raw chart working #1168
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raw chart working #1168
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WalkthroughAdds logging of extra_dimension in charts API. In charts service, deduplicates selected columns, reworks column mapping, and implements consistent extra_dimension grouping across raw and aggregated chart paths, including multi-series bar/line handling and revised pie slice generation. Table preview aligns with new deduped column ordering. No API signatures changed. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant Client
participant API as Charts API
participant Service as Charts Service
participant DB as Data Source
Client->>API: get_chart_data(request: chart_type, x/y, metrics, extra_dimension?)
API->>Service: build_chart_query(params)
activate Service
Note right of Service: Deduplicate axis cols -> projection order
Service->>DB: execute_chart_query(query)
DB-->>Service: rows
Service->>Service: transform_data_for_chart(rows, chart_type, extra_dimension?)
alt raw or aggregated with extra_dimension
Note right of Service: Group by extra_dimension -> multi-series
else no extra_dimension
Note right of Service: Single-series per chart_type
end
Service-->>API: chart series + x-axis + labels
deactivate Service
API-->>Client: response
sequenceDiagram
autonumber
participant Service
participant DB
Service->>Service: get_chart_data_table_preview(params)
Note right of Service: Collect x/y/metrics/extra_dimension
Service->>Service: Deduplicate columns (ordered)
Service->>DB: query with projected unique columns
DB-->>Service: rows
Service-->>Service: Build column_mapping from ordered uniques
Service-->>Service: Preview table data
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## main #1168 +/- ##
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- Coverage 52.57% 52.29% -0.28%
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Lines 11358 11418 +60
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Actionable comments posted: 4
🧹 Nitpick comments (3)
ddpui/core/charts/charts_service.py (2)
1124-1144: Preview (raw): keep ordered de-dup and de-duplicate logic in one place.Mirror the ordered logic and consider extracting a small helper to avoid drift between build_chart_query/execute_chart_query/preview.
- columns_to_add = set() - column_order = [] # Preserve insertion order - if payload.x_axis: - if payload.x_axis not in columns_to_add: - columns_to_add.add(payload.x_axis) - column_order.append(payload.x_axis) - if payload.y_axis: - if payload.y_axis not in columns_to_add: - columns_to_add.add(payload.y_axis) - column_order.append(payload.y_axis) - if payload.extra_dimension: - if payload.extra_dimension not in columns_to_add: - columns_to_add.add(payload.extra_dimension) - column_order.append(payload.extra_dimension) - - # Build mapping using the unique column order - for col_index, col_name in enumerate(column_order): + ordered_columns: list[str] = [] + for col in [payload.x_axis, payload.y_axis, payload.extra_dimension]: + if col and col not in ordered_columns: + ordered_columns.append(col) + + # Build mapping using the unique column order + for col_index, col_name in enumerate(ordered_columns): column_mapping.append((col_name, col_index)) columns.append(col_name)I can extract a helper like:
def _ordered_unique_columns(*cols: str | None) -> list[str]: out = [] for c in cols: if c and c not in out: out.append(c) return outand wire it in all three places. Want me to push that refactor?
557-579: Use ordered de-dup for raw columns and fix/remove the unused column_mapping
- Replace the set+manual checks with an ordered de-dup (e.g. iterate [payload.x_axis, payload.y_axis, payload.extra_dimension] and append if not already present) in both places that build column_mapping in ddpui/core/charts/charts_service.py (around l.555 and l.1120).
- execute_query currently ignores column_mapping — PostgresClient.execute and BigqueryClient.execute convert rows to dicts ([dict(row) for row in rows]), so the mapping is never applied. Either:
- Implement mapping inside execute_query to convert tuple results -> dicts using column_mapping, or
- Remove the column_mapping parameter and stop building it in execute_chart_query (also update execute_query's docstring).
ddpui/api/charts_api.py (1)
516-516: Include extra_dimension in logs — good; consider DEBUG level to reduce noise.This line is verbose for INFO (logs schema/table/metrics each request). Suggest DEBUG.
- logger.info( + logger.debug( f"Columns - x_axis: {payload.x_axis}, y_axis: {payload.y_axis}, dimension_col: {payload.dimension_col}, extra_dimension: {payload.extra_dimension}, metrics: {payload.metrics}" )
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📒 Files selected for processing (2)
ddpui/api/charts_api.py(1 hunks)ddpui/core/charts/charts_service.py(6 hunks)
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ddpui/core/charts/charts_service.py (1)
ddpui/datainsights/query_builder.py (1)
add_column(31-34)
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Comment on lines
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| if payload.chart_type == "bar": | ||
| if payload.computation_type == "raw": | ||
| return { | ||
| "xAxisData": [ | ||
| convert_value(safe_get_value(row, payload.x_axis, null_label)) | ||
| for row in results | ||
| ], | ||
| "series": [ | ||
| { | ||
| "name": payload.y_axis, | ||
| "data": [ | ||
| convert_value( | ||
| safe_get_value(row, payload.y_axis, null_label), preserve_none=True | ||
| ) | ||
| for row in results | ||
| ], | ||
| } | ||
| ], | ||
| "legend": [payload.y_axis], | ||
| } | ||
| # Handle extra dimension for grouping/stacking in raw charts | ||
| if payload.extra_dimension: | ||
| # Group data by extra dimension | ||
| grouped_data = {} | ||
| x_values = set() | ||
|
|
||
| for row in results: | ||
| x_val = convert_value(safe_get_value(row, payload.x_axis, null_label)) | ||
| y_val = convert_value( | ||
| safe_get_value(row, payload.y_axis, null_label), preserve_none=True | ||
| ) | ||
| extra_val = convert_value( | ||
| safe_get_value(row, payload.extra_dimension, null_label) | ||
| ) | ||
|
|
||
| x_values.add(x_val) | ||
|
|
||
| if extra_val not in grouped_data: | ||
| grouped_data[extra_val] = {} | ||
| grouped_data[extra_val][x_val] = y_val | ||
|
|
||
| # Build series data for each extra dimension value | ||
| x_axis_data = sorted(list(x_values)) | ||
| series_data = [] | ||
| legend_data = [] | ||
|
|
||
| for extra_val, data_dict in grouped_data.items(): | ||
| series_values = [data_dict.get(x_val, 0) for x_val in x_axis_data] | ||
| series_data.append({"name": str(extra_val), "data": series_values}) | ||
| legend_data.append(str(extra_val)) | ||
|
|
||
| return { | ||
| "xAxisData": x_axis_data, | ||
| "series": series_data, | ||
| "legend": legend_data, | ||
| } | ||
| else: | ||
| # Simple raw chart without extra dimension | ||
| return { | ||
| "xAxisData": [ | ||
| convert_value(safe_get_value(row, payload.x_axis, null_label)) | ||
| for row in results | ||
| ], | ||
| "series": [ | ||
| { | ||
| "name": payload.y_axis, | ||
| "data": [ | ||
| convert_value( | ||
| safe_get_value(row, payload.y_axis, null_label), | ||
| preserve_none=True, | ||
| ) | ||
| for row in results | ||
| ], | ||
| } | ||
| ], | ||
| "legend": [payload.y_axis], | ||
| } |
There was a problem hiding this comment.
Bug: y-axis nulls become "Unknown" strings in raw bar.
Using safe_get_value on numeric y injects the null label into series data. Use row.get(...) and convert_value(..., preserve_none=True) to keep None numeric-safe.
- y_val = convert_value(
- safe_get_value(row, payload.y_axis, null_label), preserve_none=True
- )
+ y_val = convert_value(row.get(payload.y_axis), preserve_none=True)Also consider retaining original x order instead of sorted() if the dataset order is meaningful.
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Suggested change
| if payload.chart_type == "bar": | |
| if payload.computation_type == "raw": | |
| return { | |
| "xAxisData": [ | |
| convert_value(safe_get_value(row, payload.x_axis, null_label)) | |
| for row in results | |
| ], | |
| "series": [ | |
| { | |
| "name": payload.y_axis, | |
| "data": [ | |
| convert_value( | |
| safe_get_value(row, payload.y_axis, null_label), preserve_none=True | |
| ) | |
| for row in results | |
| ], | |
| } | |
| ], | |
| "legend": [payload.y_axis], | |
| } | |
| # Handle extra dimension for grouping/stacking in raw charts | |
| if payload.extra_dimension: | |
| # Group data by extra dimension | |
| grouped_data = {} | |
| x_values = set() | |
| for row in results: | |
| x_val = convert_value(safe_get_value(row, payload.x_axis, null_label)) | |
| y_val = convert_value( | |
| safe_get_value(row, payload.y_axis, null_label), preserve_none=True | |
| ) | |
| extra_val = convert_value( | |
| safe_get_value(row, payload.extra_dimension, null_label) | |
| ) | |
| x_values.add(x_val) | |
| if extra_val not in grouped_data: | |
| grouped_data[extra_val] = {} | |
| grouped_data[extra_val][x_val] = y_val | |
| # Build series data for each extra dimension value | |
| x_axis_data = sorted(list(x_values)) | |
| series_data = [] | |
| legend_data = [] | |
| for extra_val, data_dict in grouped_data.items(): | |
| series_values = [data_dict.get(x_val, 0) for x_val in x_axis_data] | |
| series_data.append({"name": str(extra_val), "data": series_values}) | |
| legend_data.append(str(extra_val)) | |
| return { | |
| "xAxisData": x_axis_data, | |
| "series": series_data, | |
| "legend": legend_data, | |
| } | |
| else: | |
| # Simple raw chart without extra dimension | |
| return { | |
| "xAxisData": [ | |
| convert_value(safe_get_value(row, payload.x_axis, null_label)) | |
| for row in results | |
| ], | |
| "series": [ | |
| { | |
| "name": payload.y_axis, | |
| "data": [ | |
| convert_value( | |
| safe_get_value(row, payload.y_axis, null_label), | |
| preserve_none=True, | |
| ) | |
| for row in results | |
| ], | |
| } | |
| ], | |
| "legend": [payload.y_axis], | |
| } | |
| if payload.chart_type == "bar": | |
| if payload.computation_type == "raw": | |
| # Handle extra dimension for grouping/stacking in raw charts | |
| if payload.extra_dimension: | |
| # Group data by extra dimension | |
| grouped_data = {} | |
| x_values = set() | |
| for row in results: | |
| x_val = convert_value(safe_get_value(row, payload.x_axis, null_label)) | |
| y_val = convert_value(row.get(payload.y_axis), preserve_none=True) | |
| extra_val = convert_value( | |
| safe_get_value(row, payload.extra_dimension, null_label) | |
| ) | |
| x_values.add(x_val) | |
| if extra_val not in grouped_data: | |
| grouped_data[extra_val] = {} | |
| grouped_data[extra_val][x_val] = y_val | |
| # Build series data for each extra dimension value | |
| x_axis_data = sorted(list(x_values)) | |
| series_data = [] | |
| legend_data = [] | |
| for extra_val, data_dict in grouped_data.items(): | |
| series_values = [data_dict.get(x_val, 0) for x_val in x_axis_data] | |
| series_data.append({"name": str(extra_val), "data": series_values}) | |
| legend_data.append(str(extra_val)) | |
| return { | |
| "xAxisData": x_axis_data, | |
| "series": series_data, | |
| "legend": legend_data, | |
| } | |
| else: | |
| # Simple raw chart without extra dimension | |
| return { | |
| "xAxisData": [ | |
| convert_value(safe_get_value(row, payload.x_axis, null_label)) | |
| for row in results | |
| ], | |
| "series": [ | |
| { | |
| "name": payload.y_axis, | |
| "data": [ | |
| convert_value( | |
| safe_get_value(row, payload.y_axis, null_label), | |
| preserve_none=True, | |
| ) | |
| for row in results | |
| ], | |
| } | |
| ], | |
| "legend": [payload.y_axis], | |
| } |
🤖 Prompt for AI Agents
In ddpui/core/charts/charts_service.py around lines 621 to 679, fix the raw
bar-chart branch so numeric y nulls are preserved as None (not replaced by the
null label) and original x order is retained when extra_dimension is present:
when extracting y values use row.get(payload.y_axis, None) (not safe_get_value)
and pass that to convert_value(..., preserve_none=True); for x values, preserve
input order instead of calling sorted() by collecting x values into a list while
tracking a seen set (append new x when first encountered) and use that ordered
list for xAxisData and for building series with data_dict.get(x_val, 0).
Comment on lines
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| elif payload.chart_type == "pie": | ||
| if payload.computation_type == "raw": | ||
| # For raw data, count occurrences | ||
| value_counts = {} | ||
| for row in results: | ||
| key = handle_null_value(safe_get_value(row, payload.x_axis, null_label), null_label) | ||
| value_counts[key] = value_counts.get(key, 0) + 1 | ||
| # For truly raw data, each record becomes a separate pie slice | ||
| pie_data = [] | ||
| for i, row in enumerate(results): | ||
| x_value = handle_null_value( | ||
| safe_get_value(row, payload.x_axis, null_label), null_label | ||
| ) | ||
|
|
||
| if payload.y_axis: | ||
| # Use Y-axis value as the slice value | ||
| y_value = convert_value( | ||
| safe_get_value(row, payload.y_axis, null_label), preserve_none=True | ||
| ) | ||
| slice_value = y_value if y_value is not None else 0 | ||
| # Create unique slice name combining X and Y values | ||
| slice_name = f"{x_value} ({slice_value})" | ||
| else: | ||
| # If no Y-axis, each record gets value of 1 | ||
| slice_value = 1 | ||
| # Create unique slice name with index to avoid duplicates | ||
| slice_name = f"{x_value} #{i+1}" | ||
|
|
||
| pie_data.append({"value": slice_value, "name": slice_name}) | ||
|
|
||
| series_name = payload.y_axis if payload.y_axis else f"Records by {payload.x_axis}" | ||
| return { | ||
| "pieData": [{"value": count, "name": name} for name, count in value_counts.items()], | ||
| "seriesName": payload.x_axis, | ||
| "pieData": pie_data, | ||
| "seriesName": series_name, | ||
| } |
There was a problem hiding this comment.
Bug: pie slice values can become non-numeric "Unknown".
For raw pie with y_axis, safe_get_value converts None to a string label, breaking ECharts numeric expectations. Fetch raw and preserve None.
- y_value = convert_value(
- safe_get_value(row, payload.y_axis, null_label), preserve_none=True
- )
+ y_value = convert_value(row.get(payload.y_axis), preserve_none=True)Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In ddpui/core/charts/charts_service.py around lines 791 to 820, the raw pie
branch currently converts None Y values into the null label string which
produces non-numeric slice values; change the Y-axis value retrieval to fetch
the raw value while preserving None (i.e., call safe_get_value for
payload.y_axis with preserve_none=True or otherwise avoid mapping None to
null_label), then pass that raw value into convert_value with preserve_none=True
so that None stays None and you can set slice_value = y_value if y_value is not
None else 0; keep slice_name logic but only include the numeric y_value when it
is not None to avoid embedding the null label into the numeric field.
Comment on lines
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| if payload.computation_type == "raw": | ||
| return { | ||
| "xAxisData": [ | ||
| convert_value(safe_get_value(row, payload.x_axis, null_label)) | ||
| for row in results | ||
| ], | ||
| "series": [ | ||
| { | ||
| "name": payload.y_axis, | ||
| "data": [ | ||
| convert_value( | ||
| safe_get_value(row, payload.y_axis, null_label), preserve_none=True | ||
| ) | ||
| for row in results | ||
| ], | ||
| } | ||
| ], | ||
| "legend": [payload.y_axis], | ||
| } | ||
| # Check if we have extra_dimension for grouping | ||
| if payload.extra_dimension: | ||
| # Group data by extra dimension | ||
| grouped_data = {} | ||
| x_values = set() | ||
| for row in results: | ||
| x_val = convert_value(safe_get_value(row, payload.x_axis, null_label)) | ||
| y_val = convert_value( | ||
| safe_get_value(row, payload.y_axis, null_label), preserve_none=True | ||
| ) | ||
| extra_val = convert_value( | ||
| safe_get_value(row, payload.extra_dimension, null_label) | ||
| ) | ||
|
|
||
| x_values.add(x_val) | ||
| if extra_val not in grouped_data: | ||
| grouped_data[extra_val] = {} | ||
| grouped_data[extra_val][x_val] = y_val | ||
|
|
||
| # Build series data for each extra dimension value | ||
| x_axis_data = sorted(list(x_values)) | ||
| series_data = [] | ||
| legend_data = [] | ||
|
|
||
| for extra_val, data_dict in grouped_data.items(): | ||
| series_values = [data_dict.get(x_val, 0) for x_val in x_axis_data] | ||
| series_data.append({"name": str(extra_val), "data": series_values}) | ||
| legend_data.append(str(extra_val)) | ||
|
|
||
| return { | ||
| "xAxisData": x_axis_data, | ||
| "series": series_data, | ||
| "legend": legend_data, | ||
| } | ||
| else: | ||
| # Simple raw chart without extra dimension | ||
| return { | ||
| "xAxisData": [ | ||
| convert_value(safe_get_value(row, payload.x_axis, null_label)) | ||
| for row in results | ||
| ], | ||
| "series": [ | ||
| { | ||
| "name": payload.y_axis, | ||
| "data": [ | ||
| convert_value( | ||
| safe_get_value(row, payload.y_axis, null_label), | ||
| preserve_none=True, | ||
| ) | ||
| for row in results | ||
| ], | ||
| } | ||
| ], | ||
| "legend": [payload.y_axis], | ||
| } | ||
| else: # aggregated |
There was a problem hiding this comment.
Bug: y-axis null handling in raw line mirrors bar issue.
Apply the same fix to avoid "Unknown" in numeric series.
- y_val = convert_value(
- safe_get_value(row, payload.y_axis, null_label), preserve_none=True
- )
+ y_val = convert_value(row.get(payload.y_axis), preserve_none=True)- "data": [
- convert_value(
- safe_get_value(row, payload.y_axis, null_label),
- preserve_none=True,
- )
- for row in results
- ],
+ "data": [
+ convert_value(row.get(payload.y_axis), preserve_none=True)
+ for row in results
+ ],📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| if payload.computation_type == "raw": | |
| return { | |
| "xAxisData": [ | |
| convert_value(safe_get_value(row, payload.x_axis, null_label)) | |
| for row in results | |
| ], | |
| "series": [ | |
| { | |
| "name": payload.y_axis, | |
| "data": [ | |
| convert_value( | |
| safe_get_value(row, payload.y_axis, null_label), preserve_none=True | |
| ) | |
| for row in results | |
| ], | |
| } | |
| ], | |
| "legend": [payload.y_axis], | |
| } | |
| # Check if we have extra_dimension for grouping | |
| if payload.extra_dimension: | |
| # Group data by extra dimension | |
| grouped_data = {} | |
| x_values = set() | |
| for row in results: | |
| x_val = convert_value(safe_get_value(row, payload.x_axis, null_label)) | |
| y_val = convert_value( | |
| safe_get_value(row, payload.y_axis, null_label), preserve_none=True | |
| ) | |
| extra_val = convert_value( | |
| safe_get_value(row, payload.extra_dimension, null_label) | |
| ) | |
| x_values.add(x_val) | |
| if extra_val not in grouped_data: | |
| grouped_data[extra_val] = {} | |
| grouped_data[extra_val][x_val] = y_val | |
| # Build series data for each extra dimension value | |
| x_axis_data = sorted(list(x_values)) | |
| series_data = [] | |
| legend_data = [] | |
| for extra_val, data_dict in grouped_data.items(): | |
| series_values = [data_dict.get(x_val, 0) for x_val in x_axis_data] | |
| series_data.append({"name": str(extra_val), "data": series_values}) | |
| legend_data.append(str(extra_val)) | |
| return { | |
| "xAxisData": x_axis_data, | |
| "series": series_data, | |
| "legend": legend_data, | |
| } | |
| else: | |
| # Simple raw chart without extra dimension | |
| return { | |
| "xAxisData": [ | |
| convert_value(safe_get_value(row, payload.x_axis, null_label)) | |
| for row in results | |
| ], | |
| "series": [ | |
| { | |
| "name": payload.y_axis, | |
| "data": [ | |
| convert_value( | |
| safe_get_value(row, payload.y_axis, null_label), | |
| preserve_none=True, | |
| ) | |
| for row in results | |
| ], | |
| } | |
| ], | |
| "legend": [payload.y_axis], | |
| } | |
| else: # aggregated | |
| if payload.computation_type == "raw": | |
| # Check if we have extra_dimension for grouping | |
| if payload.extra_dimension: | |
| # Group data by extra dimension | |
| grouped_data = {} | |
| x_values = set() | |
| for row in results: | |
| x_val = convert_value(safe_get_value(row, payload.x_axis, null_label)) | |
| y_val = convert_value(row.get(payload.y_axis), preserve_none=True) | |
| extra_val = convert_value( | |
| safe_get_value(row, payload.extra_dimension, null_label) | |
| ) | |
| x_values.add(x_val) | |
| if extra_val not in grouped_data: | |
| grouped_data[extra_val] = {} | |
| grouped_data[extra_val][x_val] = y_val | |
| # Build series data for each extra dimension value | |
| x_axis_data = sorted(list(x_values)) | |
| series_data = [] | |
| legend_data = [] | |
| for extra_val, data_dict in grouped_data.items(): | |
| series_values = [data_dict.get(x_val, 0) for x_val in x_axis_data] | |
| series_data.append({"name": str(extra_val), "data": series_values}) | |
| legend_data.append(str(extra_val)) | |
| return { | |
| "xAxisData": x_axis_data, | |
| "series": series_data, | |
| "legend": legend_data, | |
| } | |
| else: | |
| # Simple raw chart without extra dimension | |
| return { | |
| "xAxisData": [ | |
| convert_value(safe_get_value(row, payload.x_axis, null_label)) | |
| for row in results | |
| ], | |
| "series": [ | |
| { | |
| "name": payload.y_axis, | |
| "data": [ | |
| convert_value(row.get(payload.y_axis), preserve_none=True) | |
| for row in results | |
| ], | |
| } | |
| ], | |
| "legend": [payload.y_axis], | |
| } | |
| else: # aggregated |
🤖 Prompt for AI Agents
In ddpui/core/charts/charts_service.py around lines 865-921, the grouped
raw-path currently fills missing x-axis points with 0 (series_values =
[data_dict.get(x_val, 0) ...]) which causes numeric series to show
"Unknown"/incorrect bars; change the default to None so missing points remain
nulls (series_values = [data_dict.get(x_val, None) ...]) and ensure the grouped
y values are converted with preserve_none=True (leave that conversion in place)
so charts receive None for missing data instead of 0 or the string "Unknown".
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Summary by CodeRabbit
New Features
Bug Fixes