Likelihood to Recommend 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).
Read full review Great for organizations that need to centralize different types of logging across integration, qa, sandbox and production environments. Would be less impactful for organizations that don't have complex logging needs.
Read full review Pros 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. Read full review Providing in-depth insights A related content feature which really helps us to figure out which service is breaking the infrastructure. The Log explorer which helps us to explore the entire log and pin point the errors/issues. Fast and powerful log investigation Read full review Cons 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. Read full review Integrating the system with Slack channels Saving the logs in different cloud provider Viewing the logs at more fine grained level based on user roles and permissions Read full review Likelihood to Renew I'm a Splunk specialist, and I'm involved in its use and improvement.
Read full review Usability It gives access to data features for every level of users: from managers and executives to Analysts, each one with the correct level of observation and analysis.
Read full review Support Rating Splunk support is very quick and efficient. Pre-sale specialists are very skilled and available.
Read full review Implementation Rating Follow a training before starting.
Read full review Alternatives Considered 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
Read full review I had not considered other products because Splunk Log Observer was in use in my company before I started working there.
Read full review Return on Investment 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. Read full review Significantly reduced the MTTR (Mean Time To Recovery), which in turn has improved the end-user experience tremendously. Meets compliance requirements of security policies, audit, regulation, and forensics. Helps us to track/manage the resource usage on our cloud instances which has a direct implication on the recurring cost. Read full review ScreenShots