Graylog vs Prometheus

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Graylog vs Prometheus: What are the differences?

Introduction

This Markdown code provides a comparison between Graylog and Prometheus. Graylog and Prometheus are both widely used open-source monitoring tools, but they differ in various aspects. The following are key differences between Graylog and Prometheus.

  1. Data Collection and Storage: Graylog is primarily designed for log management and analysis. It collects log data from various sources and stores it in a centralized repository, providing a comprehensive view of system events. On the other hand, Prometheus focuses on time series data collection, specifically monitoring metrics and providing powerful querying capabilities with a built-in time series database. It is more suited for monitoring and alerting.

  2. Monitoring Approach: Graylog is primarily centered around log-based monitoring. It scans and analyzes logs, allowing users to search, filter, and perform analysis on log data. It provides features like full-text search, log tailing, and message parsing. In contrast, Prometheus adopts a pull-based monitoring approach, where it actively polls and scrapes metrics from instrumented applications and infrastructure components. It focuses on monitoring metrics related to performance, resource usage, and other custom-defined metrics.

  3. Alerting and Event Notification: Graylog provides extensive alerting capabilities, allowing users to define conditions based on log data and trigger notifications, such as email, Slack messages, or webhooks. It offers flexibility in defining sophisticated alert conditions and actions easily. On the other hand, Prometheus has a native alerting system that supports rule-based alert definitions and notification integrations. It provides a set of powerful alerting expressions and offers various notification options.

  4. Scalability and High Availability: Graylog supports horizontal scalability and high availability through its clustering capabilities. With a cluster of Graylog nodes, it can handle larger log volumes and provide fault tolerance. Prometheus architecture is designed for horizontal scalability as well, where multiple Prometheus servers can be federated together. However, it doesn't inherently provide high availability out of the box, and additional measures like data replication or using a separate HA solution may be required.

  5. Data Visualization and Dashboards: Graylog offers basic data visualization capabilities, allowing users to create dashboards with various widgets like line charts, tables, and histograms. It also supports creating real-time histograms and time-series charts based on log data. In contrast, Prometheus relies on third-party visualization tools like Grafana for creating interactive and customizable dashboards. Grafana integrates seamlessly with Prometheus and provides a vast array of visualization options.

  6. Ecosystem and Integrations: Graylog has a rich ecosystem of plugins and integrations, enabling seamless integration with other tools and systems. It supports various data sources and outputs, facilitating integration with external systems like Elasticsearch, Kafka, and more. Prometheus, on the other hand, has a focused ecosystem centered around monitoring. It has extensive integrations with popular tools and platforms for data collection, visualization, and alerting.

In summary, Graylog is primarily focused on log management and analysis, with extensive log-based monitoring capabilities and alerting features. On the other hand, Prometheus is designed for time series data collection, focusing on monitoring metrics with a pull-based approach, and providing a powerful querying language and alerting system.

Advice on Graylog and Prometheus
Susmita Meher
Senior SRE at African Bank · | 4 upvotes · 791.1K views
Needs advice
on
GrafanaGrafanaGraphiteGraphite
and
PrometheusPrometheus

Looking for a tool which can be used for mainly dashboard purposes, but here are the main requirements:

  • Must be able to get custom data from AS400,
  • Able to display automation test results,
  • System monitoring / Nginx API,
  • Able to get data from 3rd parties DB.

Grafana is almost solving all the problems, except AS400 and no database to get automation test results.

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Replies (1)
Sakti Behera
Technical Specialist, Software Engineering at AT&T · | 3 upvotes · 576.5K views
Recommends
on
GrafanaGrafanaPrometheusPrometheus

You can look out for Prometheus Instrumentation (https://prometheus.io/docs/practices/instrumentation/) Client Library available in various languages https://prometheus.io/docs/instrumenting/clientlibs/ to create the custom metric you need for AS4000 and then Grafana can query the newly instrumented metric to show on the dashboard.

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Sunil Chaudhari
Needs advice
on
MetricbeatMetricbeat
and
PrometheusPrometheus

Hi, We have a situation, where we are using Prometheus to get system metrics from PCF (Pivotal Cloud Foundry) platform. We send that as time-series data to Cortex via a Prometheus server and built a dashboard using Grafana. There is another pipeline where we need to read metrics from a Linux server using Metricbeat, CPU, memory, and Disk. That will be sent to Elasticsearch and Grafana will pull and show the data in a dashboard.

Is it OK to use Metricbeat for Linux server or can we use Prometheus?

What is the difference in system metrics sent by Metricbeat and Prometheus node exporters?

Regards, Sunil.

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Replies (2)
Matthew Rothstein
Recommends
on
PrometheusPrometheus

If you're already using Prometheus for your system metrics, then it seems like standing up Elasticsearch just for Linux host monitoring is excessive. The node_exporter is probably sufficient if you'e looking for standard system metrics.

Another thing to consider is that Metricbeat / ELK use a push model for metrics delivery, whereas Prometheus pulls metrics from each node it is monitoring. Depending on how you manage your network security, opting for one solution over two may make things simpler.

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Recommends
on
InstanaInstana

Hi Sunil! Unfortunately, I don´t have much experience with Metricbeat so I can´t advise on the diffs with Prometheus...for Linux server, I encourage you to use Prometheus node exporter and for PCF, I would recommend using the instana tile (https://www.instana.com/supported-technologies/pivotal-cloud-foundry/). Let me know if you have further questions! Regards Jose

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Mat Jovanovic
Head of Cloud at Mats Cloud · | 3 upvotes · 720.2K views
Needs advice
on
DatadogDatadogGrafanaGrafana
and
PrometheusPrometheus

We're looking for a Monitoring and Logging tool. It has to support AWS (mostly 100% serverless, Lambdas, SNS, SQS, API GW, CloudFront, Autora, etc.), as well as Azure and GCP (for now mostly used as pure IaaS, with a lot of cognitive services, and mostly managed DB). Hopefully, something not as expensive as Datadog or New relic, as our SRE team could support the tool inhouse. At the moment, we primarily use CloudWatch for AWS and Pandora for most on-prem.

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Replies (2)
Lucas Rincon
Recommends
on
InstanaInstana

this is quite affordable and provides what you seem to be looking for. you can see a whole thing about the APM space here https://www.apmexperts.com/observability/ranking-the-observability-offerings/

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Recommends
on
DatadogDatadog

I worked with Datadog at least one year and my position is that commercial tools like Datadog are the best option to consolidate and analyze your metrics. Obviously, if you can't pay the tool, the best free options are the mix of Prometheus with their Alert Manager and Grafana to visualize (that are complementary not substitutable). But I think that no use a good tool it's finally more expensive that use a not really good implementation of free tools and you will pay also to maintain its.

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Decisions about Graylog and Prometheus
Leonardo Henrique da Paixão
Junior QA Tester at SolarMarket · | 15 upvotes · 358.1K views

The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.

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Pros of Graylog
Pros of Prometheus
  • 19
    Open source
  • 13
    Powerfull
  • 8
    Well documented
  • 6
    Alerts
  • 5
    User authentification
  • 5
    Flexibel query and parsing language
  • 3
    User management
  • 3
    Easy query language and english parsing
  • 3
    Alerts and dashboards
  • 2
    Easy to install
  • 1
    A large community
  • 1
    Manage users and permissions
  • 1
    Free Version
  • 47
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
  • 22
    Extensive integrations
  • 19
    Easy to setup
  • 12
    Beautiful Model and Query language
  • 7
    Easy to extend
  • 6
    Nice
  • 3
    Written in Go
  • 2
    Good for experimentation
  • 1
    Easy for monitoring

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Cons of Graylog
Cons of Prometheus
  • 1
    Does not handle frozen indices at all
  • 12
    Just for metrics
  • 6
    Bad UI
  • 6
    Needs monitoring to access metrics endpoints
  • 4
    Not easy to configure and use
  • 3
    Supports only active agents
  • 2
    Written in Go
  • 2
    TLS is quite difficult to understand
  • 2
    Requires multiple applications and tools
  • 1
    Single point of failure

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