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Top Observability tools for DevOps Engineers and SREs

dborta profile image Nir Sharma Originally published at squadcast.com ・7 min read

Better visibility is the first step to improved system stability. Our latest blog outlines Top Observability tools for DevOps Engineers & SREs to help you get started on your journey to gain valuable insights into your infrastructure.

“We can't fix something which we can't observe” - whether it's a steam engine or a complex microservice based cloud deployment, great observability makes troubleshooting things easier. Having a clear view of your system makes early recognition and preemptive solving of problems possible. Getting the right data at the right time with associated context is a game changer for those who want better system stability.

In this blog post, we have collated a list of observability tools in the areas of log aggregation, APM, time series databases, distributed tracing, and metrics collection tools. While this is not an in-depth look at the strengths and weaknesses of these tools, it's a good starting point to get started on your journey to better observability.

The list contains a mix of on-premise, hybrid, and SaaS platforms. Also, some of the tools featured here are open-source products or built on the foundation of other open-source software.

First up, we look at some log aggregation tools:

Fluentd is an open-source data collection tool. It is used to analyse data from event and application logs. It is a centralizing layer for consolidating different log inputs and outputs.

Features:

  • Flexible plugin system that allows the community to extend its usability.
  • Fluentd is written in C and Ruby and requires very little system resources.
  • Supports Unified Logging with JSON

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ELK is a stack that includes three common open source projects: Elasticsearch, Logstash, and Kibana. ELK allows you to collect logs from your applications, review and analyse these logs to create visualisations for better monitoring and troubleshooting.

Features:

  • Highly scalable and resilient
  • Encrypted communications are supported
  • Role based access control
  • Support for several integrations

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Graylog is another centralised log aggregation tool that allows real-time search of large amounts of data. It uses the Elasticsearch and MongoDB frameworks. It also functions as a repository for capturing and storing machine data. Graylog has paid plans for enterprises.

Features:

  • Extended log collection using Sidecar
  • Graphical log analysis
  • Free marketplace of extensions
  • Simple UI for administration

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Loggly is a log data processing SaaS solution. It has log tracking tools to help you monitor and analyse the logs generated from your infrastructure. Since it is a SaaS product you can start using it without installing any additional hardware or software. Loggly has freemium and paid plans.

Features:

  • Proactive monitoring: View app performance, system behavior, and unusual activity across the stack.
  • Analyze and visualize data to answer key questions, track SLA compliance, and spot trends.
  • Integrates with Slack, GitHub, Jira, Microsoft Teams, custom webhooks, and more.

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Next up, here’s some APM (Application Performance Monitoring) tools.

Opsview is a highly scalable monitoring platform that is used by enterprises. Opsview Cloud, gives its users an unified view of their organization's IT infrastructure as well as uncovering opportunities for automation. Opsview is suitable for small to medium businesses as well. Opsview is a paid tool with a free demo available.

Features:

  • Automatically find hosts, identify them and bulk configure them with ease, saving time and effort.
  • Visualize your on-premises or cloud infrastructure in your NOC with ease.
  • Encrypt database connections, communication between slave and master servers, login credentials and more
  • Configure intelligent alerts using one of many built-in notification methods.

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Zenoss offers monitoring services for IT infrastructure. It is agentless and uses a collector tool to collect system information and sends it to a central server for analysis. Zenoss captures data in real-time and places it in context. Zenoss is a paid tool.

Features:

  • Monitoring of containers
  • AI-guided anomaly detection & capacity planning
  • Root-cause isolation with Service Impact
  • Business intelligence and Log Analytics

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List of top distributed tracing tools for monitoring microservice based applications.

Wavefront (Tanzu Observability) offers insight into your cloud platforms with detailed metrics, traces, logs, and relevant analytics. It has a host of integrations to major cloud hosting and incident management platforms.

Features:

  • Get instant insights, customized for each team, with one-click analytics-driven dashboards.
  • Measure what matters most using advanced analytics-driven custom metrics.
  • Identify the root cause in seconds across any cloud, any application or any siloed tool.

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Lightstep is a product that provides visibility into complex deployments. This includes analysis of redundancies and automatic root causes analysis from collected data. It also has the ability to automatically detect changes in your infrastructure. Lightstep has paid as well as freemium versions.

Features:

  • Lightstep's correlation engine finds the cause for every effect, even across service boundaries.
  • Instantly detect everything from minor fluctuations to major deployments anywhere in your system.
  • Automatically detect the root cause of issues and resolve performance regressions immediately.

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OpenTelemetry is an open-source, vendor-neutral set of tools, APIs, SDKs with broad support for most languages and frameworks. It lets you collect telemetry data from your applications and send it to other tools for analysis.

Features:

  • Automatic instrumentation agents that can collect telemetry from some applications without requiring code changes
  • Language-specific integrations for popular web frameworks that capture relevant traces and metrics
  • OpenTelemetry Collector, which can collect data from OpenTelemetry SDKs and other sources, and then export this telemetry to any supported backend

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Next up are some time series databases.

Datastax is a time series database that is built using Apache Cassandra (No SQL). Cassandra is widely used when time series data needs to be stored. It is preferred since it allows for easy scalability.

Features:

  • DSE graph and DSE search
  • Advanced replication and analytics
  • Tiered storage and DSE multi-instance capabilities

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Warp 10 is a time series database that has its own analytics language and engine (Warpscript). It can be used to collect, store and analyse data. It is used in the aggregation and analysis of sensor data for IoT applications and others that require time sensitive data. Due to its GTS (Geo-timestamped) data, it is preferred for use in IoT.

Features:

  • WarpLib, a library dedicated to sensor data analysis with more than 1000 functions and extension capabilities
  • Standalone version can run on a Raspberry Pi as well as on a beefy server, with no external dependencies
  • Integration with Pig, Spark, Flink, NiFi, Kafka Streams and Storm for batch and streaming analysis

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Lastly here are some preferred tools used for metrics collection.

Logstash is a lightweight, open source, server-side data processing framework for storing, converting and transmitting data from a number of sources to their target destination. It ingests, converts and transmits data dynamically independent of their format or complexity. Logstash also has tight integration with Elasticsearch.

Features:

  • Seamless integration with Elasticsearch, Beats, and Kibana
  • Logstash is completely free and the source code is available freely on GitHub.
  • Highly extensible - it is easy to create additional filters for Logstash

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Kafka is an open-source distributed event dissemination platform with support for high-performance data pipelines, streaming analytics, data integration, and more. It is widely used for mission critical applications for its zero message loss capabilities. Kafka is widely used by organisations in the insurance, banking, manufacturing, and telecom industries.

Features:

  • Kafka supports deriving new data streams using the data streams from producers
  • The Kafka cluster can easily manage failures
  • Kafka uses a Distributed commit log, messages remain on disk

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You can never have enough visibility into your infrastructure. With the advent of microservices architecture the resulting observability tools must rise to the challenge of discovering and analysing dependencies.

Although this is not an exhaustive list of both the available tools and the listed features, as stated earlier, it is important to identify the kind of metrics you need to observe and understand how you can make this data more actionable before choosing an observability tool. You can also visit the respective websites to know more about each tool and how it can help you.

Regardless of the kind of platform you are running, we are sure that the tools listed here will be useful to you. On similar lines, for a more detailed look at the top monitoring tools used by DevOps/SREs, head over to this blog.

Squadcast is an incident management tool that ingests data from various monitoring sources and supports tooling in your techstack to provide actionable alerts, reduce MTTR and eliminate unplanned downtime. Try for free now or schedule a demo to explore SRE best practices in incident management with better collaboration and transparency, increasing the overall reliability of your service.

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