I want to make use of the geoip logstash plugin to get geolocation info about some IP addresses seen in my logs;
I also want to be able to visualize such info on kibana;
I am going through a short overview of the process;
What the tutorial does not mention, is what are the geoip.* fields necessary for producing the map visualizations;
I want to keep only the strictly necessary fields and discard the rest;
Will keeping only geoip.longtitute and geoip.latitude do the job?
edit: At this point in time I am just using
{ geoip { source => "my_incoming_ip" } }
in my logstash filter;
It turns out the following field is necessary for producing the map visualization
geoip.location {
"lat": 38.7163,
"lon": -78.1704
}
The others can be ommited (i.e. mutate/remove)
Related
I am trying to get a list which shows me all sources ES is receiving messages from. I am pretty new with this topic and trying to get deeper into it. I am searching basically for a solution to see the total amount of sources sending logs to my central logging solution and in best case also provided my a list with the source names.
Does anyone have an idea how to get such information querying Elasticsearch?
Yes, this is possible, though the solution depends on how your data looks.
Users typically index data in Elasticsearch so that it contains more than just the raw log lines. This is done automatically if you're using Filebeat. Otherwise, you'd do something (add a field using Logstash, rely on a host field in syslog, etc) to ensure you have a field that contains your "source" identifier:
{
"message": "my super valuable logline",
"source": "my_kinda_awesome_app"
}
given ^^ you can identify all sources (and record counts!) with a terms aggregation like:
{
"aggs": {
"my_sources": {
"terms": { "field": "source" }
}
}
}
Kibana makes this all easier since you don't need to know/write ES queries and can do stuff visually.
I have setup logging like described in https://quarkus.io/guides/centralized-log-management with an ELK Stack using version 7.7.
My logstash pipeline looks like the proposed example:
input {
gelf {
port => 12201
}
}
output {
stdout {}
elasticsearch {
hosts => ["http://elasticsearch:9200"]
}
}
Most Messages are showing up in my Kibana using logstash.* as an Index pattern. But some Messages are dropped.
2020-05-28 15:30:36,565 INFO [io.quarkus] (Quarkus Main Thread) Quarkus 1.4.2.Final started in 38.335s. Listening on: http://0.0.0.0:8085
The Problem seems to be, that the fields MessageParam0, MessageParam1, MessageParam2 etc. are mapped to the type that first appeared in the logs but actually contain multiple datatypes. The Elasticsearch log shows Errors like ["org.elasticsearch.index.mapper.MapperParsingException: failed to parse field [MessageParam1].
Is there any way in the Quarkus logging-gelf extension to correctly map the values?
ELK can auto-create your Elasticsearch index mapping by looking at the first indexed document. This is a very convenient functionality, but it comes with some drawback.
For example, if you have a field that can contains numbers or strings, if the first document contains a number for this field, the mapping will be created with a number field so you will not be able to index a document containing a String inside this field ...
The only workaround for this is to create the mapping upfront (you can only defines the fields that causing the issue, the other fields will be created automatically).
This is an ELK issue, there is nothing we can do at Quarkus side.
I'm trying to write logs to an Elasticsearch index from a Kubernetes cluster. Fluent-bit is being used to read stdout and it enriches the logs with metadata including pod labels. A simplified example log object is
{
"log": "This is a log message.",
"kubernetes": {
"labels": {
"app": "application-1"
}
}
}
The problem is that a few other applications deployed to the cluster have labels of the following format:
{
"log": "This is another log message.",
"kubernetes": {
"labels": {
"app.kubernetes.io/name": "application-2"
}
}
}
These applications are installed via Helm charts and the newer ones are following the label and selector conventions as laid out here. The naming convention for labels and selectors was updated in Dec 2018, seen here, and not all charts have been updated to reflect this.
The end result of this is that depending on which type of label format makes it into an Elastic index first, trying to send the other type in will throw a mapping exception. If I create a new empty index and send in the namespaced label first, attempting to log the simple app label will throw this exception:
object mapping for [kubernetes.labels.app] tried to parse field [kubernetes.labels.app] as object, but found a concrete value
The opposite situation, posting the namespaced label second, results in this exception:
Could not dynamically add mapping for field [kubernetes.labels.app.kubernetes.io/name]. Existing mapping for [kubernetes.labels.app] must be of type object but found [text].
What I suspect is happening is that Elasticsearch sees the periods in the field name as JSON dot notation and is trying to flesh it out as an object. I was able to find this PR from 2015 which explicitly disallows periods in field names however it seems to have been reversed in 2016 with this PR. There is also this multi-year thread from 2015-2017 discussing this issue but I was unable to find anything recent involving the latest versions.
My current thoughts on moving forward is to standardize the Helm charts we are using to have all of the labels use the same convention. This seems like a band-aid on the underlying issue though which is that I feel like I'm missing something obvious in the configuration of Elasticsearch and dynamic field mappings.
Any help here would be appreciated.
I opted to use the Logstash mutate filter with the rename option as described here:
https://www.elastic.co/guide/en/logstash/current/plugins-filters-mutate.html#plugins-filters-mutate-rename
The end result looked something like this:
filter {
mutate {
'[kubernetes][labels][app]' => '[kubernetes][labels][app.kubernetes.io/name]'
'[kubernetes][labels][chart]' => '[kubernetes][labels][helm.sh/chart]'
}
}
Although personally I've never encountered the exact same issue, I had similar problems when I indexed some test data and afterwards changed the structure of the document that should have been indexed (especially when "unflattening" data structures).
Your interpretation of the error message is correct. When you first index the document
{
"log": "This is another log message.",
"kubernetes": {
"labels": {
"app.kubernetes.io/name": "application-2"
}
}
}
Elasticsearch will recognize the app as an object/structure due to dynamic mapping.
When you then try to index the document
{
"log": "This is a log message.",
"kubernetes": {
"labels": {
"app": "application-1"
}
}
}
the previously, dynamically created mapping defined the field app as an object with sub-fields but elasticsearch encounters a concrete value, namely "application-1".
I suggest that you setup an index template to define the correct mappings. For the 'outdated' logging-versions I suggest to pre-process the particular documents either through an elasticsearch ingest-pipeline or with e.g. Logstash to get the documents in the correct format.
Hope that helps.
I use ELK to get some info on my rabbitmq stuff.
Here my conf logstash side
json {
source => "message"
}
But in kibana I have to prefix all my fields with json.xxx:
json.sender, json.sender.raw,json.programld, json.programId.raw ...
How can I not have this json.-prefix in my field names, so that I only have to have: sender, programId, etc.?
Best regards and thanks for your help !
Bonus question : what are all these .'raw' I must use in kibana ?
According to the doc:
By default it will place the parsed JSON in the root (top level) of
the Logstash event, but this filter can be configured to place the
JSON into any arbitrary event field, using the target configuration.
So it feels like your json is wrapped in a container named "json" or you're setting the "target" in logstash without showing us.
As for ".raw", the default elasticsearch mapping will analyze the data you put in a field, so changing "/var/log/messages" into three words: [var, log, messages]" which can make it hard to search. To keep you from having to worry about this at the beginning, logstash creates a ".raw" version of each string, which is not analyzed.
You'll eventually make your own mappings, and you can make the original field not_analyzed, so you won't need the .raw versions anymore.
We are populating Elasticsearch via logstash. The thing is that I see some unnecessary fields that I had like to remove like for example:
#version
file
geoip
host
message
offset
tags
Is it possible to do this by defining/extending a dynamic template? If yes, how? If no, can we do this via logstash configuration?
Your help is much appreciated.
You can remove fields using really any logstash filter - when the filter succeeds, it will remove the field.
It makes sense to me to use mutate:
filter {
mutate {
remove_field => [ "file" ]
}
}
That said, most of these fields are incredibly useful and really should not be removed.