Zabbix is an open-source network performance monitoring software. It includes prebuilt official and community-developed templates for integrating with networks, applications, and endpoints, and can automate some monitoring processes.
Well, I am not a decision-maker here, but I believe Zabbix has been adopted as a default choice to be integrated with Nokia OpenStack because of its simplicity of usage & other products were not matured at that time. Single GUI can be used for infrastructure as well as workload …
I have had feedback that Splunk is a more out-of-the-box solution. With some fine tuning, it is possible to get the same robust functionality from a Logstash and Zabbix integration. The setup is more taxing, but you avoid paying the costly Splunk fees. So it all really depends …
Perfect for projects where Elasticsearch makes sense: if you decide to employ ES in a project, then you will almost inevitably use LogStash, and you should anyways. Such projects would include: 1. Data Science (reading, recording or measure web-based Analytics, Metrics) 2. Web Scraping (which was one of our earlier projects involving LogStash) 3. Syslog-ng Management: While I did point out that it can be a bit of an electric boo-ga-loo in finding an errant configuration item, it is still worth it to implement Syslog-ng management via LogStash: being able to fine-tune your log messages and then pipe them to other sources, depending on the data being read in, is incredibly powerful, and I would say is exemplar of what modern Computer Science looks like: Less Specialization in mathematics, and more specialization in storing and recording data (i.e. Less Engineering, and more Design).
Zabbix is very well suited for infrastructure monitoring i.e. the underlying host servers, basically, compute nodes. However, it has limited FM & PM capabilities for the workloads, i.e., the virtual machines (VMs). Zabbix has an easy-to-use GUI which can be explored easily & provides good filtering of the data.
Logstash design is definitely perfect for the use case of ELK. Logstash has "drivers" using which it can inject from virtually any source. This takes the headache from source to implement those "drivers" to store data to ES.
Logstash is fast, very fast. As per my observance, you don't need more than 1 or 2 servers for even big size projects.
Data in different shape, size, and formats? No worries, Logstash can handle it. It lets you write simple rules to programmatically take decisions real-time on data.
You can change your data on the fly! This is the CORE power of Logstash. The concept is similar to Kafka streams, the difference being the source and destination are application and ES respectively.
Collecting hardware data - CPU, Memory, Network, and Disk Metrics are collected and reported on.
Flexible design - It is very easy to build out even very large environments via the templating system. You can also start where you are - network monitoring, server monitoring, etc. and then build it out from there as time and resources permit.
Provides a "plugin architecture" (via XML templates) to allow end users to extend it to monitor all kinds of equipment, software, or other metrics that are not already added into the software already.
Very complete documentation. Almost every aspect of Zabbix has been documented and reported on.
Cost - Zabbix is FOSS software and always free. Support is reasonably priced and readily available.
Since it's a Java product, JVM tuning must be done for handling high-load.
The persistent queue feature is nice, but I feel like most companies would want to use Kafka as a general storage location for persistent messages for all consumers to use. Using some pipeline of "Kafka input -> filter plugins -> Kafka output" seems like a good solution for data enrichment without needing to maintain a custom Kafka consumer to accomplish a similar feature.
I would like to see more documentation around creating a distributed Logstash cluster because I imagine for high ingestion use cases, that would be necessary.
In a busy Zabbix environment, it can easily overwhelm the underlying database. Plan on having SSDs and a significant server infrastructure to keep up with more than a hundred hosts.
Building out Zabbix metrics that suit your environment can be very time consuming. When choosing a monitoring platform like Zabbix, expect a steep learning curve and to invest significant resources to make the tool valuable.
This is less important than it has been in the past, but current versions of Zabbix still do not handle IPMI checks of hardware very well. We needed to write our own wrapper for IPMI checks rather than using the built in IPMI poller.
It is free. It didn't cost anything to implement (other than my time and the cost incurred for it) and it is filling a badly needed gap in our IT infrastructure. Support is available if we have issues and can be done annually or paid for on a per incident basis as needed. Expansion, updates, and all other future lifecycle activities are likewise free of cost, so as long as someone is able to implement/maintain the software (and the OSS project is maintained) then I imagine the company will never leave it.
The setup is the most time-consuming portion of using zabbix. It takes a lot of effort to shape it into a usable format and even then it can get very messy. It's not exactly intuitive and as mentioned the UI seems a bit antiquated. If I was to roll out a monitoring solution from scratch, I'd probably look for alternatives which are easier to use and maintain.
We are a mainly Windows environment, so it would be useful if we could have used Active Directory to deploy agents. As of version 4.2, Zabbix has announced a new agent MSI file to allow exactly that. Unfortunately, we didn't have that option. Also, for Linux and MAC deployments, there is no simple way to deploy that. Using remote scripts you may be able to create something, but most places will opt for either SNMP (agentless) or manual installation of agents to add to Zabbix. A way of deploying agents via discovery would go a long way to helping in the adoption of the tool.
MongoDB and Azure SQL Database are just that: Databases, and they allow you to pipe data into a database, which means that alot of the log filtering becomes a simple exercise of querying information from a DBMS. However, LogStash was chosen for it's ease of integration into our choice of using ELK Elasticsearch is an obvious inclusion: Using Logstash with it's native DevOps stack its really rational
We're using the Solarwinds suite as our global monitoring standard, but it is very complex and its licensing model makes it difficult to monitor a wide range of technologies. So, we're using Zabbix as a complement on our monitoring process. Zabbix is a way more flexible and has free integrations to a wide range of technologies. It is also more 'user friendly' and easy to manage.
Positive: Learning curve was relatively easy for our team. We were up and running within a sprint.
Positive: Managing Logstash has generally been easy. We configure it, and usually, don't have to worry about misbehavior.
Negative: Updating/Rehydrating Logstash servers have been little challenging. We sometimes even loose data while Logstash is down. It requires more in-depth research and experiments to figure the fine-grained details.
Negative: This is now one more application/skill/server to manage. Like any other servers, it requires proper grooming or else you will get in trouble. This is also a single point of failure which can have the ability to make other servers useless if it is not running.
Zabbix simply makes it easier to identify, and subsequently resolve problems quickly
Zabbix gives one web page to look at to see a list of all on-going issue in a single place
Zabbix can automate response to alerts. For example, Zabbix allows you the customization to take a monitored server out of production rotation if it is identified as unhealthy
