I'm having some issues getting elasticsearch to interpret an epoch millis timestamp field. I have some old bro logs I want to ingest and have them be in the proper orders and spacing. Thanks to Logstash filter to convert "$epoch.$microsec" to "$epoch_millis"
I've been able to convert the field holding the bro timestamp to the proper length of digits. I've also inserted a mapping into elasticsearch for that field, and it says that the type is "Date" with the format being the default. However, when I go and look at the entries it still has a little "t" next to it instead of a little clock. And hence I can't use it for my filter view reference in kibana.
Anyone have any thoughts or have dealt with this before? Unfortunately it's a stand alone system so I would have to manually enter any of the configs I'm using.
I did try and convert my field "ts" back to an integer after using the method described in the link above. So It should be a logstash integer before hitting the elasticsearch mapping.
So I ended up just deleting all my mappings in Kibana, and elasticsearch. I then resubmitted and this time it worked. Must have been some old junk in there that was messing me up. But now it's working great!
Related
Is there a way to get the date and time that an elastic search document was written?
I am running es queries via spark and would prefer NOT to look through all documents that I have already processed. Instead I would like read the only documents that were ingested between the last time the program ran and now.
What is the best most efficient way to do this?
I have looked at;
updating to add a field with an array with booleans for if its been looked at by which analytic. The negative is waiting for the update to occur.
index per time frame method, which would be to break down the current indexes into smaller ones so by hour.The negative I see is the number of open file descriptors.
??
Elasticsearch version 5.6
I posted the question on the elasticsearch discussion board and it appears using the ingest pipeline is the best option.
I am running es queries via spark and would prefer NOT to look through
all documents that I have already processed. Instead I would like read
the only documents that were ingested between the last time the
program ran and now.
A workaround could be :
While inserting data using Logstash to Elasticsearch, Logstash appends a #timestamp key to the document which represents the time (in UTC) at which the document is created or we can use an ingest pipline
After that we can query based on the timestamp.
For more on this please have a look at :
Mapping changes
There is no way to ask ES to insert a timestamp at index time
Elasticsearch doesn't have such functionality.
You need manually save with each document date. In this case you will be able to search by date range.
i have an issue which i suspect is quite basic but i have been stuck on this for too long and i fear i am missing something so basic that i can't see it by now.
we are using the ELK stack today for log analysis of our application logs.
logs are created by the JAVA application into JSON format, shipped using filebeat into logstash which in turn processes the input and queues it into ES.
some of the messages contain unstructured data in the message field which i currently cannot parse into separate fields so i need to catch them in the message field. problem is this:
the string i need to catch is: "57=1" this is an indication of something which i need to filter documents upon. i need to get documents which contain this exact string.
no matter what i try i can't get kibana to match this. it seems to always ignore the equal char and match either 57 or 1.
please advise.
thanks
You may check the Elasticsearch mapping on the field type of the referring field. If it is analyzed, the '=' may not have been indexed due to the default-analyzer. (source 1, source 2)
So im working on a system that logs bad data sent to an api and what the full request was. Would love to be able to see this in Kibana.
Issue is the datatypes could be random, so when I send them to the bad_data field it fails if it dosen't match the original mapping.
Anyone have a suggestion for the right way to handle this?
(2.X Es is required due to a sub dependancy)
You could use ignore_malformed flag in your field mappings. In that case wrong format values will not be indexed and your document will be saved.
See elastic documentation for more information.
If you want to be able to query such fields as original text you could use fields in your mapping for multi-type indexing, to get fast queries on raw text values.
I've been given a set up where I have a program creating live data and posting them into Elasticsearch.
I am trying to visualise this data in Kibana, but I'm coming across many problems such as numbers for a field being of type string instead of integers or there being certain missing fields.
But mainly for now certain fields being integer instead of string would be useful. How do I go about this? Is it possible?
I have no access to source code of the system creating the live events data.
Thanks in advance.
Update: I should also mention additionally that for now I am restricted to Elasticsearch version 2.4
If your data is coming straight into Elasticsearch, your options are limited.
The best option is to have the program that is creating the data send valid, properly formatted data.
If that's not an option, you can set your Elasticsearch mapping to force the field to be numeric. This will have the side-effect of dropping all documents where this field is not numeric.
There is also the elasticsearch injest node, which allows for some (logstash-like) transformations of the data. Converting the type is one such allowed "processor".
We are using ELK and shoving all syslogs into Elasticsearch.
I have a log type like whose message field looks like:
"message":"11/04/2016 12:04:09 PM|There are now 8 active connections#015"
I would like to use Kibana to parse the message to get the number of active connections over time and then graph that in Kibana.
Am I thinking of how to do this correctly?
The reading I've done seems to be telling me to set up a filter in Logstash...but that seems like the wrong place to parse the message field for this single log line type, given the amount of messages/logs and message/log types getting sent through Logstash.
Is there a way to parse the message field for this number and then graph that count over time in Kibana?
Kibana is not meant to do this kind of parsing. There are a few options you can use:
You could write an analyser that analyses this string. It can be
done, but I would not do it like this.
Use logstash, but you already suggested that yourself. If you feel
log stash is to heavy and you have a choice for the version to use,
go for option three.
Use ingest, this is a new feature of elasticsearch. This is kind of
a lightweight logstash that comes pre-packaged with elastic, it
support patterns with grok that can do this.