Logstash vs Retrace

• 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.

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• It combines code profiling with application logger.
• It is safe for production usage.

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• 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.

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• Need to improve their mobile support
• Documentation improvment

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• 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.

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• We shorten the time we fix app issues
• We spend a lot less time digging through data
• We can be proactive and see issues as they are trending before the completely explode
• I have APM on all production servers so I can catch issues as they happen
• I no longer get 2000 email on an issue but rather one full of data

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