prometheus brings the power of real-time graphs to the land of open source ( reference included ). It's lightweight and doesn't seem overkill if you're a startup company and do not have a heavy traffic load. Great for starting out on small to mid-scale. as traffic rises, you …
Apache Cassandra is a NoSQL database and well suited where you need highly available, linearly scalable, tunable consistency and high performance across varying workloads. It has worked well for our use cases, and I shared my experiences to use it effectively at the last Cassandra summit! http://bit.ly/1Ok56TK It is a NoSQL database, finally you can tune it to be strongly consistent and successfully use it as such. However those are not usual patterns, as you negotiate on latency. It works well if you require that. If your use case needs strongly consistent environments with semantics of a relational database or if the use case needs a data warehouse, or if you need NoSQL with ACID transactions, Apache Cassandra may not be the optimum choice.
This program works from the roots of the problem and creates a professional matrix for each of its users. This will give them more skills and resources to carry out tasks and reduce the difficulties of operating each of the processes of my work, as well as being An ally for the manipulation and operability of all your master data; Prometheus is very easy to recommend since it is a program that fulfills its mission.
Continuous availability: as a fully distributed database (no master nodes), we can update nodes with rolling restarts and accommodate minor outages without impacting our customer services.
Linear scalability: for every unit of compute that you add, you get an equivalent unit of capacity. The same application can scale from a single developer's laptop to a web-scale service with billions of rows in a table.
Amazing performance: if you design your data model correctly, bearing in mind the queries you need to answer, you can get answers in milliseconds.
Time-series data: Cassandra excels at recording, processing, and retrieving time-series data. It's a simple matter to version everything and simply record what happens, rather than going back and editing things. Then, you can compute things from the recorded history.
Cassandra runs on the JVM and therefor may require a lot of GC tuning for read/write intensive applications.
Requires manual periodic maintenance - for example it is recommended to run a cleanup on a regular basis.
There are a lot of knobs and buttons to configure the system. For many cases the default configuration will be sufficient, but if its not - you will need significant ramp up on the inner workings of Cassandra in order to effectively tune it.
Customer Service: since this is an open-source tool, customer service is not that great. Generally, you get all answers to your problems in online forums, but in case you got stuck, nobody will assist you in a channelised manner. You will have to find the way out on your own, and it may become frustrating at times.
More metrics for dashboards shall be added per the application being monitored. Standards metrics will work in most cases but may not in specific applications. Therefore, customised metrics shall be created for some of the industry-standard niche applications.
I would recommend Cassandra DB to those who know their use case very well, as well as know how they are going to store and retrieve data. If you need a guarantee in data storage and retrieval, and a DB that can be linearly grown by adding nodes across availability zones and regions, then this is the database you should choose.
It is usable and one can learn if few people in the team are already using it. It can be difficult to understand at the beginning because of non intuitive UI and syntax of the rules. So, I've gone for 7 points as there is some room for improvement in user interface and rules syntax.
We evaluated MongoDB also, but don't like the single point failure possibility. The HBase coupled us too tightly to the Hadoop world while we prefer more technical flexibility. Also HBase is designed for "cold"/old historical data lake use cases and is not typically used for web and mobile applications due to its performance concern. Cassandra, by contrast, offers the availability and performance necessary for developing highly available applications. Furthermore, the Hadoop technology stack is typically deployed in a single location, while in the big international enterprise context, we demand the feasibility for deployment across countries and continents, hence finally we are favor of Cassandra
Highly customized pricing plans to choose from. Lower pricing for the same features compared to competitors. Easy to reach the support team, which provided detailed documentation and helped set up the Prometheus. Monitoring metrics gets very easy after the integration with Grafana. It also has a sophisticated alert setting mechanism to ensure we don't miss anything critical.
I have no experience with this but from the blogs and news what I believe is that in businesses where there is high demand for scalability, Cassandra is a good choice to go for.
Since it works on CQL, it is quite familiar with SQL in understanding therefore it does not prevent a new employee to start in learning and having the Cassandra experience at an industrial level.
The ROI mentioned during the purchase has not been achieved, however this could be due to lack of data from our side. 2 years of implementation is too early to calculate and confirm the ROI.
