Now the elasticsearch has many indexes. These indexes pattern like aaa-2016_12_14.log and bbb-2016_12_14.log and others. And it will generate new index everyday, it pattern will be the xxx-YYYY_mm_dd.I want the kibana can automatic import these index to the index pattern. These index patterns will similarly like the aaa,bbb and so on.
Thanks for your reply.
Yes you could do that by having a wildcard pattern for your indexes in Kibana. Make sure you create the index in Kibana with the same name which you've created the index in ES. Your index pattern in Kibana could look something like this:
aaa-2015* <-- this could create an index with all your months
aaa-2015-01* <--- this could create an index within a month
I'm not sure this works through out all the versions. Keep an eye on that. This SO might be helpful.
Related
I added an index pattern in Kibana for the elastic search index and executed some transactions multiple times.
But I cannot see the time range histogram or any option to select in the Discover dashboard.
Pls, refer following screenshot.
Recreate index pattern with #timestamp field after deleting the existing index pattern.
Based on the availability of #timestamp field, It seems you are adding a timeseries logs to Elasticsearch. Recreate the index pattern again by referring to the following link and I believe the issue will be fixed: https://www.elastic.co/guide/en/kibana/current/tutorial-define-index.html#_create_an_index_pattern_for_time_series_data
I deleted all my index patterns (I still have few indexes in elasticsearch, also visible from management in kibana). Right now I wanted to create new (first) index pattern, but there is message:
No default index pattern. You must select or create one to continue.
but I can't create one because of screen is only showing message above and:
Checking for Elasticsearch data
so basically, I need to have default index pattern to create one, but I can't assign any index to be default, because I don't have any index pattern :(
I am on Windows and I am using version 6.5.4 of Kibana and Elasticsearch.
Do you have any idea how to proceed?
Hive tables can partition Date Field data into keys within a table.
Can I also do the elasticsearch index?
I would like to be able to partition an index by date using specific field values within the index.
I would appreciate it if you have any of these techniques, even if you are not necessarily using partitioning with specific field values.
Thank you.
Sure, you can define a index with a YYYY.MM.dd format.
This is what Logstash does, by default
In Kibana, you can do wildcard searches on logstash-* or logstash-2018.*. Not sure if you can do the same with the regular search API
In the context of ELK (Elasticsearch, Logstash, Kibana), I learnt that Logstash has FILTER to make use of grok to divide log messages into different fields. According to my understanding, it only helps to make the unstructured log data into more structured data. But I do no have any idea about how Elasticsearch can make use of the fields (done by grok) to improve the querying performance? Is it possible to build indices on base of the fields like in traditional relational database?
From Elasticsearch: The Definitive Guide
Inverted index
Relational databases add an index, such as a B-tree index, to specific columns in
order to improve the speed of data retrieval. Elasticsearch and Lucene use a
structure called an inverted index for exactly the same purpose.
By default, every field in a document is indexed (has an inverted
index) and thus is searchable. A field without an inverted index is
not searchable. We discuss inverted indexes in more detail in Inverted Index.
So you not need to do anything special. Elasticsearch already indexes all the fields by default.
I am trying to implement an analyzer (uppercase) and index some documents after that in elasticsearch. My question is, am i following the correct procedure?
Implement your analyzer (containing index and type name), which would create the index if it doesnt exist
Then index the documents with the same index and type name as above during which stream of text would pass through the analyzer and then would be saved in index.
Is this the correct way to go about it?
I indexed some documents with and without using analyzers, checked the contents of index before/after using Facets, and they were no different.
The content is not supposed to be different. How it's indexed is. You should recognize the difference because queries would have different results, like some documents are found which weren't without the analyzers, and viceversa.
Try for instance a March Query.
The _score may and should also change