enhancement(enrichment_tables): Changes to support GeoIP Anonymous IP database

[publicfacingusername]

The following geoip enrichment tables are currently supported:

GeoLite2-ASN
GeoIP2-ISP
GeoIP2-Connection-Type
GeoIP2-City
GeoLite2-City

I'd like to add support for:

GeoIP2-Anonymous-IP

https://dev.maxmind.com/geoip/docs/databases/anonymous-ip

[bits-bot]

All committers have signed the CLA.

[publicfacingusername]

See issue: #20945

[publicfacingusername]

Please note my local test failed. Working on making the appropriate changes then will reset.

error[E0277]: can't compare `std::collections::BTreeMap<KeyString, vector_lib::event::Value>` with `std::collections::BTreeMap<std::string::String, vector_lib::event::Value>`
   --> src/enrichment_tables/geoip.rs:489:9
    |
489 |         assert_eq!(values, expected);
    |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ no implementation for `std::collections::BTreeMap<KeyString, vector_lib::event::Value> == std::collections::BTreeMap<std::string::String, vector_lib::event::Value>`
    |
    = help: the trait `PartialEq<std::collections::BTreeMap<std::string::String, vector_lib::event::Value>>` is not implemented for `std::collections::BTreeMap<KeyString, vector_lib::event::Value>`
    = help: the following other types implement trait `PartialEq<Rhs>`:
              <std::collections::BTreeMap<K, V, A> as PartialEq>
              <std::collections::BTreeMap<K, V> as PartialEq<rkyv::collections::btree_map::ArchivedBTreeMap<AK, AV>>>
    = note: this error originates in the macro `assert_eq` (in Nightly builds, run with -Z macro-backtrace for more info)

error[E0004]: non-exhaustive patterns: `DatabaseKind::AnonymousIp` not covered
   --> src/enrichment_tables/geoip.rs:129:28
    |
129 |         let result = match dbkind {
    |                            ^^^^^^ pattern `DatabaseKind::AnonymousIp` not covered
    |
note: `DatabaseKind` defined here
   --> src/enrichment_tables/geoip.rs:24:10
    |
24  | pub enum DatabaseKind {
    |          ^^^^^^^^^^^^
...
29  |     AnonymousIp,
    |     ----------- not covered
    = note: the matched value is of type `DatabaseKind`
help: ensure that all possible cases are being handled by adding a match arm with a wildcard pattern or an explicit pattern as shown
    |
132 ~             DatabaseKind::City => dbreader.lookup::<City>(ip).map(|_| ()),
133 ~             DatabaseKind::AnonymousIp => todo!(),
    |

For more information about this error, try `rustc --explain E0004`.
error: could not compile `vector` (lib) due to 1 previous error

[jorgehermo9]

May you add a changelog fragment too, please? https://github.com/vectordotdev/vector/blob/master/changelog.d/README.md

[publicfacingusername]

Testing completed successfully after newly committed changes.

Summary [  90.783s] 2151 tests run: 2151 passed (2 slow, 2 flaky), 11 skipped
   FLAKY 2/4 [   0.120s] vector sources::journald::tests::emits_cursor
   FLAKY 2/4 [   0.123s] vector sources::journald::tests::excludes_matches

[publicfacingusername]

May you add a changelog fragment too, please? https://github.com/vectordotdev/vector/blob/master/changelog.d/README.md

Done!

[jszwedko]

Thanks for this @publicfacingusername !

[github-actions]

Regression Detector Results

Run ID: af50357e-6195-427e-8fa7-7d5b2b11dba4 Metrics dashboard

Baseline: 4227e96
Comparison: dc50263

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

Significant changes in experiment optimization goals

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

perf	experiment	goal	Δ mean %	Δ mean % CI	links
✅	http_text_to_http_json	ingress throughput	+5.07	[+4.89, +5.25]

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI	links
✅	file_to_blackhole	egress throughput	+16.60	[+8.91, +24.30]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	links
✅	file_to_blackhole	egress throughput	+16.60	[+8.91, +24.30]
✅	http_text_to_http_json	ingress throughput	+5.07	[+4.89, +5.25]
➖	syslog_log2metric_humio_metrics	ingress throughput	+4.13	[+4.02, +4.24]
➖	socket_to_socket_blackhole	ingress throughput	+2.76	[+2.68, +2.84]
➖	otlp_grpc_to_blackhole	ingress throughput	+2.66	[+2.54, +2.78]
➖	syslog_humio_logs	ingress throughput	+1.35	[+1.18, +1.51]
➖	datadog_agent_remap_blackhole_acks	ingress throughput	+0.93	[+0.82, +1.04]
➖	otlp_http_to_blackhole	ingress throughput	+0.67	[+0.53, +0.80]
➖	splunk_hec_route_s3	ingress throughput	+0.18	[-0.13, +0.50]
➖	http_to_http_noack	ingress throughput	+0.15	[+0.07, +0.23]
➖	http_to_s3	ingress throughput	+0.09	[-0.17, +0.35]
➖	http_to_http_json	ingress throughput	+0.02	[-0.01, +0.05]
➖	splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.00	[-0.12, +0.12]
➖	splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.01	[-0.11, +0.10]
➖	splunk_hec_indexer_ack_blackhole	ingress throughput	-0.03	[-0.11, +0.06]
➖	fluent_elasticsearch	ingress throughput	-0.32	[-0.81, +0.17]
➖	datadog_agent_remap_blackhole	ingress throughput	-0.33	[-0.44, -0.22]
➖	datadog_agent_remap_datadog_logs_acks	ingress throughput	-0.66	[-0.82, -0.50]
➖	http_to_http_acks	ingress throughput	-0.75	[-2.07, +0.57]
➖	syslog_splunk_hec_logs	ingress throughput	-1.08	[-1.17, -0.99]
➖	datadog_agent_remap_datadog_logs	ingress throughput	-1.16	[-1.35, -0.97]
➖	syslog_regex_logs2metric_ddmetrics	ingress throughput	-1.48	[-1.63, -1.32]
➖	syslog_log2metric_splunk_hec_metrics	ingress throughput	-1.69	[-1.80, -1.58]
➖	http_elasticsearch	ingress throughput	-1.73	[-1.93, -1.54]
➖	syslog_loki	ingress throughput	-1.78	[-1.86, -1.71]
➖	syslog_log2metric_tag_cardinality_limit_blackhole	ingress throughput	-2.57	[-2.66, -2.47]

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".

[github-actions]

Regression Detector Results

Run ID: 2b42f793-3bce-4bf4-8a78-e5a4c7b8e144 Metrics dashboard

Baseline: 96de0ac
Comparison: 7f286c4

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.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI	links
➖	file_to_blackhole	egress throughput	-5.96	[-12.63, +0.71]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	links
➖	datadog_agent_remap_datadog_logs	ingress throughput	+3.69	[+3.51, +3.88]
➖	syslog_log2metric_splunk_hec_metrics	ingress throughput	+2.79	[+2.68, +2.89]
➖	socket_to_socket_blackhole	ingress throughput	+2.62	[+2.56, +2.69]
➖	http_elasticsearch	ingress throughput	+2.56	[+2.34, +2.78]
➖	syslog_loki	ingress throughput	+2.31	[+2.21, +2.41]
➖	syslog_regex_logs2metric_ddmetrics	ingress throughput	+2.09	[+1.94, +2.24]
➖	otlp_http_to_blackhole	ingress throughput	+1.88	[+1.73, +2.03]
➖	http_text_to_http_json	ingress throughput	+1.00	[+0.87, +1.14]
➖	otlp_grpc_to_blackhole	ingress throughput	+0.51	[+0.40, +0.63]
➖	http_to_http_acks	ingress throughput	+0.40	[-0.92, +1.72]
➖	datadog_agent_remap_blackhole_acks	ingress throughput	+0.31	[+0.22, +0.40]
➖	http_to_http_noack	ingress throughput	+0.17	[+0.09, +0.26]
➖	syslog_log2metric_humio_metrics	ingress throughput	+0.14	[+0.00, +0.29]
➖	http_to_http_json	ingress throughput	+0.04	[+0.00, +0.08]
➖	http_to_s3	ingress throughput	+0.02	[-0.24, +0.29]
➖	splunk_hec_indexer_ack_blackhole	ingress throughput	+0.01	[-0.07, +0.09]
➖	splunk_hec_to_splunk_hec_logs_noack	ingress throughput	+0.01	[-0.09, +0.11]
➖	splunk_hec_to_splunk_hec_logs_acks	ingress throughput	-0.00	[-0.15, +0.14]
➖	datadog_agent_remap_datadog_logs_acks	ingress throughput	-0.31	[-0.49, -0.13]
➖	fluent_elasticsearch	ingress throughput	-0.33	[-0.82, +0.17]
➖	syslog_log2metric_tag_cardinality_limit_blackhole	ingress throughput	-0.44	[-0.55, -0.34]
➖	syslog_humio_logs	ingress throughput	-0.81	[-0.97, -0.65]
➖	splunk_hec_route_s3	ingress throughput	-0.90	[-1.22, -0.58]
➖	datadog_agent_remap_blackhole	ingress throughput	-1.00	[-1.11, -0.88]
➖	syslog_splunk_hec_logs	ingress throughput	-1.00	[-1.10, -0.90]
➖	file_to_blackhole	egress throughput	-5.96	[-12.63, +0.71]

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".