When comparing jKool vs Prometheus, the Slant community recommends Prometheus for most people. In the question“What are the best server performance monitoring tools?” Prometheus is ranked 2nd while jKool is ranked 19th. The most important reason people chose Prometheus is:
Available as Docker images on Quay.io or Docker Hub, read article [Monitoring Containerized Application Health with Docker](https://blog.sixeyed.com/monitoring-containerized-application-health-with-docker/).
Ranked in these QuestionsQuestion Ranking
Pro Subscriptions to real-time updates
Subscriptions to real-time updates from infrastructure systems gives users proactive detection of application anomalies, as well as performance, availability and capacity issues. This enables users to capture perishable insights into application issues that they would normally miss.
Pro Offers free option
JKool offers free option limited by daily data volume and retention. Easy to sign up and try. No commitment or credit card required to signup.
Pro Simple English-like query language
jKool uses JKQL (english like query language) to search real-time and historical events, metrics and transaction streams. It includes verbs like GET, SUBSCRIBE, COMPARE, etc. Compare allows comparison of events, activities and metrics.
Pro Open source connectors
jKool offers open source connectors available hosted on GitHub: syslog, log4j, slf4j, logback, jmx, Spark, HDFS, JMS, MQTT, Logstash etc.
Pro Analyze logs: syslog, log4j, logback and others
You can use the jKool log collectors to stream syslogs and other logs to a jKool and be able to view and analyze your logs in a graphical oriented way. jKool can show error rates, anomalies, exceptions, groups, time buckets, time windows, aggregations and apply math functions across all your logs.
Pro Supports Logstash as a datasource
jKool can consume incoming streams directly from Logstash. Combine Logstash with other streams to deliver a unified application view. Logstash integration is open source.
Pro True multi-tenancy
Supports concepts of data repositories, organizations, teams and users which allows logical data separation and access control.
Pro Real-time search
Search for events, transcations, metrics streams in real-time before data is indexed.
Pro Automatic anomaly detection
Helps to proactively detect potential anomalous behavior and prevent problems from occurring.
Pro Unique Visualization
Visualization is generated dynamically based on a data query as opposed to the typical canned views that have nothing to do with the specific query.
Pro More than log analyzer
jKool does much more than just log analysis. Examples: end-user monitoring, application performance, transaction tracking, business metrics and IoT. Developers can use APIs (java, REST) to build extensions for streaming, visualization. jKool provides a unified model for all time series machine data.
Pro Simple, easy to use, great UI
There are really only 2 things a user needs to do to show charts, graphs: 1) create a dashboard 2) Create viewlets. Each viewlets is bound with a JQKL query. JQKL queries can be typed in or built via a query builder for beginners. Pretty easy to get up and running without learning JQKL. There is also a UI tutorial once you logon to jKool.
Pro Self service dashboard for users with different responsibilities.
Provides situational awareness to users from their own perspective. The benefits are translated to reduction of support calls and visibility for each user, from their own angles into root causes of problems within a couple of clicks.
Pro Unified analytics support
Combines analytics for events, metrics (name, value pairs) and transactions with real-time and historical analytics in a single platform.
Pro Transaction Tracking & Discovery
jKool is able to automatically connect/stitch multiple events coming from multiple sources into a business transaction(s), measure performance, completion, progress. Transactions can be grouped into user defined sets (payment, claims, etc).
Pro Simple model: Stream Data->Run Queries->Analyze
Easy to use cloud based log and application analytics supporting standards such as Syslog, log4j, log files, end-user monitoring, transaction tracking. No schemas to define, agentless, no servers or storage to setup. Provides true transaction stitching and analytics -- track all transactions, 100% of the time. On-premise and cloud based (docker support available).
Pro Docker image
Available as Docker images on Quay.io or Docker Hub, read article Monitoring Containerized Application Health with Docker.
Pro Powerful query language
The Prometheus query language allows you to slice and dice the dimensional data for ad-hoc exploration, graphing, and alerting.
Alerts are defined using the same powerful query language described above. A separate binary, the Alertmanager, handles alert notifications and aggregations and enables silencing by any label set. If an alert fires, Alertmanager can send an email or page you through an external alerting service like PagerDuty.
Nagios plugin is also offered as a bridge from the parts that are already monitored by Prometheus to the existing Nagios alerting set-up that many companies have in place.
The one-stop solution for the fanciest dashboards is PromDash, a GUI-based dashboard builder with a SQL backend. It talks to any number of Prometheus servers via an HTTP API and graphs their data in highly configurable dashboards. Even Graphite graphs can be included.
Pro Scalable data collection and decentralized architecture
You can reliably monitor the many instances of your services, and independent teams can set up independent monitoring servers.
You can spin up a monitoring server where and when you want, even on your local workstation, without setting up a distributed storage backend or reconfiguring the world.
Pro A multi-dimensional data model, so that data can be sliced and diced at will, along dimensions like instance, service, endpoint, and method.
Prometheus fundamentally stores all data as time series: streams of timestamped values belonging to the same metric and the same set of labeled dimensions. Timestamps have a millisecond resolution, while values are always 64-bit floats.
The metric name specifies the general feature of a system that is measured. For example, a metric to count the total number of HTTP requests received by an API server might be called api_http_requests_total. Adding labels (key/value pairs) to this metric enables Prometheus's dimensional data model: any given combination of labels for the same metric name results in a separate time series.
Con Doesn't seem to be very popular
JKool is a relatively new tool, as such, there don't seem to be many third party guides and tutorials other than from official sources from the JKool team.
Con Needs configuration
Not as easy to deploy as some other tools.