The aim of this filter is to aggregate information available among several events (typically log lines) belonging to a same task, and finally push aggregated information into final task event.
You should be very careful to set Logstash filter workers to 1 (-w 1 flag) for this filter to work correctly
otherwise events may be processed out of sequence and unexpected results will occur.
-
with these given logs :
INFO - 12345 - TASK_START - start
INFO - 12345 - SQL - sqlQuery1 - 12
INFO - 12345 - SQL - sqlQuery2 - 34
INFO - 12345 - TASK_END - end-
you can aggregate "sql duration" for the whole task with this configuration :
filter {
grok {
match => [ "message", "%{LOGLEVEL:loglevel} - %{NOTSPACE:taskid} - %{NOTSPACE:logger} - %{WORD:label}( - %{INT:duration:int})?" ]
}
if [logger] == "TASK_START" {
aggregate {
task_id => "%{taskid}"
code => "map['sql_duration'] = 0"
map_action => "create"
}
}
if [logger] == "SQL" {
aggregate {
task_id => "%{taskid}"
code => "map['sql_duration'] += event.get('duration')"
map_action => "update"
}
}
if [logger] == "TASK_END" {
aggregate {
task_id => "%{taskid}"
code => "event.set('sql_duration', map['sql_duration'])"
map_action => "update"
end_of_task => true
timeout => 120
}
}
}-
the final event then looks like :
{
"message" => "INFO - 12345 - TASK_END - end message",
"sql_duration" => 46
}the field sql_duration is added and contains the sum of all sql queries durations.
-
If you have the same logs than example #1, but without a start log :
INFO - 12345 - SQL - sqlQuery1 - 12
INFO - 12345 - SQL - sqlQuery2 - 34
INFO - 12345 - TASK_END - end-
you can also aggregate "sql duration" with a slightly different configuration :
filter {
grok {
match => [ "message", "%{LOGLEVEL:loglevel} - %{NOTSPACE:taskid} - %{NOTSPACE:logger} - %{WORD:label}( - %{INT:duration:int})?" ]
}
if [logger] == "SQL" {
aggregate {
task_id => "%{taskid}"
code => "map['sql_duration'] ||= 0 ; map['sql_duration'] += event.get('duration')"
}
}
if [logger] == "TASK_END" {
aggregate {
task_id => "%{taskid}"
code => "event.set('sql_duration', map['sql_duration'])"
end_of_task => true
timeout => 120
}
}
}-
the final event is exactly the same than example #1
-
the key point is the "||=" ruby operator. It allows to initialize 'sql_duration' map entry to 0 only if this map entry is not already initialized
Third use case: You have no specific end event.
A typical case is aggregating or tracking user behaviour. We can track a user by its ID through the events, however once the user stops interacting, the events stop coming in. There is no specific event indicating the end of the user’s interaction.
In this case, we can enable the option 'push_map_as_event_on_timeout' to enable pushing the aggregation map as a new event when a timeout occurs. In addition, we can enable 'timeout_code' to execute code on the populated timeout event. We can also add 'timeout_task_id_field' so we can correlate the task_id, which in this case would be the user’s ID.
-
Given these logs:
INFO - 12345 - Clicked One
INFO - 12345 - Clicked Two
INFO - 12345 - Clicked Three-
You can aggregate the amount of clicks the user did like this:
filter {
grok {
match => [ "message", "%{LOGLEVEL:loglevel} - %{NOTSPACE:user_id} - %{GREEDYDATA:msg_text}" ]
}
aggregate {
task_id => "%{user_id}"
code => "map['clicks'] ||= 0; map['clicks'] += 1;"
push_map_as_event_on_timeout => true
timeout_task_id_field => "user_id"
timeout => 600 # 10 minutes timeout
timeout_tags => ['_aggregatetimeout']
timeout_code => "event.set('several_clicks', event.get('clicks') > 1)"
}
}-
After ten minutes, this will yield an event like:
{
"user_id": "12345",
"clicks": 3,
"several_clicks": true,
"tags": [
"_aggregatetimeout"
]
}Fourth use case : like example #3, you have no specific end event, but also, tasks come one after the other.
That is to say : tasks are not interlaced. All task1 events come, then all task2 events come, …
In that case, you don’t want to wait task timeout to flush aggregation map.
-
A typical case is aggregating results from jdbc input plugin.
-
Given that you have this SQL query :
SELECT country_name, town_name FROM town -
Using jdbc input plugin, you get these 3 events from :
{ "country_name": "France", "town_name": "Paris" }
{ "country_name": "France", "town_name": "Marseille" }
{ "country_name": "USA", "town_name": "New-York" }-
And you would like these 2 result events to push them into elasticsearch :
{ "country_name": "France", "towns": [ {"town_name": "Paris"}, {"town_name": "Marseille"} ] }
{ "country_name": "USA", "towns": [ {"town_name": "New-York"} ] }-
You can do that using
push_previous_map_as_eventaggregate plugin option :
filter {
aggregate {
task_id => "%{country_name}"
code => "
map['country_name'] = event.get('country_name')
map['towns'] ||= []
map['towns'] << {'town_name' => event.get('town_name')}
event.cancel()
"
push_previous_map_as_event => true
timeout => 3
}
}-
The key point is that each time aggregate plugin detects a new
country_name, it pushes previous aggregate map as a new Logstash event, and then creates a new empty map for the next country -
When 5s timeout comes, the last aggregate map is pushed as a new event
-
Finally, initial events (which are not aggregated) are dropped because useless (thanks to
event.cancel())
Fifth use case: like example #3, there is no end event.
Events keep comming for an indefinite time and you want to push the aggregation map as soon as possible after the last user interaction without waiting for the timeout.
This allows to have the aggregated events pushed closer to real time.
A typical case is aggregating or tracking user behaviour.
We can track a user by its ID through the events, however once the user stops interacting, the events stop coming in.
There is no specific event indicating the end of the user’s interaction.
The user ineraction will be considered as ended when no events for the specified user (task_id) arrive after the specified inactivity_timeout`.
If the user continues interacting for longer than timeout seconds (since first event), the aggregation map will still be deleted and pushed as a new event when timeout occurs.
The difference with example #3 is that the events will be pushed as soon as the user stops interacting for inactivity_timeout seconds instead of waiting for the end of timeout seconds since first event.
In this case, we can enable the option 'push_map_as_event_on_timeout' to enable pushing the aggregation map as a new event when inactivity timeout occurs.
In addition, we can enable 'timeout_code' to execute code on the populated timeout event.
We can also add 'timeout_task_id_field' so we can correlate the task_id, which in this case would be the user’s ID.
-
Given these logs:
INFO - 12345 - Clicked One
INFO - 12345 - Clicked Two
INFO - 12345 - Clicked Three-
You can aggregate the amount of clicks the user did like this:
filter {
grok {
match => [ "message", "%{LOGLEVEL:loglevel} - %{NOTSPACE:user_id} - %{GREEDYDATA:msg_text}" ]
}
aggregate {
task_id => "%{user_id}"
code => "map['clicks'] ||= 0; map['clicks'] += 1;"
push_map_as_event_on_timeout => true
timeout_task_id_field => "user_id"
timeout => 3600 # 1 hour timeout, user activity will be considered finished one hour after the first event, even if events keep comming
inactivity_timeout => 300 # 5 minutes timeout, user activity will be considered finished if no new events arrive 5 minutes after the last event
timeout_tags => ['_aggregatetimeout']
timeout_code => "event.set('several_clicks', event.get('clicks') > 1)"
}
}-
After five minutes of inactivity or one hour since first event, this will yield an event like:
{
"user_id": "12345",
"clicks": 3,
"several_clicks": true,
"tags": [
"_aggregatetimeout"
]
}-
the filter needs a "task_id" to correlate events (log lines) of a same task
-
at the task beginning, filter creates a map, attached to task_id
-
for each event, you can execute code using 'event' and 'map' (for instance, copy an event field to map)
-
in the final event, you can execute a last code (for instance, add map data to final event)
-
after the final event, the map attached to task is deleted (thanks to
end_of_task ⇒ true) -
an aggregate map is tied to one task_id value which is tied to one task_id pattern. So if you have 2 filters with different task_id patterns, even if you have same task_id value, they won’t share the same aggregate map.
-
in one filter configuration, it is recommanded to define a timeout option to protect the feature against unterminated tasks. It tells the filter to delete expired maps
-
if no timeout is defined, by default, all maps older than 1800 seconds are automatically deleted
-
all timeout options have to be defined in only one aggregate filter per task_id pattern (per pipeline). Timeout options are : timeout, inactivity_timeout, timeout_code, push_map_as_event_on_timeout, push_previous_map_as_event, timeout_timestamp_field, timeout_task_id_field, timeout_tags
-
if
codeexecution raises an exception, the error is logged and event is tagged '_aggregateexception'
-
extract some cool metrics from task logs and push them into task final log event (like in example #1 and #2)
-
extract error information in any task log line, and push it in final task event (to get a final event with all error information if any)
-
extract all back-end calls as a list, and push this list in final task event (to get a task profile)
-
extract all http headers logged in several lines to push this list in final task event (complete http request info)
-
for every back-end call, collect call details available on several lines, analyse it and finally tag final back-end call log line (error, timeout, business-warning, …)
-
Finally, task id can be any correlation id matching your need : it can be a session id, a file path, …
This plugin supports the following configuration options plus the [plugins-filters-aggregate-common-options] described later.
| Setting | Input type | Required |
|---|---|---|
string, a valid filesystem path |
No |
|
Yes |
||
No |
||
No |
||
string, one of |
No |
|
No |
||
No |
||
Yes |
||
No |
||
No |
||
No |
||
No |
||
No |
Also see [plugins-filters-aggregate-common-options] for a list of options supported by all filter plugins.
-
Value type is string
-
There is no default value for this setting.
The path to file where aggregate maps are stored when Logstash stops and are loaded from when Logstash starts.
If not defined, aggregate maps will not be stored at Logstash stop and will be lost. Must be defined in only one aggregate filter per pipeline (as aggregate maps are shared at pipeline level).
Example:
filter {
aggregate {
aggregate_maps_path => "/path/to/.aggregate_maps"
}
}-
This is a required setting.
-
Value type is string
-
There is no default value for this setting.
The code to execute to update map, using current event.
Or on the contrary, the code to execute to update event, using current map.
You will have a 'map' variable and an 'event' variable available (that is the event itself).
Example:
filter {
aggregate {
code => "map['sql_duration'] += event.get('duration')"
}
}-
Value type is boolean
-
Default value is
false
Tell the filter that task is ended, and therefore, to delete aggregate map after code execution.
-
Value type is number
-
There is no default value for this setting.
The amount of seconds (since the last event) after which a task is considered as expired.
When timeout occurs for a task, its aggregate map is evicted.
If 'push_map_as_event_on_timeout' or 'push_previous_map_as_event' is set to true, the task aggregation map is pushed as a new Logstash event.
inactivity_timeout can be defined for each "task_id" pattern.
inactivity_timeout must be lower than timeout.
-
Value type is string
-
Default value is
"create_or_update"
Tell the filter what to do with aggregate map.
"create": create the map, and execute the code only if map wasn’t created before
"update": doesn’t create the map, and execute the code only if map was created before
"create_or_update": create the map if it wasn’t created before, execute the code in all cases
-
Value type is boolean
-
Default value is
false
When this option is enabled, each time a task timeout is detected, it pushes task aggregation map as a new Logstash event. This enables to detect and process task timeouts in Logstash, but also to manage tasks that have no explicit end event.
-
Value type is boolean
-
Default value is
false
When this option is enabled, each time aggregate plugin detects a new task id, it pushes previous aggregate map as a new Logstash event, and then creates a new empty map for the next task.
|
Warning
|
this option works fine only if tasks come one after the other. It means : all task1 events, then all task2 events, etc… |
-
This is a required setting.
-
Value type is string
-
There is no default value for this setting.
The expression defining task ID to correlate logs.
This value must uniquely identify the task.
Example:
filter {
aggregate {
task_id => "%{type}%{my_task_id}"
}
}-
Value type is number
-
Default value is
1800
The amount of seconds (since the first event) after which a task is considered as expired.
When timeout occurs for a task, its aggregate map is evicted.
If 'push_map_as_event_on_timeout' or 'push_previous_map_as_event' is set to true, the task aggregation map is pushed as a new Logstash event.
Timeout can be defined for each "task_id" pattern.
-
Value type is string
-
There is no default value for this setting.
The code to execute to complete timeout generated event, when 'push_map_as_event_on_timeout' or 'push_previous_map_as_event' is set to true.
The code block will have access to the newly generated timeout event that is pre-populated with the aggregation map.
If 'timeout_task_id_field' is set, the event is also populated with the task_id value
Example:
filter {
aggregate {
timeout_code => "event.set('state', 'timeout')"
}
}-
Value type is array
-
Default value is
[]
Defines tags to add when a timeout event is generated and yield
Example:
filter {
aggregate {
timeout_tags => ["aggregate_timeout"]
}
}-
Value type is string
-
There is no default value for this setting.
This option indicates the timeout generated event’s field where the current "task_id" value will be set. This can help to correlate which tasks have been timed out.
By default, if this option is not set, task id value won’t be set into timeout generated event.
Example:
filter {
aggregate {
timeout_task_id_field => "task_id"
}
}-
Value type is string
-
There is no default value for this setting.
By default, timeout is computed using system time, where Logstash is running.
When this option is set, timeout is computed using event timestamp field indicated in this option. It means that when a first event arrives on aggregate filter and induces a map creation, map creation time will be equal to this event timestamp. Then, each time a new event arrives on aggregate filter, event timestamp is compared to map creation time to check if timeout happened.
This option is particularly useful when processing old logs with option push_map_as_event_on_timeout ⇒ true.
It lets to generate aggregated events based on timeout on old logs, where system time is inappropriate.
Warning : so that this option works fine, it must be set on first aggregate filter.
Example:
filter {
aggregate {
timeout_timestamp_field => "@timestamp"
}
}