Use new classic histogram conversion algorithm

[dricross]

Description of the issue

Integrate with new algorithm for converting OTel classic histograms to CloudWatch's Value/Counts. This significantly improves percentile metrics for classic histogram metrics.

Description of changes

Integrate with new algorithm in contrib repo:

License

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

Tests

Use the new tools in the contrib repo so send histogram test cases to CloudWatch and then retrieve the percentile metrics.

TestCase                                                   P10         P25         P50         P75         P90         P99       P99.9         Min         Max         Sum       Count
126 Buckets                                             125.95      314.76      629.19      944.75      1132.9      1258.2      1271.5           5        1300  5.2233e+06        8316
176 Buckets                                             175.88      440.04      880.01      1318.6      1583.1      1771.3      1797.1           5        1800  1.0182e+07       11616
225 Buckets                                              226.5      564.39      1128.8      1695.1      2033.5      2239.9      2289.3           5        2300  1.6822e+07       14916
325 Buckets                                             325.96      814.79      1628.7      2443.9      2931.6      3260.9      3296.1           5        3300  3.4983e+07       21516
Basic Histogram                                         17.913      28.327      50.986      73.413      86.886       194.4      199.43          10         200       36000         606
Cumulative bucket starts at 0                         0.010662    0.049403     0.10823     0.23481     0.40067      2.7043      11.867           0          45        6600       19086
Large Numbers                                       3.5613e+05  1.8884e+06  9.4334e+06  4.9984e+07   9.722e+07  7.2107e+08  8.7259e+08       1e+05       1e+09       6e+11        6006
Many Buckets                                            6.0464      35.102      89.752      558.59      889.85      1043.9      1090.7         0.5        1100     2.1e+06        6744
Negative and Positive Boundaries                           N/A         N/A         N/A         N/A         N/A         N/A         N/A         -50          50           0         636
No Min or Max                                           2.1182      18.084      55.369      71.599      180.26       242.8      250.74           0         300       21000         450
No Min/Max with Single Value                            142.82      143.99      145.97      147.97      149.18      149.92      149.99          50         150         600           6
Only Max Defined                                        52.465      118.33      203.07      303.27      367.55      733.64      748.35           0         750    1.05e+05         606
Only Min Defined                                        37.583      56.621      86.121       110.6      128.82      170.21       171.7          25         200       24000         306
Only Negative Boundaries                                   N/A         N/A         N/A         N/A         N/A         N/A         N/A        -200         -10      -60000         606
Positive boundaries but implied Negative Values            N/A         N/A         N/A         N/A         N/A         N/A         N/A        -100          60        1200         606
Single Bucket                                           37.763      38.306       39.23      40.176      40.754      41.106      41.141           5          75        6000         306
Tail Heavy Histogram                                    128.84      139.48       144.7      147.85      149.77      150.93         151          10         151     8.7e+05        6060
Two Buckets                                               1.78      2.6881       4.278       5.429      6.3839      9.9766      9.9977           1          10         900         186
Unbounded Histogram                                          0           0           0           0           0           0           0           0           0       21000         450
Very Small Numbers                                  5.2363e-08  7.2171e-07   1.629e-06  2.7846e-06  3.3259e-06  4.5734e-06  5.9513e-06       1e-08       6e-06      0.0009         606
Zero Counts and Sparse Data                             1.0712      2.8607      7.7614      221.86      983.31      1271.3      1489.5           0        1500     1.5e+05         606

Most percentiles fall within the expected range. A few are off by a percent or two. I believe this is due to the back-end applying another SEH1 mapping slightly modifying the values that the agent sends to CW for efficient storing.

For our accuracy tests, we see several improvments:

• Maximum error reduced from 99% to 9%
• Reduce average error from 30% to 3%
• Improve throughput for histogram conversions by 60%

Requirements

1. Run make fmt and make fmt-sh
2. Run make lint

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Integration Tests

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