perf(amber): replace SyncExecutionResource sleeps with bounded result poll

[Ma77Ball]

What changes were proposed in this PR?

• Replaces the two unconditional Thread.sleep(500) calls in SyncExecutionResource::executeWorkflowSync with a bounded readiness-poll (awaitResultsPersisted) that, per target operator, compares the expected output count from the in-memory stats store against the count committed to result storage (document.getCount), polling at 25ms with a 2s cap and early-exit.
• Wires the poll into the two success paths only (TargetResultsReady before client shutdown, TerminalStateReached(COMPLETED) before reading storage); error, console-error, and timeout paths no longer wait.
• Extracts the poll core into a package-private, injectable awaitUntil(targetIds, expectedCountOf, committedCountOf, timeout, interval, now, sleep) so the timeout and early-exit behavior is unit-testable with a fake clock.
• Removes ~1s of latency from every synchronous run: the engine commits the result writer before COMPLETED is observable, so the poll returns on its first check in the normal case, and the cap is defensive insurance for storage lag.

Any related issues, documentation, discussions?

Closes: #5713

How was this PR tested?

• Run the new unit spec: sbt "WorkflowExecutionService/testOnly org.apache.texera.web.resource.SyncExecutionResourceSpec", expecting succeeded 7, failed 0 (run under JDK 21 as CI does; the repo's JaCoCo 0.8.11 cannot instrument JDK 25 bytecode).
• The 7 cases cover: empty target list (no wait), non-positive expected count (ready), operator with no result storage (ready), already-committed counts (returns on first check), counts that land mid-poll (waits then returns), all-targets-must-be-ready, and never-landing counts (gives up exactly at the 2s cap).
• Manual end-to-end check: run a workflow through the synchronous endpoint (agent "run workflow" or "Execute To") and confirm results return with the same rows as before, now without the fixed ~1s delay.

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

Co-authored with Claude Opus 4.8 in compliance with ASF

[codecov-commenter]

Codecov Report

❌ Patch coverage is 19.23077% with 21 lines in your changes missing coverage. Please review.
✅ Project coverage is 53.05%. Comparing base (5869492) to head (9d6a21f).
✅ All tests successful. No failed tests found.

Files with missing lines	Patch %	Lines
...he/texera/web/resource/SyncExecutionResource.scala	19.23%	21 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #5714      +/-   ##
============================================
- Coverage     53.17%   53.05%   -0.13%     
  Complexity     2660     2660              
============================================
  Files          1094     1094              
  Lines         42363    42350      -13     
  Branches       4556     4554       -2     
============================================
- Hits          22528    22469      -59     
- Misses        18507    18554      +47     
+ Partials       1328     1327       -1     

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.31% <ø> (-0.14%)	⬇️	Carriedforward from 83df2b5
agent-service	34.36% <ø> (ø)		Carriedforward from 83df2b5
amber	53.45% <19.23%> (-0.09%)	⬇️
computing-unit-managing-service	1.65% <ø> (+<0.01%)	⬆️	Carriedforward from 83df2b5
config-service	56.71% <ø> (ø)		Carriedforward from 83df2b5
file-service	57.06% <ø> (ø)		Carriedforward from 83df2b5
frontend	47.86% <ø> (-0.10%)	⬇️	Carriedforward from 83df2b5
pyamber	89.84% <ø> (-0.29%)	⬇️	Carriedforward from 83df2b5
python	90.80% <ø> (ø)		Carriedforward from 83df2b5
workflow-compiling-service	58.69% <ø> (ø)		Carriedforward from 83df2b5

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

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[github-actions]

⚠️ Benchmark changes need a look

🟢 2 better · 🔴 2 worse · ⚪ 11 noise (<±5%) · 0 without baseline

Compared against main 5869492 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	437	0.267	21,821/30,886/30,886 us	🔴 -6.6% / 🟢 -11.7%
🟢	bs=100 sw=10 sl=64	938	0.572	103,720/126,469/126,469 us	🟢 -16.7% / 🟢 -9.5%
⚪	bs=1000 sw=10 sl=64	1,095	0.669	907,927/970,259/970,259 us	⚪ within ±5% / 🟢 -6.7%

Baseline details

Latest main 5869492 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	437 tuples/sec	468 tuples/sec	410.82 tuples/sec	-6.6%	+6.4%
bs=10 sw=10 sl=64	MB/s	0.267 MB/s	0.285 MB/s	0.251 MB/s	-6.3%	+6.5%
bs=10 sw=10 sl=64	p50	21,821 us	21,332 us	23,785 us	+2.3%	-8.3%
bs=10 sw=10 sl=64	p95	30,886 us	30,180 us	34,980 us	+2.3%	-11.7%
bs=10 sw=10 sl=64	p99	30,886 us	30,180 us	34,980 us	+2.3%	-11.7%
bs=100 sw=10 sl=64	throughput	938 tuples/sec	947 tuples/sec	891.94 tuples/sec	-1.0%	+5.2%
bs=100 sw=10 sl=64	MB/s	0.572 MB/s	0.578 MB/s	0.544 MB/s	-1.0%	+5.1%
bs=100 sw=10 sl=64	p50	103,720 us	105,540 us	112,277 us	-1.7%	-7.6%
bs=100 sw=10 sl=64	p95	126,469 us	151,831 us	139,802 us	-16.7%	-9.5%
bs=100 sw=10 sl=64	p99	126,469 us	151,831 us	139,802 us	-16.7%	-9.5%
bs=1000 sw=10 sl=64	throughput	1,095 tuples/sec	1,090 tuples/sec	1,041 tuples/sec	+0.5%	+5.2%
bs=1000 sw=10 sl=64	MB/s	0.669 MB/s	0.665 MB/s	0.635 MB/s	+0.6%	+5.3%
bs=1000 sw=10 sl=64	p50	907,927 us	920,944 us	972,714 us	-1.4%	-6.7%
bs=1000 sw=10 sl=64	p95	970,259 us	979,192 us	1,023,057 us	-0.9%	-5.2%
bs=1000 sw=10 sl=64	p99	970,259 us	979,192 us	1,023,057 us	-0.9%	-5.2%

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
0,10,10,64,20,457.55,200,128000,437,0.267,21820.91,30886.37,30886.37
1,100,10,64,20,2132.83,2000,1280000,938,0.572,103720.49,126468.68,126468.68
2,1000,10,64,20,18258.70,20000,12800000,1095,0.669,907926.61,970258.95,970258.95

[Ma77Ball]

/request-review @Yicong-Huang