Eliminates an O(N^2) bottleneck when consuming query results:

• Previously, StreamedResultSet yielded rows by calling iter_rows.pop(0) in a loop. Because Python lists are contiguous arrays, popping index 0 shifts all remaining elements in memory on every row yielded, causing quadratic overhead for larger result sets.
• Replaces pop(0) with direct iteration (for row in iter_rows: yield row) and detaches the list directly (iter_rows, self._rows = self._rows, []), allowing row iteration to run in linear O(N) time at C speed.

Benchmark Performance Verification

Performance verification of this PR branch (spanner-row-popping) compared against the 7-day nightly baseline of the Spanner Python client running on GCE (n2-standard-2, 2 vCPUs, closed-loop single worker):

Results Summary

Observations

• Narrow Result Sets (~90% latency reduction / ~10x throughput): With 200,000 rows and few columns, row popping in StreamedResultSet was the dominant CPU bottleneck. Removing that overhead yields a massive performance leap.
• Wide Result Sets (~13% - 17.5% latency reduction): While complex column deserialization accounts for more relative CPU time in wide rows, the row popping optimization still delivers a consistent ~13% to ~17.5% speedup across Mean, P50, and P90.