added option to insert to random shard

[rguleryuz]

adds support for "insert_distributed_one_random_shard" mentioned here by me #20335

source : https://clickhouse.com/docs/en/engines/table-engines/special/distributed#distributed-parameters

[bits-bot]

All committers have signed the CLA.

[jszwedko]

Thanks @rguleryuz ! I think this PR just needs a couple of things:

• A changelog entry (see https://github.com/vectordotdev/vector/blob/master/changelog.d/README.md)
• make generate-component-docs ran to regenerate the documentation

[rguleryuz]

Thanks @rguleryuz ! I think this PR just needs a couple of things:

* A changelog entry (see https://github.com/vectordotdev/vector/blob/master/changelog.d/README.md)

* `make generate-component-docs` ran to regenerate the documentation

done!

[aliciascott]

looks good

[jszwedko]

One comment, otherwise 👍

[jszwedko]

Suggested change

	Added new config field "insert_random_shard" to support "insert_distributed_one_random_shard" on Clickhouse sink for Distributed Table Engine. More on here https://clickhouse.com/docs/en/engines/table-engines/special/distributed
	The `clickhouse` sink now has a new configuration option, `insert_random_shard`, to tell Clickhouse to insert into a random shard (by setting `insert_distributed_one_random_shard`). See the Clickhouse [Distributed Table Engine docs](https://clickhouse.com/docs/en/engines/table-engines/special/distributed) for details.
	authors: rguleryuz

[jszwedko]

Thanks @rguleryuz !

[rguleryuz]

I guess I need to add some stuff for testing as well. Can I get some help with that :(

[github-actions]

Regression Detector Results

Run ID: 938d123d-3291-498b-ad06-178c65dd8483
Baseline: 9362d1b
Comparison: 37eb81d
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

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
✅	syslog_log2metric_humio_metrics	ingress throughput	+7.64	[+7.47, +7.82]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
✅	syslog_log2metric_humio_metrics	ingress throughput	+7.64	[+7.47, +7.82]
➖	datadog_agent_remap_blackhole_acks	ingress throughput	+3.48	[+3.36, +3.61]
➖	otlp_grpc_to_blackhole	ingress throughput	+2.49	[+2.40, +2.59]
➖	splunk_hec_route_s3	ingress throughput	+1.81	[+1.32, +2.29]
➖	datadog_agent_remap_datadog_logs_acks	ingress throughput	+1.75	[+1.66, +1.84]
➖	http_elasticsearch	ingress throughput	+1.50	[+1.42, +1.58]
➖	http_text_to_http_json	ingress throughput	+0.95	[+0.79, +1.10]
➖	fluent_elasticsearch	ingress throughput	+0.93	[+0.45, +1.41]
➖	http_to_http_acks	ingress throughput	+0.68	[-0.69, +2.04]
➖	syslog_splunk_hec_logs	ingress throughput	+0.26	[+0.14, +0.39]
➖	http_to_http_noack	ingress throughput	+0.12	[+0.03, +0.20]
➖	http_to_http_json	ingress throughput	+0.06	[-0.01, +0.14]
➖	syslog_loki	ingress throughput	+0.04	[-0.06, +0.14]
➖	splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.00	[-0.14, +0.14]
➖	splunk_hec_indexer_ack_blackhole	ingress throughput	-0.01	[-0.15, +0.14]
➖	splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.04	[-0.15, +0.08]
➖	enterprise_http_to_http	ingress throughput	-0.05	[-0.12, +0.02]
➖	syslog_humio_logs	ingress throughput	-0.13	[-0.27, +0.01]
➖	http_to_s3	ingress throughput	-0.19	[-0.47, +0.09]
➖	syslog_log2metric_tag_cardinality_limit_blackhole	ingress throughput	-0.20	[-0.33, -0.06]
➖	file_to_blackhole	egress throughput	-0.23	[-2.74, +2.29]
➖	socket_to_socket_blackhole	ingress throughput	-0.50	[-0.56, -0.43]
➖	datadog_agent_remap_datadog_logs	ingress throughput	-0.77	[-0.89, -0.65]
➖	datadog_agent_remap_blackhole	ingress throughput	-1.01	[-1.12, -0.91]
➖	syslog_log2metric_splunk_hec_metrics	ingress throughput	-1.05	[-1.24, -0.85]
➖	otlp_http_to_blackhole	ingress throughput	-1.24	[-1.36, -1.11]
➖	syslog_regex_logs2metric_ddmetrics	ingress throughput	-3.79	[-3.96, -3.62]

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: 7d2033db-f282-4589-90fa-d1fdfb8ff22b
Baseline: f391fe2
Comparison: 69f985d
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
➖	splunk_hec_route_s3	ingress throughput	+2.98	[+2.50, +3.46]
➖	datadog_agent_remap_blackhole_acks	ingress throughput	+1.80	[+1.69, +1.92]
➖	fluent_elasticsearch	ingress throughput	+1.16	[+0.67, +1.66]
➖	datadog_agent_remap_blackhole	ingress throughput	+1.00	[+0.88, +1.13]
➖	otlp_http_to_blackhole	ingress throughput	+0.78	[+0.60, +0.96]
➖	syslog_log2metric_humio_metrics	ingress throughput	+0.61	[+0.42, +0.81]
➖	syslog_loki	ingress throughput	+0.42	[+0.33, +0.50]
➖	otlp_grpc_to_blackhole	ingress throughput	+0.20	[+0.11, +0.29]
➖	http_to_http_noack	ingress throughput	+0.19	[+0.09, +0.29]
➖	http_to_s3	ingress throughput	+0.10	[-0.18, +0.38]
➖	syslog_log2metric_splunk_hec_metrics	ingress throughput	+0.05	[-0.13, +0.23]
➖	http_to_http_json	ingress throughput	+0.01	[-0.06, +0.08]
➖	splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.00	[-0.14, +0.14]
➖	splunk_hec_indexer_ack_blackhole	ingress throughput	-0.01	[-0.15, +0.14]
➖	enterprise_http_to_http	ingress throughput	-0.06	[-0.13, +0.02]
➖	splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.07	[-0.18, +0.04]
➖	http_to_http_acks	ingress throughput	-0.23	[-1.59, +1.13]
➖	file_to_blackhole	egress throughput	-0.39	[-2.90, +2.11]
➖	syslog_log2metric_tag_cardinality_limit_blackhole	ingress throughput	-0.49	[-0.65, -0.32]
➖	syslog_splunk_hec_logs	ingress throughput	-0.82	[-0.92, -0.73]
➖	datadog_agent_remap_datadog_logs_acks	ingress throughput	-0.88	[-0.97, -0.78]
➖	socket_to_socket_blackhole	ingress throughput	-1.02	[-1.10, -0.95]
➖	http_text_to_http_json	ingress throughput	-1.14	[-1.30, -0.99]
➖	syslog_regex_logs2metric_ddmetrics	ingress throughput	-1.38	[-1.56, -1.20]
➖	http_elasticsearch	ingress throughput	-1.77	[-1.87, -1.67]
➖	datadog_agent_remap_datadog_logs	ingress throughput	-2.77	[-2.89, -2.64]
➖	syslog_humio_logs	ingress throughput	-2.89	[-3.02, -2.76]

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".