chore(deps): Bump itertools from 0.12.1 to 0.13.0

[dependabot]

Bumps itertools from 0.12.1 to 0.13.0.

Changelog

Sourced from itertools's changelog.

0.13.0

Breaking

• Removed implementation of DoubleEndedIterator for ConsTuples (#853)
• Made MultiProduct fused and fixed on an empty iterator (#835, #834)
• Changed iproduct! to return tuples for maxi one iterator too (#870)
• Changed PutBack::put_back to return the old value (#880)
• Removed deprecated repeat_call, Itertools::{foreach, step, map_results, fold_results} (#878)
• Removed TakeWhileInclusive::new (#912)

Added

• Added Itertools::{smallest_by, smallest_by_key, largest, largest_by, largest_by_key} (#654, #885)
• Added Itertools::tail (#899)
• Implemented DoubleEndedIterator for ProcessResults (#910)
• Implemented Debug for FormatWith (#931)
• Added Itertools::get (#891)

Changed

• Deprecated Itertools::group_by (renamed chunk_by) (#866, #879)
• Deprecated unfold (use std::iter::from_fn instead) (#871)
• Optimized GroupingMapBy (#873, #876)
• Relaxed Fn bounds to FnMut in diff_with, Itertools::into_group_map_by (#886)
• Relaxed Debug/Clone bounds for MapInto (#889)
• Documented the use_alloc feature (#887)
• Optimized Itertools::set_from (#888)
• Removed badges in README.md (#890)
• Added "no-std" categories in Cargo.toml (#894)
• Fixed Itertools::k_smallest on short unfused iterators (#900)
• Deprecated Itertools::tree_fold1 (renamed tree_reduce) (#895)
• Deprecated GroupingMap::fold_first (renamed reduce) (#902)
• Fixed Itertools::k_smallest(0) to consume the iterator, optimized Itertools::k_smallest(1) (#909)
• Specialized Combinations::nth (#914)
• Specialized MergeBy::fold (#920)
• Specialized CombinationsWithReplacement::nth (#923)
• Specialized FlattenOk::{fold, rfold} (#927)
• Specialized Powerset::nth (#924)
• Documentation fixes (#882, #936)
• Fixed assert_equal for iterators longer than i32::MAX (#932)
• Updated the must_use message of non-lazy KMergeBy and TupleCombinations (#939)

Notable Internal Changes

• Tested iterator laziness (#792)
• Created CONTRIBUTING.md (#767)

Commits

• d5084d1 Prepare v0.13.0 release (#937)
• d7c99d5 TupleCombinations is not lazy but must be used nonetheless
• 074c7fc KMergeBy is not lazy but must be used nonetheless
• 2ad9e07 assert_equal: fix clippy::default_numeric_fallback
• 0d4efc8 Remove free function get
• 05cc0ee get(s..=usize::MAX) should be fine when s != 0
• 3c16f14 get: when is it ESI and/or DEI
• 4dd6ba0 get: panics if the range includes usize::MAX
• 7a9ce56 get(r: Range) as Skip\<Take>
• f676f2f Remove the unspecified check about .get(exhausted_range_inclusive)
• Additional commits viewable in compare view

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[dependabot]

Dependabot attempted to update this pull request, but because the branch dependabot/cargo/itertools-0.13.0 is protected it was unable to do so.

[github-actions]

Regression Detector Results

Run ID: cc1ba85e-d014-467a-86d7-1aae9b04b83c
Baseline: cddc180
Comparison: d5c23fe
Total CPUs: 7

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	syslog_loki	ingress throughput	+2.55	[+2.48, +2.61]
➖	syslog_log2metric_tag_cardinality_limit_blackhole	ingress throughput	+2.25	[+2.12, +2.37]
➖	http_text_to_http_json	ingress throughput	+1.05	[+0.91, +1.18]
➖	datadog_agent_remap_blackhole	ingress throughput	+1.02	[+0.90, +1.13]
➖	syslog_log2metric_humio_metrics	ingress throughput	+0.89	[+0.78, +1.00]
➖	syslog_splunk_hec_logs	ingress throughput	+0.73	[+0.66, +0.80]
➖	datadog_agent_remap_blackhole_acks	ingress throughput	+0.68	[+0.59, +0.76]
➖	syslog_log2metric_splunk_hec_metrics	ingress throughput	+0.63	[+0.48, +0.78]
➖	otlp_http_to_blackhole	ingress throughput	+0.33	[+0.20, +0.46]
➖	splunk_hec_route_s3	ingress throughput	+0.31	[-0.14, +0.77]
➖	http_to_s3	ingress throughput	+0.23	[-0.05, +0.51]
➖	otlp_grpc_to_blackhole	ingress throughput	+0.17	[+0.08, +0.26]
➖	http_to_http_noack	ingress throughput	+0.13	[+0.05, +0.22]
➖	syslog_humio_logs	ingress throughput	+0.07	[-0.03, +0.18]
➖	syslog_regex_logs2metric_ddmetrics	ingress throughput	+0.05	[-0.06, +0.15]
➖	http_to_http_json	ingress throughput	+0.03	[-0.04, +0.11]
➖	splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.00	[-0.14, +0.15]
➖	splunk_hec_indexer_ack_blackhole	ingress throughput	-0.00	[-0.15, +0.14]
➖	splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.04	[-0.16, +0.07]
➖	enterprise_http_to_http	ingress throughput	-0.07	[-0.13, +0.00]
➖	http_to_http_acks	ingress throughput	-0.17	[-1.53, +1.19]
➖	http_elasticsearch	ingress throughput	-0.18	[-0.25, -0.11]
➖	socket_to_socket_blackhole	ingress throughput	-0.19	[-0.28, -0.11]
➖	fluent_elasticsearch	ingress throughput	-0.23	[-0.71, +0.25]
➖	datadog_agent_remap_datadog_logs	ingress throughput	-1.11	[-1.21, -1.00]
➖	datadog_agent_remap_datadog_logs_acks	ingress throughput	-1.27	[-1.34, -1.19]
➖	file_to_blackhole	egress throughput	-1.85	[-4.34, +0.64]

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".