We are installing ELS and Kibana for log aggregation/analysis. The first system to use it is greenfield so we output structured logs from the services that make up our system. Given that we don't need to add structure to our logs I was planning on using FileBeat to ship the logs directly to ELS and not use LogStash. Is this a sensible option or does LogStash have value over and above parsing that we might need? If we do use LogStash can I use that to harvest log files or should I still use FileBeat to pump the logs to LogStash?
Logstash is useful if you need to aggregate logs from many servers and apply some common transformations and filtering to your events.
If your log events are already structured and you are ok with indexing them directly, then you can definitely have Filebeat send them directly to ES. If ES goes down (e.g. for maintenance), Filebeat will retry until it can successfully send the events.
Is this a sensible option or does LogStash have value over and above parsing that we might need?
Deciding to use Logstash or not, in your case, depends on whether you need to treat the logs before inserting them into ES.
In addition to parsing (which is apparently useless in your use case), you can use Logstash to add a location with the geoip filter, parse a date with the date filter, replace a word with another, replace a field with a hash, etc...
You can have a look at the available filters here.
If we do use LogStash can I use that to harvest log files or should I still use FileBeat to pump the logs to LogStash?
If you need Logstash and can afford to run it on the machine where your logs are, you can avoid using Filebeat, by using the file input.
But keep in mind that Logstash, especially if used for parsing, can consume a lot of resources. It is better to have it on another machine and use Filebeat to pump the logs to Logstash.
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.
I have a log file. I want to upload the log file and I want to do some query on the log file then I want to display the result. What is the simplest way to do this work? Is it possible to do with only Elastic search and Kibana without using logstash?
You need something to send the log file to elasticsearch, it could be a Logstash instance, Filebeat or a custom application/script.
With Logstash you will have more freedom if you want to parse the message or enrich the data, Filebeat is more limited on these aspects.
On my case I use both Logstash and Filebeat, I have a Logstash machine that receives the data sent by Filebeat agents installed on remote machines.
You need a log forwarder to read your log file and push them into elasticsearch cluster.
Famous log forwarders are,
logstash (which not only read log file and push log lines into elasticseach, but also you can perform intermediate filtering and formatting before you send each log line)
file-beat (a very light weight agent which read log lines from file and push into elasticsearch cannot perform intermediate filtering or formatting)
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.
For my enterprise application distributed and structured logging, I use logstash for log aggregation and elastic search as log storage. I have the clear control pushing logs from my application to logstash. On the other hand, from logstash to elastic search having very thin control.
Assume, if my elasticsearch goes down for some stupid reason, The logstash log(/var/log/logstash/logstash.log) is recording the reason clearly like the following one.
Attempted to send a bulk request to Elasticsearch configured at '["http://localhost:9200/"]', but Elasticsearch appears to be unreachable or down! {:client_config=>{:hosts=>["http://localhost:9200/"], :ssl=>nil, :transport_options=>{:socket_timeout=>0, :request_timeout=>0, :proxy=>nil, :ssl=>{}}, :transport_class=>Elasticsearch::Transport::Transport::HTTP::Manticore, :logger=>nil, :tracer=>nil, :reload_connections=>false, :retry_on_failure=>false, :reload_on_failure=>false, :randomize_hosts=>false}, :error_message=>"Connection refused", :class=>"Manticore::SocketException", :level=>:error}
How will I get noticed OR notified for the error level logs from logstash?
Should be doable with the following 3 steps:
1) Depends on how you want to get notified. If an email is sufficient you could use the Logstash email output-plugin.
But there are many more output plugins available.
2) To restrict certain events you can do stuff like that in your Logstash config (example is taken from the Elastic support site):
if [level] == "ERROR" {
output {
...
}
}
The if clause is not limited to the level field of your JSON; you are able to apply it for any of your JSON fields of course, which makes it more powerful.
3) To make this work (and not run into a logging cycle) you need either:
Start a second Logstash instance on your system (just observing the Logstash ERROR log), which should be okay from what is written here
Or you build a more complicated configuration, using just one Logstash instance. This configuration has to forward log-statements from YOUR application to Elasitcsearch while logstaments from Logstash ERROR logs are forwarded to the e.g. Logstash email output-plugin.
Side note: you may want to have a look at Filebeat which works very well with Logstash (Its from Elastic as well) and it is even more light-weighted than Logstash. It allows stuff like include_lines: ["^ERR", "^WARN"] in your configuration.
To receive input from Filebeat you will have to adopt the config to send data to Logstash and for Logstash you will have to active and use the Beats input plugin described here.