Logstash vs. Logz.io

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

Logz.io is an effective solution if your alerting needs are fairly straightforward and you don't need long-term retention of logs with easy access. If being able to maintain easy access to logs longer than this is necessary, another solution might be better. If you need a high degree of precision with alerting triggers and the ability to suppress alerts, you will need to combine Logz.io with an integration to get this or you might consider a different solution.

Incentivized

• 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

• Well documented and easy to set up.
• Their alerting mechanism is really great especially when it comes to application monitoring.
• Our data analysis in Kibana is made easy with the solution for Logz.io.
• The issue checking and fixing is escalated quickly and is reported in time.

Incentivized

• 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.

Incentivized

• Its price can be very high, so you should have good control of it to avoid exaggerated figures.
• Some functions can be confusing.
• It has limits to create subaccounts, which is a big problem for large companies.

Incentivized

I initially struggled trying to ensure the correct data was returned in the Kibana search, but I found it overall easy to use. Some of the UI is not as seamless as I'd expect, like changing the environment completely resets your search criteria and filters, which is annoying since it's a common use case to search something in multiple environments

Incentivized

Their support team is the best in the world! They supported us in most of the critical times and helped to resolve the issue in real time. Also their email support is well maintained and never a mail is missed unanswered. Kudos to the support team of logz.io for maintaining professionalism.

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

Logz.io is more affordable, less work to maintain, and has more features. It was an easy choice. After my last team had to manage their own ELK stack, this was a no brainer. It helps us be focused on our core competencies.

Incentivized

• 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.

Incentivized

• Be capable to efficiently identify problems
• Efficiently investigate issues and find the root cause
• Be able to improve the logging of services and products

Incentivized