chore(pyamber): remove dead duplicate TimedBuffer

[Ma77Ball]

What changes were proposed in this PR?

• Deleted amber/src/main/python/core/util/console_message/timed_buffer.py, a dead, unreferenced near-duplicate of the live core/util/buffer/timed_buffer.py.
• No other files change: the only live TimedBuffer user, console_message_manager.py, already imports the buffer/ version, and the sibling files in the console_message/ directory (error_message, replace_print, timestamp) remain in use and are untouched.

Any related issues, documentation, discussions?

Closes: #5980

How was this PR tested?

• Pure deletion of unreferenced code, so no test was added or changed. Confirmed dead three ways: grep -rn "console_message.timed_buffer" amber/src returns no importers, there is no __init__ re-export of the module, and no test references it.
• Reviewer can verify with cd amber/src/main/python && grep -rn "console_message.timed_buffer\|console_message import timed_buffer" ., expect zero matches outside the deleted file itself.

Was this PR authored or co-authored using generative AI tooling?

Co-authored with Claude Opus 4.8 in compliance with ASF

[github-actions]

Automated Reviewer Suggestions

Based on the git blame history of the changed files, we recommend the following reviewers:

• No candidates found from git blame history.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 56.24%. Comparing base (1aae005) to head (20d29a9).
⚠️ Report is 3 commits behind head on main.

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #5981      +/-   ##
============================================
- Coverage     56.25%   56.24%   -0.01%     
  Complexity     2992     2992              
============================================
  Files          1120     1120              
  Lines         43175    43182       +7     
  Branches       4658     4658              
============================================
+ Hits          24286    24287       +1     
- Misses        17469    17475       +6     
  Partials       1420     1420              

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.00% <ø> (ø)		Carriedforward from 1aae005
agent-service	44.59% <ø> (ø)		Carriedforward from 1aae005
amber	57.80% <ø> (ø)		Carriedforward from 1aae005
computing-unit-managing-service	0.00% <ø> (ø)		Carriedforward from 1aae005
config-service	51.56% <ø> (ø)		Carriedforward from 1aae005
file-service	59.02% <ø> (ø)		Carriedforward from 1aae005
frontend	49.30% <ø> (ø)		Carriedforward from 1aae005
notebook-migration-service	78.57% <ø> (ø)		Carriedforward from 1aae005
pyamber	90.15% <ø> (-0.05%)	⬇️
python	90.76% <ø> (ø)		Carriedforward from 1aae005
workflow-compiling-service	55.14% <ø> (ø)		Carriedforward from 1aae005

*This pull request uses carry forward flags. Click here to find out more.

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[github-actions]

⚠️ Benchmark changes need a look

🟢 0 better · 🔴 6 worse · ⚪ 9 noise (<±5%) · 0 without baseline

Compared against main ce23c01 benchmarked on this same runner, so the delta is largely free of cross-runner hardware noise. The "7d avg" column still reflects the gh-pages dashboard. Treat <±5% as noise unless repeated.

Dashboard · Run

	config	throughput	MB/s	latency	max Δ latest / 7d
🔴	bs=10 sw=10 sl=64	362	0.221	26,904/37,148/37,148 us	🔴 +16.4% / 🔴 +142.3%
🔴	bs=100 sw=10 sl=64	772	0.471	128,415/154,812/154,812 us	🔴 +5.3% / 🔴 +40.0%
⚪	bs=1000 sw=10 sl=64	898	0.548	1,112,647/1,168,059/1,168,059 us	⚪ within ±5% / 🔴 +9.8%

Baseline details

Latest main ce23c01 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	362 tuples/sec	402 tuples/sec	757.16 tuples/sec	-10.0%	-52.2%
bs=10 sw=10 sl=64	MB/s	0.221 MB/s	0.245 MB/s	0.462 MB/s	-9.8%	-52.2%
bs=10 sw=10 sl=64	p50	26,904 us	23,112 us	12,971 us	+16.4%	+107.4%
bs=10 sw=10 sl=64	p95	37,148 us	34,312 us	15,333 us	+8.3%	+142.3%
bs=10 sw=10 sl=64	p99	37,148 us	34,312 us	18,732 us	+8.3%	+98.3%
bs=100 sw=10 sl=64	throughput	772 tuples/sec	798 tuples/sec	957.66 tuples/sec	-3.3%	-19.4%
bs=100 sw=10 sl=64	MB/s	0.471 MB/s	0.487 MB/s	0.585 MB/s	-3.3%	-19.4%
bs=100 sw=10 sl=64	p50	128,415 us	121,896 us	103,982 us	+5.3%	+23.5%
bs=100 sw=10 sl=64	p95	154,812 us	151,471 us	110,583 us	+2.2%	+40.0%
bs=100 sw=10 sl=64	p99	154,812 us	151,471 us	118,369 us	+2.2%	+30.8%
bs=1000 sw=10 sl=64	throughput	898 tuples/sec	896 tuples/sec	979.6 tuples/sec	+0.2%	-8.3%
bs=1000 sw=10 sl=64	MB/s	0.548 MB/s	0.547 MB/s	0.598 MB/s	+0.2%	-8.3%
bs=1000 sw=10 sl=64	p50	1,112,647 us	1,104,609 us	1,024,553 us	+0.7%	+8.6%
bs=1000 sw=10 sl=64	p95	1,168,059 us	1,216,804 us	1,063,789 us	-4.0%	+9.8%
bs=1000 sw=10 sl=64	p99	1,168,059 us	1,216,804 us	1,096,239 us	-4.0%	+6.6%

Raw CSV

config_idx,batch_size,schema_width,string_len,num_batches,total_ms,total_tuples,total_bytes,tuples_per_sec,mb_per_sec,lat_p50_us,lat_p95_us,lat_p99_us
0,10,10,64,20,552.41,200,128000,362,0.221,26903.83,37147.67,37147.67
1,100,10,64,20,2589.34,2000,1280000,772,0.471,128414.60,154812.18,154812.18
2,1000,10,64,20,22276.22,20000,12800000,898,0.548,1112647.37,1168059.01,1168059.01