Elasticsearch Monitoring: SPM vs. Marvel

While many SPM Performance Monitoring users quickly see the benefits of SPM and adopt it in their organizations for monitoring — not just for Elasticsearch, but for their complete application stack — some Elasticsearch users evaluate SPM and compare it to Marvel from Elasticsearch.  We’ve been asked about SPM vs. Marvel enough times that we decided to put together this focused comparison to show some of the key differences and help individuals and organizations pick the right tool for their needs.

Marvel is a relatively young product that provides a detailed visualization of Elasticsearch metrics in a Kibana-based UI. It installs as an Elasticsearch plug-in and includes ‘Sense’ (a developer console), plus a replay functionality for shard allocation history.

SPM, on the other hand, offers multiple agent deployment modes, has both Cloud and On Premises versions, includes alerts and anomaly detection, is not limited to Elasticsearch monitoring, integrates with third party services, etc. The following Venn diagram shows key areas that SPM and Marvel have in common and also the areas where they differ.

SPM-vs-Marvel

Looking into the details surfaces many notable differences.  For example:

  • The SPM agent can run independently from the Elasticsearch process and an upgrade of the agent does not require a restart of Elasticsearch
  • Dashboards are defined with different philosophies: Marvel exposes each Metric in a separate chart, while SPM groups related metrics together in a single chart or in adjacent charts (thus making it easy for people to have more information in a single place without needing to jump between multiple views)
  • Both have the ability to show metrics from multiple nodes in a single chart: Marvel draws a separate line for each node, while in SPM you can choose to aggregate values or display them separately.

The following “SPM vs. Marvel Comparison Table” is a starting point to evaluate monitoring products for organization’s individual needs.

SPM vs. Marvel Comparison Table

Feature SPM by Sematext Marvel by Elasticsearch
Supported Applications Elasticsearch, Hadoop, Spark, Kafka, Storm, Cassandra, HBase, Redis, Memcached, NGINX(+), Apache, MySQL, Solr, AWS CloudWatch, JVM, … Elasticsearch
Agent deployment mode in- and out-of-process
(out-of-process allows for seamless updates without requiring Elasticsearch restarts)
in-process
(as Elasticsearch plug-in; updates require Elasticsearch restarts)
Predefined dashboard graphs organized in groups YES YES
Saving Individual Dashboards Each user can store multiple dashboards, mixing charts from all applications, including both metrics and logs. Current view can be saved, reset to defaults possible. These changes are global.
API for Custom Metrics and Business KPIs YES NO
Extra Elasticsearch Metrics
  • Circuit Breakers
  • ES Threadpools
YES

  • Circuit Breakers
  • ID Cache
  • Lucene memory
  • ES Threadpools
  • Percolator
OS and JVM Metrics YES (+)

  • JVM pool sizes
  • JVM pool utilization
YES
Correlation of Metrics with Logs, Events, Alerts, and Anomalies YES

  • SPM and Logsene integration
  • Ability to ingest and chart arbitrary external Events
NO

  • Cluster Pulse displays only Elasticsearch Events
Deployment model SaaS or On Premises On Premises
Security/User Roles &
Permissions
YES NO
Easy & Secure Sharing of Reports with internal and external organizations YES

  • via short links
  • vie embeds / iframe
  • via email
NO
Machine Learning-based Anomaly Detection YES NO
Threshold based Alerts YES NO
Heartbeat Alerts YES NO
Forwarding Alerts to 3rd parties YES

  • E-Mail
  • PagerDuty
  • Nagios / Shinken
  • HipChat
  • Slack
  • Webhooks
NO
Metrics Aggregation YES

  • Pre-aggregation at multiple granularity levels, including 1 min granularity.  Advantage: more efficient storage, scales better, faster for graphing performance over longer time periods at the expense of sub-minute precision.
YES

  • Query-time aggregation. No write or query-time aggregation.
    Advantage: 10 second precision by default at the expense of storage size, write, and read performance and memory footprint.

As an aside, most of the features in this comparison table would also apply if we compared SPM to BigDesk, ElasticHQ, Statsd, Graphite, Ganglia, Nagios, Riemann, and other application-specific monitoring or alerting tools out there.

If you have any questions about this comparison or have any feedback, please let us know!

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