LogPoint vs. Logstash

LogPoint is incredibly useful for pulling information from various log sources and combining them together to offer insights into suspicious or potentially malicious behaviour. It is not intuitive and can take some time to get used to. Once you're up and running though, it's easy to onboard new log sources. Search queries can again be tough to get used to, but LogPoint support is really helpful and can offer assistance with writing more complex searches.

Incentivized

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).

Incentivized

• Technical support team is fast and competent
• License management and cost
• Log parsing
• New logs can be provided to the support team for parser creation
• High Availability architecture does not cost more

• 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.

Incentivized

• Providing a full Cloud solution
• Having more documentation for complex deployment

Incentivized

• It is heavy i.e., intensive as of now. Need to reduce overhead to save CPU/RAM consumption
• Need to be more Kubernetes-friendly. Should support auto-scaling and K8s observability
• Initial configuration is still complex. A seamless config procedure is still required

We are confident with the solution and we are using it daily

Incentivized

Overall, LogPoint is pretty easy to get started with but faces issues with specific things (syslog on custom ports, script log collection, etc.).

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

LogPoint support is outstanding. They are incredibly helpful, and on occasions have proactively identified issues with our setup, and logged cases on our behalf before we had even noticed there was a problem. If there is a search we need to write that is beyond our skills, LogPoint support can typically write it for us within a couple of days. They are always very responsive, and I am yet to have a bad support experience.

Incentivized

Really nice person with huge skills on LogPoint

Incentivized

LogPoint is easier to implement and less expensive.

Incentivized

Logstash can be compared to other ETL frameworks or tools, but it is also complementary to several, for example, Kafka. I would not only suggest using Logstash when the rest of the ELK stack is available, but also for a self-hosted event collection pipeline for various searching systems such as Solr or Graylog, or even monitoring solutions built on top of Graphite or OpenTSDB.

Incentivized

N/A

(Cannot skip without answer)

• Keep the same team to manage more IT resources
• Having a better logs visibility

Incentivized

• Positive: LogStash is OpenSource. While this should not be directly construed as Free, it's a great start towards Free. OpenSource means that while it's free to download, there are no regular patch schedules, no support from a company, no engineer you can get on the phone / email to solve a problem. You are your own Engineer. You are your own Phone Call. You are your own ticketing system.
• Negative: Since Logstash's features are so extensive, you will often find yourself saying "I can just solve this problem better going further down / up the Stack!". This is not a BAD quality, necessarily and it really only depends on what Your Project's Aim is.
• Positive: LogStash is a dream to configure and run. A few hours of work, and you are on your way to collecting and shipping logs to their required addresses!

Incentivized