I have a theoretical question about Beats.
If I understand correctly, Logstach allows to easily parse the received data and ingest it to ElasticSearch. So my question is, does Beats send the retrieved information directly to a Logstach profile or does Beats itself modify the data and ingest it directly into ElasticSearch?
Thanks in advance for your help
EDIT :
Important message
To summarize, when I configure Filebeat, I only fill in the
information from Elastic Cloud as output. That is to say the cloud ID
and the password of elasticsearch. So I don't fill in any output
information. My question is, when I use a module (Fortinet for
example), will Filebeat process the information and send it directly
to ElasticSearch or will it send it to Logstach by default? I need to
try to understand as much as possible to write a paper on this.
You can have both the option, you can send the data from beats to directly Elasticsearch or send it to logstash where again in logstash configuration you can mention which Elasticsearch it needs to send the data.
Refer configure the output page for more details and configurations ie Elasticsearch, kafka, redis, console etc.
Related
I would like to deploy ELK stack on-premise for our custom application. So, I referred to the official docs for installation guides, installed Elasticsearch cluster and Kibana. Then comes the question: the documentation says I can process the logs from any custom app if I would like to (if built-in modules are not suitable for me), and I should just configure Filebeat so it can harvest these logs as an input. But what should be an output for Filebeat? I've heard that Elasticsearch should get processed, structured logs (for example, in JSON format) as an input; but our application produces plain text logs (as it's Java app, logs can include stack traces and other mixed data), and they should be processed and structured first... Or shouldn't they?
So, here are my questions regarding this situation:
Do I need to set Filebeat output as Logstash input to format and structure logs, and then set Logstash output as Elasticsearch input? Or I can forward logs from Filebeat straight to Elasticsearch?
Do I really need Filebeat in this situation, or maybe Logstash can be configured to read log files by its own?
Filebeat and Logstash can both work either on their own or in concert together. If all you have to do is to tail your log files and send them to Elasticsearch, without performing any processing on them, then I'd say go for Filebeat as it's more lightweight than Logstash.
If you need to perform some processing and transformation on your log files, then you have a few options depending on which solution you pick. You can leverage:
Filebeat processors
Logstash filters
Elasticsearch ingest processors
As a side note, I draw your attention on the fact that your Java app doesn't necessarily have to produce plain text logs. Using ecs-logging-java, it can also produce JSON logs ready to be ingested into Elasticsearch.
If you use the above logging library, then Filebeat would be perfectly suitable for your use case, but it depends of course on whether you need to parse and process the message field in your logs or not.
I am trying out the ELK to visualise my log file. I have tried different setups:
Logstash file input plugin https://www.elastic.co/guide/en/logstash/current/plugins-inputs-file.html
Logstash Beats input plugin https://www.elastic.co/guide/en/logstash/current/plugins-inputs-beats.html with Filebeat Logstash output https://www.elastic.co/guide/en/beats/filebeat/current/logstash-output.html
Filebeat Elasticsearch output https://www.elastic.co/guide/en/beats/filebeat/current/elasticsearch-output.html
Can someone list out their differences and when to use which setup? If it is not for here, please point me to the right place like Super User or DevOp or Server Fault.
1) To use logstash file input you need a logstash instance running on the machine from where you want to collect the logs, if the logs are on the same machine that you are already running logstash this is not a problem, but if the logs are on remote machines, a logstash instance is not always recommended because it needs more resources than filebeat.
2 and 3) For collecting logs on remote machines filebeat is recommended since it needs less resources than a logstash instance, you would use the logstash output if you want to parse your logs, add or remove fields or make some enrichment on your data, if you don't need to do anything like that you can use the elasticsearch output and send the data directly to elasticsearch.
This is the main difference, if your logs are on the same machine that you are running logstash, you can use the file input, if you need to collect logs from remote machines, you can use filebeat and send it to logstash if you want to make transformations on your data, or send directly to elasticsearch if you don't need to make transformations on your data.
Another advantage of using filebeat, even on the logstash machine, is that if your logstash instance is down, you won't lose any logs, filebeat will resend the events, using the file input you can lose events in some cases.
An additional point for large scale application is that if you have a lot of Beat (FileBeat, HeartBeat, MetricBeat...) instances, you would not want them altogether open connection and sending data directly to Elasticsearch instance at the same time.
Having too many concurrent indexing connections may result in a high bulk queue, bad responsiveness and timeouts. And for that reason in most cases, the common setup is to have Logstash placed between Beat instances and Elasticsearch to control the indexing.
And for larger scale system, the common setup is having a buffering message queue (Apache Kafka, Rabbit MQ or Redis) between Beats and Logstash for resilency to avoid congestion on Logstash during event spikes.
Figures are captured from Logz.io. They also have a good
article on this topic.
Not really familiar with (2).
But,
Logstash(1) is usually a good choice to take a content play around with it using input/output filters, match it to your analyzers, then send it to Elasticsearch.
Ex.
You point the Logstash to your MySql which takes a row modify the data (maybe do some math on it, then Concat some and cut out some words then send it to ElasticSearch as processed data).
As for Logbeat(2), it's a perfect choice to pick up an already processed data and pass it to elasticsearch.
Logstash (as the name clearly states) is mostly good for log files and stuff like that. usually you can do tiny changes to those.
Ex. I have some log files in my servers (incl errors, syslogs, process logs..)
Logstash listens to those files, automatically picks up new lines added to it and sends those to Elasticsearch.
Then you can filter some things in elasticsearch and find what's important to you.
p.s: logstash has a really good way of load balancing too many data to ES.
You can now use filebeat to send logs to elasticsearch directly or logstash (without a logstash agent, but still need a logstash server of course).
Main advantage is that logstash will allow you to custom parse each line of the logs...whereas filebeat alone will simply send the log and there is not much separation of fields.
Elasticsearch will still index and store the data.
In one of our applications we parse the application logs using logstash and indexing them into elasticsearch. Our simple architecture is logfiles ---> filebeat--->logstash-----> elasticsearch.
As we enabled multiple log files example (apachelogs, passengerlogs, application logs etc,,), logstash is not able to parse the volume of data and hence there are logs missing at elasticsearch. Is there any way to handle huge volume of data at logstash or can we have multiple logstash server to receive logs from filebeat based on the log type? for example: application logs send output logstash-1 and apachelogs to logstash-2.
Thanks in advance.
It is not currently possible to define the same output type multiple time in Filebeat.
But there is a few options to achieve what you want:
You can use the loadbalance option in filebeat to distribute your events to multiple Logstash. https://www.elastic.co/guide/en/beats/filebeat/current/logstash-output.html#loadbalance, by default beats will pick a random host and stick to it.
Use a queue, like kafka and make logstash uses the kafka input, this will allow you add more LS as you need.
I'm planning on using Elasticsearch to log all my application activities (like an audit log).
Considering how I have direct control over the application, should I directly push the data into Elasticsearch using their REST APIs or should I somehow use Logstash to feed data into Elasticsearch?
Is there any reason I should use Logstash when I can directly push data into Elasticsearch? It's an additional layer to manage.
If you need to parse different log formats (eventlog, syslog and so on), support different transports (UDP, TCP and so on) and log outputs use Logstash. If http is good for you and you collect logs only from one application use ES directly. Logstash is an additional tool. Details are here.
I installed elasticsearch and kibana, and I'm following the tutorial.
https://www.elastic.co/guide/en/elasticsearch/reference/current/_index_and_query_a_document.html
And I'm perfectly inserting and reading data, e.g.:
PUT /customer/external/1?pretty
{
"name": "John Doe"
}
So, that makes me wonder, what do I need logstash or filebeats for?
My plan is to log each web request on a website to elasticsearch for analytics.
Do I need to install logstash? I don't understand what would I need it for.
(I don't plan to store it on a file)I will read the request info(e.g. ip address, time, user_id, etc) from a PHP script and simply send it through a HTTP REST REQUEST...as the example above to the elasticsearch server which will save the data anyway. So, I don't see any reason to store the data on the webserver(that is data duplicity), and If I wanted to, why would I need logstash anyway...I can just read a .log file and send it to elasticsearch....like this example: https://www.elastic.co/guide/en/elasticsearch/reference/current/_exploring_your_data.html
No, you do not have to install Logstash, if you plan to collect, normalize and write your application data yourself. As you correctly assumed, Logstash would be a replacement for your PHP script.
Nevertheless, you might still consider to have a look at Logstash. Since it is developed and maintained by same company taking care of Elastic Search, you could benefit from upcoming changes and optimizations.
As you can read from the introduction, Logstash is a tool to read data from multiple sources, normalize it and write the result to multiple destinations. For more details on which sources, filters and oputputs Logstash offers, you should also take a look at the pipeline documentation.