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To add a Logstash filter, put a filter { ... } block between the pipeline’s input and output blocks. The input receives events from agents or brokers, filters turn unstructured content into reliable fields, and the output forwards those events to your central store, such as Elasticsearch. A practical text-log chain is grok → date → mutate; use the json filter when the payload is JSON rather than a plain log line.
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This guide shows how to build that chain, test it safely, handle JSON and non-JSON events, and avoid the ordering and timestamp mistakes that make centralized logs difficult to search.
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Where the filter fits in a centralized pipeline
A Logstash pipeline has three stages in this order:
- Input receives events, for example from a Beats listener or Kafka.
- Filter parses, enriches, renames, converts, or removes fields. This section is optional.
- Output sends the resulting events to a destination such as Elasticsearch.
Centralized logging does not require a separate “centralized filter” product. The central part is one shared Logstash pipeline receiving events from many hosts. Every event then passes through the same filter rules before it is indexed or forwarded. A parsing change in that shared pipeline can therefore affect every sender, which is why configuration testing and a controlled rollout matter.
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A minimal pipeline shape
input {
beats {
port => 5044
}
}
filter {
# parsing and normalization rules go here
}
output {
stdout { codec => rubydebug }
}The Beats-on-port-5044 example is the shape shown in the Elastic 8.19 “Parsing Logs with Logstash” documentation. Its filter block is commented out so that you can add parsing incrementally. Replace the input and output with the plugins used in your environment; the filter stage remains in the middle.
Choose filters by the transformation you need
| Filter | What it does | Typical input | Result to verify |
|---|---|---|---|
| grok | Extracts named fields from free-form text using reusable patterns. | A line such as a timestamp, level, and message separated by spaces. | Fields such as ts, level, and msg. |
| date | Parses a field and assigns the event’s canonical @timestamp. |
The field produced by grok or another parser. | @timestamp reflects when the event occurred, not merely when Logstash received it. |
| mutate | Renames, converts, normalizes, or removes fields. | Parsed fields with inconsistent names or types. | Searchable names and correct data types, such as an integer status. |
| json | Expands JSON held in one field into structured fields. | A whole-line JSON payload in message, or JSON that should remain nested. |
Top-level fields or an object under the target field you select. |
Do not apply every filter to every event. A JSON application log and a plain-text system log need different parsing paths. If a JSON parser receives a non-JSON line, it records a parse failure instead of producing useful fields.
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1. Extract fields with grok
Grok matches a regular expression assembled from named patterns. Common built-in patterns include TIMESTAMP_ISO8601, LOGLEVEL, and GREEDYDATA. The named portion before the colon becomes the destination field.
grok {
match => {
"message" => "%{TIMESTAMP_ISO8601:ts} %{LOGLEVEL:level} %{GREEDYDATA:msg}"
}
}For a line such as 2026-10-14T07:15:22Z INFO Player session started, this pattern creates ts, level, and msg. Keep the original message while you are developing the pattern; removing it too early makes failures harder to diagnose.
When senders use more than one line format, provide an array of patterns. Grok tries them in the order written:
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grok {
match => {
"message" => [
"%{TIMESTAMP_ISO8601:ts} %{LOGLEVEL:level} %{GREEDYDATA:msg}",
"%{TIMESTAMP_ISO8601:ts} [%{LOGLEVEL:level}] %{GREEDYDATA:msg}"
]
}
}Keep alternatives narrowly scoped. A pattern that accepts almost anything can appear to work while silently putting malformed data into the wrong fields.
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2. Set the event timestamp with date
Grok only extracts text. The date filter interprets that text and writes the event’s @timestamp. Use ISO8601 for a standard ISO-8601 value, or specify a custom format for another layout.
date {
match => ["ts", "ISO8601"]
target => "@timestamp"
remove_field => ["ts"]
}If the source timestamp is local time rather than UTC, set the date filter’s timezone option to the zone in which that log was generated. Without that qualification, identical wall-clock text can represent different instants when events come from hosts in different regions. Remove the temporary ts field only after the date parse is working; retaining it during initial testing lets you compare the source value with @timestamp.
3. Normalize with mutate
Use mutate after parsing to make fields consistent for queries, dashboards, and mappings.
mutate {
convert => { "status" => "integer" }
lowercase => ["level"]
remove_field => ["message"]
}Converting a status value to an integer prevents a numeric range query from being treated as text. Lowercasing the level makes INFO, Info, and info comparable. Remove the raw message only when the extracted fields contain everything you need; otherwise keep it for investigation.
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Filter order is part of the design
Logstash executes separate filter plugins in the order they appear in the configuration. A common text pipeline is grok first, date second, and mutate third: grok creates the timestamp field, date consumes it, and mutate performs final cleanup.
Inside one mutate block, operations have a documented internal order rather than following the order of individual lines. The sequence is:
- coerce
- rename
- update
- replace
- convert
- gsub
- uppercase
- capitalize
- lowercase
- strip
- split
- join
- merge
- copy
That matters when one operation depends on another. For example, a rename is completed before a convert, so converting the new field name in the same mutate block is valid. If a dependency is not obvious, split it into two mutate blocks and make the sequence explicit. This also makes troubleshooting easier because you can inspect the event between stages.
Parse JSON without damaging plain-text events
The json filter expands JSON stored in a field. For a whole-line JSON event, point source at message:
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source => "message"
}By default, parsed keys are placed into the event. Set target when you want the object kept under a namespace instead:
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json {
source => "message"
target => "app"
}Use the target form when different applications might emit keys with the same name, or when preserving the original event layout is useful. A nested object can then be queried as app.user, app.request_id, and so on.
Use a branch when formats are mixed
Applying json to every event is unsafe when the same centralized input carries both JSON and plain text. A non-JSON line receives a parse-failure tag rather than useful fields. Branch on a reliable characteristic of the event, such as a source field added by the input or a JSON-looking message, and run only the matching parser.
filter {
if [log_format] == "json" {
json {
source => "message"
target => "app"
}
} else {
grok {
match => {
"message" => "%{TIMESTAMP_ISO8601:ts} %{LOGLEVEL:level} %{GREEDYDATA:msg}"
}
}
date {
match => ["ts", "ISO8601"]
target => "@timestamp"
remove_field => ["ts"]
}
}
mutate {
lowercase => ["level"]
}
}The branch above assumes the sender or an earlier stage supplies log_format. If no such field exists, establish one at the input or split pipelines by source rather than guessing from message text. The important rule is that each parser should see only the format it understands.
Why a single json-then-grok example can mislead
A frequently copied example places json first and then applies grok to message. That combination needs validation: parsing JSON does not automatically turn a JSON line into the plain-text layout expected by a grok pattern, and non-JSON messages will produce a JSON parse failure. Build the date step yourself after confirming which field contains the timestamp, and test the exact event formats your senders emit.
A complete starter configuration
The following configuration is a conservative text-log example. It extracts fields, converts the timestamp, normalizes the level, and keeps the raw message out of the final event only after the preceding steps have succeeded.
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input {
beats {
port => 5044
}
}
filter {
grok {
match => {
"message" => "%{TIMESTAMP_ISO8601:ts} %{LOGLEVEL:level} %{GREEDYDATA:msg}"
}
}
date {
match => ["ts", "ISO8601"]
target => "@timestamp"
remove_field => ["ts"]
}
mutate {
convert => { "status" => "integer" }
lowercase => ["level"]
remove_field => ["message"]
}
}
output {
stdout { codec => rubydebug }
}This is a starting point, not a universal production template. Add a JSON branch for JSON applications, add patterns for every legitimate text format, and decide deliberately whether retaining message is required for incident response or compliance.
Validate before sending events to your central store
Check syntax without starting the pipeline
Run Logstash’s configuration test against the file:
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bin/logstash -f first-pipeline.conf --config.test_and_exitA successful syntax check means Logstash can read the configuration; it does not prove that a grok pattern matches your real logs or that a timestamp is interpreted in the intended timezone. Treat parser behavior as a separate test.
Test with representative fixtures
- Collect at least one real line for every text format and one valid JSON event for each JSON schema.
- Include edge cases: a different log level, a missing optional value, a timestamp with an offset, and a malformed line.
- Run the pipeline with a console-style output such as
stdout { codec => rubydebug }while developing. - Confirm that extracted fields have the expected names and values.
- Compare the original timestamp field with
@timestamp, including the intended timezone conversion. - Check that numeric fields are numbers, not quoted strings, and that fields you intend to remove are gone only after parsing succeeds.
Do not use a successful startup as evidence that parsing is correct. A syntactically valid pattern can still fail on production variants, and an incorrect date can silently place events in the wrong time window.
Roll out in a controlled way
Because a shared pipeline receives events from many hosts, deploy a new filter to a limited source or a separate test pipeline first when your operating model allows it. Watch parse-failure tags, field completeness, event timestamps, and index-mapping errors before widening the change. Keep the previous configuration available so you can restore service quickly if an unexpected format appears.
Troubleshoot the failures that matter most
| Symptom | Likely cause | What to inspect |
|---|---|---|
| No extracted fields | The grok pattern does not match the sender’s exact spacing, punctuation, or prefix. | Print the raw message, compare it character by character with the pattern, and add a second pattern only for a documented alternate format. |
| Events have the ingestion time | The date filter did not find the field, used the wrong format, or interpreted a local timestamp without its timezone. | Check the field name produced by grok, the match value, and the timezone setting. |
| JSON parse failures on ordinary logs | The json filter runs on events that are not JSON. | Use a format branch or separate input/pipeline path so plain text bypasses json. |
| Queries treat numbers as text | The field was never converted, or conversion ran before the field was created. | Place mutate after the parser and verify the resulting type in debug output. |
| A needed field disappears | remove_field removed the raw source before the parse was confirmed. |
Temporarily remove the cleanup directive, validate extraction, then reintroduce it deliberately. |
| One format works and another fails | A single pattern assumes all senders use the same layout. | Use grok’s pattern array for genuine text variants, or branch by a reliable source/format field. |
Version and compatibility checks
Plugin versions are released independently of Logstash core. RubyGems lists logstash-filter-grok 4.4.4 as released on February 11, 2026, and logstash-filter-mutate 3.6.0 as released on March 26, 2026. Those dates identify gem releases, not a Logstash core release, and a particular Logstash distribution may bundle different versions. Check the plugins included with the Logstash version you actually run before relying on a newly documented option or behavior.
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Production checklist
- Input, filter, and output blocks are present in that order, with the filter block used only where parsing is needed.
- Every grok pattern is matched against representative lines from each sender format.
- The date filter consumes the actual timestamp field and specifies
ISO8601or the correct custom layout. - A non-UTC source has an explicit timezone policy.
- Mutate runs after fields exist, and dependent operations respect its documented internal order.
- JSON parsing is limited to JSON events; nested data uses
targetwhen namespace isolation is required. - The raw message is retained until parsing is verified and is removed only for a deliberate reason.
bin/logstash -f first-pipeline.conf --config.test_and_exitpasses before deployment.- Debug output confirms field names, timestamp values, and numeric types.
- Plugin versions are checked against the Logstash release and bundle used by your organization.
What a good centralized filter achieves
A useful filter does more than make an event look tidy. It gives every host a consistent schema: the same level field can be filtered regardless of capitalization, statuses can be aggregated numerically, and searches can use the event’s true occurrence time. Grok handles extraction, date establishes temporal meaning, mutate enforces naming and types, and json preserves structure when the source already provides it. Keeping those responsibilities separate, ordering them intentionally, and testing each source format turns a shared Logstash pipeline into a dependable foundation for centralized logging.
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