I am not understanding the concept why file beat is required while we have a logstash.
With filebeat you are able to collect and forward logfiles from one or many remote servers.
There is also a option to add source specific fields to your log entries.
You have several output options like elasticsearch or logstash for further analysis/filtering/modification.
Just imagine 20 or 200 machines running services like databases, webservers, hosting applications and containers. And now you need to collect all the logs...
only with logstash you'll be pretty limited in this scenario
Beats are light-weight agents used primarily for forwarding events from multiple sources. Beats have a small footprint and use fewer system resources than Logstash.
Logstash has a larger footprint, but provides a broad array of input, filter, and output plugins for collecting, enriching, and transforming data from a variety of sources.
Please note though that filebeat is also capable of parsing for most use cases using Ingest Node as described here.
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I am currently setting up the central logging system (using ELK) which is estimated to get log data from 100 of micro services and could expand more. Requirement is to have minimum latency and highly available solution
Right now I am stuck on how design should look like.
While studying over internet, I got the below approach as widely used for such requirements
Microservice -> filebeat -> kafka -> logstash -> ElasticSearch -> Kibana
However, I am struggling to understand if filebeat is really useful in this case.
What if I directly stream logs to Kafka which then ships it to logstash ? This will help me to overcome the maintenance of log files and also there will be one component less to monitor and maintain.
I see an advantage of using kafka over filebeat that it can act as a buffer in conditions if the data being shipped is very high in volume or when the ES cluster is unreachable. Source : https://www.elastic.co/blog/just-enough-kafka-for-the-elastic-stack-part1
I want to understand if there is any real benefit of having filebeat that I am unable to realise.
Filebeat can be installed on each of your servers or nodes. Filebeat collects and quickly sends logs. It is very fast and lightweight, written in go.
In your case, the advantage is that you don't have to spend time developing the same functionality for collecting and sending logs. You just use and configure the Filebeat for your logging architecture. This is very convenient.
Another description of Filebeat is available at the link.
I am building an alert using Elasticsearch and I need to access the data for one of the logstash nodes for how many events it is receiving per second. On the Monitoring (formally Marvel) tab, this information is readily available in graph format. Is there anyway to get that same information using an ELK API aside from scripting something?
Take a look at the Logstash Metrics filter plugin
I am a newbie in the ElasticSearch's wonderful world so please be indulgent.
I am thinking about an import and synchronisation strategy for a Microsoft sql data source and if I did not misunderstand, I can use the input plugins JDBC or Beats.
But I don't see what are the deeps differences between them,
what are their usefulness? When use one or other one?
What are their benefits and their drawbacks?
Thank you if you can help me
They serve different purposes. Beats is another offering of the Elastic Stack, which is basically a platform for collecting and shipping data (logs, network packets, any kind of metrics, protocol data, etc) from the periphery of your architecture. Even though Beats also allows you to listen on the MySQL protocol and collect all kinds of metrics from your DB, it has nothing to do with loading data from your DB and load it into Elasticsearch. For that you can use the jdbc input plugin whose job is mainly to run a given query on regular time intervals and send each retrieved DB record as event through the Logstash pipeline to be processed further and sent to a variety of different outputs.
In a v1.0 of a .Net data crawler, I created a Windows service that would read URLs from a database and based on some logic would select what to crawl on a specified interval.
This was single-threaded and worked well for a low number of endpoints, but scaling is obviously an issue.
I'm trying to find out how to do this using the ElasticSearch (ELK) stack and came across HTTPBeat,
a Beat to poll HTTP endpoints in a regular interval and ship the
result to the configured output channel, e.g. Logstash and
Elasticsearch.
In looking at the documentation, you have to add URLs to the config.yaml file. Not what I'm looking for as the list of URLs could change and we may not want all URLs crawled at the same time.
Then there's RSS for Logstash, which is a command-line tool - again, not what I'm looking for.
Is there a way to make use of the Beats daemon to read from the ElasticSearch database to do work based on database values - crawls, etc?
To take this to the enterprise level, do Beats or any other component of the ElasticSearch ecosystem use message queuing or a spooler (like FileBeats does - is this built into Beats?)?
We want to set up a common logging interface across all the product teams in our company. We chose ELK for this and i want some advice regarding the set up:
One way is to have centralized ELK set up and all teams can use some sort of log forwarder e.g. FileBeat to send logs to common logstash. The issue with this i feel is : If teams want to use filters on the logs for analyzing log messages, they would need to access the common ELK machine to add filters as Beats doesn't support groking or any other filtering.
Second way is to have different logstash servers per team and all those will point to common Elastic Search server. This way teams are free to modify/add grok filters.
Please enlighten me if i am missing something or may be i am wrong in understanding. Other ideas are welcome.
Have you considered using fluentd instead? Lightweight, similar to filebeat, and allows groking and parsing.
Of course, your other alternative is to use a centralized Logstash instance and have different configuration files for each entity.