflowServiceTests(unittest.TestCase):
self.assertEqual(2, run.output_row_count)
self.assertEqual(3, run.input_row_count)
self.assertEqual(1, len(run.source_fingerprints))
self.assertEqual(3, response.input_row_count)
self.assertEqual(run.source_fingerprints, response.source_fingerprints)
self.assertFalse(hasattr(run, "result_rows"))
self.assertEqual(
1,
self.session.scalar(select(func.count()).select_from(DataflowRun)),
)
def test_failed_preview_keeps_upstream_lineage_and_failed_node_diagnostic(self) -> None:
graph = sample_graph()
graph.nodes[0].config["rows"] = [{"id": 1, "amount": "not-a-number"}]
pipeline = create_pipeline(
self.session,
tenant_id="tenant-1",
actor_id="user-1",
payload=PipelineCreateRequest(
name="Invalid runtime value",
graph=graph,
editor_mode="graph",
),
)
self.session.commit()
response = preview_pipeline(
self.session,
tenant_id="tenant-1",
actor_id="user-1",
payload=PipelinePreviewRequest(pipeline_id=pipeline.id),
)
self.session.commit()
run = self.session.scalar(select(DataflowRun).where(DataflowRun.id == response.run_id))
self.assertEqual("failed", response.status)
self.assertEqual(
[("source", "succeeded"), ("filter", "failed")],
[(item.node_id, item.status) for item in response.node_diagnostics],
)
self.assertEqual(1, run.input_row_count)
self.assertEqual(1, len(run.source_fingerprints))
self.assertEqual(1, response.input_row_count)
self.assertEqual(run.source_fingerprints, response.source_fingerprints)
self.assertEqual("preview.execution", run.diagnostics[-1]["code"])
if __name__ == "__main__":
unittest.main()

100
webui/src/api/dataflow.ts
View File

@@ -2,6 +2,7 @@ import { apiFetch, type ApiSettings } from "@govoplan/core-webui";
export type PipelineStatus = "draft" | "active" | "archived";
export type EditorMode = "graph" | "sql";
export type NodeCategory = "load" | "combine" | "filter" | "transform" | "output";
export type GraphPosition = { x: number; y: number };
export type PipelineGraphNode = {
@@ -101,10 +102,109 @@ export type PipelinePreview = {
truncated: boolean;
diagnostics: DataflowDiagnostic[];
node_diagnostics: NodePreviewDiagnostic[];
source_fingerprints: Record<string, unknown>[];
input_row_count: number;
definition_hash: string;
executor_version: string;
};
export type NodePortDefinition = {
id: string;
label: string;
required: boolean;
multiple: boolean;
minimum_connections: number;
};
export type NodeConfigFieldDefinition = {
id: string;
label: string;
kind: string;
required: boolean;
description?: string | null;
options: Array<[string, string]>;
};
export type NodeTypeDefinition = {
type: string;
category: NodeCategory;
category_label: string;
label: string;
description: string;
icon: string;
input_ports: NodePortDefinition[];
output_ports: NodePortDefinition[];
config_fields: NodeConfigFieldDefinition[];
default_config: Record<string, unknown>;
sql_support: "full" | "partial" | "none";
};
export type TabularSourceColumn = {
name: string;
data_type: string;
nullable: boolean;
};
export type TabularSource = {
ref: string;
provider: string;
source_name: string;
name: string;
description?: string | null;
columns: TabularSourceColumn[];
schema_version: string;
fingerprint: string;
row_count?: number | null;
byte_count?: number | null;
updated_at?: string | null;
capabilities: string[];
};
export type TabularSourceCatalogue = {
available: boolean;
writable: boolean;
sources: TabularSource[];
};
export async function listDataflowNodeTypes(settings: ApiSettings): Promise<NodeTypeDefinition[]> {
const response = await apiFetch<{ nodes: NodeTypeDefinition[] }>(
settings,
"/api/v1/dataflow/node-types"
);
return response.nodes;
}
export function listDataflowSources(
settings: ApiSettings,
query = ""
): Promise<TabularSourceCatalogue> {
const suffix = query.trim() ? `?query=${encodeURIComponent(query.trim())}` : "";
return apiFetch<TabularSourceCatalogue>(settings, `/api/v1/dataflow/sources${suffix}`);
}
export function createDataflowSourceSnapshot(
settings: ApiSettings,
payload: {
name: string;
source_name: string;
description?: string | null;
} & (
{
format: "json";
rows: Record<string, unknown>[];
} | {
format: "csv";
csv_text: string;
delimiter: string;
}
)
): Promise<TabularSource> {
return apiFetch<TabularSource>(settings, "/api/v1/dataflow/sources/snapshots", {
method: "POST",
body: JSON.stringify(payload)
});
}
export async function listDataflowPipelines(settings: ApiSettings): Promise<Pipeline[]> {
const response = await apiFetch<{ pipelines: Pipeline[] }>(settings, "/api/v1/dataflow/pipelines");
return response.pipelines;

68
webui/src/features/dataflow/DataflowCanvas.tsx
View File

@@ -15,12 +15,13 @@ import {
} from "@xyflow/react";
import type {
DataflowDiagnostic,
NodeTypeDefinition,
NodePreviewDiagnostic,
PipelineGraph,
PipelineGraphNode
} from "../../api/dataflow";
import DataflowNode, { type DataflowFlowNode } from "./DataflowNode";
import { newNode } from "./model";
import { FALLBACK_NODE_LIBRARY, newNode } from "./model";
const nodeTypes = { dataflow: DataflowNode };
@@ -28,6 +29,7 @@ type DataflowCanvasProps = {
graph: PipelineGraph;
diagnostics: DataflowDiagnostic[];
nodeDiagnostics: NodePreviewDiagnostic[];
nodeLibrary: NodeTypeDefinition[];
selectedNodeId: string | null;
readOnly: boolean;
onGraphChange: (graph: PipelineGraph) => void;
@@ -38,6 +40,7 @@ export default function DataflowCanvas({
graph,
diagnostics,
nodeDiagnostics,
nodeLibrary,
selectedNodeId,
readOnly,
onGraphChange,
@@ -52,8 +55,16 @@ export default function DataflowCanvas({
() => new Map(nodeDiagnostics.map((item) => [item.node_id, item.output_rows])),
[nodeDiagnostics]
);
const definitions = useMemo(
() => new Map(nodeLibrary.map((definition) => [definition.type, definition])),
[nodeLibrary]
);
const nodes = useMemo<DataflowFlowNode[]>(
() => graph.nodes.map((node) => ({
() => graph.nodes.map((node) => {
const definition = definitions.get(node.type)
?? FALLBACK_NODE_LIBRARY.find((item) => item.type === node.type)
?? FALLBACK_NODE_LIBRARY[0];
return {
id: node.id,
type: "dataflow",
position: node.position,
@@ -61,18 +72,22 @@ export default function DataflowCanvas({
data: {
label: node.label,
transformType: node.type,
definition,
config: node.config,
hasError: errorNodeIds.has(node.id),
outputRows: rowCounts.get(node.id)
}
})),
[errorNodeIds, graph.nodes, rowCounts, selectedNodeId]
};
}),
[definitions, errorNodeIds, graph.nodes, rowCounts, selectedNodeId]
);
const edges = useMemo<Edge[]>(
() => graph.edges.map((edge) => ({
id: edge.id,
source: edge.source,
target: edge.target,
sourceHandle: edge.source_port ?? "output",
targetHandle: edge.target_port ?? "input",
type: "smoothstep",
className: "dataflow-edge"
})),
@@ -113,10 +128,30 @@ export default function DataflowCanvas({
const source = graph.nodes.find((node) => node.id === connection.source);
const target = graph.nodes.find((node) => node.id === connection.target);
if (!source || !target || source.type === "output" || target.type.startsWith("source.")) return false;
const targetAlreadyConnected = graph.edges.some(
(edge) => edge.target === connection.target && edge.source !== connection.source
const sourcePort = connection.sourceHandle ?? "output";
const targetPort = connection.targetHandle ?? "input";
const sourceDefinition = definitions.get(source.type);
const targetDefinition = definitions.get(target.type);
if (
!sourceDefinition?.output_ports.some((port) => port.id === sourcePort)
|| !targetDefinition?.input_ports.some((port) => port.id === targetPort)
) {
return false;
}
const duplicate = graph.edges.some(
(edge) =>
edge.source === connection.source
&& edge.target === connection.target
&& (edge.source_port ?? "output") === sourcePort
&& (edge.target_port ?? "input") === targetPort
);
return !targetAlreadyConnected;
if (duplicate) return false;
const port = targetDefinition.input_ports.find((item) => item.id === targetPort);
const targetPortConnections = graph.edges.filter(
(edge) => edge.target === connection.target && (edge.target_port ?? "input") === targetPort
);
if (!port?.multiple && targetPortConnections.length) return false;
return !wouldCreateCycle(graph, connection.source, connection.target);
};
const onConnect = (connection: Connection) => {
@@ -138,7 +173,7 @@ export default function DataflowCanvas({
const type = event.dataTransfer.getData("application/x-govoplan-dataflow-node");
if (!type) return;
const position = instance.screenToFlowPosition({ x: event.clientX, y: event.clientY });
const node = newNode(type, position);
const node = newNode(type, position, nodeLibrary);
onGraphChange({ ...graph, nodes: [...graph.nodes, node] });
onSelectNode(node.id);
};
@@ -199,6 +234,23 @@ export default function DataflowCanvas({
);
}
function wouldCreateCycle(graph: PipelineGraph, source: string, target: string): boolean {
const outgoing = new Map<string, string[]>();
graph.edges.forEach((edge) => {
outgoing.set(edge.source, [...(outgoing.get(edge.source) ?? []), edge.target]);
});
const pending = [target];
const visited = new Set<string>();
while (pending.length) {
const nodeId = pending.pop();
if (!nodeId || visited.has(nodeId)) continue;
if (nodeId === source) return true;
visited.add(nodeId);
pending.push(...(outgoing.get(nodeId) ?? []));
}
return false;
}
export function updateGraphNode(
graph: PipelineGraph,
updatedNode: PipelineGraphNode

68
webui/src/features/dataflow/DataflowNode.tsx
View File

@@ -1,19 +1,11 @@
import type { ComponentType } from "react";
import {
Braces,
Columns3,
Database,
Filter,
ListEnd,
Sigma,
SortAsc,
Unplug
} from "lucide-react";
import { Handle, Position, type Node, type NodeProps } from "@xyflow/react";
import type { NodeTypeDefinition } from "../../api/dataflow";
import { dataflowNodeIcon } from "./nodeIcons";
export type DataflowNodeData = {
label: string;
transformType: string;
definition: NodeTypeDefinition;
config: Record<string, unknown>;
hasError?: boolean;
outputRows?: number;
@@ -21,21 +13,8 @@ export type DataflowNodeData = {
export type DataflowFlowNode = Node<DataflowNodeData, "dataflow">;
const iconByType: Record<string, ComponentType<{ size?: number; strokeWidth?: number }>> = {
"source.inline": Braces,
"source.reference": Database,
filter: Filter,
select: Columns3,
aggregate: Sigma,
sort: SortAsc,
limit: ListEnd,
output: Unplug
};
export default function DataflowNode({ data, selected }: NodeProps<DataflowFlowNode>) {
const Icon = iconByType[data.transformType] ?? Braces;
const isSource = data.transformType.startsWith("source.");
const isOutput = data.transformType === "output";
const Icon = dataflowNodeIcon(data.definition.icon);
return (
<div
@@ -46,42 +25,51 @@ export default function DataflowNode({ data, selected }: NodeProps<DataflowFlowN
data.hasError ? "has-error" : ""
].filter(Boolean).join(" ")}
>
{!isSource ? (
{data.definition.input_ports.map((port, index) => (
<Handle
key={port.id}
id={port.id}
type="target"
position={Position.Left}
className="dataflow-node-handle dataflow-node-handle-input"
style={{ top: portPosition(index, data.definition.input_ports.length) }}
/>
) : null}
))}
{data.definition.input_ports.length > 1
? data.definition.input_ports.map((port, index) => (
<span
key={port.id}
className="dataflow-node-port-label"
style={{ top: portPosition(index, data.definition.input_ports.length) }}
>
{port.label}
</span>
))
: null}
<span className="dataflow-node-icon" aria-hidden="true">
<Icon size={17} strokeWidth={1.8} />
</span>
<span className="dataflow-node-copy">
<strong>{data.label}</strong>
<small>{transformLabel(data.transformType)}</small>
<small>{data.definition.label}</small>
</span>
{typeof data.outputRows === "number" ? (
<span className="dataflow-node-count">{data.outputRows}</span>
) : null}
{!isOutput ? (
{data.definition.output_ports.map((port, index) => (
<Handle
key={port.id}
id={port.id}
type="source"
position={Position.Right}
className="dataflow-node-handle dataflow-node-handle-output"
style={{ top: portPosition(index, data.definition.output_ports.length) }}
/>
) : null}
))}
</div>
);
}
function transformLabel(type: string): string {
if (type === "source.inline") return "Inline source";
if (type === "source.reference") return "Connector source";
if (type === "filter") return "Filter";
if (type === "select") return "Projection";
if (type === "aggregate") return "Aggregation";
if (type === "sort") return "Sort";
if (type === "limit") return "Limit";
if (type === "output") return "Output";
return type;
function portPosition(index: number, count: number): string {
return `${((index + 1) / (count + 1)) * 100}%`;
}

357
webui/src/features/dataflow/DataflowPage.tsx
View File

@@ -3,32 +3,27 @@ import {
useEffect,
useMemo,
useState,
type ComponentType,
type DragEvent
} from "react";
import {
Braces,
CheckCircle2,
Code2,
Columns3,
Database,
Filter,
ListEnd,
Network,
Play,
Plus,
RefreshCw,
RotateCcw,
Save,
Sigma,
SortAsc,
Trash2,
TriangleAlert
TriangleAlert,
Upload
} from "lucide-react";
import {
Button,
ConfirmDialog,
Dialog,
DismissibleAlert,
FormField,
IconButton,
LoadingFrame,
SegmentedControl,
@@ -44,24 +39,29 @@ import { ReactFlowProvider } from "@xyflow/react";
import {
compileDataflowSql,
createDataflowPipeline,
createDataflowSourceSnapshot,
deleteDataflowPipeline,
listDataflowNodeTypes,
listDataflowPipelines,
listDataflowSources,
previewDataflowPipeline,
renderDataflowSql,
updateDataflowPipeline,
validateDataflowPipeline,
type DataflowDiagnostic,
type EditorMode,
type NodeCategory,
type NodeTypeDefinition,
type NodePreviewDiagnostic,
type Pipeline,
type PipelineGraphNode,
type PipelinePreview
type PipelinePreview,
type TabularSource
} from "../../api/dataflow";
import DataflowCanvas, { updateGraphNode } from "./DataflowCanvas";
import NodeInspector from "./NodeInspector";
import {
NODE_LABELS,
PALETTE_NODE_TYPES,
FALLBACK_NODE_LIBRARY,
draftFingerprint,
draftFromPipeline,
newNode,
@@ -70,18 +70,12 @@ import {
sourceNodes,
type PipelineDraft
} from "./model";
import { dataflowNodeIcon } from "./nodeIcons";
type ResultTab = "preview" | "diagnostics";
type SnapshotFormat = "json" | "csv";
const paletteIcons: Record<string, ComponentType<{ size?: number; strokeWidth?: number }>> = {
"source.inline": Braces,
filter: Filter,
select: Columns3,
aggregate: Sigma,
sort: SortAsc,
limit: ListEnd,
output: Network
};
const NODE_CATEGORY_ORDER: NodeCategory[] = ["load", "combine", "filter", "transform", "output"];
export default function DataflowPage({ settings, auth }: { settings: ApiSettings; auth: AuthInfo }) {
const { requestNavigation, requestDiscard } = useUnsavedChanges();
@@ -101,9 +95,20 @@ export default function DataflowPage({ settings, auth }: { settings: ApiSettings
const [resultOpen, setResultOpen] = useState(false);
const [resultTab, setResultTab] = useState<ResultTab>("preview");
const [deleteOpen, setDeleteOpen] = useState(false);
const [snapshotOpen, setSnapshotOpen] = useState(false);
const [nodeLibrary, setNodeLibrary] = useState<NodeTypeDefinition[]>(FALLBACK_NODE_LIBRARY);
const [sources, setSources] = useState<TabularSource[]>([]);
const [sourceCatalogueAvailable, setSourceCatalogueAvailable] = useState(false);
const [sourceCatalogueWritable, setSourceCatalogueWritable] = useState(false);
const canWrite = hasScope(auth, "dataflow:pipeline:write") || hasScope(auth, "dataflow:pipeline:admin");
const canRun = hasScope(auth, "dataflow:pipeline:run") || hasScope(auth, "dataflow:pipeline:admin");
const canImportSources = canWrite
&& sourceCatalogueWritable
&& (
hasScope(auth, "connectors:source:write")
|| hasScope(auth, "connectors:source:admin")
);
const dirty = Boolean(draft) && draftFingerprint(draft) !== draftFingerprint(savedDraft);
const selectedNode = useMemo(
() => draft?.graph.nodes.find((node) => node.id === selectedNodeId) ?? null,
@@ -116,6 +121,14 @@ export default function DataflowPage({ settings, auth }: { settings: ApiSettings
`${pipeline.name} ${pipeline.description ?? ""} ${pipeline.status}`.toLocaleLowerCase().includes(query)
);
}, [pipelines, search]);
const paletteGroups = useMemo(
() => NODE_CATEGORY_ORDER.map((category) => ({
category,
label: nodeLibrary.find((item) => item.category === category)?.category_label ?? category,
nodes: nodeLibrary.filter((item) => item.category === category)
})).filter((group) => group.nodes.length),
[nodeLibrary]
);
const loadPipelines = useCallback(async (preferredId?: string | null) => {
setLoading(true);
@@ -145,6 +158,33 @@ export default function DataflowPage({ settings, auth }: { settings: ApiSettings
void loadPipelines();
}, []);
useEffect(() => {
let cancelled = false;
void listDataflowNodeTypes(settings)
.then((items) => {
if (!cancelled && items.length) setNodeLibrary(items);
})
.catch(() => {
if (!cancelled) setNodeLibrary(FALLBACK_NODE_LIBRARY);
});
void listDataflowSources(settings)
.then((catalogue) => {
if (cancelled) return;
setSources(catalogue.sources);
setSourceCatalogueAvailable(catalogue.available);
setSourceCatalogueWritable(catalogue.writable);
})
.catch(() => {
if (cancelled) return;
setSources([]);
setSourceCatalogueAvailable(false);
setSourceCatalogueWritable(false);
});
return () => {
cancelled = true;
};
}, [settings]);
const discardDraft = useCallback(() => {
if (savedDraft) {
const reset = structuredClone(savedDraft);
@@ -383,7 +423,7 @@ export default function DataflowPage({ settings, auth }: { settings: ApiSettings
const node = newNode(type, {
x: 120 + draft.graph.nodes.length * 70,
y: 100 + (draft.graph.nodes.length % 4) * 80
});
}, nodeLibrary);
updateGraph({ ...draft.graph, nodes: [...draft.graph.nodes, node] });
setSelectedNodeId(node.id);
};
@@ -528,27 +568,41 @@ export default function DataflowPage({ settings, auth }: { settings: ApiSettings
{draft.editorMode === "graph" ? (
<aside className="dataflow-palette" aria-label="Transform palette">
<div className="dataflow-panel-heading">
<strong>Transforms</strong>
<strong>Nodes</strong>
{canImportSources ? (
<IconButton
label="Import tabular snapshot"
icon={<Upload size={15} />}
variant="ghost"
onClick={() => setSnapshotOpen(true)}
/>
) : null}
</div>
<div className="dataflow-palette-items">
{PALETTE_NODE_TYPES.map((type) => {
const Icon = paletteIcons[type] ?? Database;
const disabled = !canWrite || uniqueNodeExists(draft, type);
{paletteGroups.map((group) => (
<section key={group.category} className="dataflow-palette-group">
<h3>{group.label}</h3>
{group.nodes.map((definition) => {
const Icon = dataflowNodeIcon(definition.icon);
const disabled = !canWrite || uniqueNodeExists(draft, definition.type);
return (
<button
key={type}
key={definition.type}
type="button"
title={definition.description}
draggable={!disabled}
disabled={disabled}
onDragStart={(event) => startPaletteDrag(event, type)}
onClick={() => addNode(type)}
onDragStart={(event) => startPaletteDrag(event, definition.type)}
onClick={() => addNode(definition.type)}
>
<Icon size={16} />
<span>{NODE_LABELS[type]}</span>
<span>{definition.label}</span>
<Plus size={14} className="dataflow-palette-add" />
</button>
);
})}
</section>
))}
</div>
</aside>
) : null}
@@ -559,6 +613,7 @@ export default function DataflowPage({ settings, auth }: { settings: ApiSettings
graph={draft.graph}
diagnostics={diagnostics}
nodeDiagnostics={nodeDiagnostics}
nodeLibrary={nodeLibrary}
selectedNodeId={selectedNodeId}
readOnly={!canWrite}
onGraphChange={updateGraph}
@@ -589,6 +644,9 @@ export default function DataflowPage({ settings, auth }: { settings: ApiSettings
</section>
<NodeInspector
node={selectedNode}
nodeLibrary={nodeLibrary}
sources={sources}
sourceCatalogueAvailable={sourceCatalogueAvailable}
readOnly={!canWrite}
onChange={(node: PipelineGraphNode) => updateGraph(updateGraphNode(draft.graph, node))}
onDelete={(nodeId) => {
@@ -634,10 +692,218 @@ export default function DataflowPage({ settings, auth }: { settings: ApiSettings
onCancel={() => setDeleteOpen(false)}
onConfirm={() => void removePipeline()}
/>
<SourceSnapshotDialog
open={snapshotOpen}
settings={settings}
onClose={() => setSnapshotOpen(false)}
onCreated={(source) => {
setSources((current) => [
source,
...current.filter((item) => item.ref !== source.ref)
]);
setSourceCatalogueAvailable(true);
setSnapshotOpen(false);
if (!draft) return;
const node = newNode(
"source.reference",
{
x: 100 + draft.graph.nodes.length * 60,
y: 90 + (draft.graph.nodes.length % 4) * 90
},
nodeLibrary
);
node.label = source.name;
node.config = {
...node.config,
source_ref: source.ref,
source_name: source.source_name,
expected_fingerprint: source.fingerprint,
source_columns: source.columns
};
updateGraph({ ...draft.graph, nodes: [...draft.graph.nodes, node] });
setSelectedNodeId(node.id);
}}
/>
</main>
);
}
function SourceSnapshotDialog({
open,
settings,
onClose,
onCreated
}: {
open: boolean;
settings: ApiSettings;
onClose: () => void;
onCreated: (source: TabularSource) => void;
}) {
const [name, setName] = useState("");
const [sourceName, setSourceName] = useState("");
const [description, setDescription] = useState("");
const [format, setFormat] = useState<SnapshotFormat>("json");
const [rowsText, setRowsText] = useState("[]");
const [csvText, setCsvText] = useState("");
const [delimiter, setDelimiter] = useState(",");
const [fileInputKey, setFileInputKey] = useState(0);
const [busy, setBusy] = useState(false);
const [error, setError] = useState("");
useEffect(() => {
if (!open) return;
setName("");
setSourceName("");
setDescription("");
setFormat("json");
setRowsText("[]");
setCsvText("");
setDelimiter(",");
setFileInputKey((current) => current + 1);
setError("");
}, [open]);
const create = async () => {
setError("");
let rows: Record<string, unknown>[] = [];
if (format === "json") {
try {
const parsed: unknown = JSON.parse(rowsText);
if (!Array.isArray(parsed) || parsed.some((row) => !isRecord(row))) {
throw new Error("Rows must be a JSON array of objects.");
}
rows = parsed;
} catch (parseError) {
setError(parseError instanceof Error ? parseError.message : "Rows could not be parsed.");
return;
}
} else if (!csvText.trim()) {
setError("Choose a CSV file or paste CSV data.");
return;
}
setBusy(true);
try {
const source = await createDataflowSourceSnapshot(settings, {
name: name.trim(),
source_name: sourceName.trim(),
description: description.trim() || null,
...(format === "json"
? { format, rows }
: { format, csv_text: csvText, delimiter })
});
onCreated(source);
} catch (createError) {
setError(apiErrorMessage(createError));
} finally {
setBusy(false);
}
};
const loadCsvFile = async (file: File | undefined) => {
if (!file) return;
setError("");
try {
setCsvText(await file.text());
const baseName = file.name.replace(/\.[^.]+$/, "");
setName((current) => current || baseName);
setSourceName((current) => current || sourceNameFromFile(baseName));
} catch {
setError("The selected CSV file could not be read.");
}
};
return (
<Dialog
open={open}
title="Import tabular snapshot"
className="dataflow-source-dialog"
onClose={() => {
if (!busy) onClose();
}}
footer={(
<>
<Button onClick={onClose} disabled={busy}>Cancel</Button>
<Button
variant="primary"
onClick={() => void create()}
disabled={busy || !name.trim() || !sourceName.trim()}
>
{busy ? "Importing..." : "Import"}
</Button>
</>
)}
>
<div className="dataflow-source-dialog-fields">
{error ? (
<DismissibleAlert tone="danger" resetKey={error}>{error}</DismissibleAlert>
) : null}
<div className="dataflow-source-dialog-format">
<SegmentedControl<SnapshotFormat>
ariaLabel="Snapshot format"
options={[
{ id: "json", label: "JSON" },
{ id: "csv", label: "CSV" }
]}
value={format}
onChange={setFormat}
/>
</div>
<FormField label="Name">
<input value={name} onChange={(event) => setName(event.target.value)} />
</FormField>
<FormField label="Logical source name">
<input
value={sourceName}
onChange={(event) => setSourceName(event.target.value)}
pattern="[A-Za-z_][A-Za-z0-9_]*"
placeholder="monthly_input"
/>
</FormField>
<FormField label="Description">
<input value={description} onChange={(event) => setDescription(event.target.value)} />
</FormField>
{format === "csv" ? (
<>
<FormField label="CSV file">
<input
key={fileInputKey}
type="file"
accept=".csv,.tsv,text/csv,text/tab-separated-values,text/plain"
onChange={(event) => void loadCsvFile(event.target.files?.[0])}
/>
</FormField>
<FormField label="Delimiter">
<select value={delimiter} onChange={(event) => setDelimiter(event.target.value)}>
<option value=",">Comma</option>
<option value=";">Semicolon</option>
<option value={"\t"}>Tab</option>
<option value="|">Pipe</option>
</select>
</FormField>
<FormField label="CSV data">
<textarea
className="dataflow-json-editor"
value={csvText}
onChange={(event) => setCsvText(event.target.value)}
spellCheck={false}
/>
</FormField>
</>
) : (
<FormField label="Rows">
<textarea
className="dataflow-json-editor"
value={rowsText}
onChange={(event) => setRowsText(event.target.value)}
spellCheck={false}
/>
</FormField>
)}
</div>
</Dialog>
);
}
function ResultPanel({
tab,
onTabChange,
@@ -702,9 +968,17 @@ function PreviewTable({ preview }: { preview: PipelinePreview | null }) {
))}
</tbody>
</table>
{preview.truncated ? (
<div className="dataflow-preview-truncated">Showing {preview.rows.length} of {preview.total_rows} rows</div>
) : null}
<div className="dataflow-preview-summary">
<span>
Showing {preview.rows.length} of {preview.total_rows} output rows
</span>
<span>{preview.input_row_count} input rows</span>
<span>
{preview.source_fingerprints.length} source
{preview.source_fingerprints.length === 1 ? "" : "s"}
</span>
<span>{preview.executor_version}</span>
</div>
</div>
);
}
@@ -747,7 +1021,6 @@ function DiagnosticsPanel({
function uniqueNodeExists(draft: PipelineDraft, type: string): boolean {
if (type === "output") return draft.graph.nodes.some((node) => node.type === "output");
if (type.startsWith("source.")) return draft.graph.nodes.some((node) => node.type.startsWith("source."));
return false;
}
@@ -762,6 +1035,22 @@ function formatCell(value: unknown): string {
return String(value);
}
function sourceNameFromFile(value: string): string {
const normalized = value
.normalize("NFKD")
.replace(/[\u0300-\u036f]/g, "")
.replace(/[^A-Za-z0-9_]+/g, "_")
.replace(/^_+|_+$/g, "")
.toLocaleLowerCase()
.slice(0, 120);
if (!normalized) return "imported_source";
return (/^[A-Za-z_]/.test(normalized) ? normalized : `source_${normalized}`).slice(0, 120);
}
function isRecord(value: unknown): value is Record<string, unknown> {
return typeof value === "object" && value !== null && !Array.isArray(value);
}
function apiErrorMessage(error: unknown): string {
if (!isApiError(error)) return error instanceof Error ? error.message : "The request failed.";
try {

170
webui/src/features/dataflow/NodeInspector.tsx
View File

@@ -1,16 +1,31 @@
import { useEffect, useState } from "react";
import { Trash2 } from "lucide-react";
import { Button, DismissibleAlert, FormField } from "@govoplan/core-webui";
import type { PipelineGraphNode } from "../../api/dataflow";
import type {
NodeTypeDefinition,
PipelineGraphNode,
TabularSource
} from "../../api/dataflow";
type NodeInspectorProps = {
node: PipelineGraphNode | null;
nodeLibrary: NodeTypeDefinition[];
sources: TabularSource[];
sourceCatalogueAvailable: boolean;
readOnly: boolean;
onChange: (node: PipelineGraphNode) => void;
onDelete: (nodeId: string) => void;
};
export default function NodeInspector({ node, readOnly, onChange, onDelete }: NodeInspectorProps) {
export default function NodeInspector({
node,
nodeLibrary,
sources,
sourceCatalogueAvailable,
readOnly,
onChange,
onDelete
}: NodeInspectorProps) {
const [rowsText, setRowsText] = useState("");
const [aggregateText, setAggregateText] = useState("");
const [sortText, setSortText] = useState("");
@@ -34,6 +49,7 @@ export default function NodeInspector({ node, readOnly, onChange, onDelete }: No
);
}
const definition = nodeLibrary.find((item) => item.type === node.type);
const updateConfig = (patch: Record<string, unknown>) => {
onChange({ ...node, config: { ...node.config, ...patch } });
};
@@ -76,7 +92,7 @@ export default function NodeInspector({ node, readOnly, onChange, onDelete }: No
<div className="dataflow-panel-heading">
<span>
<strong>Inspector</strong>
<small>{node.type}</small>
<small>{definition?.label ?? node.type}</small>
</span>
<Button
variant="ghost"
@@ -111,6 +127,50 @@ export default function NodeInspector({ node, readOnly, onChange, onDelete }: No
/>
</FormField>
) : null}
{node.type === "source.reference" ? (
<>
<FormField label="Source">
<select
value={textValue(node.config.source_ref)}
onChange={(event) => {
const source = sources.find((item) => item.ref === event.target.value);
if (!source) {
updateConfig({
source_ref: "",
expected_fingerprint: ""
});
return;
}
updateConfig({
source_ref: source.ref,
source_name: source.source_name,
expected_fingerprint: source.fingerprint,
source_columns: source.columns
});
}}
disabled={readOnly || !sourceCatalogueAvailable}
>
<option value="">
{sourceCatalogueAvailable ? "Choose a source" : "Connectors unavailable"}
</option>
{sources.map((source) => (
<option key={source.ref} value={source.ref}>
{source.name} ({source.row_count ?? "?"} rows)
</option>
))}
</select>
</FormField>
{textValue(node.config.expected_fingerprint) ? (
<FormField label="Pinned fingerprint">
<input
value={textValue(node.config.expected_fingerprint)}
readOnly
title={textValue(node.config.expected_fingerprint)}
/>
</FormField>
) : null}
</>
) : null}
{node.type === "source.inline" ? (
<FormField label="Rows">
<textarea
@@ -160,6 +220,65 @@ export default function NodeInspector({ node, readOnly, onChange, onDelete }: No
) : null}
</>
) : null}
{node.type === "distinct" ? (
<FormField label="Key columns">
<input
value={stringList(node.config.columns).join(", ")}
onChange={(event) => updateConfig({ columns: commaList(event.target.value) })}
placeholder="All columns"
disabled={readOnly}
/>
</FormField>
) : null}
{node.type === "combine.union" ? (
<FormField label="Duplicates">
<select
value={textValue(node.config.mode) || "all"}
onChange={(event) => updateConfig({ mode: event.target.value })}
disabled={readOnly}
>
<option value="all">Keep all rows</option>
<option value="distinct">Remove duplicates</option>
</select>
</FormField>
) : null}
{node.type === "combine.join" ? (
<>
<FormField label="Join type">
<select
value={textValue(node.config.join_type) || "inner"}
onChange={(event) => updateConfig({ join_type: event.target.value })}
disabled={readOnly}
>
<option value="inner">Matching rows</option>
<option value="left">All left rows</option>
<option value="right">All right rows</option>
<option value="full">All rows</option>
</select>
</FormField>
<FormField label="Left keys">
<input
value={stringList(node.config.left_keys).join(", ")}
onChange={(event) => updateConfig({ left_keys: commaList(event.target.value) })}
disabled={readOnly}
/>
</FormField>
<FormField label="Right keys">
<input
value={stringList(node.config.right_keys).join(", ")}
onChange={(event) => updateConfig({ right_keys: commaList(event.target.value) })}
disabled={readOnly}
/>
</FormField>
<FormField label="Right-column prefix">
<input
value={textValue(node.config.right_prefix)}
onChange={(event) => updateConfig({ right_prefix: event.target.value })}
disabled={readOnly}
/>
</FormField>
</>
) : null}
{node.type === "select" ? (
<FormField label="Columns">
<input
@@ -190,6 +309,51 @@ export default function NodeInspector({ node, readOnly, onChange, onDelete }: No
</FormField>
</>
) : null}
{node.type === "derive" ? (
<>
<FormField label="Output column">
<input
value={textValue(node.config.target_column)}
onChange={(event) => updateConfig({ target_column: event.target.value })}
disabled={readOnly}
/>
</FormField>
<FormField label="Operation">
<select
value={textValue(node.config.operation) || "copy"}
onChange={(event) => updateConfig({ operation: event.target.value })}
disabled={readOnly}
>
<option value="copy">Copy</option>
<option value="upper">Uppercase</option>
<option value="lower">Lowercase</option>
<option value="trim">Trim whitespace</option>
<option value="concat">Concatenate</option>
<option value="coalesce">First non-empty</option>
<option value="add">Add</option>
<option value="subtract">Subtract</option>
<option value="multiply">Multiply</option>
<option value="divide">Divide</option>
</select>
</FormField>
<FormField label="Source columns">
<input
value={stringList(node.config.source_columns).join(", ")}
onChange={(event) => updateConfig({ source_columns: commaList(event.target.value) })}
disabled={readOnly}
/>
</FormField>
{textValue(node.config.operation) === "concat" ? (
<FormField label="Separator">
<input
value={textValue(node.config.separator)}
onChange={(event) => updateConfig({ separator: event.target.value })}
disabled={readOnly}
/>
</FormField>
) : null}
</>
) : null}
{node.type === "sort" ? (
<FormField label="Sort fields">
<textarea

177
webui/src/features/dataflow/model.ts
View File

@@ -4,7 +4,8 @@ import type {
PipelineGraph,
PipelineGraphNode,
PipelinePayload,
PipelineStatus
PipelineStatus,
NodeTypeDefinition
} from "../../api/dataflow";
export type PipelineDraft = {
@@ -18,26 +19,125 @@ export type PipelineDraft = {
editorMode: EditorMode;
};
export const NODE_LABELS: Record<string, string> = {
"source.inline": "Inline source",
"source.reference": "Connector source",
filter: "Filter rows",
select: "Select columns",
aggregate: "Aggregate",
sort: "Sort rows",
limit: "Limit rows",
output: "Output"
};
const input = [{ id: "input", label: "Input", required: true, multiple: false, minimum_connections: 1 }];
const output = [{ id: "output", label: "Output", required: true, multiple: false, minimum_connections: 1 }];
export const PALETTE_NODE_TYPES = [
"source.inline",
export const FALLBACK_NODE_LIBRARY: NodeTypeDefinition[] = [
nodeType("source.inline", "load", "Load", "Inline data", "Enter a small JSON table.", "braces", [], output, {
source_name: "inline_source",
rows: []
}),
nodeType(
"source.reference",
"load",
"Load",
"Connector source",
"Load a governed tabular source.",
"database",
[],
output,
{ source_ref: "", source_name: "connector_source", expected_fingerprint: "" }
),
nodeType(
"combine.union",
"combine",
"Combine",
"Append rows",
"Append two or more inputs.",
"combine",
[{ id: "input", label: "Inputs", required: true, multiple: true, minimum_connections: 2 }],
output,
{ mode: "all" }
),
nodeType(
"combine.join",
"combine",
"Combine",
"Join tables",
"Match two inputs using key columns.",
"git-merge",
[
{ id: "left", label: "Left", required: true, multiple: false, minimum_connections: 1 },
{ id: "right", label: "Right", required: true, multiple: false, minimum_connections: 1 }
],
output,
{ join_type: "inner", left_keys: [""], right_keys: [""], right_prefix: "right_" }
),
nodeType("filter", "filter", "Filter", "Filter rows", "Keep matching rows.", "filter", input, output, {
column: "",
operator: "eq",
value: ""
}),
nodeType(
"distinct",
"filter",
"Filter",
"Remove duplicates",
"Keep one row per key.",
"list-filter",
input,
output,
{ columns: [] }
),
nodeType(
"select",
"transform",
"Transform",
"Select columns",
"Choose and rename fields.",
"columns-3",
input,
output,
{ fields: [{ column: "", alias: "" }] }
),
nodeType(
"derive",
"transform",
"Transform",
"Derive column",
"Create a constrained calculated field.",
"variable",
input,
output,
{ target_column: "", operation: "copy", source_columns: [""], separator: " " }
),
nodeType(
"aggregate",
"transform",
"Transform",
"Aggregate",
"Group and summarize rows.",
"sigma",
input,
output,
{ group_by: [], aggregates: [{ function: "count", column: "*", alias: "row_count" }] }
),
nodeType(
"sort",
"limit",
"output"
] as const;
"transform",
"Transform",
"Sort rows",
"Order rows by fields.",
"arrow-up-down",
input,
output,
{ fields: [{ column: "", direction: "asc" }] }
),
nodeType("limit", "transform", "Transform", "Limit rows", "Keep the first rows.", "list-end", input, output, {
count: 100
}),
nodeType(
"output",
"output",
"Output",
"Preview output",
"Expose the terminal table.",
"panel-top-open",
input,
[],
{}
)
];
export function draftFromPipeline(pipeline: Pipeline): PipelineDraft {
return {
@@ -152,26 +252,45 @@ export function draftFingerprint(draft: PipelineDraft | null): string {
});
}
export function newNode(type: string, position: { x: number; y: number }): PipelineGraphNode {
export function newNode(
type: string,
position: { x: number; y: number },
library: NodeTypeDefinition[] = FALLBACK_NODE_LIBRARY
): PipelineGraphNode {
const id = `${type.replace(".", "-")}-${crypto.randomUUID()}`;
const config: Record<string, unknown> = defaultNodeConfig(type);
const definition = library.find((item) => item.type === type);
const config = structuredClone(definition?.default_config ?? {});
return {
id,
type,
label: NODE_LABELS[type] ?? type,
label: definition?.label ?? type,
position,
config
};
}
function defaultNodeConfig(type: string): Record<string, unknown> {
if (type === "source.inline") return { source_name: "new_source", rows: [] };
if (type === "filter") return { column: "", operator: "eq", value: "" };
if (type === "select") return { fields: [{ column: "", alias: "" }] };
if (type === "aggregate") {
return { group_by: [], aggregates: [{ function: "count", column: "*", alias: "row_count" }] };
}
if (type === "sort") return { fields: [{ column: "", direction: "asc" }] };
if (type === "limit") return { count: 100 };
return {};
function nodeType(
type: string,
category: NodeTypeDefinition["category"],
categoryLabel: string,
label: string,
description: string,
icon: string,
inputPorts: NodeTypeDefinition["input_ports"],
outputPorts: NodeTypeDefinition["output_ports"],
defaultConfig: Record<string, unknown>
): NodeTypeDefinition {
return {
type,
category,
category_label: categoryLabel,
label,
description,
icon,
input_ports: inputPorts,
output_ports: outputPorts,
config_fields: [],
default_config: defaultConfig,
sql_support: ["combine.union", "combine.join", "distinct", "derive"].includes(type) ? "partial" : "full"
};
}

34
webui/src/features/dataflow/nodeIcons.ts Normal file
View File

@@ -0,0 +1,34 @@
import {
ArrowUpDown,
Braces,
Columns3,
Combine,
Database,
Filter,
GitMerge,
ListEnd,
ListFilter,
PanelTopOpen,
Sigma,
Variable,
type LucideIcon
} from "lucide-react";
const icons: Record<string, LucideIcon> = {
"arrow-up-down": ArrowUpDown,
braces: Braces,
"columns-3": Columns3,
combine: Combine,
database: Database,
filter: Filter,
"git-merge": GitMerge,
"list-end": ListEnd,
"list-filter": ListFilter,
"panel-top-open": PanelTopOpen,
sigma: Sigma,
variable: Variable
};
export function dataflowNodeIcon(icon: string): LucideIcon {
return icons[icon] ?? Braces;
}

98
webui/src/styles/dataflow.css
View File

@@ -240,7 +240,7 @@
.dataflow-editor {
flex: 1 1 auto;
display: grid;
grid-template-columns: 170px minmax(0, 1fr) minmax(260px, 310px);
grid-template-columns: 210px minmax(0, 1fr) minmax(260px, 310px);
min-height: 0;
overflow: hidden;
}
@@ -297,10 +297,32 @@
.dataflow-palette-items {
display: grid;
gap: 4px;
height: calc(100% - 44px);
padding: 8px;
overflow: auto;
}
.dataflow-palette-group {
display: grid;
gap: 2px;
}
.dataflow-palette-group + .dataflow-palette-group {
margin-top: 6px;
padding-top: 8px;
border-top: var(--border-line);
}
.dataflow-palette-group h3 {
margin: 0;
color: var(--muted);
font-size: 10px;
font-weight: 700;
letter-spacing: 0;
padding: 3px 8px;
text-transform: uppercase;
}
.dataflow-palette-items button {
display: grid;
grid-template-columns: 18px minmax(0, 1fr) 14px;
@@ -425,6 +447,17 @@
border-left-color: #b7791f;
}
.dataflow-node-combine-union,
.dataflow-node-combine-join {
min-height: 64px;
border-left-color: #2f7d6d;
}
.dataflow-node-distinct {
border-left-color: #b7791f;
}
.dataflow-node-derive,
.dataflow-node-aggregate {
border-left-color: #76569b;
}
@@ -507,6 +540,24 @@
background: var(--accent);
}
.dataflow-node-port-label {
position: absolute;
left: 8px;
max-width: 42px;
overflow: hidden;
color: var(--muted);
font-size: 8px;
line-height: 1;
pointer-events: none;
text-overflow: ellipsis;
transform: translateY(-50%);
white-space: nowrap;
}
.dataflow-node-combine-join .dataflow-node-icon {
margin-left: 31px;
}
.dataflow-inspector-fields {
display: grid;
gap: 12px;
@@ -546,6 +597,27 @@
color: var(--danger-text);
}
.dataflow-source-dialog {
width: min(720px, calc(100vw - 32px));
}
.dataflow-source-dialog-fields {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 12px;
}
.dataflow-source-dialog-fields .alert,
.dataflow-source-dialog-format,
.dataflow-source-dialog-fields .form-field:last-child {
grid-column: 1 / -1;
}
.dataflow-source-dialog-fields .dataflow-json-editor {
width: 100%;
min-height: 260px;
}
.dataflow-sql-workbench {
display: flex;
height: 100%;
@@ -651,9 +723,12 @@
background: var(--primary-soft);
}
.dataflow-preview-truncated {
.dataflow-preview-summary {
position: sticky;
bottom: 0;
display: flex;
flex-wrap: wrap;
gap: 6px 16px;
padding: 6px 10px;
border-top: var(--border-line);
background: var(--panel-header);
@@ -754,7 +829,7 @@
}
.dataflow-editor {
grid-template-columns: 150px minmax(0, 1fr) 270px;
grid-template-columns: 180px minmax(0, 1fr) 270px;
}
.dataflow-editor.is-sql {
@@ -839,6 +914,23 @@
overflow-x: auto;
}
.dataflow-palette-group {
display: flex;
flex: 0 0 auto;
}
.dataflow-palette-group + .dataflow-palette-group {
margin-top: 0;
padding-top: 0;
padding-left: 6px;
border-top: 0;
border-left: var(--border-line);
}
.dataflow-palette-group h3 {
display: none;
}
.dataflow-palette-items button {
min-width: 128px;
}