enhancement(file sink, aws_s3 sink, gcp_cloud_storage): configurable filename timezone

[kates]

The PR will make timezone for timestamp in filenames configurable. It will use the global timezone from the SinkContext (cx.globals.timezone) but can be overridden in the sinks. As an example,

timezone = "Asia/Singapore"

[sources.in]
type = "file"
data_dir = "./data_dir/"
include = ["./events/*.log"]


[sinks.out]
inputs = ["in"]
type = "file"
path = "./logs/vector-%Y-%m-%d-%H-%M.log"
encoding.codec = "text"

[sinks.s3_sink]
type = "aws_s3"
inputs = ["in"]
bucket = "s3-demo-bucket"
filename_extension = "json.gz"
endpoint = "http://127.0.0.1:9000"
filename_time_format = "vector-%Y-%m-%d-%H-%M"
filename_append_uuid = false
key_prefix = "date=%Y%m%d/"
timezone = "Australia/Melbourne"

[sinks.s3_sink.batch]
timeout_secs = 120

[sinks.s3_sink.encoding]
codec = "json"

[sinks.s3_sink.auth]
access_key_id = "accesskeyid"
secret_access_key = "secretaccesskey"

[sinks.gcs_sink]
type = "gcp_cloud_storage"
inputs = ["in"]
bucket = "gcs-demo-bucket"
filename_extension = "json.gz"
filename_time_format = "vector-%Y-%m-%d-%H-%M"
filename_append_uuid = false
key_prefix = "date=%Y%m%d/"
timezone = "Asia/Kolkata"
credentials_path = "./credentials.json"

[sinks.gcs_sink.encoding]
codec = "json"

[sinks.gcs_sink.batch]
timeout_secs = 120

We specified Asia/Singapore as global timezone. The File Sink will have the filename timestamp formatted with Asia/Singapore. The S3 Sink will have the filename timestamp formatted with Australia/Melbourne timezone. The GCP Cloud Storage Sink will have its filename timestamp formatted with Asia/Kolkata timezone.

[StephenWakely]

Ah, the integration tests are going to need to be updated with the new fields. For example here.

If you need to run the integration tests you can run cargo vdev int test aws, cargo vdev int test file and cargo vdev int test gcp.

It also looks like a number of files need to be run through rustfmt to ensure they are formatted correctly. You can see the files in the CI run here.

[StephenWakely]

Formatting still needs fixing. You can check this by running cargo vdev check fmt and then running the files that need formatting through rustfmt. Most editors should have the ability to automatically run the file through rustfmt, it might be worth looking in to setting that up.

[StephenWakely]

This still doesn't compile all the tests. My apologies, I incorrectly said there were file integration tests. These are actually just handled by the unit tests, running - cargo vdev test.

I realise we don't really have a list of the various jobs that need to pass before the PR can get merged. I've just pushed a PR that adds this to our CONTRIBUTING docs. You may find it useful, any feedback on it would be appreciated.

[kates]

cargo vdev test

Sorry for all the oversights. It's my first time trying to contribute to a large project.

[StephenWakely]

Sorry for all the oversights. It's my first time trying to contribute to a large project.

No worries at all! It highlighted some shortcomings in our developer documentation. We want the contribution process to be as seamless as possible.

[bruceg]

There appears to still be a clippy error with the changes to the S3 sink. Fixing that will let CI progress to the other checks.

[StephenWakely]

Perfect. Thank you!

[github-actions]

Regression Detector Results

Run ID: 707fae87-c252-415f-adc5-ac711046ec2c
Baseline: bd7df4a
Comparison: 5a55567
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
splunk_hec_route_s3	ingress throughput	+1.95	[+1.43, +2.46]	100.00%
otlp_http_to_blackhole	ingress throughput	+1.70	[+1.54, +1.86]	100.00%
syslog_regex_logs2metric_ddmetrics	ingress throughput	+1.14	[+1.06, +1.22]	100.00%
syslog_loki	ingress throughput	+1.13	[+1.09, +1.16]	100.00%
syslog_log2metric_splunk_hec_metrics	ingress throughput	+0.95	[+0.82, +1.07]	100.00%
http_to_http_acks	ingress throughput	+0.71	[-0.60, +2.02]	62.90%
fluent_elasticsearch	ingress throughput	+0.68	[+0.22, +1.13]	98.61%
socket_to_socket_blackhole	ingress throughput	+0.23	[+0.18, +0.28]	100.00%
http_elasticsearch	ingress throughput	+0.22	[+0.16, +0.28]	100.00%
http_to_s3	ingress throughput	+0.17	[-0.10, +0.45]	70.67%
http_to_http_noack	ingress throughput	+0.13	[+0.03, +0.23]	97.37%
http_to_http_json	ingress throughput	+0.01	[-0.06, +0.08]	21.93%
file_to_blackhole	egress throughput	+0.01	[-2.27, +2.29]	0.55%
splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.00	[-0.14, +0.14]	0.96%
splunk_hec_indexer_ack_blackhole	ingress throughput	-0.00	[-0.14, +0.14]	2.84%
splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.06	[-0.18, +0.05]	61.56%
enterprise_http_to_http	ingress throughput	-0.07	[-0.14, -0.01]	92.93%
syslog_splunk_hec_logs	ingress throughput	-0.09	[-0.13, -0.05]	99.97%
http_text_to_http_json	ingress throughput	-0.19	[-0.30, -0.08]	99.46%
syslog_humio_logs	ingress throughput	-0.31	[-0.42, -0.20]	100.00%
datadog_agent_remap_datadog_logs_acks	ingress throughput	-0.49	[-0.56, -0.42]	100.00%
datadog_agent_remap_datadog_logs	ingress throughput	-0.57	[-0.66, -0.48]	100.00%
datadog_agent_remap_blackhole	ingress throughput	-0.76	[-0.84, -0.67]	100.00%
syslog_log2metric_humio_metrics	ingress throughput	-0.79	[-0.92, -0.67]	100.00%
otlp_grpc_to_blackhole	ingress throughput	-1.13	[-1.23, -1.03]	100.00%
datadog_agent_remap_blackhole_acks	ingress throughput	-1.29	[-1.37, -1.20]	100.00%

[smadan2703]

@jszwedko any idea when this can be released ? Waiting for this feature to released.

[jszwedko]

It should be released this week.