I have recently installed Kibana4 but I am beginning to understand that dashboards are designed differently from Kibana3 i.e., to embed multiple visualizations which are designed individually into every dashboard. I already have a lot of dashboards designed in Kibana3 so I would like to know if there is a way to load them to kibana4 instead of creating everything from scratch.
To the best I know, there is no way to do that. Not just the formats, but the queries sent to ES backend are quite different. Kibana 3 used to use facets a lot for segmentation which is a deprecated feature and Kibana4 got rid of that.
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I'm new to using elastic search, and I'm trying to find a datastore for our application where we can also add a front end for analytics, in this case Kibana. I'm planning to use them as a datastore for dr/cr transactions on our billing system.
Most use case I read is towards data analytics and searching related. I don't see a use case wherein it is used as a regular datastore for an application. So I'm worried I might use it on a wrong use case.
I was hoping if anyone can add their insights on this. Like why or why not use Elastic Search as authoritative/primary datastore for applications.
You should read a official blog of elasticsearch, where they clearly mentioned that databases must be robust and should not stop working unless you tell to do it.
From the robustness section of same blog
A database should be robust, especially if it is your authoritative
system of record. Ideally, a costly query should be possible to
cancel, and you certainly don't want the database to stop working
unless you tell it to.
Unfortunately, Elasticsearch (and the components it's made of) does
not currently handle OutOfMemory-errors very well. We cover this in
more depth in Elasticsearch in Production, OutOfMemory-Caused Crashes.
It is very important to provide Elasticsearch with enough memory and
be careful before running searches with unknown memory requirements on
a production cluster.
In short, you shouldn't use Elasticsearch as a primary data-store where you can't afford to loose the data.
I am trying to create a local index for my notes which comprises mainly of markdown files, text files, codes in python, javascript and dart.
I came across Solr and Elasticsearch.
But the main differences are focused around online use and distributedness.
Which can be a better choice if i need a good integrarion with javascript through electronjs?
Keeping in mind that the files are on local storage and there is not much focus on distributedness but on integration with javascript frontend and efficiency on local system.
Elasticsearch is more popular among newer developers due to its ease of use. But if you are already used to working with Solr, stay with it because there is no specific advantage of migrating to Elasticsearch.
I believe for your use case either of them would work.
However, If you need it to handle analytical queries in addition to searching text, Elasticsearch is the better choice
In terms of popularity, a larger community, documentations I would say elasticsearch is the winner, You can look at the below google trends
You can use the solr along with Apache Tika.
Apache Tika help in extracting the content/Text of different file system.
Using the above the you can index the metadata of the files and content of the files to the Apache solr.
You get admin tool for the analysis of the index and the fields to determine if you able to achieve the desired result.
Assuming I have many Python processes running on an automation server such as Jenkins, let's say I want to use Python's native logging module and, other than writing to the Jenkins console or to a log file, I want to store & centralize the logs somewhere.
I thought of using ELK for that, but then I realized that I can just as well create a dedicated log table in an existing database (I'm using Redshift), use something like Grafana for log dashboards/visualization and save myself the trouble of deploying a new system (most of the people in my team are familiar with Redshift but not with ElasticSearch).
Although it sounds straightforward, I feel like I'm not looking at the big picture and that I would be missing some powerful capabilities that components like Logstash were written for the in the first place. What would these capabilities be and how would it be advantageous to use ELK instead of my solution?
Thank you!
I have implemented a full ELK stack in my company in the past year.
The project was huge and took a lot of time to properly implement. The advantages of using ELK and not implementing our own centralized logging solution would be:
Not needing to re-invent the wheel- There is already a product that is doing just that. (and the installation part is extremely easy)
It is battle tested and can stand huge amount of logs in a short time.
As your business and product grows and shift you will need to parse more logs with different structure which will mean DB changes on self built system. logstash will give you endless possibilities of filtering and parsing those new formatted logs.
It has Cluster and HA capabilities, and you can scale your logging system vertically and horizontally.
Very easy to maintain and change over time.
It can send the needed output to a variety of products including Zabbix, Grafana, elasticsearch and many more.
Kibana will give you ability to view the logs, build graphs and dashboards, alerts and more...
The options with ELK are really endless and the more I work with it, the more I find new ways it can help me. not just from viewing logs on distributed remote server systems, but also security alerts and SLA graphs and many other insights.
I'm trying to get some suggestions as I setup my data system. I'd like to setup a system for web crawling. It'll crawl probably a few hundred/thousand sites on a regular basis.
I'm aware of Nutch and have used Nutch, however I'd like to know if others know of a better crawler than Nutch.
I'm also using Elasticsearch as the indexer and its quite hard to get Nutch to work with newer versions of ES.
You can take a look at StormCrawler is based on Apache Storm and is not only a full-featured crawler but also has a focus on Near Real Time crawling. ES is usually very updated, at the moment of this writing, supports ES v6.1.1 (https://github.com/DigitalPebble/storm-crawler/blob/master/external/elasticsearch/pom.xml#L20) so this could work you. Keep in mind that is a different approach & technologie than Nutch, although it uses some of the ideas behind Apache Nutch.
Also, in https://github.com/BruceDone/awesome-crawler you can find a list of a lot of crawlers written in a lot of different languages.
I am new to Elasticsearch, Logstash and Kibana by using this tutorial i just played around it. Now I want to know how to create real production application for Log Analysis including login and securities model. Is there any way to achieve with kibana.
A fairly vague question, but...
In Kibana 3, you setup dashboards that are made up of "panels". Each panel can be a different type (histogram, pie chart, etc). Each panel can chart one or more queries.
In Kibana 4 (still in beta), it's a more explicit multi-step process. You create "visualizations", which, like panels, are components that you'll use in dashboards.