Likely bug in microservices 2021 traces (in MSRTQps)

The `HTTP_MCR` and `HTTP_RT` metrics appear to have the exact same values, but for different indices.

Here's an example for `MSRTQps_0.csv` using Python `pandas`.

Data file used:
```
>>> import pandas as pd
>>> df = pd.read_csv("MSRTQps_0.csv")
```

Visual inspection (see the "value" column):
```
>>> df[df["metric"]=="HTTP_RT"].sort_values(by=["value"])
Unnamed: 0 timestamp msname msinstanceid metric value
2507931 2507931 360000 2abd05990aa9eb81a3eaa333829ec355eaee57fbecc9e8... 1241e56851129f2832769f898f4ae42ebed8416c5c4476... HTTP_RT 0.010833
423481 423481 120000 2abd05990aa9eb81a3eaa333829ec355eaee57fbecc9e8... 329f1b8f8580c79eaca3420cb81bae4e74caf2a715f633... HTTP_RT 0.012962
13906180 13906180 1740000 2abd05990aa9eb81a3eaa333829ec355eaee57fbecc9e8... 2f4b9b40b5a24ba9b3f0be87f6c674799cccf1f3f2d9f6... HTTP_RT 0.013814
15452257 15452257 1020000 2abd05990aa9eb81a3eaa333829ec355eaee57fbecc9e8... 425f1aae309527bc491a79f91a315fc5a8a515d1698605... HTTP_RT 0.013995
831144 831144 300000 2abd05990aa9eb81a3eaa333829ec355eaee57fbecc9e8... 65caf889c5079f53deecaee74542ce793b02669c4243ed... HTTP_RT 0.014112
... ... ... ... ... ... ...
258159 258159 1560000 d17f58c4324b523992e8f479804b65a5b965f94877139b... fa7057b1ee80ed348191db81bc2a4c415a023dd9d0b966... HTTP_RT 60581.531568
9532330 9532330 180000 d17f58c4324b523992e8f479804b65a5b965f94877139b... b1950cb1269c9b1ea7db2e0a75ba2e25859a2822539aa1... HTTP_RT 62518.301342
9535894 9535894 1380000 d17f58c4324b523992e8f479804b65a5b965f94877139b... f053370b71a161fe6f14ef1447e16f87d272a1a1741f01... HTTP_RT 62879.484608
562767 562767 1500000 d17f58c4324b523992e8f479804b65a5b965f94877139b... 944ca9882cbcdc712c5ff9ece46ad2f316a2cbb3f9f3c1... HTTP_RT 65206.585728
9196703 9196703 840000 d17f58c4324b523992e8f479804b65a5b965f94877139b... 9a6d5b9e6e74fb54f287de03966275924a824f4462ba2d... HTTP_RT 94560.483449

[674161 rows x 6 columns]
>>> df[df["metric"]=="HTTP_MCR"].sort_values(by=["value"])
Unnamed: 0 timestamp msname msinstanceid metric value
2507940 2507940 360000 2abd05990aa9eb81a3eaa333829ec355eaee57fbecc9e8... 1241e56851129f2832769f898f4ae42ebed8416c5c4476... HTTP_MCR 0.010833
423651 423651 120000 2abd05990aa9eb81a3eaa333829ec355eaee57fbecc9e8... 329f1b8f8580c79eaca3420cb81bae4e74caf2a715f633... HTTP_MCR 0.012962
13906160 13906160 1740000 2abd05990aa9eb81a3eaa333829ec355eaee57fbecc9e8... 2f4b9b40b5a24ba9b3f0be87f6c674799cccf1f3f2d9f6... HTTP_MCR 0.013814
15452177 15452177 1020000 2abd05990aa9eb81a3eaa333829ec355eaee57fbecc9e8... 425f1aae309527bc491a79f91a315fc5a8a515d1698605... HTTP_MCR 0.013995
831091 831091 300000 2abd05990aa9eb81a3eaa333829ec355eaee57fbecc9e8... 65caf889c5079f53deecaee74542ce793b02669c4243ed... HTTP_MCR 0.014112
... ... ... ... ... ... ...
258177 258177 1560000 d17f58c4324b523992e8f479804b65a5b965f94877139b... fa7057b1ee80ed348191db81bc2a4c415a023dd9d0b966... HTTP_MCR 60581.531568
9532340 9532340 180000 d17f58c4324b523992e8f479804b65a5b965f94877139b... b1950cb1269c9b1ea7db2e0a75ba2e25859a2822539aa1... HTTP_MCR 62518.301342
9535905 9535905 1380000 d17f58c4324b523992e8f479804b65a5b965f94877139b... f053370b71a161fe6f14ef1447e16f87d272a1a1741f01... HTTP_MCR 62879.484608
562739 562739 1500000 d17f58c4324b523992e8f479804b65a5b965f94877139b... 944ca9882cbcdc712c5ff9ece46ad2f316a2cbb3f9f3c1... HTTP_MCR 65206.585728
9196731 9196731 840000 d17f58c4324b523992e8f479804b65a5b965f94877139b... 9a6d5b9e6e74fb54f287de03966275924a824f4462ba2d... HTTP_MCR 94560.483449

[674161 rows x 6 columns]
>>>
```

Equality check:
```
>>> rt_vals=df[df["metric"]=="HTTP_RT"]["value"].sort_values().reset_index().drop("index",axis=1)
>>> mcr_vals=df[df["metric"]=="HTTP_MCR"]["value"].sort_values().reset_index().drop("index",axis=1)
>>> rt_vals.equals(mcr_vals)
True
```

This seems like a bug (the call rate and response time should certainly be different across so many functions). Or am I misunderstanding something about the trace?

Thanks!

Start by loading MSRTQps_0.csv with Python pandas and reproduce the HTTP_RT and HTTP_MCR equality check shown in the issue. Trace how these metrics are defined or generated, then determine whether the matching values are an error or expected; done means documenting the explanation or identifying the affected trace data for correction.

Written by the indexing model from the issue text.