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).
Great for standard web application performance monitoring, analytics and error reporting. Shows line level code errors, gives insight into performance issues (plugins, API issues, etc.). Automation and scheduled scanning in production gives client visibility into 'after deployment' value. Also lets a relatively small number of developers keep tabs on a handful of different site/applications without needing a bunch of tools. The UI is pretty complicated and can be overwhelming for new users. Documentation could be better for the learning curve,
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.
Great web interface. Lots of data available in a really clean format, with filtering options and more.
Per-user exception tracking. User is complaining about something being broken? Look up their account ID in Sentry and you can see if they've run into any exceptions (with device information included, of course).
Source map uploading. Took a little while to figure this out but now we have our deploy script upload sourcemaps to Sentry on each deployment, meaning we get to see stack traces that aren't obfuscated!
Very generous free tier – 10,000 events per month. We're nowhere near that yet.
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.
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 used Rollbar but didn't like the configuration its not easy. And also doesn't support wide features like Sentry although its a cheaper option but doesn't have the dash-boarding like Sentry and its was not easy to integrate webhooks for different purposes. Somehow many people in company where not able to understand Rollbar dashboard who were very much used to Sentry.
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.
We had to take it down later due to internal reasons and majorly because of cost-cutting process
If someone has a unstable system and have no way to figure out what to do, can use sentry at least temporarily along with some other APM to fix their system faster
