Bump hyper from 0.14.26 to 0.14.27

[dependabot]

Bumps hyper from 0.14.26 to 0.14.27.

Release notes

Sourced from hyper's releases.

v0.14.27

Bug Fixes

• http1:
  • send error on Incoming body when connection errors (#3256) (b107655f, closes #3253)
  • properly end chunked bodies when it was known to be empty (#3254) (32422c47, closes #3252)

Features

• client: include connection info in Client::send_request errors (#2749)

Changelog

Sourced from hyper's changelog.

v0.14.27 (2023-06-26)

Bug Fixes

• http1:
  • send error on Incoming body when connection errors (#3256) (b107655f, closes #3253)
  • properly end chunked bodies when it was known to be empty (#3254) (32422c47, closes #3252)

Features

• client: include connection info in Client::send_request errors (#2749)

Commits

• d77c259 v0.14.27
• a7b2c82 chore(lib): disable log feature of tower dependency
• b107655 fix(http1): send error on Incoming body when connection errors (#3256)
• 32422c4 fix(http1): properly end chunked bodies when it was known to be empty (#3254)
• 297dc4c feat(client): include connection info in Client::send_request errors (#2749)
• See full diff in compare view

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[github-actions]

Regression Detector Results

Run ID: 5d64f265-deba-4b0b-80f5-23630a60a21e
Baseline: d217017
Comparison: 4b034d9
Total lading-target CPUs: 4

Explanation

A regression test is an integrated performance test for lading-target in a repeatable rig, with varying configuration for lading-target. What follows is a statistical summary of a brief lading-target run for each configuration across SHAs given above. The goal of these tests are to determine quickly if lading-target performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

1. The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.

2. Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
blackhole_from_apache_common_http	ingress throughput	+0.02	[-0.04, +0.07]	30.28%
apache_common_http_both_directions_this_doesnt_make_sense	ingress throughput	-0.05	[-0.06, -0.04]	100.00%

[blt]

@dependabot rebase

[github-actions]

Regression Detector Results

Run ID: 304522cd-74d8-4d45-a230-c1502dc743f1
Baseline: aa72e38
Comparison: f4f4020
Total lading-target CPUs: 4

Explanation

A regression test is an integrated performance test for lading-target in a repeatable rig, with varying configuration for lading-target. What follows is a statistical summary of a brief lading-target run for each configuration across SHAs given above. The goal of these tests are to determine quickly if lading-target performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

1. The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.

2. Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
blackhole_from_apache_common_http	ingress throughput	+0.18	[+0.13, +0.23]	100.00%
apache_common_http_both_directions_this_doesnt_make_sense	ingress throughput	+0.07	[+0.05, +0.09]	100.00%

[GeorgeHahn]

@dependabot squash and merge