Detect duplicated transactions
Requirement
A charge that appears twice with the same merchant, amount, and day is probably a double-swipe. Find every such group in July's transactions and list each transaction involved so the user can review them — but do not flag the same merchant and amount on different days (a repeat coffee is not a duplicate). The data is in the code below; both versions must print the lines shown under Expected output.
Expected output
Possible duplicate charges: 2026-07-08 Noodle Bar $18.90 2026-07-08 Noodle Bar $18.90 2026-07-21 StreamFlix $9.99 2026-07-21 StreamFlix $9.99
Side by side
Native Dart
FxDart
Why they differ
The algorithm is group–keep–flatten, and FxDart writes it as exactly
those three words: groupBy the merchant|amount|day key,
filter the groups with more than one member,
flatMap the survivors back into individual transactions
(map + join format them). Native Dart has
none of the three as vocabulary: grouping becomes a
putIfAbsent loop, keeping-and-flattening becomes nested
for loops with an if between them. Both are
correct; only one still looks like the sentence that specified it.
Benchmark
N = 100
Time Tie
Peak memory Tie
N = 10,000
Time Tie
Peak memory FxDart wins
N = 1,000,000
Time Tie
Peak memory Tie
Bars are medians of repeated timed iterations in fresh processes per side (small N is batched for timer resolution). Sides within 5% of each other — or within 0.6 ms, a difference no person can perceive — count as a tie; close relative races are re-measured up to 5 runs. In an app, anything under a few milliseconds is invisible to the user regardless of which bar is shorter. Memory is peak process RSS. The Dart VM and the dataset are identical on both sides, so the difference between the two bars is what the pipeline itself holds onto.