When comparing Flume vs jKool, the Slant community recommends Flume for most people. In the question“What are the best log aggregation & monitoring tools?” Flume is ranked 14th while jKool is ranked 29th.
Ranked in these QuestionsQuestion Ranking
Pro Flume is transactional (no lost when duplicating streams), and can be backuped by Kafka
Pro Hadoop integration
Flume was created to efficiently move log data to Apache Hadoop's HDFS.
Pro Free and open source
Licensed under Apache 2.0.
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).
Con Hard to manage
Since Flume cannot do multiplex connections, it's extremely hard to manage. It's essentially one socket per log type.
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.