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  1. Stackups
  2. DevOps
  3. Monitoring
  4. Cloud Monitoring
  5. Amazon CloudWatch vs Logstash

Amazon CloudWatch vs Logstash

OverviewComparisonAlternatives

Overview

Amazon CloudWatch
Amazon CloudWatch
Stacks12.0K
Followers8.2K
Votes214
Logstash
Logstash
Stacks12.3K
Followers8.8K
Votes103
GitHub Stars14.7K
Forks3.5K

Amazon CloudWatch vs Logstash: What are the differences?

Introduction:

Amazon CloudWatch and Logstash are two popular tools used for monitoring and analyzing logs in a cloud environment. While both of them serve the same purpose, there are several key differences between the two.

  1. Scalability and Integration: Amazon CloudWatch is a fully managed service provided by AWS, which makes it highly scalable and seamlessly integrates with other AWS services. On the other hand, Logstash is an open-source tool that can be installed and configured on any server, providing flexibility but requiring manual setup for integration with other services.

  2. Data Collection and Transformation: CloudWatch primarily focuses on collecting and monitoring logs from AWS resources, such as EC2 instances, S3 buckets, and Lambda functions. It offers limited options for transforming the collected logs. In contrast, Logstash is capable of collecting logs from various sources, including AWS resources, and provides powerful data transformation capabilities, allowing users to enrich, filter, and parse logs before sending them to the desired destination.

  3. Alerting and Notification: CloudWatch offers advanced alerting and notification capabilities, allowing users to set up alarms based on predefined metrics and send notifications via various channels, such as email, SMS, or even triggering AWS Lambda functions. Logstash, being a log collection and transformation tool, does not provide built-in alerting and notification features. Users will need to implement additional tools or services to achieve similar functionality.

  4. Cost Structure: Amazon CloudWatch pricing is based on several factors, including the number of metrics collected, log ingestion volume, data retention, and alarms created. The cost can vary significantly depending on the usage. On the other hand, Logstash is an open-source tool, which means there is no direct cost associated with using it. However, users will need to consider server hosting costs and potentially additional storage costs if logs need to be retained for an extended period.

  5. Search and Analysis Capabilities: CloudWatch focuses on real-time monitoring and offers basic filtering and searching capabilities. It provides a web console to visualize data and perform simple analysis. Logstash, being a part of the Elastic Stack, offers advanced searching and analysis features using Elasticsearch. Users can perform complex queries, visualize data with Kibana, and leverage the power of full-text search and data analytics.

  6. Community and Support: Amazon CloudWatch is backed by AWS, which ensures reliable support and continuous improvements. It has a large user base and extensive documentation available. Logstash, being an open-source tool, has an active community that provides support through forums, blogs, and documentation. Users can also benefit from frequent updates and new features introduced by the community.

In summary, Amazon CloudWatch offers a highly scalable and integrated cloud-based monitoring solution with advanced alerting capabilities, while Logstash provides a flexible open-source log collection and transformation tool with powerful data enrichment and search functionalities. The choice between the two depends on specific requirements, integration needs, and budget considerations.

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Detailed Comparison

Amazon CloudWatch
Amazon CloudWatch
Logstash
Logstash

It helps you gain system-wide visibility into resource utilization, application performance, and operational health. It retrieve your monitoring data, view graphs to help take automated action based on the state of your cloud environment.

Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.

Basic Monitoring for Amazon EC2 instances: ten pre-selected metrics at five-minute frequency, free of charge.;Detailed Monitoring for Amazon EC2 instances: seven pre-selected metrics at one-minute frequency, for an additional charge.;Amazon EBS volumes: eight pre-selected metrics at five-minute frequency, free of charge.;Elastic Load Balancers: thirteen pre-selected metrics at one-minute frequency, free of charge.;Amazon RDS DB instances: thirteen pre-selected metrics at one-minute frequency, free of charge.;Amazon SQS queues: eight pre-selected metrics at five-minute frequency, free of charge.;Amazon SNS topics: four pre-selected metrics at five-minute frequency, free of charge.;Amazon ElastiCache nodes: twenty-nine pre-selected metrics at one-minute frequency, free of charge.;Amazon DynamoDB tables: seven pre-selected metrics at five-minute frequency, free of charge.;AWS Storage Gateways: eleven pre-selected gateway metrics and five pre-selected storage volume metrics at five-minute frequency, free of charge.;Amazon Elastic MapReduce job flows: twenty-three pre-selected metrics at five-minute frequency, free of charge.;Auto Scaling groups: seven pre-selected metrics at one-minute frequency, optional and charged at standard pricing.;Estimated charges on your AWS bill: you can also choose to enable metrics to monitor your AWS charges. The number of metrics depends on the AWS products and services that you use, and these metrics are free of charge. Learn more about this option.
Centralize data processing of all types;Normalize varying schema and formats;Quickly extend to custom log formats;Easily add plugins for custom data source
Statistics
GitHub Stars
-
GitHub Stars
14.7K
GitHub Forks
-
GitHub Forks
3.5K
Stacks
12.0K
Stacks
12.3K
Followers
8.2K
Followers
8.8K
Votes
214
Votes
103
Pros & Cons
Pros
  • 76
    Monitor aws resources
  • 46
    Zero setup
  • 30
    Detailed Monitoring
  • 23
    Backed by Amazon
  • 19
    Auto Scaling groups
Cons
  • 2
    Poor Search Capabilities
Pros
  • 69
    Free
  • 18
    Easy but powerful filtering
  • 12
    Scalable
  • 2
    Kibana provides machine learning based analytics to log
  • 1
    Well Documented
Cons
  • 4
    Memory-intensive
  • 1
    Documentation difficult to use
Integrations
No integrations available
Kibana
Kibana
Elasticsearch
Elasticsearch
Beats
Beats

What are some alternatives to Amazon CloudWatch, Logstash?

Papertrail

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Logmatic

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Loggly

Loggly

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

Logentries

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Graylog

Graylog

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

Stackdriver

Stackdriver

Google Stackdriver provides powerful monitoring, logging, and diagnostics. It equips you with insight into the health, performance, and availability of cloud-powered applications, enabling you to find and fix issues faster.

Sematext

Sematext

Sematext pulls together performance monitoring, logs, user experience and synthetic monitoring that tools organizations need to troubleshoot performance issues faster.

Fluentd

Fluentd

Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure.

Lumigo

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.

ELK

ELK

It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.

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