chore(deps): Update VRL to use KeyString type wrapper

[bruceg]

This bumps the VRL rev to the commit introducing the KeyString type wrapper and updates the code to match the new types. Mostly this involves a lot of .into() but there are a few type changes as well. I am putting this out in advance of the VRL release as I had most of the changes done already and it will quickly get out of date again. This should be performance-neutral at this point.

[pront]

I don't see any functional changes, just changing types and using .into() vs to_string().

[datadog-vectordotdev]

Datadog Report

Branch report: bruceg/vrl-keystring
Commit report: 91c80e7

✅ vector: 0 Failed, 0 New Flaky, 2279 Passed, 0 Skipped, 37m 48.52s Wall Time

[github-actions]

Regression Detector Results

Run ID: 3f1736ca-9c26-445e-8667-b374f834ee32
Baseline: 515ce43
Comparison: 9733dd6
Total vector CPUs: 7

Explanation

A regression test is an integrated performance test for vector in a repeatable rig, with varying configuration for vector. What follows is a statistical summary of a brief vector run for each configuration across SHAs given above. The goal of these tests are to determine quickly if vector 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
datadog_agent_remap_blackhole	ingress throughput	+1.90	[+1.82, +1.98]	100.00%
datadog_agent_remap_blackhole_acks	ingress throughput	+1.63	[+1.54, +1.72]	100.00%
datadog_agent_remap_datadog_logs	ingress throughput	+1.03	[+0.94, +1.12]	100.00%
datadog_agent_remap_datadog_logs_acks	ingress throughput	+0.75	[+0.67, +0.83]	100.00%
file_to_blackhole	egress throughput	+0.65	[-1.82, +3.13]	33.62%
http_to_s3	ingress throughput	+0.24	[-0.04, +0.51]	84.32%
fluent_elasticsearch	ingress throughput	+0.16	[-0.30, +0.61]	42.73%
http_to_http_noack	ingress throughput	+0.13	[+0.05, +0.22]	99.08%
otlp_grpc_to_blackhole	ingress throughput	+0.06	[-0.04, +0.16]	65.98%
http_to_http_json	ingress throughput	+0.02	[-0.05, +0.09]	38.82%
splunk_hec_indexer_ack_blackhole	ingress throughput	+0.00	[-0.13, +0.14]	2.34%
splunk_hec_to_splunk_hec_logs_acks	ingress throughput	-0.00	[-0.14, +0.14]	0.30%
http_text_to_http_json	ingress throughput	-0.00	[-0.12, +0.12]	2.97%
splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.08	[-0.20, +0.03]	76.54%
enterprise_http_to_http	ingress throughput	-0.08	[-0.16, -0.01]	92.37%
http_to_http_acks	ingress throughput	-0.09	[-1.39, +1.21]	8.96%
syslog_log2metric_humio_metrics	ingress throughput	-0.34	[-0.43, -0.24]	100.00%
splunk_hec_route_s3	ingress throughput	-0.42	[-0.92, +0.08]	83.09%
syslog_humio_logs	ingress throughput	-0.49	[-0.58, -0.40]	100.00%
syslog_splunk_hec_logs	ingress throughput	-0.59	[-0.63, -0.55]	100.00%
otlp_http_to_blackhole	ingress throughput	-0.63	[-0.78, -0.48]	100.00%
syslog_regex_logs2metric_ddmetrics	ingress throughput	-0.83	[-0.90, -0.76]	100.00%
syslog_loki	ingress throughput	-0.90	[-0.94, -0.87]	100.00%
syslog_log2metric_splunk_hec_metrics	ingress throughput	-2.14	[-2.27, -2.00]	100.00%
socket_to_socket_blackhole	ingress throughput	-2.35	[-2.41, -2.28]	100.00%