Likelihood to Recommend
Cloud watch is great and essential if you decide to invest in AWS and have any need to monitor the health of all aspects of your VPC resources, or at the organizational level (multiple accounts). Another benefit of the service is constant upgrades at no additional costs; the software evolves to develop modules and interface improvements. For first-time users in AWS, this is going to take a bit to understand, so the learning curve to this metrics environment can seem overwhelming at first glance/use.
Read full review Perfect
for projects where
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 Pros It provides lot many out of the box dashboard to observe the health and usage of your cloud deployments. Few examples are CPU usage, Disk read/write, Network in/out etc. It is possible to stream CloudWatch log data to Amazon Elasticsearch to process them almost real time. If you have setup your code pipeline and wants to see the status, CloudWatch really helps. It can trigger lambda function when certain cloudWatch event happens and lambda can store the data to S3 or Athena which Quicksight can represent. Read full review 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 Cons Memory metrics on EC2 are not available on CloudWatch. Depending on workloads if we need visibility on memory metrics we use Solarwinds Orion with the agent installed. For scalable workloads, this involves customization of images being used. Visualization out of the box. But this can easily be addressed with other solutions such as Grafana. By design, this is only used for AWS workloads so depending on your environment cannot be used as an all in one solution for your monitoring. Read full review 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 Support Rating
Support is effective, and we were able to get any problems that we couldn't get solved through community discussion forums solved for us by the AWS support team. For example, we were assisted in one instance where we were not sure about the best metrics to use in order to optimize an auto-scaling group on EC2. The support team was able to look at our metrics and give a useful recommendation on which metrics to use.
Read full review Alternatives Considered
I believe that CloudWatch is a better solution to use with AWS services and resources in terms of cost and ease of integration with AWS infrastructure services. But keep in mind that
is better at aggregating application-level metrics. We chose CloudWatch because of its capabilities to integrate and monitor AWS services in almost real-time.
Read full review MongoDB
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
is an obvious inclusion: Using Logstash with it's native DevOps stack its really rational
Read full review Return on Investment We were able to set up log streaming, retention, and simple downtime alerts within a few hours, having no prior experience with CloudWatch, freeing up our engineers to focus on more important business goals. CloudWatch log groups have made it relatively easy to detect and diagnose issues in production by allowing us to aggregate logs across servers, correlate failures, isolate misbehaving servers, etc. Thanks to CloudWatch, we are generally able to identify, understand and mitigate most production fires within 10-15 minutes. Choosing CloudWatch to manage log aggregation has saved us quite a bit of time and money over the past year. Generally, 3rd-party log aggregation solutions tend to get quite expensive unless you self-host, in which case you typically need to spend a fair amount of time setting up, maintaining, and monitoring these services. Read full review 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 ScreenShots Amazon CloudWatch Screenshots