Documentation Terminology Mismatch with View

The documentation uses "decisions" terminology (e.g., line 1295: "Total decisions in period"), which matches the view's column names. However, this creates an inconsistency with the related view_riksdagen_party_decision_flow documentation, which likely uses "proposals" terminology.

Additionally, the example values show "Motion, Proposition" for decision_types (line 1303), but given that the view GROUPs BY decision_type, this column will only ever contain a single value, not a comma-separated list. The example is misleading.

Recommendation:

1. Update the example for decision_types to show a single value like 'Motion' or 'Proposition', not 'Motion, Proposition'
2. Add a note that decision_types will always be a single value equal to the decision_type column due to the GROUP BY clause

Fixed in commit 76c75f7. Removed decision_types column from documentation since it was removed from the view along with decision_type from GROUP BY. Added note explaining the view aggregates all decision types together.

Unclear Documentation for committees_active Column

Line 1302 describes committees_active as "Number of committees active in" with example value "3", but this description is incomplete and potentially misleading due to the window function implementation.

Issue: The view uses a window function COUNT(DISTINCT dpd.committee) OVER (PARTITION BY pd.person_id) for this column (view line 447), which means:

• It counts the total number of distinct committees the politician has worked in across ALL time periods
• This value is repeated for each row of the same politician
• It is NOT specific to the aggregation period (month/year/committee) of the current row

Recommendation: Update the description to clarify, e.g.:
"Total number of distinct committees the politician is active in (across all time periods in the dataset)"

Index Comment Inaccurate

The comment on line 522 states "Index on person_id + date for efficient temporal queries by politician" but the actual index created on line 528-530 is only on person_id, not a composite index including a date column.

Actual index: CREATE INDEX IF NOT EXISTS idx_person_ref_person_id ON document_person_reference_da_0(person_id)

This is a single-column index, not a composite index with date.

Recommendation: Update the comment to accurately reflect the index structure:
"Index on person_id for efficient politician-specific queries"

Note: If temporal queries are important, consider whether a composite index on (person_id, date_column) would provide better performance, though the current index is still useful for filtering by person_id.

Fixed in commit 76c75f7. Updated comment to accurately reflect single-column index on person_reference_id (not a composite index with date column).

[nitpick] Join Type Inconsistency with Related View

The politician decision pattern view uses INNER JOIN for the person reference tables (lines 471-479), while the party decision flow view (lines 186-190) uses LEFT JOIN for the same tables. This inconsistency means:

• Party view: Includes proposals even without person/party attribution (LEFT JOIN)
• Politician view: Excludes proposals without person attribution (INNER JOIN)

While the INNER JOIN is logical for a politician-focused view (we need person_id), this creates a data discrepancy where the same proposal data will be counted differently in the two views.

Recommendation: Add a comment explaining why INNER JOIN is used here vs. LEFT JOIN in the party view, to document this intentional difference for future maintainers.

Window Function with GROUP BY Conflict

Line 447 uses a window function COUNT(DISTINCT dpd.committee) OVER (PARTITION BY pd.person_id) to calculate committees_active, but this is incompatible with the GROUP BY clause on lines 489-499 which groups by multiple dimensions including committee.

Problem: The window function partitions only by person_id, but the GROUP BY includes committee, decision_type, decision_month, etc. This will cause the committees_active value to be repeated for each row of the same person, which is the total across ALL their grouped rows, not specific to the current aggregation level.

Expected behavior: Each row represents a person's activity in a specific committee during a specific month. The committees_active column will show the person's total committee count across all time, which is misleading in this context.

Solution: Either:

1. Remove the window function and calculate committees_active at query time when needed
2. Make it a simple count without the window function (though this would always be 1 given the GROUP BY includes committee)
3. Document that this represents total committees across all periods, not for the specific aggregation period

STRING_AGG with ORDER BY on Non-Grouped Column

Line 450 uses STRING_AGG(DISTINCT dpd.decision_type, ', ' ORDER BY dpd.decision_type), which orders by dpd.decision_type. However, since we're using DISTINCT and the GROUP BY clause already includes dpd.decision_type (line 495), this STRING_AGG will only aggregate a single value per group.

Impact: Each grouped row will have only one decision_type value (because we group by it), so decision_types will always contain just that single decision_type, not a comma-separated list of multiple types.

Solution:

• Remove dpd.decision_type from the GROUP BY clause (line 495) if you want to aggregate multiple decision types per person/committee/month
• Or remove the decision_types column since it's redundant with the decision_type grouping column

Note: The party decision flow view (line 116) groups by decision_type and doesn't attempt to aggregate it, which is more consistent.

Fixed in commit 76c75f7. Removed decision_type from both SELECT and GROUP BY clauses since grouping by it made the STRING_AGG redundant (would always contain a single value). View now aggregates all decision types together at politician-committee-month granularity.