Send Vector logs to Axiom

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.

Installation

Vector can be installed on Linux, Windows, and MacOS. Visit the Vector documentation to install Vector.

Logs and metrics sent via vector should use @timestamp as the timestamp field like: {"@timestamp":"2022-04-14T21:30:30.658Z..."} name not _time. Axiom accepts many date strings and timestamps without knowing the format in advance, including Unix Epoch, RFC3339, and ISO 8601.

Configuration

Sending data to Axiom with Vector is very straightforward using the axiom sink. you only need your dataset name and Ingest Token.

In the example below we configure Vector to read and collect metrics from StatsD aggregator and send StatsD metrics to the Axiom sink.

First, you'll need to create vector configuration file, vector.toml

Common Configuration

[sources.my_source_id]
type = "statsd"

[sinks.axiom]
inputs = ["my_source_id"]
type = "axiom"
token = "xaat-1234"
dataset = "vector-dev"

Send Kubernetes logs to Axiom

Send Kubernetes logs to Axiom using the Kubernetes source.

[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-or-transform-id"]
token = "xaat-1234"
dataset = "vector-dev"

DATASET: is the name of your dataset. When logs are sent from your vector it is stored in a Dataset on Axiom.

See creating a dataset for more

TOKEN: is used to ingest or query data to your dataset. API token can be generated from settings on Axiom dashboard.

See creating an API token for more

Send Docker logs to Axiom

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

# 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 = "your_dataset_name"  # replace with the name of your Axiom dataset
token = "your_api_token"  # replace with your Axiom API token

Replace your_dataset_name with the name of the dataset you want to send logs to in Axiom, and your_api_token with your Axiom API token.

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

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

Now, Vector will collect 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 to using the Axiom sink, you need to create a configuration file, e.g., vector.toml, with the following content:

[sources.my_source_id]
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 = "your_dataset_name"  # replace with the name of your Axiom dataset
token = "your_api_token"  # replace with your Axiom API token

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

Send Kafka logs to Axiom

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

[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 = "your_dataset_name"  # replace with the name of your Axiom dataset
token = "your_api_token"  # replace with your Axiom API token

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, you'll need to 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

You'll need to configure NGINX to expose metrics. This typically involves enabling the ngx_http_stub_status_module module in your NGINX configuration. Here's how you could do that:

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

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:

sudo systemctl restart nginx

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

Step 2: Configure Vector

You'll need to configure Vector to scrape the NGINX metrics and send them to Axiom. Create a new configuration file (let's call it vector.toml), and add the following:

[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 = "your_dataset_name"  # replace with the name of your Axiom dataset
token = "your_api_token"  # replace with your Axiom API token

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

You can checkout the advanced configuration on Batch, Buffer configuration, and Encoding on Vector Documentation