“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 best observability tools in devops in the areas of log aggregation, APM, time series databases, distributed tracing and metrics collection tools. While this is not an indepth 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.
Top Observability Tools
First up, we look at some log aggregation tools:
Fluentd
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.
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.
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.
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.
Next up, here’s some APM (Application Performance Monitoring) tools.
Opsview
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.
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.
List of top distributed tracing tools for monitoring microservice based applications.
Wavefront
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.
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.
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
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
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
Lastly here are some preferred tools used for metrics collection.
Logstash
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
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
Sentry is a well known application monitoring or client-side performance monitoring tool that allows cross-functional visibility into the application’s health and performance. Assists software development lifecycle by notifying issues to developers with stack traces and trail of events.
Features:
Provides Full-stack Monitoring capabilities
Gives elaborate context about the errors and status of the application
Automatic capturing of unhandled expressions
Application monitoring comes with customised dashboard and query builder
Google Stackdriver, now known as Google cloud’s operations suite, is effective in monitoring, observing, improving and troubleshooting the applications and system performances on a Google cloud environment. It even has a freemium version for you to try out its functions and capabilities.
Features:
It collects various performance metrics, traces and logs across your Google cloud applications
It has built-in observability metrics on scale that helps gain visibility towards performance characteristics
It provides real-time data analytics and log management characteristics
It has got out-of- box built-in dashboard features to set up alerts, performance indicators, notification rules across your existing infrastructure
Amazon Cloudwatch is one of the prominent observability tools that provides monitoring and management services with actionable data insights for on-prem, AWS hybrid, infrastructure, application and services. It can be leveraged as a single platform that accumulates various information and data logs on all of the performance metrics.
Features:
Enables monitoring of complete stack of architecture
Container Insights helps in monitoring, alerting and troubleshooting of containerized microservices application
Collects and summarizes lambda metrics, container metrics and logs
Elastic Observability is specifically designed to provide granular insights and context about the behaviour of applications that are running in your infrastructure. It facilitates a single stack of data that contains logs, uptime data, metrics, user experience data, application traces and synthetics. Users can search, monitor and apply analytics on a real-time basis across the environment.
Features:
Synthetic monitoring provides simulation of end-user actions with rich and repeatable data
Provides data on logs, metrics, uptime, UX and APM metrics
SolarWinds AppOptics is a simple and powerful solution for APM and infrastructure monitoring applications. It enhances application performance monitoring and is cost-effective for cloud-native and hybrid IT infrastructure environments. It has got a wide range of products such as IT service management, network, systems, database and IT security management solutions.
Features:
It has Full-stack performance monitoring tools
It reduces MTTR by troubleshooting context-based issues across database and applications performance
Monitors and observe the entire system health in a single unified dashboard
Customised monitoring solution for every environment
Dynatrace is an automatic and intelligent observability tool that helps in the faster transformation to cloud infrastructure. It is designed to resolve complexities across the cloud architecture with intelligent and automatic observability in a single platform.
Features:
Intelligent observability across logs, traces, metrics, behaviour, UX, entity-relationship and vulnerability scores.
Continuous automation over configuration, discovery, performance, deployment and more
Enables AI assistance and cross-functional team collaboration
It has got user experience and business analytics features to enhance customer experience
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.
Discover the best devops observability tools for engineers & SREs. Enhance your system with top monitoring tools for logs, metrics, and traces
Table of Contents:
“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 best observability tools in devops in the areas of log aggregation, APM, time series databases, distributed tracing and metrics collection tools. While this is not an indepth 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.
Top Observability Tools
First up, we look at some log aggregation tools:
Fluentd
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.
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.
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.
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.
Next up, here’s some APM (Application Performance Monitoring) tools.
Opsview
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.
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.
List of top distributed tracing tools for monitoring microservice based applications.
Wavefront
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.
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.
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
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
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
Lastly here are some preferred tools used for metrics collection.
Logstash
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
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
Sentry is a well known application monitoring or client-side performance monitoring tool that allows cross-functional visibility into the application’s health and performance. Assists software development lifecycle by notifying issues to developers with stack traces and trail of events.
Features:
Provides Full-stack Monitoring capabilities
Gives elaborate context about the errors and status of the application
Automatic capturing of unhandled expressions
Application monitoring comes with customised dashboard and query builder
Google Stackdriver, now known as Google cloud’s operations suite, is effective in monitoring, observing, improving and troubleshooting the applications and system performances on a Google cloud environment. It even has a freemium version for you to try out its functions and capabilities.
Features:
It collects various performance metrics, traces and logs across your Google cloud applications
It has built-in observability metrics on scale that helps gain visibility towards performance characteristics
It provides real-time data analytics and log management characteristics
It has got out-of- box built-in dashboard features to set up alerts, performance indicators, notification rules across your existing infrastructure
Amazon Cloudwatch is one of the prominent observability tools that provides monitoring and management services with actionable data insights for on-prem, AWS hybrid, infrastructure, application and services. It can be leveraged as a single platform that accumulates various information and data logs on all of the performance metrics.
Features:
Enables monitoring of complete stack of architecture
Container Insights helps in monitoring, alerting and troubleshooting of containerized microservices application
Collects and summarizes lambda metrics, container metrics and logs
Elastic Observability is specifically designed to provide granular insights and context about the behaviour of applications that are running in your infrastructure. It facilitates a single stack of data that contains logs, uptime data, metrics, user experience data, application traces and synthetics. Users can search, monitor and apply analytics on a real-time basis across the environment.
Features:
Synthetic monitoring provides simulation of end-user actions with rich and repeatable data
Provides data on logs, metrics, uptime, UX and APM metrics
SolarWinds AppOptics is a simple and powerful solution for APM and infrastructure monitoring applications. It enhances application performance monitoring and is cost-effective for cloud-native and hybrid IT infrastructure environments. It has got a wide range of products such as IT service management, network, systems, database and IT security management solutions.
Features:
It has Full-stack performance monitoring tools
It reduces MTTR by troubleshooting context-based issues across database and applications performance
Monitors and observe the entire system health in a single unified dashboard
Customised monitoring solution for every environment
Dynatrace is an automatic and intelligent observability tool that helps in the faster transformation to cloud infrastructure. It is designed to resolve complexities across the cloud architecture with intelligent and automatic observability in a single platform.
Features:
Intelligent observability across logs, traces, metrics, behaviour, UX, entity-relationship and vulnerability scores.
Continuous automation over configuration, discovery, performance, deployment and more
Enables AI assistance and cross-functional team collaboration
It has got user experience and business analytics features to enhance customer experience
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.
What you should do now
Schedule a demo with Squadcast to learn about the platform, answer your questions, and evaluate if Squadcast is the right fit for you.
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