Kibana vs. Logstash

Great for teams big and small that want a single pane of glass for understanding their systems, from dev, to staging, to production. Well-suited for teams that need to preserve logs for long-term compliance reasons, and also mine their logs for useful operational insights. Highly recommended as both an open source project and a commercial offering with fantastic paid support.

Incentivized

Logstash is a must in an ELK stack, which I am sure is going to be the #1 case. At any point when you have several sources, Logstash can be the common point to aggregate, and categorize those data. Then send this new data to its destination. Very handy. It is free and open source. It may not be appropriate to analyze data-sets dependent on each other but from a different data source. Reason being Logstash works on data at hand, and not wait for other data to arrive. It would be unwise for Logstashh to handle complicated, long-running transformations because this is injected and ejected. The faster you do it, the safer.

Incentivized

• searching
• near real-time

Incentivized

• Plugin ecosystem allows modular extensions.
• Tight integration into the Elastic.com products of Beats and Elasticsearch, so minimal setup is required when using those tools.
• Filter plugins are powerful for extracting and enriching input data.

Incentivized

• Improved tutorial/ user guidance
• Improved labeling for sources
• Ease of login and sharing with coworkers

Incentivized

• Memory: Logstash is a HOG, if you are deploying it on commodity (i.e. cheap and old) hardware: You will need at least 2GB, just for Logstash. So don't expect to run your entire ELK stack on one AMD Athlon machine.
• Overlap: Logstash fills in an area of the ELK stack that makes the most sense: as a log file transformer / shipper. However, if you start breaking that stack, with the addition of other components- you start seeing where features of Logstash may be implemented or solved in the additional components much easier (or better, or to a higher degree of resolution)
• More Overlap: Since my team employs Syslog-ng extensively- Logstash can sometimes get in the way (and this may be a problem for DevOps stacks overall): You can configure Syslog to record certain information from a source, filter that data, and even export that data in a particular format. Logstash will pick that data up, and then parse it. However, if you don't keep your Syslog-ng configuration files, and your Logstash configuration files in sync, your results will not be what you expected, and this will translate into (sometimes) hours/days of work, hunting down a line item in a configuration file.

Incentivized

Its usability is generally good and it provides teams with a basic to intermediate understanding about data visualization. It is very user-friendly when it comes to creating dashboards. The UI is very good and simple. Its integration with other tools for alerting and reporting is amazing. But its advance features have a learning curve and a first timer needs some time to use the advance features.

Incentivized

As I said earlier, for a production-grade OpenStack Telco cloud, Logstash brings high value in flexibility, compliance, and troubleshooting efficiency. However, this brings a higher infra & ops cost on resources, but that is not a problem in big datacenters because there is no resource crunch in terms of servers or CPU/RAM

I have not contacted Kibana support.

Incentivized

Well when it comes to using Kibana when compared to Datadog, I can say that Kibana is pretty [...] cheap. Apart from APM and Datadog hosted agents, Kibana gives a good competition to Datadog for real time log analysis as well as metrics analysis. While OpsGenie is a great tool for alerting, it lacks visualization when compared to Kibana. Grafana is another opensource tool that gives a lot of insights like Kibana but Grafana cannot be easily integrated with OpenSearch.

Incentivized

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

Incentivized

• First stop when diagnosing production performance issues.

Incentivized

• It is very difficult to give any figures on ROI, as it depends on many factors, and in a Telcocloud environment, it is much complex to find out; however, I would give some points below on ROI
• ROI based on flexibility is very high, as it reduces the time to find RCA
• ROI based on integration is very high because it supports multi-vendor environments, avoiding vendor lock-in & works across multi-cloud setups
• ROI on resource consumption is less because Logstash in 2-3 times more resource-intensive as compared to its lightweight alternatives resulting in latency