JavaMelody vs SignalFx

Overview

SignalFx

Stacks53

Followers110

Votes22

JavaMelody

Stacks1

Followers6

Votes2

GitHub Stars3.0K

Forks754

SignalFx vs JavaMelody: What are the differences?

SignalFx: Monitoring and Operational Intelligence for the Cloud. We provide operational intelligence for today’s elastic architectures through monitoring specifically designed for microservices and containers with: -powerful and proactive alerting -metrics aggregation -visualization into time series data; JavaMelody: Monitoring of JavaEE applications. It is used to monitor Java or Java EE application servers in QA and production environments It is not a tool to simulate requests from users, it is a tool to measure and calculate statistics on real operation of an application depending on the usage of the application by users. It is mainly based on statistics of requests and on evolution charts..

SignalFx belongs to "Performance Monitoring" category of the tech stack, while JavaMelody can be primarily classified under "Java Tools".

Some of the features offered by SignalFx are:

Beautiful streaming visualizations
Meaningful, fast alerting using SignalFlow analytics
High resolution metrics up to 1 sec

On the other hand, JavaMelody provides the following key features:

give facts about the average response times and number of executions
make decisions when trends are bad, before problems become too serious
optimize based on the more limiting response times

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Manual

Detailed Comparison

SignalFx	JavaMelody
We provide operational intelligence for today’s elastic architectures through monitoring specifically designed for microservices and containers with: -powerful and proactive alerting -metrics aggregation -visualization into time series data	It is used to monitor Java or Java EE application servers in QA and production environments. It is not a tool to simulate requests from users, it is a tool to measure and calculate statistics on real operation of an application depending on the usage of the application by users. It is mainly based on statistics of requests and on evolution charts.
Beautiful streaming visualizations; Meaningful, fast alerting using SignalFlow analytics; High resolution metrics up to 1 sec; Filter & aggregate by dimesions like source, geo, customer, etc; Build custom real-time analytics pipelines; Use dynamic alert thresholds like moving averages; Get alerts in PagerDuty, VictorOps, HipChat, Slack, & more; Overlay events like alerts, pushes, CI runs, etc; Integrations with AWS services, Docker, Mesos, Kubernetes, Kafka, Cassandra, Mongo, MySQL & much more; Use OSS agents and metrics libraries like collectd, StatsD, etc; Provides OSS libraries in Java, Python, Go, Node, Ruby, etc; Provides OSS proxy to consume existing metrics; REST API;	give facts about the average response times and number of executions; make decisions when trends are bad, before problems become too serious; optimize based on the more limiting response times; find the root causes of response times; verify the real improvement after optimizations
Statistics
GitHub Stars -	GitHub Stars 3.0K
GitHub Forks -	GitHub Forks 754
Stacks 53	Stacks 1
Followers 110	Followers 6
Votes 22	Votes 2
Pros & Cons
Pros 5 High cardinality 5 Scalability 4 Easy to install 4 Fastest alerts 4 World class customer support	Pros 1 Open source 1 Easy to setup
Integrations
Golang Redis VictorOps AWS OpsWorks Amazon Route 53 Kafka Python New Relic Chef PagerDuty	No integrations available

What are some alternatives to SignalFx, JavaMelody?

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