fix(dev): fix issues when using container tools and cargo is not installed locally

[hhromic]

This PR fixes a number of issues I found when using ENVIRONMENT=true with make.
The summary of changes are below and correspond to each commit in this PR.

• fix(dev): fix error in make when cargo is not installed
  Fixes the following "no such file" errors when using make on a system without cargo installed:

  $ make environment ENVIRONMENT_AUTOBUILD=false
  make: cargo: No such file or directory
  make: cargo: No such file or directory
  make: cargo: No such file or directory
  make: cargo: No such file or directory
  make: cargo: No such file or directory
  make: cargo: No such file or directory
  make: cargo: No such file or directory
  make: cargo: No such file or directory
  make: cargo: No such file or directory
  make: cargo: No such file or directory
  docker pull docker.io/timberio/vector-dev:sha-3eadc96742a33754a5859203b58249f6a806972a
  make: cargo: No such file or directory
  sha-3eadc96742a33754a5859203b58249f6a806972a: Pulling from timberio/vector-dev
  Digest: sha256:23310245d469f0ae911da8de0be9a8d091e4df9f69a2591e9ab8df95405ceffc
  Status: Image is up to date for timberio/vector-dev:sha-3eadc96742a33754a5859203b58249f6a806972a
  docker.io/timberio/vector-dev:sha-3eadc96742a33754a5859203b58249f6a806972a
  Entering environment...
  

  It should be not necessary for a local machine to have cargo or Rust installed locally (the point of make environnment).
  The problem is in the Makefile, where VERSION is assigned from the result of cargo vdev version. This PR fixes the issue by checking if cargo is available in the PATH first, or simply assign the value unknown if not.

• fix(dev): fix build make target when cargo is not installed
  Fixes the following error when using make build ENVIRONMENT=true:

  Makefile:212: *** "Please install Rust: https://www.rust-lang.org/tools/install".  Stop.
  

  It should be not necessary for a local machine to have cargo or Rust installed locally (the point of ENVIRONMENT=true).
  Also switch to using command (POSIX compatible) instead of which for detecting cargo.
  Details in https://mywiki.wooledge.org/BashFAQ/081.

• fix(dev): fix detection of container tool in make
  Fixes the following error when ENVIRONMENT=true and neither Docker or Podman are available:

  make: podman: No such file or directory
  make: *** [Makefile:174: build] Error 127
  

  Podman was being used as fallback even if also not available. A descriptive error is now reported instead in this situation.

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[hhromic]

Sorry for all the force pushes, made some minor mistakes but it should be all good now for review.
Btw, the spell-checker is complaining about ifneq not being recognised as valid but it is in fact a valid keyword in Makefile.

[neuronull]

Hey, thanks for suggesting these changes @hhromic ! We'll get this reviewed.

Regarding the spell checker, that can be circumvented by adding the ifneq to .github/actions/spelling/allow.txt

[hhromic]

Regarding the spell checker, that can be circumvented by adding the ifneq to .github/actions/spelling/allow.txt

Ah good to know!
Would you like me to add it in this PR?

EDIT: I added it now. Thanks for the tip!

[jszwedko]

Thanks @hhromic !

[github-actions]

Regression Detector Results

Run ID: 5e96966b-4edd-43d5-b125-5239b9f54df9
Baseline: e6f2ccc
Comparison: 36111b5
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
file_to_blackhole	egress throughput	+4.32	[+0.50, +8.13]	85.24%
syslog_regex_logs2metric_ddmetrics	ingress throughput	+1.85	[+1.50, +2.20]	100.00%
http_to_s3	ingress throughput	+1.72	[+1.51, +1.93]	100.00%
syslog_splunk_hec_logs	ingress throughput	+1.38	[+1.30, +1.46]	100.00%
http_to_http_acks	ingress throughput	+1.22	[-0.03, +2.46]	79.01%
datadog_agent_remap_datadog_logs	ingress throughput	+0.81	[+0.70, +0.92]	100.00%
datadog_agent_remap_blackhole_acks	ingress throughput	+0.54	[+0.41, +0.67]	100.00%
http_text_to_http_json	ingress throughput	+0.40	[+0.33, +0.47]	100.00%
otlp_grpc_to_blackhole	ingress throughput	+0.39	[+0.29, +0.48]	100.00%
socket_to_socket_blackhole	ingress throughput	+0.25	[+0.20, +0.29]	100.00%
datadog_agent_remap_datadog_logs_acks	ingress throughput	+0.23	[+0.12, +0.35]	98.99%
http_to_http_noack	ingress throughput	+0.06	[+0.00, +0.12]	80.31%
enterprise_http_to_http	ingress throughput	+0.02	[-0.01, +0.04]	48.82%
fluent_elasticsearch	ingress throughput	+0.00	[-0.00, +0.00]	22.56%
splunk_hec_indexer_ack_blackhole	ingress throughput	+0.00	[-0.04, +0.04]	0.65%
http_to_http_json	ingress throughput	-0.00	[-0.04, +0.04]	5.09%
splunk_hec_to_splunk_hec_logs_acks	ingress throughput	-0.01	[-0.08, +0.06]	12.54%
splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.01	[-0.06, +0.03]	32.01%
syslog_log2metric_humio_metrics	ingress throughput	-0.22	[-0.30, -0.14]	99.96%
syslog_loki	ingress throughput	-0.53	[-0.58, -0.47]	100.00%
otlp_http_to_blackhole	ingress throughput	-0.77	[-0.91, -0.63]	100.00%
syslog_humio_logs	ingress throughput	-0.87	[-0.95, -0.78]	100.00%
splunk_hec_route_s3	ingress throughput	-1.00	[-1.13, -0.87]	100.00%
datadog_agent_remap_blackhole	ingress throughput	-1.04	[-1.16, -0.92]	100.00%
syslog_log2metric_splunk_hec_metrics	ingress throughput	-2.80	[-2.92, -2.68]	100.00%