Jaeger vs Thanos

Overview

Jaeger

Stacks340

Followers464

Votes25

GitHub Stars22.0K

Forks2.7K

Thanos

Stacks100

Followers126

Votes0

Jaeger vs Thanos: What are the differences?

Introduction

In this analysis, we will outline the key differences between Jaeger and Thanos, two popular tools used for observability and monitoring in distributed systems.

Storage Strategy: Jaeger uses a centralized storage strategy where all traces and metrics are stored in a single backend storage system, while Thanos follows a decentralized storage strategy allowing storage across multiple backends.
Query Capabilities: Jaeger provides querying and visualization capabilities for tracing data, while Thanos focuses on long-term storage and global querying for Prometheus metrics data.
Data Retention: Jaeger typically has shorter data retention periods due to its centralized storage approach, whereas Thanos allows for longer data retention periods with its distributed storage setup.
High Availability: Thanos inherently provides high availability with its decentralized architecture and replication capabilities, while Jaeger may require additional configurations for achieving high availability.
Scalability: Thanos is designed for horizontal scalability with its ability to handle large volumes of Prometheus metrics data, while Jaeger may have limitations in scaling as it primarily focuses on trace data.
Community Adoption: Jaeger has gained significant adoption in the tracing community especially with its integration in popular frameworks, whereas Thanos is more commonly used for Prometheus metrics storage and analysis solutions.

In Summary, Jaeger and Thanos differ in their storage strategies, query capabilities, data retention, high availability, scalability, and community adoption.

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CLI (Node.js)

Manual

Detailed Comparison

Jaeger	Thanos
Jaeger, a Distributed Tracing System	Thanos is a set of components that can be composed into a highly available metric system with unlimited storage capacity. It can be added seamlessly on top of existing Prometheus deployments and leverages the Prometheus 2.0 storage format to cost-efficiently store historical metric data in any object storage while retaining fast query latencies. Additionally, it provides a global query view across all Prometheus installations and can merge data from Prometheus HA pairs on the fly.
-	Global querying view across all connected Prometheus servers; Deduplication and merging of metrics collected from Prometheus HA pairs; Seamless integration with existing Prometheus setups; Any object storage as its only, optional dependency; Downsampling historical data for massive query speedup; Cross-cluster federation; Fault-tolerant query routing; Simple gRPC "Store API" for unified data access across all metric data; Easy integration points for custom metric providers
Statistics
GitHub Stars 22.0K	GitHub Stars -
GitHub Forks 2.7K	GitHub Forks -
Stacks 340	Stacks 100
Followers 464	Followers 126
Votes 25	Votes 0
Pros & Cons
Pros 7 Open Source 7 Easy to install 6 Feature Rich UI 5 CNCF Project	No community feedback yet
Integrations
Golang Elasticsearch Cassandra	Prometheus

What are some alternatives to Jaeger, Thanos?

Grafana

Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.

Kibana

Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.

Prometheus

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Nagios

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

Netdata

Netdata collects metrics per second & presents them in low-latency dashboards. It's designed to run on all of your physical & virtual servers, cloud deployments, Kubernetes clusters & edge/IoT devices, to monitor systems, containers & apps

Zabbix

Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics.

Sensu

Sensu is the future-proof solution for multi-cloud monitoring at scale. The Sensu monitoring event pipeline empowers businesses to automate their monitoring workflows and gain deep visibility into their multi-cloud environments.

Graphite

Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand

Lumigo

Lumigo is an observability platform built for developers, unifying distributed tracing with payload data, log management, and real-time metrics to help you deeply understand and troubleshoot your systems.

StatsD

It is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP or TCP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

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Jaeger vs Thanos: What are the differences?

Introduction

In this analysis, we will outline the key differences between Jaeger and Thanos, two popular tools used for observability and monitoring in distributed systems.

Storage Strategy: Jaeger uses a centralized storage strategy where all traces and metrics are stored in a single backend storage system, while Thanos follows a decentralized storage strategy allowing storage across multiple backends.
Query Capabilities: Jaeger provides querying and visualization capabilities for tracing data, while Thanos focuses on long-term storage and global querying for Prometheus metrics data.
Data Retention: Jaeger typically has shorter data retention periods due to its centralized storage approach, whereas Thanos allows for longer data retention periods with its distributed storage setup.
High Availability: Thanos inherently provides high availability with its decentralized architecture and replication capabilities, while Jaeger may require additional configurations for achieving high availability.
Scalability: Thanos is designed for horizontal scalability with its ability to handle large volumes of Prometheus metrics data, while Jaeger may have limitations in scaling as it primarily focuses on trace data.
Community Adoption: Jaeger has gained significant adoption in the tracing community especially with its integration in popular frameworks, whereas Thanos is more commonly used for Prometheus metrics storage and analysis solutions.

In Summary, Jaeger and Thanos differ in their storage strategies, query capabilities, data retention, high availability, scalability, and community adoption.