Solarwinds® Kiwi Syslog® Server is a syslog management tool for network and systems engineers. It receives syslog messages and SNMP traps from network devices (routers, switches, firewalls, etc.), and Linux®/Unix® hosts. Users can filter and view these messages based on time, hostname, severity, etc., and set up custom alerts. Kiwi Syslog Server has built-in actions to react appropriately to syslog messages. There are also log archival and clean-up features to help comply with security policies.
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).
To monitor syslog events Kiwi syslog much helpful and needed .Its saving human efforts and cost.Easy to check on GUI panel flow and status of server ,start and stop services we can do them from GUI panel it self . Recent version also no need C++ libraries to install .We can store the ingested events and archive based on our threshold criteria .We can import and export INI file which contain everything what we have configured
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.
Collection of SNMP traps a reliable and stable collection server for these is crucial to troubleshooting and time to ROS. Kiwi excels at this.
Easy to install set up and train users on.
The free version is a good free tool and handy to use for personal labs and other smalle use cases.
SNMP traps to user readable format is great, sometimes syslog and smnp messages can be hard to interpret and read with out a knowledge of how to do this.
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.
Kiwi Syslog has the best usability of any syslog server. While not being able to offer the most features, the ones it does have are intuitive and easy to work with. Everything that it has is where you think it should be. If you can't find it in the menus, it doesn't exist.
Because the solution is so simple to use and implement, support wasn't very necessary. The one time I did call them to better understand where logs were stored, they were very helpful and friendly. Kiwi has been around for some time and not a lot has changed over the years, so support for it is pretty straightforward and quick.
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
PRTG is a great package and very useful, but the jump from the free 100 sensor price model to the first tier of the paid model is WAY too expensive. SolarWinds Kiwi Syslog Server is very inexpensive and provides us with the results we needed for log monitoring.
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.
