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Send logs from Vector

Vector is an amazing piece of software (in Rust obviously) and brings a new fresh wind in the observability space, it is well-known for collecting logs from every part of your infrastructure, transforming and aggregating them, and finally forwarding them to a sink.

In this guide, we will show you how to connect it to Quickwit.

Start Quickwit server

# Create Quickwit data dir.
mkdir qwdata
./quickwit run

Taking advantage of Quickwit's native support for logs

Let's embrace the OpenTelemetry standard and take advantage of Quickwit features. With the native support for OpenTelemetry standards, Quickwit already comes with an index called otel-logs_v0_6 that is compatible with the OpenTelemetry logs data model. This means we can start pushing log data without any prior usual index setup.

Here is the OpenTelemetry index configuration for reference.

version: 0.6

index_id: otel-logs-v0_6

mode: strict
- name: timestamp_nanos
type: datetime
input_formats: [unix_timestamp]
output_format: unix_timestamp_nanos
indexed: false
fast: true
precision: milliseconds
- name: observed_timestamp_nanos
type: datetime
input_formats: [unix_timestamp]
output_format: unix_timestamp_nanos
- name: service_name
type: text
tokenizer: raw
- name: severity_text
type: text
tokenizer: raw
fast: true
- name: severity_number
type: u64
fast: true
- name: body
type: json
- name: attributes
type: json
tokenizer: raw
fast: true
- name: dropped_attributes_count
type: u64
indexed: false
- name: trace_id
type: bytes
- name: span_id
type: bytes
- name: trace_flags
type: u64
indexed: false
- name: resource_attributes
type: json
tokenizer: raw
fast: true
- name: resource_dropped_attributes_count
type: u64
indexed: false
- name: scope_name
type: text
indexed: false
- name: scope_version
type: text
indexed: false
- name: scope_attributes
type: json
indexed: false
- name: scope_dropped_attributes_count
type: u64
indexed: false

timestamp_field: timestamp_nanos

commit_timeout_secs: 5

default_search_fields: [body.message]

Setup Vector

Our sink here will be Quickwit ingest API To keep it simple in this tutorial, we will use a log source called demo_logs that generates logs in a given format. Let's choose the common syslog format (Vector does not generate logs in the OpenTelemetry format directly!) and use the transform feature to map the syslog format into the OpenTelemetry format.

type = "demo_logs"
format = "syslog"
count = 100000
interval = 0.001

inputs = [ "generate_syslog"]
type = "remap"
source = '''
structured = parse_syslog!(.message)
.timestamp_nanos, err = to_unix_timestamp(structured.timestamp, unit: "nanoseconds")
.body = structured
.service_name = structured.appname
.resource_attributes.source_type = .source_type = structured.hostname = structured.appname
.attributes.syslog.procid = structured.procid
.attributes.syslog.facility = structured.facility
.attributes.syslog.version = structured.version
.severity_text = if includes(["emerg", "err", "crit", "alert"], structured.severity) {
} else if structured.severity == "warning" {
} else if structured.severity == "debug" {
} else if includes(["info", "notice"], structured.severity) {
} else {
.scope_name = structured.msgid

# useful to see the logs in the terminal
# [sinks.emit_syslog]
# inputs = ["remap_syslog"]
# type = "console"
# encoding.codec = "json"

type = "http"
method = "post"
inputs = ["remap_syslog"]
encoding.codec = "json"
framing.method = "newline_delimited"
uri = ""

Download the above Vector config file.

curl -o vector.toml

Now let's start Vector so that we can start sending logs to Quickwit.

docker run -v $(pwd)/vector.toml:/etc/vector/vector.toml:ro -p 8383:8383 --net=host timberio/vector:0.25.0-distroless-libc

Search logs

Quickwit is now ingesting logs coming from Vector and you can search them either with curl or by using the UI:

  • curl -XGET
  • Open your browser at and play with it!

Compute aggregation on severity_text

For aggregations, we can't use yet Quickwit UI but we can use cURL.

Let's craft a nice aggregation query to count how many INFO, DEBUG, WARN, and ERROR per minute (all datetime are stored in microseconds thus the interval of 60_000_000 microseconds) we have:

"query": "*",
"max_hits": 0,
"aggs": {
"count_per_minute": {
"histogram": {
"field": "timestamp_nanos",
"interval": 60000000
"aggs": {
"severity_text_count": {
"terms": {
"field": "severity_text"
curl -XPOST -H "Content-Type: application/json" --data @aggregation-query.json

Going further

Now you can also deploy Grafana and connect to Quickwit as data source for query, dashboard, alerts and more!