Logs is generated records of computer, networks, and other IT system, which document system activities. To help mitigate variety of risks and meet compliance regulations, you will need a Log analysis. In today Logstash vs Filebeat article, we will compare these two similar yet different tools to give you information regarding what they can offer to you and if exist, how they differ from each other. If you are interested on using one or both of them, you may want to check our article below.
In this article, we are going to give you information about:
– What are Logstash and Filebeat
– What Logstash and Filebeat can offer to you
– Logstash vs Filebeat
About Logstash and Filebeat
As you may already know, Logstash is one of open source data collection engine you can use to collect your logs with its real-time pipelining capabilities. The product is part of ELK stack and according to them, the tool is able to dynamically unify data from disparate sources and normalized them to any of your preferred destination. For the diverse advance downstream analytics and visualization use case it will also cleanse and democratize your data. The tool capabilities also expanded to give you an accelerated insights.
At the other hand, Filebeat is a type of data shippers that you can install as agents on your servers to send operational data to Elasticsearch and the Filebeat itself is a lightweight Log shipper you can use as a simple way to forward and centralized log files. For those who are using ELK probably already know why these two shouldn’t be compared but those who don’t will see the point where Logstash and Filebeat equally can be used to send logs from a file-based data source to a supported destination.
Even though, these two have similar function in a glance, the similarity actually stops at sending logs because they are used in tandem when you are building pipeline with ELK Stack since they carry different function.
The difference of these two can be told in a story about how they were exist now. Logstash is obviously created earlier and initially used to handle the streaming of a large amount of log data from many sources and after the creator Jordan Sissel joined Elastic team, the tool becomes an integral part of ELK Stack from its previous standalone tool. Read also: Logstash vs Fluentd here.
The main role of the tool is handling the tasks of pulling and receiving data from multiple systems as well as transforming them into a meaningful set of fields and streaming the output to your preferred destination for storage. However, since long ago, Logstash is having an issue about performance because it requires Java Virtual Machine to run and combined with implementation in Ruby, it will consume so much memory when used with multiple pipelines and advanced filtering.
Lumberjack and Filebeat Capabilities
To deal with this problem, Lumberjack is developed and used to handle data extraction, which of course designed to be lightweight shipper for collecting logs before sending them off for processing in another platform like Logstash. It is written in in Go and initially made to develop more efficient a network protocol for handling large bulks of data with support encryption and low memory footprint. Sometime later, it is renamed into Logstash-Forwarder that only leave the original its network protocol while the actual logging program is actually the Forwarder.
The newer version of Lumberjack protocol is what we know as Beats now. What set them apart from each other are support for JSON nesting in a message, the ability to ack in mid-window and better in handling of back pressure with efficient window-size reduction. Filebeat itself is called the next generation of Logstash-Forwarder.
Of course there are another member of Beat family and you can use them as lightweight agents installed on the different servers in your infrastructure for shipping metrics or logs. For log files, you can use Filebeat, for network metrics you can use Packetbeat, for server metrics you can use Metricbeat and many other.
This is why we can’t compare Logstash with Filebeat. If you are logging files you will almost always need both of them in combination because Filebeat will only give you timestamp and message fields while to get the Transformation just like in ETL, you will still need Logstash to serve as the aggregator for multiple logging pipelines.
Despite Filebeat being one of the best log file shippers available today with its lightweight, extremely reliable, supports SSL and TLS encryption as well as support back pressure with a good built-in recovery mechanism, it still won’t be able to turn your logs into an easy to analyze structured log messaged with filters for log enhancements.
To put it simply, Logstash is acting as an aggregator that will pull your data from many sources before pushing them down into the pipeline, which is usually Elasticsearch but it may also into a buffering component in larger production environments. At the other side, Filebeat and other Beats family acts as a lightweight agent that are deployed in the edge host to pump data into your Logstash for filtering, aggregation and enrichment.
Now, let’s compare Logstash with Filebeat. As it has been described above, both of them are different tools that shouldn’t be compared for they are serving different functions even though they equally used to send logs from a file-based data. Logstash main function is to handle the tasks of pulling and receiving data from multiple system then transforming them into a meaningful set of fields and streaming them into your preferred storage we called stashing.
At the other hand, Filebeat, which is a part of Beats family is used as a lightweight agents that you can installed on different servers in your infrastructure for shipping logs. It is extremely reliable and support both SSL and TLS as well as support back pressure with good built-in recovery mechanism.
Logstash vs Filebeat
|- Act as an aggregator||- Act as log shipper|
|- Created earlier||- Created later|
|- High memory consumption||- Lightweight|
All in all, the decision is all yours to make. In our opinion, both of them complement each other and there is no point on comparing them because mostly you will need both when logging files. However, if you are only need the timestamp and message fields, you can only use Filebeat but if you also need the Transformation part, you will still need Logstash.