Vector configuration optimisation for memory usage reduction in metric ingestion via Kafka

[JoiKKo]

Hello to all,

I'm working on a metrics ingestion pipeline with Vector, where the source is a Kafka cluster and the approximate throughput is 15 MB/s. The messages we receive have an average size between 300 and 2000 bytes.

To control the memory usage, I have made some adjustments in the librdkafka_options section and in the sinks configuration. Below, I share an extract of my configuration:

`[api]
enabled = true

[sources.kafka_in]
type = "kafka"
bootstrap_servers = "kafka1.example.com:9093,kafka2.example.com:9093,kafka3.example.com:9093"
group_id = "vector2kafka-XXXXXXXX.metrics"
topics = ["metrics_topic"]
tls.enabled = true
tls.verify_certificate = false
tls.ca_file = "/certs/ca.crt"
tls.crt_file = "/certs/tls.crt"
tls.key_file = "/certs/tls.key"
decoding.codec = "native"
auto_offset_reset = "earliest"
commit_interval_ms = 2000
fetch_wait_max_ms = 10
session_timeout_ms = 10000

librdkafka_options = {
"fetch.message.max.bytes" = "8192",
"queued.min.messages" = "100",
"queued.max.messages.kbytes" = "8192",
"socket.receive.buffer.bytes" = "65536"
}

acknowledgements.enabled = true

[sources.internal_metrics]
type = "internal_metrics"

[transforms.filter_metrics]
type = "filter"
inputs = ["kafka_in"]
condition = 'exists(.tags.leanix) && .tags.leanix == "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX" && exists(.tags.environment) && .tags.environment != "production"'

[transforms.route_metrics]
type = "route"
inputs = ["filter_metrics"]
route.private = 'exists(.tags.private) && .tags.private == "true"'
route.default = '!(exists(.tags.private) && .tags.private == "true")'

[sinks.vmbucket1_public]
type = "prometheus_remote_write"
inputs = ["route_metrics.default"]
endpoint = "http://vm-bucket-1.example.internal:8480/insert/0/prometheus/api/v1/write"
compression = "snappy"
healthcheck = false
batch.timeout_secs = 1
batch.max_events = 300
buffer.max_events = 500

[sinks.vmbucket1_private]
type = "prometheus_remote_write"
inputs = ["route_metrics.private"]
endpoint = "http://vm-bucket-1.example.internal:8480/insert/103/prometheus/api/v1/write"
compression = "snappy"
healthcheck = false
batch.timeout_secs = 1
batch.max_events = 300
buffer.max_events = 500

[sinks.prometheus_exporter]
type = "prometheus_exporter"
inputs = ["internal_metrics"]
address = "0.0.0.0:9201"`

My question is the following:

Are there any additional settings or parameters I can adjust in Vector (or librdkafka_options) to further optimize memory usage, without compromising throughput, in a 15 MB/s metric environment? Also, what methods do you recommend for verifying and measuring the actual size of messages in my Kafka topic?

To give you more context, I am currently using a customised solution that consumes 50% less resources than Vector....

I welcome any suggestions or experiences you can share, thank you in advance!