Send data from Vector to Axiom

This page explains how to configure Vector to read and collect metrics from your sources using the Axiom sink.

Vector is a lightweight and ultra-fast tool for building observability pipelines. It has a built-in support for shipping logs to Axiom through the axiom sink.

Prerequisites

Create an Axiom account.
Create a dataset in Axiom where you send your data.
Create an API token in Axiom with permissions to ingest data to the dataset you have created.

Installation

Follow the quickstart guide in the Vector documentation to install Vector, and to configure sources and sinks.

Configuration

Send data to Axiom with Vector using the file method and the axiom sink.

The example below configures Vector to read and collect logs from files and send them to Axiom.

Create a vector configuration file vector.toml with the following content:
TOML
```
[sources.VECTOR_SOURCE_ID]
type = "file"
include = ["PATH_TO_LOGS"]

[sinks.SINK_ID]
type = "axiom"
inputs = ["VECTOR_SOURCE_ID"]
token = "API_TOKEN"
dataset = "DATASET_NAME"
region = "AXIOM_DOMAIN"
```
Info
Replace VECTOR_SOURCE_ID with the Vector source ID.
Replace PATH_TO_LOGS with the path to the log files. For example, /var/log/**/*.log.
Replace SINK_ID with the sink ID.
Replace API_TOKEN with the Axiom API token you have generated. For added security, store the API token in an environment variable.

Replace DATASET_NAME with the name of the Axiom dataset where you send your data.
Replace AXIOM_DOMAIN with the base domain of your edge deployment. For more information, see Edge deployments.
Run Vector to send logs to Axiom.

Example with data transformation

The example below deletes a field before sending the data to Axiom:

TOML

[sources.VECTOR_SOURCE_ID]
type = "file"
include = ["PATH_TO_LOGS"]

[transforms.filter_json_fields]
type = "remap"
inputs = ["VECTOR_SOURCE_ID"]
source = '''
  . = del(.FIELD_TO_REMOVE)
'''

[sinks.SINK_ID]
type = "axiom"
inputs = ["filter_json_fields"]
token = "API_TOKEN"
dataset = "DATASET_NAME"
region = "AXIOM_DOMAIN"

Send Kubernetes logs to Axiom

Send Kubernetes logs to Axiom using the Kubernetes source.

TOML

[sources.my_source_id]
type = "kubernetes_logs"
auto_partial_merge = true
ignore_older_secs = 600
read_from = "beginning"
self_node_name = "${VECTOR_SELF_NODE_NAME}"
exclude_paths_glob_patterns = [ "**/exclude/**" ]
extra_field_selector = "metadata.name!=pod-name-to-exclude"
extra_label_selector = "my_custom_label!=my_value"
extra_namespace_label_selector = "my_custom_label!=my_value"
max_read_bytes = 2_048
max_line_bytes = 32_768
fingerprint_lines = 1
glob_minimum_cooldown_ms = 60_000
delay_deletion_ms = 60_000
data_dir = "/var/lib/vector"
timezone = "local"

[sinks.axiom]
type = "axiom"
inputs = ["my_source_id"]
token = "API_TOKEN"
dataset = "DATASET_NAME"
region = "AXIOM_DOMAIN"

Send Docker logs to Axiom

To send Docker logs using the Axiom sink, you need to create a configuration file, for example, vector.toml, with the following content:

TOML

# Define the Docker logs source
[sources.docker_logs]
type = "docker_logs"
docker_host = "unix:///var/run/docker.sock"

# Define the Axiom sink
[sinks.axiom]
type = "axiom"
inputs = ["docker_logs"]
dataset = "DATASET_NAME"
token = "API_TOKEN"
region = "AXIOM_DOMAIN"

Run Vector: Start Vector with the configuration file you just created:

shell

vector --config /path/to/vector.toml

Vector collects logs from Docker and forward them to Axiom using the Axiom sink. You can view and analyze your logs in your dataset.

Send AWS S3 logs to Axiom

To send AWS S3 logs using the Axiom sink, create a configuration file, for example, vector.toml, with the following content:

TOML

[sources.my_s3_source]
type = "aws_s3"
bucket = "my-bucket"  # replace with your bucket name
region = "us-west-2"  # replace with the AWS region of your bucket

[sinks.axiom]
type = "axiom"
inputs = ["my_s3_source"]
dataset = "DATASET_NAME"
token = "API_TOKEN"
region = "AXIOM_DOMAIN"

Finally, run Vector with the configuration file using vector --config ./vector.toml. This starts Vector and begins reading logs from the specified S3 bucket and sending them to the specified Axiom dataset.

Send Kafka logs to Axiom

To send Kafka logs using the Axiom sink, you need to create a configuration file, for example, vector.toml, with the following code:

TOML

[sources.my_kafka_source]
type = "kafka" # must be: kafka
bootstrap_servers = "10.14.22.123:9092" # your Kafka bootstrap servers
group_id = "my_group_id" # your Kafka consumer group ID
topics = ["my_topic"] # the Kafka topics to consume from
auto_offset_reset = "earliest" # start reading from the beginning

[sinks.axiom]
type = "axiom"
inputs = ["my_kafka_source"]  # connect the Axiom sink to your Kafka source
dataset = "DATASET_NAME"  # replace with the name of your Axiom dataset
token = "API_TOKEN"  # replace with your Axiom API token
region = "AXIOM_DOMAIN"

Finally, you can start Vector with your configuration file: vector --config /path/to/your/vector.toml

Send NGINX metrics to Axiom

To send NGINX metrics using Vector to the Axiom sink, first enable NGINX to emit metrics, then use Vector to capture and forward those metrics. Here is a step-by-step guide:

Step 1: Enable NGINX Metrics

Configure NGINX to expose metrics. This typically involves enabling the ngx_http_stub_status_module module in your NGINX configuration.

Open your NGINX configuration file (often located at /etc/nginx/nginx.conf) and in your server block, add:

shell

location /metrics {
  stub_status;
  allow 127.0.0.1; # only allow requests from localhost
  deny all; # deny all other hosts
}

Restart or reload NGINX to apply the changes:

shell

sudo systemctl restart nginx

This exposes basic NGINX metrics at the /metrics endpoint on your server.

Step 2: Configure Vector

Configure Vector to scrape the NGINX metrics and send them to Axiom. Create a new configuration file (vector.toml), and add the following:

TOML

[sources.nginx_metrics]
type = "nginx_metrics" # must be: nginx_metrics
endpoints = ["http://localhost/metrics"] # the endpoint where NGINX metrics are exposed

[sinks.axiom]
type = "axiom" # must be: axiom
inputs = ["nginx_metrics"] # use the metrics from the NGINX source
dataset = "DATASET_NAME"  # replace with the name of your Axiom dataset
token = "API_TOKEN"  # replace with your Axiom API token
region = "AXIOM_DOMAIN"

Finally, you can start Vector with your configuration file: vector --config /path/to/your/vector.toml

Send Syslog logs to Axiom

To send Syslog logs using the Axiom sink, you need to create a configuration file, for example, vector.toml, with the following code:

TOML

[sources.my_source_id]
type="syslog"
address="0.0.0.0:6514"
max_length=102_400
mode="tcp"

[sinks.axiom]
type="axiom"
inputs = [ "my_source_id" ] # required
dataset="DATASET_NAME" # replace with the name of your Axiom dataset
token="API_TOKEN" # replace with your Axiom API token
region = "AXIOM_DOMAIN"

Send Prometheus metrics to Axiom

To send Prometheus scrape metrics using the Axiom sink, you need to create a configuration file, for example, vector.toml, with the following code:

TOML

# Define the Prometheus source that scrapes metrics
[sources.my_prometheus_source]
type = "prometheus_scrape"  # scrape metrics from a Prometheus endpoint
endpoints = ["http://localhost:9090/metrics"]  # replace with your Prometheus endpoint

# Define Axiom sink where logs will be sent
[sinks.axiom]
type = "axiom"  # Axiom type
inputs = ["my_prometheus_source"]  # connect the Axiom sink to your Prometheus source
dataset = "DATASET_NAME"  # replace with the name of your Axiom dataset
token = "API_TOKEN"  # replace with your Axiom API token
region = "AXIOM_DOMAIN"

Check out the advanced configuration on Batch, Buffer configuration, and Encoding on Vector Documentation

Timestamp in legacy Vector versions

If you use Vector version v0.41.1 (released on September 11, 2024) or earlier, use the @timestamp field instead of _time to specify the timestamp in the event data you send to Axiom. For example: {"@timestamp":"2022-04-14T21:30:30.658Z..."}. For more information, see Requirements of the timestamp field. In the case of Vector version v0.41.1 or earlier, the requirements explained on the page apply to the @timestamp field, not to _time.

If you use Vector version v0.42.0 (released on October 21, 2024) or newer, use the _time field as usual for other collectors.

Upgrade from legacy Vector version

If you upgrade from Vector version v0.41.1 or earlier to a newer version, change all references from the timestamp field to the _time field and remap the logic.

Example vrl file:

text

# Set time explicitly rather than allowing Axiom to default to the current time
. = set!(value: ., path: ["_time"],  data: .timestamp)

# Remove the original value as it’s effectively a duplicate
del(.timestamp)

Example Vector configuration file:

TOML

# ...

[transforms.migrate]
type = "remap"
inputs = [ "k8s"]
file= 'example.vrl' # See above

[sinks.debug]
type = "axiom"
inputs = [ "migrate" ]
dataset = "DATASET_NAME" # No change
token = "API_TOKEN" # No change
region = "AXIOM_DOMAIN"

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

Set compression algorithm

Upgrading to Vector version v0.42.0 or newer automatically enables the zstd compression algorithm by default.

To set another compression algorithm, use the example below:

TOML

# ...

[transforms.migrate]
type = "remap"
inputs = [ "k8s"]
file= 'example.vrl' # See above

[sinks.debug]
type = "axiom"
compression = "gzip" # Set the compression algorithm
inputs = [ "migrate" ]
dataset = "DATASET_NAME" # No change
token = "API_TOKEN" # No change
region = "AXIOM_DOMAIN"

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