Log enrichment
You can perform different types of log enrichment with Data Prepper, including:
- Filtering.
- Extracting key-value pairs from strings.
- Mutating events.
- Mutating strings.
- Converting lists to maps.
- Processing incoming timestamps.
Filtering
Use the drop_events processor to filter out specific log events before sending them to a sink. For example, if you’re collecting web request logs and only want to store unsuccessful requests, you can create the following pipeline, which drops any requests for which the response is less than 400 so that only log events with HTTP status codes of 400 and higher remain.
log-pipeline:
source:
...
processor:
- grok:
match:
log: [ "%{COMMONAPACHELOG_DATATYPED}" ]
- drop_events:
drop_when: "/response < 400"
sink:
- opensearch:
...
index: failure_logs
The drop_when option specifies which events to drop from the pipeline.
Extracting key-value pairs from strings
Log data often includes strings of key-value pairs. For example, if a user queries a URL that can be paginated, the HTTP logs might contain the following HTTP query string:
page=3&q=my-search-term
To perform analysis using the search terms, you can extract the value of q from a query string. The key_value processor provides robust support for extracting keys and values from strings.
The following example combines the split_string and key_value processors to extract query parameters from an Apache log line:
pipeline
...
processor:
- grok:
match:
message: [ "%{COMMONAPACHELOG_DATATYPED}" ]
- split_string:
entries:
- source: request
delimiter: "?"
- key_value:
source: "/request/1"
field_split_characters: "&"
value_split_characters: "="
destination: query_params
Mutating events
The different mutate event processors let you rename, copy, add, and delete event entries.
In this example, the first processor sets the value of the debug key to true if the key already exists in the event. The second processor only sets the debug key to true if the key doesn’t exist in the event because overwrite_if_key_exists is set to true.
...
processor:
- add_entries:
entries:
- key: "debug"
value: true
...
processor:
- add_entries:
entries:
- key: "debug"
value: true
overwrite_if_key_exists: true
...
You can also use a format string to construct new entries from existing events. For example, ${date}-${time} will create a new entry based on the values of the existing entries date and time.
For example, the following pipeline adds new event entries dynamically from existing events:
processor:
- add_entries:
entries:
- key: "key_three"
format: "${key_one}-${key_two}
Consider the following incoming event:
{
"key_one": "value_one",
"key_two": "value_two"
}
The processor transforms it into an event with a new key named key_three, which combines values of other keys in the original event, as shown in the following example:
{
"key_one": "value_one",
"key_two": "value_two",
"key_three": "value_one-value_two"
}
Mutating strings
The various mutate string processors offer tools that you can use to manipulate strings in incoming data. For example, if you need to split a string into an array, you can use the split_string processor:
...
processor:
- split_string:
entries:
- source: "message"
delimiter: "&"
...
The processor will transform a string such as a&b&c into ["a", "b", "c"].
Converting lists to maps
The list_to_map processor, which is one of the mutate event processors, converts a list of objects in an event to a map.
For example, consider the following processor configuration:
...
processor:
- list_to_map:
key: "name"
source: "A-car-as-list"
target: "A-car-as-map"
value_key: "value"
flatten: true
...
The following processor will convert an event that contains a list of objects to a map like this:
{
"A-car-as-list": [
{
"name": "make",
"value": "tesla"
},
{
"name": "model",
"value": "model 3"
},
{
"name": "color",
"value": "white"
}
]
}
{
"A-car-as-map": {
"make": "tesla",
"model": "model 3",
"color": "white"
}
}
As another example, consider an incoming event with the following structure:
{
"mylist" : [
{
"somekey" : "a",
"somevalue" : "val-a1",
"anothervalue" : "val-a2"
},
{
"somekey" : "b",
"somevalue" : "val-b1",
"anothervalue" : "val-b2"
},
{
"somekey" : "b",
"somevalue" : "val-b3",
"anothervalue" : "val-b4"
},
{
"somekey" : "c",
"somevalue" : "val-c1",
"anothervalue" : "val-c2"
}
]
}
You can define the following options in the processor configuration:
...
processor:
- list_to_map:
key: "somekey"
source: "mylist"
target: "myobject"
value_key: "value"
flatten: true
...
The processor modifies the event by removing mylist and adding the new myobject object:
{
"myobject" : {
"a" : [
{
"somekey" : "a",
"somevalue" : "val-a1",
"anothervalue" : "val-a2"
}
],
"b" : [
{
"somekey" : "b",
"somevalue" : "val-b1",
"anothervalue" : "val-b2"
},
{
"somekey" : "b",
"somevalue" : "val-b3",
"anothervalue" : "val-b4"
}
"c" : [
{
"somekey" : "c",
"somevalue" : "val-c1",
"anothervalue" : "val-c2"
}
]
}
}
In many cases, you may want to flatten the array for each key. In these situations, you must choose only one object to retain. The processor offers a choice of either first or last. For example, consider the following:
...
processor:
- list_to_map:
key: "somekey"
source: "mylist"
target: "myobject"
flatten: true
...
The incoming event structure is then flattened accordingly:
{
"myobject" : {
"a" : {
"somekey" : "a",
"somevalue" : "val-a1",
"anothervalue" : "val-a2"
},
"b" : {
"somekey" : "b",
"somevalue" : "val-b1",
"anothervalue" : "val-b2"
}
"c" : {
"somekey" : "c",
"somevalue" : "val-c1",
"anothervalue" : "val-c2"
}
}
}
Processing incoming timestamps
The date processor parses the timestamp key from incoming events by converting it to International Organization for Standardization (ISO) 8601 format:
...
processor:
- date:
match:
- key: timestamp
patterns: ["dd/MMM/yyyy:HH:mm:ss"]
destination: "@timestamp"
source_timezone: "America/Los_Angeles"
destination_timezone: "America/Chicago"
locale: "en_US"
...
If the preceding pipeline processes the following event:
{"timestamp": "10/Feb/2000:13:55:36"}
It converts the event to the following format:
{
"timestamp":"10/Feb/2000:13:55:36",
"@timestamp":"2000-02-10T15:55:36.000-06:00"
}
Generating timestamps
The date processor can generate timestamps for incoming events if you specify @timestamp for the destination option:
...
processor:
- date:
from_time_received: true
destination: "@timestamp"
...
Deriving punctuation patterns
The substitute_string processor (which is one of the mutate string processors) lets you derive a punctuation pattern from incoming events. In the following example pipeline, the processor will scan incoming Apache log events and derive punctuation patterns from them:
processor:
- substitute_string:
entries:
- source: "message"
from: "[a-zA-Z0-9_]+"
to:""
- source: "message"
from: "[ ]+"
to: "_"
The following incoming Apache HTTP log will generate a punctuation pattern:
[{"message":"10.10.10.11 - admin [19/Feb/2015:15:50:36 -0500] \"GET /big2.pdf HTTP/1.1\" 200 33973115 0.202 \"-\" \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/40.0.2214.111 Safari/537.36\""}]
{"message":"..._-_[//:::_-]_\"_/._/.\"_._\"-\"_\"/._(;_)_/._(,_)_/..._/.\""}
You can count these generated patterns by passing them through the aggregate processor with the count action.