Date filter plugin

Parses date/time strings from a field and, by default, uses the parsed value as the event's @timestamp. This is the standard way to make log timestamps accurate in OpenSearch once an event reaches Logstash.

  • Package: logstash-filter-date
  • Coverage source: default/bundled
  • Official catalog entry: Yes

Plugin overview

date is used in the Logstash filter stage. Parses date/time values and sets the event timestamp.

Typical use cases

  • Parse incoming log payloads into structured fields for querying and dashboards.
  • Tag failed operations and route them to dedicated troubleshooting views.

Input and output behavior

  • Flow: Parses date/time strings and updates timestamp fields.
  • Input: works on events that match your surrounding if conditions.
  • Output target: target (default: "@timestamp").
  • Important options: target, match, tag_on_failure, locale.
  • Failure signaling: uses tag_on_failure (default: ["_dateparsefailure"]) so failed events can be routed or inspected.

Options

Required

  • No required plugin-specific options.

Optional

  • locale (type: string; default: none) — Locale used when parsing month or day names (for example en).
  • match (type: array; default: []) — Array containing the source field name followed by one or more date format patterns to try.
  • precision (type: string; default: ms) — Number of digits to use for sub-second precision.
  • tag_on_failure (type: array; default: ["_dateparsefailure"]) — Tags applied to events whose date could not be parsed (defaults to _dateparsefailure).
  • target (type: string; default: "@timestamp") — Target field for the parsed timestamp (defaults to @timestamp).
  • timezone (type: string; default: none) — Timezone to use when the source string has no offset (for example Europe/London).

Example configuration

filter {
  date {
    match    => [ "[event][start]", "ISO8601", "yyyy-MM-dd HH:mm:ss" ]
    target   => "@timestamp"
    timezone => "UTC"
    tag_on_failure => [ "_dateparsefailure" ]
  }
}

Common options configuration

All Logstash filter plugins support these shared options:

  • add_field (type: hash; default: {}) — Adds fields when the filter succeeds. Supports dynamic field names and values.
  • add_tag (type: array; default: []) — Adds one or more tags when the filter succeeds.
  • enable_metric (type: boolean; default: true) — Enables or disables metric collection for this plugin instance.
  • id (type: string; default: none) — Sets an explicit plugin instance ID for monitoring and troubleshooting.
  • periodic_flush (type: boolean; default: false) — Calls the filter flush method at regular intervals.
  • remove_field (type: array; default: []) — Removes fields when the filter succeeds. Supports dynamic field names.
  • remove_tag (type: array; default: []) — Removes tags when the filter succeeds.
filter {
  date {
    add_field => { "pipeline_stage" => "parsed" }
    add_tag => ["parsed", "logstash_filter"]
    enable_metric => true
    id => "my_filter_instance"
    periodic_flush => false
    remove_field => ["tmp_field"]
    remove_tag => ["temporary"]
  }
}

Apply in Logit.io

  1. Open your stack in Logit.io and navigate to Logstash Pipelines.
  2. In the filter { ... } section, add a date block.
  3. Save your pipeline changes, then restart the Logstash pipeline if prompted.
  4. Send sample events and verify parsed/enriched fields in OpenSearch Dashboards.

Validation checklist

  • Confirm the date block compiles without syntax errors.
  • Verify expected new/updated fields exist in sample documents.
  • Verify unexpected fields are not removed unless explicitly configured.
  • Confirm tags added on success/failure align with your alerting and routing rules.

Troubleshooting

  • If events are unchanged, verify your filter condition (if ...) matches incoming events.
  • If the pipeline fails to start, validate braces/quotes and retry with a minimal filter block.
  • Check for tag_on_failure tags (default: ["_dateparsefailure"]) to quickly isolate parse/mutation failures.
  • If throughput drops, reduce expensive operations and test with representative sample volume.

References