Remap with VRL

Modify your observability data as it passes through your topology using Vector Remap Language (VRL)

status: beta egress: stream state: stateless
input: logs input: metrics input: traces
output: logs output: metrics output: traces

This is the recommended transform for parsing, shaping, and transforming data in Vector. It implements the Vector Remap Language (VRL), an expression-oriented language designed for processing observability data (logs and metrics) in a safe and performant manner.

Please refer to the VRL reference when writing VRL scripts.

Configuration

Example configurations

{
  "transforms": {
    "my_transform_id": {
      "type": "remap",
      "inputs": [
        "my-source-or-transform-id"
      ]
    }
  }
}
[transforms.my_transform_id]
type = "remap"
inputs = [ "my-source-or-transform-id" ]

transforms:
  my_transform_id:
    type: remap
    inputs:
      - my-source-or-transform-id

{
  "transforms": {
    "my_transform_id": {
      "type": "remap",
      "inputs": [
        "my-source-or-transform-id"
      ],
      "drop_on_abort": true,
      "file": "./my/program.vrl",
      "files": [
        "['./my/program.vrl', './my/program2.vrl']"
      ],
      "metric_tag_values": "single",
      "source": ". = parse_json!(.message)\n.new_field = \"new value\"\n.status = to_int!(.status)\n.duration = parse_duration!(.duration, \"s\")\n.new_name = del(.old_name)",
      "timezone": "local"
    }
  }
}
[transforms.my_transform_id]
type = "remap"
inputs = [ "my-source-or-transform-id" ]
drop_on_abort = true
file = "./my/program.vrl"
files = [ "['./my/program.vrl', './my/program2.vrl']" ]
metric_tag_values = "single"
source = """
. = parse_json!(.message)
.new_field = "new value"
.status = to_int!(.status)
.duration = parse_duration!(.duration, "s")
.new_name = del(.old_name)"""
timezone = "local"

transforms:
  my_transform_id:
    type: remap
    inputs:
      - my-source-or-transform-id
    drop_on_abort: true
    file: ./my/program.vrl
    files:
      - "['./my/program.vrl', './my/program2.vrl']"
    metric_tag_values: single
    source: |-
      . = parse_json!(.message)
      .new_field = "new value"
      .status = to_int!(.status)
      .duration = parse_duration!(.duration, "s")
      .new_name = del(.old_name)      
    timezone: local

drop_on_abort

optional bool

Drops any event that is manually aborted during processing.

If a VRL program is manually aborted (using abort) when processing an event, this option controls whether the original, unmodified event is sent downstream without any modifications or if it is dropped.

Additionally, dropped events can potentially be diverted to a specially-named output for further logging and analysis by setting reroute_dropped.

default: true

drop_on_error

optional bool

Drops any event that encounters an error during processing.

Normally, if a VRL program encounters an error when processing an event, the original, unmodified event is sent downstream. In some cases, you may not want to send the event any further, such as if certain transformation or enrichment is strictly required. Setting drop_on_error to true allows you to ensure these events do not get processed any further.

Additionally, dropped events can potentially be diverted to a specially named output for further logging and analysis by setting reroute_dropped.

default: false

file

optional string literal

File path to the Vector Remap Language (VRL) program to execute for each event.

If a relative path is provided, its root is the current working directory.

Required if source is missing.

Examples 
"./my/program.vrl"

files

optional [string]

File paths to the Vector Remap Language (VRL) programs to execute for each event.

If a relative path is provided, its root is the current working directory.

Required if source or file are missing.

Array string literal
Examples 
[
  "['./my/program.vrl', './my/program2.vrl']"
]

graph

optional object

Extra graph configuration

Configure output for component when generated with graph command

graph.node_attributes

optional object

Node attributes to add to this component’s node in resulting graph

They are added to the node as provided

graph.node_attributes.*
required string literal
A single graph node attribute in graphviz DOT language.
Examples 
{
  "color": "red",
  "name": "Example Node",
  "width": "5.0"
}

inputs

required [string]

A list of upstream source or transform IDs.

Wildcards (*) are supported.

See configuration for more info.

Array string literal
Examples 
[
  "my-source-or-transform-id",
  "prefix-*"
]

metric_tag_values

optional string literal enum

When set to single, metric tag values are exposed as single strings, the same as they were before this config option. Tags with multiple values show the last assigned value, and null values are ignored.

When set to full, all metric tags are exposed as arrays of either string or null values.

Enum options string literal
OptionDescription
fullAll tags are exposed as arrays of either string or null values.
singleTag values are exposed as single strings, the same as they were before this config option. Tags with multiple values show the last assigned value, and null values are ignored.
default: single

reroute_dropped

optional bool

Reroutes dropped events to a named output instead of halting processing on them.

When using drop_on_error or drop_on_abort, events that are “dropped” are processed no further. In some cases, it may be desirable to keep the events around for further analysis, debugging, or retrying.

In these cases, reroute_dropped can be set to true which forwards the original event to a specially-named output, dropped. The original event is annotated with additional fields describing why the event was dropped.

default: false

source

optional string remap_program

The Vector Remap Language (VRL) program to execute for each event.

Required if file is missing.

Examples 
". = parse_json!(.message)\n.new_field = \"new value\"\n.status = to_int!(.status)\n.duration = parse_duration!(.duration, \"s\")\n.new_name = del(.old_name)"

timezone

optional string literal

The name of the timezone to apply to timestamp conversions that do not contain an explicit time zone.

This overrides the global timezone option. The time zone name may be any name in the TZ database, or local to indicate system local time.

Examples 
"local"
"America/New_York"
"EST5EDT"

Input Types

The following table lists all telemetry data types supported by the component across possible configurations. Be aware that the available data types may differ based on the specified codec configuration.

Logs

Log events are supported.

Metrics

The following metrics are supported:
counter distribution gauge histogram set summary

Traces

Trace events are supported.

Outputs

<component_id>

Default output stream of the component. Use this component’s ID as an input to downstream transforms and sinks.

dropped

This transform also implements an additional dropped output. When the drop_on_error or drop_on_abort configuration values are set to true and reroute_dropped is also set to true, events that result in runtime errors or aborts will be dropped from the default output stream and sent to the dropped output instead. For a transform component named foo, this output can be accessed by specifying foo.dropped as the input to another component. Events sent to this output will be in their original form, omitting any partial modification that took place before the error or abort.

Output Types

Metrics

The modified input metric event.

Traces

The modified input trace event.

Logs

Warning

The fields shown below will be different if log namespacing is enabled. See Log Namespacing for more details

The modified input log event.

Telemetry

Metrics

link

component_discarded_events_total

counter
The number of events dropped by this component.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
intentional
True if the events were discarded intentionally, like a filter transform, or false if due to an error.
pid optional
The process ID of the Vector instance.

component_errors_total

counter
The total number of errors encountered by this component.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
error_type
The type of the error
host optional
The hostname of the system Vector is running on.
pid optional
The process ID of the Vector instance.
stage
The stage within the component at which the error occurred.

component_received_event_bytes_total

counter
The number of event bytes accepted by this component either from tagged origins like file and uri, or cumulatively from other origins.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
container_name optional
The name of the container from which the data originated.
file optional
The file from which the data originated.
host optional
The hostname of the system Vector is running on.
mode optional
The connection mode used by the component.
peer_addr optional
The IP from which the data originated.
peer_path optional
The pathname from which the data originated.
pid optional
The process ID of the Vector instance.
pod_name optional
The name of the pod from which the data originated.
uri optional
The sanitized URI from which the data originated.

component_received_events_count

histogram

A histogram of the number of events passed in each internal batch in Vector’s internal topology.

Note that this is separate than sink-level batching. It is mostly useful for low level debugging performance issues in Vector due to small internal batches.

component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
container_name optional
The name of the container from which the data originated.
file optional
The file from which the data originated.
host optional
The hostname of the system Vector is running on.
mode optional
The connection mode used by the component.
peer_addr optional
The IP from which the data originated.
peer_path optional
The pathname from which the data originated.
pid optional
The process ID of the Vector instance.
pod_name optional
The name of the pod from which the data originated.
uri optional
The sanitized URI from which the data originated.

component_received_events_total

counter
The number of events accepted by this component either from tagged origins like file and uri, or cumulatively from other origins.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
container_name optional
The name of the container from which the data originated.
file optional
The file from which the data originated.
host optional
The hostname of the system Vector is running on.
mode optional
The connection mode used by the component.
peer_addr optional
The IP from which the data originated.
peer_path optional
The pathname from which the data originated.
pid optional
The process ID of the Vector instance.
pod_name optional
The name of the pod from which the data originated.
uri optional
The sanitized URI from which the data originated.

component_sent_event_bytes_total

counter
The total number of event bytes emitted by this component.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
output optional
The specific output of the component.
pid optional
The process ID of the Vector instance.

component_sent_events_total

counter
The total number of events emitted by this component.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
output optional
The specific output of the component.
pid optional
The process ID of the Vector instance.

utilization

gauge
A ratio from 0 to 1 of the load on a component. A value of 0 would indicate a completely idle component that is simply waiting for input. A value of 1 would indicate a that is never idle. This value is updated every 5 seconds.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
pid optional
The process ID of the Vector instance.

Examples

Parse Syslog logs

Given this event...
{
  "log": {
    "message": "\u003c102\u003e1 2020-12-22T15:22:31.111Z vector-user.biz su 2666 ID389 - Something went wrong"
  }
}
...and this configuration...
transforms:
  my_transform_id:
    type: remap
    inputs:
      - my-source-or-transform-id
    source: . |= parse_syslog!(.message)

[transforms.my_transform_id]
type = "remap"
inputs = [ "my-source-or-transform-id" ]
source = ". |= parse_syslog!(.message)"

{
  "transforms": {
    "my_transform_id": {
      "type": "remap",
      "inputs": [
        "my-source-or-transform-id"
      ],
      "source": ". |= parse_syslog!(.message)"
    }
  }
}
...this Vector event is produced:
{
  "appname": "su",
  "facility": "ntp",
  "hostname": "vector-user.biz",
  "message": "Something went wrong",
  "msgid": "ID389",
  "procid": 2666,
  "severity": "info",
  "timestamp": "2020-12-22T15:22:31.111Z",
  "version": 1
}

Parse key/value (logfmt) logs

Given this event...
{
  "log": {
    "message": "@timestamp=\"Sun Jan 10 16:47:39 EST 2021\" level=info msg=\"Stopping all fetchers\" tag#production=stopping_fetchers id=ConsumerFetcherManager-1382721708341 module=kafka.consumer.ConsumerFetcherManager"
  }
}
...and this configuration...
transforms:
  my_transform_id:
    type: remap
    inputs:
      - my-source-or-transform-id
    source: . = parse_key_value!(.message)

[transforms.my_transform_id]
type = "remap"
inputs = [ "my-source-or-transform-id" ]
source = ". = parse_key_value!(.message)"

{
  "transforms": {
    "my_transform_id": {
      "type": "remap",
      "inputs": [
        "my-source-or-transform-id"
      ],
      "source": ". = parse_key_value!(.message)"
    }
  }
}
...this Vector event is produced:
{
  "@timestamp": "Sun Jan 10 16:47:39 EST 2021",
  "id": "ConsumerFetcherManager-1382721708341",
  "level": "info",
  "module": "kafka.consumer.ConsumerFetcherManager",
  "msg": "Stopping all fetchers",
  "tag#production": "stopping_fetchers"
}

Parse custom logs

Given this event...
{
  "log": {
    "message": "2021/01/20 06:39:15 +0000 [error] 17755#17755: *3569904 open() \"/usr/share/nginx/html/test.php\" failed (2: No such file or directory), client: xxx.xxx.xxx.xxx, server: localhost, request: \"GET /test.php HTTP/1.1\", host: \"yyy.yyy.yyy.yyy\""
  }
}
...and this configuration...
transforms:
  my_transform_id:
    type: remap
    inputs:
      - my-source-or-transform-id
    source: >-
      . |= parse_regex!(.message, r'^(?P<timestamp>\d+/\d+/\d+ \d+:\d+:\d+
      \+\d+) \[(?P<severity>\w+)\] (?P<pid>\d+)#(?P<tid>\d+):(?:
      \*(?P<connid>\d+))? (?P<message>.*)$')


      # Coerce parsed fields

      .timestamp = parse_timestamp(.timestamp, "%Y/%m/%d %H:%M:%S %z") ?? now()

      .pid = to_int!(.pid)

      .tid = to_int!(.tid)


      # Extract structured data

      message_parts = split(.message, ", ", limit: 2)

      structured = parse_key_value(message_parts[1], key_value_delimiter: ":", field_delimiter: ",") ?? {}

      .message = message_parts[0]

      . = merge(., structured)      

[transforms.my_transform_id]
type = "remap"
inputs = [ "my-source-or-transform-id" ]
source = """
. |= parse_regex!(.message, r'^(?P<timestamp>\\d+/\\d+/\\d+ \\d+:\\d+:\\d+ \\+\\d+) \\[(?P<severity>\\w+)\\] (?P<pid>\\d+)#(?P<tid>\\d+):(?: \\*(?P<connid>\\d+))? (?P<message>.*)$')

# Coerce parsed fields
.timestamp = parse_timestamp(.timestamp, "%Y/%m/%d %H:%M:%S %z") ?? now()
.pid = to_int!(.pid)
.tid = to_int!(.tid)

# Extract structured data
message_parts = split(.message, ", ", limit: 2)
structured = parse_key_value(message_parts[1], key_value_delimiter: ":", field_delimiter: ",") ?? {}
.message = message_parts[0]
. = merge(., structured)"""

{
  "transforms": {
    "my_transform_id": {
      "type": "remap",
      "inputs": [
        "my-source-or-transform-id"
      ],
      "source": ". |= parse_regex!(.message, r'^(?P<timestamp>\\d+/\\d+/\\d+ \\d+:\\d+:\\d+ \\+\\d+) \\[(?P<severity>\\w+)\\] (?P<pid>\\d+)#(?P<tid>\\d+):(?: \\*(?P<connid>\\d+))? (?P<message>.*)$')\n\n# Coerce parsed fields\n.timestamp = parse_timestamp(.timestamp, \"%Y/%m/%d %H:%M:%S %z\") ?? now()\n.pid = to_int!(.pid)\n.tid = to_int!(.tid)\n\n# Extract structured data\nmessage_parts = split(.message, \", \", limit: 2)\nstructured = parse_key_value(message_parts[1], key_value_delimiter: \":\", field_delimiter: \",\") ?? {}\n.message = message_parts[0]\n. = merge(., structured)"
    }
  }
}
...this Vector event is produced:
{
  "client": "xxx.xxx.xxx.xxx",
  "connid": "3569904",
  "host": "yyy.yyy.yyy.yyy",
  "message": "open() \"/usr/share/nginx/html/test.php\" failed (2: No such file or directory)",
  "pid": 17755,
  "request": "GET /test.php HTTP/1.1",
  "server": "localhost",
  "severity": "error",
  "tid": 17755,
  "timestamp": "2021-01-20T06:39:15Z"
}

Multiple parsing strategies

Given this event...
{
  "log": {
    "message": "\u003c102\u003e1 2020-12-22T15:22:31.111Z vector-user.biz su 2666 ID389 - Something went wrong"
  }
}
...and this configuration...
transforms:
  my_transform_id:
    type: remap
    inputs:
      - my-source-or-transform-id
    source: >-
      structured =
        parse_syslog(.message) ??
        parse_common_log(.message) ??
        parse_regex!(.message, r'^(?P<timestamp>\d+/\d+/\d+ \d+:\d+:\d+) \[(?P<severity>\w+)\] (?P<pid>\d+)#(?P<tid>\d+):(?: \*(?P<connid>\d+))? (?P<message>.*)$')
      . = merge(., structured)      

[transforms.my_transform_id]
type = "remap"
inputs = [ "my-source-or-transform-id" ]
source = """
structured =
  parse_syslog(.message) ??
  parse_common_log(.message) ??
  parse_regex!(.message, r'^(?P<timestamp>\\d+/\\d+/\\d+ \\d+:\\d+:\\d+) \\[(?P<severity>\\w+)\\] (?P<pid>\\d+)#(?P<tid>\\d+):(?: \\*(?P<connid>\\d+))? (?P<message>.*)$')
. = merge(., structured)"""

{
  "transforms": {
    "my_transform_id": {
      "type": "remap",
      "inputs": [
        "my-source-or-transform-id"
      ],
      "source": "structured =\n  parse_syslog(.message) ??\n  parse_common_log(.message) ??\n  parse_regex!(.message, r'^(?P<timestamp>\\d+/\\d+/\\d+ \\d+:\\d+:\\d+) \\[(?P<severity>\\w+)\\] (?P<pid>\\d+)#(?P<tid>\\d+):(?: \\*(?P<connid>\\d+))? (?P<message>.*)$')\n. = merge(., structured)"
    }
  }
}
...this Vector event is produced:
{
  "appname": "su",
  "facility": "ntp",
  "hostname": "vector-user.biz",
  "message": "Something went wrong",
  "msgid": "ID389",
  "procid": 2666,
  "severity": "info",
  "timestamp": "2020-12-22T15:22:31.111Z",
  "version": 1
}

Modify metric tags

Given this event...
{
  "metric": {
    "counter": {
      "value": 102
    },
    "kind": "incremental",
    "name": "user_login_total",
    "tags": {
      "email": "vic@vector.dev",
      "host": "my.host.com",
      "instance_id": "abcd1234"
    }
  }
}
...and this configuration...
transforms:
  my_transform_id:
    type: remap
    inputs:
      - my-source-or-transform-id
    source: |-
      .tags.environment = get_env_var!("ENV") # add
      .tags.hostname = del(.tags.host) # rename
      del(.tags.email)      

[transforms.my_transform_id]
type = "remap"
inputs = [ "my-source-or-transform-id" ]
source = """
.tags.environment = get_env_var!("ENV") # add
.tags.hostname = del(.tags.host) # rename
del(.tags.email)"""

{
  "transforms": {
    "my_transform_id": {
      "type": "remap",
      "inputs": [
        "my-source-or-transform-id"
      ],
      "source": ".tags.environment = get_env_var!(\"ENV\") # add\n.tags.hostname = del(.tags.host) # rename\ndel(.tags.email)"
    }
  }
}
...this Vector event is produced:
{
  "counter": {
    "value": 102
  },
  "kind": "incremental",
  "name": "user_login_total",
  "tags": {
    "environment": "production",
    "hostname": "my.host.com",
    "instance_id": "abcd1234"
  }
}

Emitting multiple logs from JSON

Given this event...
{
  "log": {
    "message": "[{\"message\": \"first_log\"}, {\"message\": \"second_log\"}]"
  }
}
...and this configuration...
transforms:
  my_transform_id:
    type: remap
    inputs:
      - my-source-or-transform-id
    source: ". = parse_json!(.message) # sets `.` to an array of objects"

[transforms.my_transform_id]
type = "remap"
inputs = [ "my-source-or-transform-id" ]
source = ". = parse_json!(.message) # sets `.` to an array of objects"

{
  "transforms": {
    "my_transform_id": {
      "type": "remap",
      "inputs": [
        "my-source-or-transform-id"
      ],
      "source": ". = parse_json!(.message) # sets `.` to an array of objects"
    }
  }
}
...this Vector event is produced:
[{"log":{"message":"first_log"}},{"log":{"message":"second_log"}}]

Emitting multiple non-object logs from JSON

Given this event...
{
  "log": {
    "message": "[5, true, \"hello\"]"
  }
}
...and this configuration...
transforms:
  my_transform_id:
    type: remap
    inputs:
      - my-source-or-transform-id
    source: ". = parse_json!(.message) # sets `.` to an array"

[transforms.my_transform_id]
type = "remap"
inputs = [ "my-source-or-transform-id" ]
source = ". = parse_json!(.message) # sets `.` to an array"

{
  "transforms": {
    "my_transform_id": {
      "type": "remap",
      "inputs": [
        "my-source-or-transform-id"
      ],
      "source": ". = parse_json!(.message) # sets `.` to an array"
    }
  }
}
...this Vector event is produced:
[{"log":{"message":5}},{"log":{"message":true}},{"log":{"message":"hello"}}]

How it works

Emitting multiple log events

Multiple log events can be emitted from remap by assigning an array to the root path .. One log event is emitted for each input element of the array.

If any of the array elements isn’t an object, a log event is created that uses the element’s value as the message key. For example, 123 is emitted as:

{
  "message": 123
}

Event Data Model

You can use the remap transform to handle all event types (log, metric, trace). The remap transform cannot convert from one event type to another, the input type must match the output type. For example, a log event can only be remap-ed and outputted as a log event and cannot be converted to a metric.

For converting from one type to another, please see Log to Metric and Metric to Log transforms.

Log events in the remap transform correspond directly to Vector’s log schema, which means that the transform has access to the whole event and no restrictions on how the event can be modified.

With metric events, VRL is much more restrictive. Below is a field-by-field breakdown of VRL’s access to metrics:

FieldAccessSpecific restrictions (if any)
typeRead only
kindRead/writeYou can set kind to either incremental or absolute but not to an arbitrary value.
nameRead/write
timestampRead/write/deleteYou can only assign a valid VRL timestamp value, not a VRL string.
interval_msRead/write/deleteYou can assign it a non zero 32 byte unsigned interval
namespaceRead/write/delete
tagsRead/write/deleteThe tags field must be a VRL object in which all keys and values are strings.

It’s important to note that if you try to perform a disallowed action, such as deleting the type field using del(.type), Vector doesn’t abort the VRL program or throw an error. Instead, it ignores the disallowed action.

Lazy Event Mutation

When you make changes to an event through VRL’s path assignment syntax, the change isn’t immediately applied to the actual event. If the program fails to run to completion, any changes made until that point are dropped and the event is kept in its original state.

If you want to make sure your event is changed as expected, you have to rewrite your program to never fail at runtime (the compiler can help you with this).

Alternatively, if you want to ignore/drop events that caused the program to fail, you can set the drop_on_error configuration value to true.

Learn more about runtime errors in the Vector Remap Language reference.

Vector Remap Language

The Vector Remap Language (VRL) is a restrictive, fast, and safe language we designed specifically for mapping observability data. It avoids the need to chain together many fundamental Vector transforms to accomplish rudimentary reshaping of data.

The intent is to offer the same robustness of full language runtime (ex: Lua) without paying the performance or safety penalty.

Learn more about Vector’s Remap Language in the Vector Remap Language reference.

State

This component is stateless, meaning its behavior is consistent across each input.
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