diff --git a/.yarn/install-state.gz b/.yarn/install-state.gz
index 10bf6d91d..cdbb10428 100644
Binary files a/.yarn/install-state.gz and b/.yarn/install-state.gz differ
diff --git a/data/comparisons/firebase-alternatives.mdx b/data/comparisons/firebase-alternatives.mdx
index a3ff82864..533375235 100644
--- a/data/comparisons/firebase-alternatives.mdx
+++ b/data/comparisons/firebase-alternatives.mdx
@@ -1,13 +1,20 @@
---
-
title: Top 7 Firebase Alternatives for App Development in 2024
slug: firebase-alternatives
-date: 2024-11-30
+date: 2024-08-10
tags: [tools-comparision]
authors: [anubhav_omar]
description: Discover the best Firebase alternatives for app development in 2024. Compare features, pros, and cons of Supabase, Back4App, AWS Amplify, and more.
-keywords: [Firebase alternatives, app development, Backend-as-a-Service, Supabase, Back4App, AWS Amplify, Kinsta]
-
+keywords:
+ [
+ Firebase alternatives,
+ app development,
+ Backend-as-a-Service,
+ Supabase,
+ Back4App,
+ AWS Amplify,
+ Kinsta,
+ ]
---
Firebase has long been a go-to Backend-as-a-Service (BaaS) platform for app developers. However, as technology evolves, so do the needs of developers. Whether itâs due to pricing, feature limitations, or the desire for more control over backend infrastructure, many developers are seeking alternatives to Firebase. This guide will walk you through the top 7 Firebase alternatives for app development in 2024, helping you make an informed decision for your next project.
@@ -33,7 +40,11 @@ These factors drive many developers to seek alternatives that offer more control
### 1. Supabase: The Open Source Powerhouse
-
+
Supabase is an open-source alternative to Firebase that provides a real-time database, authentication, and serverless functions. Built on PostgreSQL, it offers a powerful and scalable solution for app developers.
@@ -63,7 +74,11 @@ Comparison with Firebase
### 2. Back4App: The Comprehensive Solution
-
+
Back4App is a backend-as-a-service (BaaS) platform that uses Parse Server, the open-source version of the Parse backend. It simplifies backend development and management, providing developers with a robust platform for building modern applications.
@@ -93,7 +108,11 @@ Comparison with Firebase
### 3. AWS Amplify: The Cloud Giant's Offering
-
+
Amazon Web Services (AWS) Amplify is a comprehensive development platform designed to provide the tools necessary to build scalable mobile and web applications. AWS Amplify offers a suite of services that mirror many of the core functionalities of Firebase, such as authentication, analytics, and storage, while leveraging the robust infrastructure of AWS.
@@ -123,7 +142,11 @@ Comparison with Firebase
### 4. Kinsta: The WordPress-Friendly Option
-
+
Kinsta is a managed cloud hosting provider that focuses on performance and ease of use. While it is primarily known for WordPress hosting, Kinsta's platform can also be used for hosting modern web applications.
@@ -153,7 +176,11 @@ Comparison with Firebase
### 5. Parse: The Community-Driven Platform
-
+
Parse is an open-source backend framework initially developed by Facebook. It offers a robust set of features for backend development, including a database, push notifications, and authentication.
@@ -183,7 +210,11 @@ Comparison with Firebase
### 6. Kuzzle: The IoT-Ready Alternative
-
+
Kuzzle is an open-source backend solution designed to support high-performance, scalable applications. It offers real-time capabilities, data storage, and advanced search functionalities.
@@ -213,7 +244,11 @@ Comparison with Firebase
### 7. Appwrite: The Developer-Centric Choice
-
+
Appwrite is an open-source backend-as-a-service platform that provides a comprehensive set of tools and APIs to help developers build modern applications. It focuses on simplicity and developer experience.
@@ -246,182 +281,179 @@ Comparison with Firebase
### đ Top Firebase Alternatives for Real-time Databases
1. Supabase: Open-source Firebase Alternative
-
- Supabase is an open-source Firebase alternative that's gaining traction. It offers:
-
- - PostgreSQL database with real-time capabilities
- - Built-in authentication and authorization
- - Storage solutions for large files
- - Serverless functions (in beta)
-
- Supabase shines in its open-source nature, allowing for greater customization and community-driven development.
-
+
+ Supabase is an open-source Firebase alternative that's gaining traction. It offers:
+
+ - PostgreSQL database with real-time capabilities
+ - Built-in authentication and authorization
+ - Storage solutions for large files
+ - Serverless functions (in beta)
+
+ Supabase shines in its open-source nature, allowing for greater customization and community-driven development.
+
2. MongoDB Realm: Scalable Solution for Mobile and Web Apps
-
- MongoDB Realm provides a robust alternative to Firebase, especially for apps requiring a flexible data model. Key features include:
-
- - Real-time sync across devices
- - Offline-first architecture
- - Built-in user authentication
- - Server-side logic with Realm Functions
-
- MongoDB Realm excels in scenarios where you need a more powerful query language and better support for complex data structures.
-
+
+ MongoDB Realm provides a robust alternative to Firebase, especially for apps requiring a flexible data model. Key features include:
+
+ - Real-time sync across devices
+ - Offline-first architecture
+ - Built-in user authentication
+ - Server-side logic with Realm Functions
+
+ MongoDB Realm excels in scenarios where you need a more powerful query language and better support for complex data structures.
+
3. AWS Amplify: Comprehensive Backend Solution from Amazon
-
- AWS Amplify offers a set of tools and services for building scalable mobile and web applications. It provides:
-
- - GraphQL and REST APIs
- - Authentication and authorization
- - Serverless functions
- - Analytics and monitoring
-
- AWS Amplify is particularly useful if you're already using other AWS services or need enterprise-grade scalability.
-
+
+ AWS Amplify offers a set of tools and services for building scalable mobile and web applications. It provides:
+
+ - GraphQL and REST APIs
+ - Authentication and authorization
+ - Serverless functions
+ - Analytics and monitoring
+
+ AWS Amplify is particularly useful if you're already using other AWS services or need enterprise-grade scalability.
+
4. Appwrite: Self-hosted Backend Server
-
- Appwrite is a self-hosted backend server that you can run on your own infrastructure. It offers:
-
- - Database management
- - User authentication
- - Storage solutions
- - Cloud functions
- - Realtime APIs
-
- Appwrite is ideal for developers who want complete control over their backend infrastructure while still benefiting from a Firebase-like experience.
-
- Comparing Real-time Database Alternatives
-
- When evaluating Firebase alternatives for real-time databases, consider these factors:
-
- 1. Performance:
- - Supabase: Excellent for small to medium-sized applications
- - MongoDB Realm: Scales well for large, complex datasets
- - AWS Amplify: Enterprise-grade performance with proper configuration
- - Appwrite: Performance depends on your hosting setup
- 2. Pricing:
- - Supabase: Free tier available, pay-as-you-go model
- - MongoDB Realm: Free tier, then usage-based pricing
- - AWS Amplify: Pay-per-use model, can be cost-effective with proper optimization
- - Appwrite: Free (self-hosted), costs depend on your infrastructure
- 3. Ease of Integration:
- - Supabase: Excellent documentation and SDKs for popular frameworks
- - MongoDB Realm: Good integration with mobile platforms, some learning curve for web
- - AWS Amplify: Comprehensive SDKs, but can be complex for beginners
- - Appwrite: Easy integration with various platforms and frameworks
- 4. Community Support and Documentation:
- - Supabase: Growing community, excellent documentation
- - MongoDB Realm: Large community, extensive documentation
- - AWS Amplify: Vast ecosystem, comprehensive but sometimes overwhelming docs
- - Appwrite: Active community, clear and concise documentation
+
+ Appwrite is a self-hosted backend server that you can run on your own infrastructure. It offers:
+
+ - Database management
+ - User authentication
+ - Storage solutions
+ - Cloud functions
+ - Realtime APIs
+
+ Appwrite is ideal for developers who want complete control over their backend infrastructure while still benefiting from a Firebase-like experience.
+
+ Comparing Real-time Database Alternatives
+
+ When evaluating Firebase alternatives for real-time databases, consider these factors:
+
+ 1. Performance:
+ - Supabase: Excellent for small to medium-sized applications
+ - MongoDB Realm: Scales well for large, complex datasets
+ - AWS Amplify: Enterprise-grade performance with proper configuration
+ - Appwrite: Performance depends on your hosting setup
+ 2. Pricing:
+ - Supabase: Free tier available, pay-as-you-go model
+ - MongoDB Realm: Free tier, then usage-based pricing
+ - AWS Amplify: Pay-per-use model, can be cost-effective with proper optimization
+ - Appwrite: Free (self-hosted), costs depend on your infrastructure
+ 3. Ease of Integration:
+ - Supabase: Excellent documentation and SDKs for popular frameworks
+ - MongoDB Realm: Good integration with mobile platforms, some learning curve for web
+ - AWS Amplify: Comprehensive SDKs, but can be complex for beginners
+ - Appwrite: Easy integration with various platforms and frameworks
+ 4. Community Support and Documentation:
+ - Supabase: Growing community, excellent documentation
+ - MongoDB Realm: Large community, extensive documentation
+ - AWS Amplify: Vast ecosystem, comprehensive but sometimes overwhelming docs
+ - Appwrite: Active community, clear and concise documentation
### đ Authentication Alternatives to Firebase
1. Auth0: Flexible, Feature-rich Authentication Service
-
- Auth0 is a robust authentication and authorization platform that offers:
-
- - Support for various identity providers
- - Customizable login flows
- - Multi-factor authentication
- - Extensive documentation and SDKs
-
- Auth0 is an excellent choice for projects requiring advanced authentication features and enterprise-grade security.
-
+
+ Auth0 is a robust authentication and authorization platform that offers:
+
+ - Support for various identity providers
+ - Customizable login flows
+ - Multi-factor authentication
+ - Extensive documentation and SDKs
+
+ Auth0 is an excellent choice for projects requiring advanced authentication features and enterprise-grade security.
+
2. Okta: Enterprise-grade Identity Management
-
- Okta provides a comprehensive identity management solution with:
-
- - Single sign-on (SSO) capabilities
- - Adaptive multi-factor authentication
- - API access management
- - User lifecycle management
-
- Okta is ideal for large-scale applications and enterprises with complex identity requirements.
-
+
+ Okta provides a comprehensive identity management solution with:
+
+ - Single sign-on (SSO) capabilities
+ - Adaptive multi-factor authentication
+ - API access management
+ - User lifecycle management
+
+ Okta is ideal for large-scale applications and enterprises with complex identity requirements.
+
3. Supabase Auth: Built-in Auth for Supabase Users
-
- If you're already using Supabase for your database needs, Supabase Auth offers:
-
- - Email and password authentication
- - Social login providers
- - JWT tokens for session management
- - Row-level security in the database
-
- Supabase Auth is a great option for developers looking for a simple, integrated authentication solution.
-
+
+ If you're already using Supabase for your database needs, Supabase Auth offers:
+
+ - Email and password authentication
+ - Social login providers
+ - JWT tokens for session management
+ - Row-level security in the database
+
+ Supabase Auth is a great option for developers looking for a simple, integrated authentication solution.
### âď¸Â Cloud Functions and Serverless Alternatives
1. AWS Lambda: Serverless Computing Pioneer
-
- AWS Lambda allows you to run code without provisioning or managing servers. It offers:
-
- - Support for multiple programming languages
- - Automatic scaling
- - Pay-per-execution pricing model
- - Integration with other AWS services
-
- AWS Lambda is suitable for applications with varying workloads and those already using the AWS ecosystem.
-
+
+ AWS Lambda allows you to run code without provisioning or managing servers. It offers:
+
+ - Support for multiple programming languages
+ - Automatic scaling
+ - Pay-per-execution pricing model
+ - Integration with other AWS services
+
+ AWS Lambda is suitable for applications with varying workloads and those already using the AWS ecosystem.
+
2. Google Cloud Functions: Direct Firebase Alternative
-
- Google Cloud Functions provides a serverless execution environment that's closely related to Firebase Functions. It features:
-
- - Support for Node.js, Python, and Go
- - Automatic scaling and load balancing
- - Integration with Google Cloud services
- - Pay-per-use pricing
-
- Google Cloud Functions is a natural choice for those looking to migrate from Firebase while staying within the Google Cloud ecosystem.
-
+
+ Google Cloud Functions provides a serverless execution environment that's closely related to Firebase Functions. It features:
+
+ - Support for Node.js, Python, and Go
+ - Automatic scaling and load balancing
+ - Integration with Google Cloud services
+ - Pay-per-use pricing
+
+ Google Cloud Functions is a natural choice for those looking to migrate from Firebase while staying within the Google Cloud ecosystem.
+
3. Azure Functions: Microsoft's Serverless Offering
-
- Azure Functions is Microsoft's answer to serverless computing, providing:
-
- - Support for multiple languages including C#, JavaScript, and Python
- - Integration with Azure and third-party services
- - Flexible scaling options
- - Comprehensive monitoring and logging
-
- Azure Functions is particularly useful for developers working with .NET technologies or those already using Azure services.
-
+
+ Azure Functions is Microsoft's answer to serverless computing, providing:
+
+ - Support for multiple languages including C#, JavaScript, and Python
+ - Integration with Azure and third-party services
+ - Flexible scaling options
+ - Comprehensive monitoring and logging
+
+ Azure Functions is particularly useful for developers working with .NET technologies or those already using Azure services.
### đž Storage Solutions to Replace Firebase Storage
1. Amazon S3: Industry-standard Object Storage
-
- Amazon S3 (Simple Storage Service) is a highly scalable object storage service. It offers:
-
- - Virtually unlimited storage capacity
- - High durability and availability
- - Fine-grained access controls
- - Integration with other AWS services
-
- Amazon S3 is suitable for applications of any size requiring reliable and scalable storage.
-
+
+ Amazon S3 (Simple Storage Service) is a highly scalable object storage service. It offers:
+
+ - Virtually unlimited storage capacity
+ - High durability and availability
+ - Fine-grained access controls
+ - Integration with other AWS services
+
+ Amazon S3 is suitable for applications of any size requiring reliable and scalable storage.
+
2. DigitalOcean Spaces: Simple, Scalable Storage Option
-
- DigitalOcean Spaces provides S3-compatible object storage with:
-
- - Simple pricing model
- - Built-in CDN for faster content delivery
- - Easy-to-use web interface
- - API compatibility with S3
-
- DigitalOcean Spaces is a good choice for developers looking for a straightforward, cost-effective storage solution.
-
+
+ DigitalOcean Spaces provides S3-compatible object storage with:
+
+ - Simple pricing model
+ - Built-in CDN for faster content delivery
+ - Easy-to-use web interface
+ - API compatibility with S3
+
+ DigitalOcean Spaces is a good choice for developers looking for a straightforward, cost-effective storage solution.
+
3. Cloudinary: Specialized Media Asset Management
-
- Cloudinary focuses on media asset management, offering:
-
- - Automatic image and video optimization
- - On-the-fly transformations
- - Content delivery network (CDN)
- - AI-powered tagging and categorization
-
- Cloudinary is ideal for applications that heavily rely on image and video content.
-
+
+ Cloudinary focuses on media asset management, offering:
+
+ - Automatic image and video optimization
+ - On-the-fly transformations
+ - Content delivery network (CDN)
+ - AI-powered tagging and categorization
+
+ Cloudinary is ideal for applications that heavily rely on image and video content.
## How to Choose the Right Firebase Alternative
@@ -534,38 +566,38 @@ Firebase's NoSQL databases, Firestore and Realtime Database, are great for simpl
### Step-by-Step Guide on Preparing for Migration
1. Data Export:
- - Backup Your Data: Begin by creating backups of all your data stored in Firebase. This ensures you have a safe copy in case anything goes wrong during the migration.
- - Export Data: Use Firebase's data export tools to download your datasets. This can often be done through the Firebase console or via Firebase CLI commands.
+ - Backup Your Data: Begin by creating backups of all your data stored in Firebase. This ensures you have a safe copy in case anything goes wrong during the migration.
+ - Export Data: Use Firebase's data export tools to download your datasets. This can often be done through the Firebase console or via Firebase CLI commands.
2. Service Mapping:
- - Identify Services: List all the Firebase services your app currently uses, such as Firestore, Realtime Database, Authentication, and Cloud Functions.
- - Match Services: Find equivalent services in your chosen alternative. For example, if migrating to Back4App, map Firestore to Back4App's real-time database and Firebase Authentication to Back4App's user management features.
+ - Identify Services: List all the Firebase services your app currently uses, such as Firestore, Realtime Database, Authentication, and Cloud Functions.
+ - Match Services: Find equivalent services in your chosen alternative. For example, if migrating to Back4App, map Firestore to Back4App's real-time database and Firebase Authentication to Back4App's user management features.
3. Testing:
- - Set Up a Staging Environment: Before making any changes to your live app, create a staging environment. This allows you to test the new setup without affecting your users.
- - Run Tests: Execute comprehensive tests to ensure data integrity and functionality. This includes testing data queries, user authentication, and other critical features.
+ - Set Up a Staging Environment: Before making any changes to your live app, create a staging environment. This allows you to test the new setup without affecting your users.
+ - Run Tests: Execute comprehensive tests to ensure data integrity and functionality. This includes testing data queries, user authentication, and other critical features.
### Tools and Resources to Assist in the Migration Process
1. Data Migration Tools:
- - Firebase CLI: Use Firebase CLI for exporting data and configurations.
- - Custom Scripts: Write custom scripts to automate data transfer to the new platform. Languages like Python and Node.js are commonly used for such tasks.
+ - Firebase CLI: Use Firebase CLI for exporting data and configurations.
+ - Custom Scripts: Write custom scripts to automate data transfer to the new platform. Languages like Python and Node.js are commonly used for such tasks.
2. Documentation and Guides:
- - Official Docs: Refer to the official documentation of your chosen platform. For instance, Back4App and Parse have extensive guides on migrating from Firebase.
- - Community Forums: Engage with community forums and support channels. Platforms like Stack Overflow and GitHub Discussions can provide valuable insights and troubleshooting tips.
+ - Official Docs: Refer to the official documentation of your chosen platform. For instance, Back4App and Parse have extensive guides on migrating from Firebase.
+ - Community Forums: Engage with community forums and support channels. Platforms like Stack Overflow and GitHub Discussions can provide valuable insights and troubleshooting tips.
3. Third-Party Services:
- - Database Migration Services: Companies like AWS Database Migration Service can help automate and manage the data migration process.
- - Managed Services: Consider using managed services that specialize in migrations to ensure a smooth transition.
+ - Database Migration Services: Companies like AWS Database Migration Service can help automate and manage the data migration process.
+ - Managed Services: Consider using managed services that specialize in migrations to ensure a smooth transition.
### Best Practices for Minimizing Downtime and Ensuring Data Integrity
1. Incremental Migration:
- - Phased Approach: Migrate data and services incrementally rather than all at once. This approach reduces the risk of downtime and makes it easier to identify and fix issues.
- - Dual Running: Run both Firebase and the new platform in parallel during the transition phase. This ensures that you can switch back to Firebase if any critical issues arise.
+ - Phased Approach: Migrate data and services incrementally rather than all at once. This approach reduces the risk of downtime and makes it easier to identify and fix issues.
+ - Dual Running: Run both Firebase and the new platform in parallel during the transition phase. This ensures that you can switch back to Firebase if any critical issues arise.
2. Data Validation:
- - Consistency Checks: Regularly validate data integrity during migration. Ensure that the data in the new platform matches the data in Firebase.
- - Automated Testing: Implement automated tests to continuously check data consistency and application functionality.
+ - Consistency Checks: Regularly validate data integrity during migration. Ensure that the data in the new platform matches the data in Firebase.
+ - Automated Testing: Implement automated tests to continuously check data consistency and application functionality.
3. Communication Plan:
- - User Notifications: Inform your users about the migration and any potential downtime. Clear communication helps manage user expectations and reduces frustration.
- - Support Channels: Provide dedicated support channels to address user concerns promptly during the migration period.
+ - User Notifications: Inform your users about the migration and any potential downtime. Clear communication helps manage user expectations and reduces frustration.
+ - Support Channels: Provide dedicated support channels to address user concerns promptly during the migration period.
## What Are the Key Features to Look for in a Firebase Alternative?
@@ -574,47 +606,47 @@ Firebase's NoSQL databases, Firestore and Realtime Database, are great for simpl
Real-time data handling is crucial for many modern applications, especially those requiring instant updates like chat apps, live dashboards, and collaborative tools. It's important to consider:
1. Real-Time Data Synchronization:
- - Instant Updates: Look for platforms that offer real-time databases where changes are instantly reflected across all clients.
- - Data Consistency: Ensure the platform maintains data consistency across all devices, even during network disruptions.
+ - Instant Updates: Look for platforms that offer real-time databases where changes are instantly reflected across all clients.
+ - Data Consistency: Ensure the platform maintains data consistency across all devices, even during network disruptions.
2. Offline Capabilities:
- - Local Caching: Check if the platform supports local data caching so users can access and interact with data even when offline.
- - Automatic Sync: Ensure that the platform can automatically synchronize data once the device reconnects to the internet.
+ - Local Caching: Check if the platform supports local data caching so users can access and interact with data even when offline.
+ - Automatic Sync: Ensure that the platform can automatically synchronize data once the device reconnects to the internet.
### Authentication and User Management Features Essential for Modern Apps
User authentication and management are foundational elements for any app. These features must be robust and secure to protect user data and provide a seamless user experience.
1. Authentication Methods:
- - Multiple Auth Providers: Opt for platforms supporting various authentication methods, including email/password, social logins (like Google, Facebook), and phone number authentication.
- - Custom Authentication: Ensure the platform allows for custom authentication methods to meet specific security requirements.
+ - Multiple Auth Providers: Opt for platforms supporting various authentication methods, including email/password, social logins (like Google, Facebook), and phone number authentication.
+ - Custom Authentication: Ensure the platform allows for custom authentication methods to meet specific security requirements.
2. User Management:
- - User Roles and Permissions: Look for features that let you manage user roles and permissions effectively. This is crucial for apps with different user levels, such as admin and regular user roles.
- - Profile Management: Ensure the platform provides easy ways to manage user profiles and handle tasks like password resets and email verifications.
+ - User Roles and Permissions: Look for features that let you manage user roles and permissions effectively. This is crucial for apps with different user levels, such as admin and regular user roles.
+ - Profile Management: Ensure the platform provides easy ways to manage user profiles and handle tasks like password resets and email verifications.
### Scalability and Performance Metrics That Ensure Long-Term Viability
For your app to grow and handle increasing traffic and data loads, scalability and performance are key factors.
1. Horizontal and Vertical Scaling:
- - Horizontal Scaling: Verify that the platform supports horizontal scaling, allowing you to add more servers to handle increased load.
- - Vertical Scaling: Check if the platform allows vertical scaling, enabling you to increase the capacity of existing servers.
+ - Horizontal Scaling: Verify that the platform supports horizontal scaling, allowing you to add more servers to handle increased load.
+ - Vertical Scaling: Check if the platform allows vertical scaling, enabling you to increase the capacity of existing servers.
2. Performance Metrics:
- - Latency: Look for low latency data operations. High latency can negatively impact user experience, especially in real-time applications.
- - Throughput: Evaluate the platform's throughput capabilities to ensure it can handle a large number of transactions per second.
+ - Latency: Look for low latency data operations. High latency can negatively impact user experience, especially in real-time applications.
+ - Throughput: Evaluate the platform's throughput capabilities to ensure it can handle a large number of transactions per second.
### Integration Capabilities with Other Tools and Services
Integration with other tools and services enhances your app's functionality and streamlines development processes.
1. APIs and SDKs:
- - Comprehensive APIs: Ensure the platform offers comprehensive APIs that allow you to interact with its services programmatically.
- - Language Support: Check if the platform provides SDKs for the programming languages and frameworks you use, such as JavaScript, Python, or Swift.
+ - Comprehensive APIs: Ensure the platform offers comprehensive APIs that allow you to interact with its services programmatically.
+ - Language Support: Check if the platform provides SDKs for the programming languages and frameworks you use, such as JavaScript, Python, or Swift.
2. Third-Party Integrations:
- - Analytics and Monitoring: Look for platforms that easily integrate with analytics and monitoring tools like Google Analytics, New Relic, or Datadog.
- - Payment Gateways: Ensure compatibility with payment gateways if your app requires payment processing. Popular options include Stripe, PayPal, and Square.
+ - Analytics and Monitoring: Look for platforms that easily integrate with analytics and monitoring tools like Google Analytics, New Relic, or Datadog.
+ - Payment Gateways: Ensure compatibility with payment gateways if your app requires payment processing. Popular options include Stripe, PayPal, and Square.
3. DevOps and CI/CD:
- - CI/CD Integration: Verify that the platform can integrate with your Continuous Integration and Continuous Deployment (CI/CD) pipelines, using tools like Jenkins, GitHub Actions, or CircleCI.
- - DevOps Tools: Check for compatibility with DevOps tools and practices, including Docker, Kubernetes, and Terraform.
+ - CI/CD Integration: Verify that the platform can integrate with your Continuous Integration and Continuous Deployment (CI/CD) pipelines, using tools like Jenkins, GitHub Actions, or CircleCI.
+ - DevOps Tools: Check for compatibility with DevOps tools and practices, including Docker, Kubernetes, and Terraform.
Selecting the right Firebase alternative involves evaluating these key features to ensure your app remains robust, scalable, and user-friendly. Each of these features plays a critical role in the overall success and sustainability of your application.
@@ -659,4 +691,4 @@ For real-time capabilities:
- Parse and Appwrite also offer strong real-time features.
- The best choice depends on your specific use case and scalability requirements.
-##
\ No newline at end of file
+##
diff --git a/data/guides/golang-logrus.mdx b/data/guides/golang-logrus.mdx
index 27f98dbe7..691abbe96 100644
--- a/data/guides/golang-logrus.mdx
+++ b/data/guides/golang-logrus.mdx
@@ -1,187 +1,202 @@
---
-
title: Golang Logrus - Complete Guide to logging in Golang with Logrus
slug: golang-logrus
date: 2024-07-11
tags: [logging]
authors: [aayush_sharma]
description: In this tutorial, we will discover Golang logrus and some of the best practices provided by logrus for implementing structured logging in Golang applications. We will set up the Golang application and practice different logging formats logrus provides.
-keywords: [logrus, logging, logging logrus, golang, logrus golang, opentelemetry golang, opentelemetry logrus]
-
+keywords:
+ [
+ logrus,
+ logging,
+ logging logrus,
+ golang,
+ logrus golang,
+ opentelemetry golang,
+ opentelemetry logrus,
+ ]
---
-
-Logging is the record of events that occur in an application, providing critical insights into its behavior, performance, and potential problems. In Golang, the built-in log package helps monitor and debug applications.
+Logging is the record of events that occur in an application, providing critical insights into its behavior, performance, and potential problems. In Golang, the built-in log package helps monitor and debug applications.
However, for more advanced logging needs, Golang offers logrus, a powerful package designed for structured logging. This guide will explore the features and best practices of using logrus for implementing structured logging in Golang applications.
## Prerequisites
-- Golang Installed in your system
+-
+ Golang Installed in your system
+
- Familiarity with [Logging in Golang](https://signoz.io/guides/golang-log/)
## Quick Guide: How to use logrus in Golang?
### What is logrus?
-`logrus` is a popular organized logger designed for `Golang`. It is renowned for being compatible with the common library logger and having an easy-to-use API. The built-in log package in Go is expanded by Logrus to offer structured logging features.
+`logrus` is a popular organized logger designed for `Golang`. It is renowned for being compatible with the common library logger and having an easy-to-use API. The built-in log package in Go is expanded by Logrus to offer structured logging features.
-Compared to plain text logs, structured logging logs are easier to interpret programmatically because of their format. An overview of logrus's features and the reasons developers like it over alternative logging frameworks are covered in the later sections of this guide.
+Compared to plain text logs, structured logging logs are easier to interpret programmatically because of their format. An overview of logrus's features and the reasons developers like it over alternative logging frameworks are covered in the later sections of this guide.
-The current version of logrus is in maintenance mode, but security updates and bug fixes are still being implemented to prevent changes from breaking in future releases.
+The current version of logrus is in maintenance mode, but security updates and bug fixes are still being implemented to prevent changes from breaking in future releases.
### Importing logrus
@@ -204,7 +219,7 @@ package main
import (
"github.com/sirupsen/logrus"
- // other imports
+ // other imports
)
func main() {
@@ -212,9 +227,9 @@ func main() {
}
```
-To implement the logging convention and decrease confusion for the reviewers, logrus package is aliased as `log` from the logrus package as shown in the below sample of code.
+To implement the logging convention and decrease confusion for the reviewers, logrus package is aliased as `log` from the logrus package as shown in the below sample of code.
-Note that it's completely api-compatible with the stdlib logger, so you can replace your `log` imports everywhere with `log "github.com/sirupsen/logrus"` and you'll now have the flexibility of Logrus.
+Note that it's completely api-compatible with the stdlib logger, so you can replace your `log` imports everywhere with `log "github.com/sirupsen/logrus"` and you'll now have the flexibility of Logrus.
You can now use the logrus library within the log namespace:
@@ -223,7 +238,7 @@ package main
import (
log "github.com/sirupsen/logrus"
- // other imports
+ // other imports
)
func main() {
@@ -267,9 +282,13 @@ func main() {
Output
-
+
-The default logger for logrus prints the logs statement with color coding.
+The default logger for logrus prints the logs statement with color coding.
### What is Structured Logging?
@@ -287,16 +306,15 @@ Example of structured logging
```json
{
- "time":"2024-06-25T03:02:16.291247+05:30",
- "level":"INFO",
- "msg":"logrus Groups",
- "Usage":{
- "Item1":"random string",
- "Item2":453666601985459141,
- "Item3":7914595825068479671
- }
+ "time": "2024-06-25T03:02:16.291247+05:30",
+ "level": "INFO",
+ "msg": "logrus Groups",
+ "Usage": {
+ "Item1": "random string",
+ "Item2": 453666601985459141,
+ "Item3": 7914595825068479671
+ }
}
-
```
The above-structured logging output, when converted into text-based logging, looks like the example below
@@ -312,33 +330,19 @@ Let's dive deep into the logrus libraries and explore the advanced features of `
In logrus, levels allow developers to classify the importance of the log messages and control what gets logged based on the environment (e.g., development, testing, production). It has seven logging levels:
- Trace: This level is used to log very detailed information, finer-grained than Debug. Generally not enabled in production because of the verbose output, which may be costly in terms of performance and storage.
-
- Use Case: Trace code and explain behavior, frequently with step-by-step execution.
-
+ Use Case: Trace code and explain behavior, frequently with step-by-step execution.
- Debug: This level is used for detailed information, primarily useful for diagnosing problems. Debug logging is usually enabled only in development settings.
-
- Use Case: Diagnosing a complex issue by logging detailed information about the application's state.
-
+ Use Case: Diagnosing a complex issue by logging detailed information about the application's state.
- Info: Provides general information about the application's operations, offering insights into its normal functioning.
-
- Use Case: Logging the successful completion of an operation or regular status updates.
-
+ Use Case: Logging the successful completion of an operation or regular status updates.
- Warn: Indicates that something unexpected happened or there might be a potential issue (e.g., 'disk space low'). The application continues to work as expected.
-
- Use Case: Alerting about deprecated API usage or low disk space warnings.
-
+ Use Case: Alerting about deprecated API usage or low disk space warnings.
- Error: Represents serious issues where the application has encountered a problem and cannot perform some functions.
-
- Use Case: Logging errors that prevent a function from executing properly, such as database connection failures.
-
+ Use Case: Logging errors that prevent a function from executing properly, such as database connection failures.
- Fatal: This level logs a message, then calls `os.Exit(1)`. Suitable for errors that require immediate program termination.Suitable for errors that require immediate program termination.
-
- Use Case: It is useful for severe errors that prevent further execution of the application in a safe state.
-
+ Use Case: It is useful for severe errors that prevent further execution of the application in a safe state.
- Panic: This level has the highest level of severity, it logs a message and then panics.
-
- Use Case: Often used for unrecoverable errors where the application cannot recover without losing data or state significantly.
-
+ Use Case: Often used for unrecoverable errors where the application cannot recover without losing data or state significantly.
Example code for the above levels
@@ -363,8 +367,11 @@ func main() {
Output
-
-
+
In the above code you might have come across a line - `log.SetLevel(log.TraceLevel)`, it is the `SetLevel` function that is used to set the logger level in logrus. It logs entries with that severity or anything above it. By default, it is set to `InfoLevel`.
@@ -490,20 +497,16 @@ Here are the key reasons why monitoring logs is important:
7. Proactive maintenance
8. Support and customer service
-To cover all the above major components, you can make use of tools like [SigNoz](https://signoz.io/).
+To cover all the above major components, you can make use of tools like [SigNoz](https://signoz.io/).
[SigNoz](https://signoz.io/)Â is a full-stack open-source application performance monitoring and observability tool that can be used in place of DataDog and New Relic. SigNoz is built to give SaaS like user experience combined with the perks of open-source software. Developer tools should be developed first, and SigNoz was built by developers to address the gap between SaaS vendors and open-source software.
Key architecture features:
- Logs, Metrics, and traces under a single dashboard
-
- SigNoz provides logs, metrics, and traces all under a single dashboard. You can also correlate these telemetry signals to debug your application issues quickly.
-
+ SigNoz provides logs, metrics, and traces all under a single dashboard. You can also correlate these telemetry signals to debug your application issues quickly.
- Native OpenTelemetry support
-
- SigNoz is built to support OpenTelemetry natively, which is quietly becoming the world standard to generate and manage telemetry data.
-
+ SigNoz is built to support OpenTelemetry natively, which is quietly becoming the world standard to generate and manage telemetry data.
## Tracking Logs Using Signoz
@@ -647,7 +650,7 @@ receivers:
- targets:
# - localhost:8888
filelog/app:
- include: [ ] #include the full path to your log file
+ include: [] #include the full path to your log file
start_at: end
processors:
batch:
@@ -665,11 +668,11 @@ extensions:
zpages: {}
exporters:
otlp:
- endpoint: ".{region}.signoz.cloud:443"
+ endpoint: '.{region}.signoz.cloud:443'
tls:
insecure: false
headers:
- "signoz-access-token": ""
+ 'signoz-access-token': ''
logging:
verbosity: normal
service:
@@ -688,7 +691,11 @@ service:
After running the above application and making the correct configurations, you can navigate to the SigNoz logs dashboard to see all the logs sent to SigNoz.
-
+
SigNoz localhost showcasing the log output of the above application.
@@ -725,15 +732,14 @@ Sensitive information may occasionally be accidentally included in logs, raising
- Use Logrus Hooks for Sensitive Data: To sanitize or encrypt log entries before storage or transmission, use Logrus Hooks if sensitive data has to be recorded.
- Safe Log Storage: Make sure that log files and log data are kept safely. If logs are kept in a public cloud or a distributed system, use encryption to protect them.
-## Other Logging Libraries vs Loguru
-
-| Libraries | Advantages | Disadvantages |
-| --- | --- | --- |
-| **Logrus** | - **Structured Logging:** Supports logging in a JSON format, making it easier for machines to read and process logs. - **Extensibility:** Offers flexibility with custom formatters, levels, and hooks that integrate with various external systems. - **Contextual Information:** Easy to add contextual information to logs with fields, improving diagnosability of log entries. | - **Performance:** Not the fastest due to its use of reflection and more abstract design. - **API Stability:** Has had breaking changes in the past, though it has stabilized now. |
-| **Zap** | - **High Performance:** Avoids reflection and uses a zero-allocation philosophy, logging with minimal impact on application performance. - **Structured Logging:** Encodes logs in JSON and provides APIs for adding structured context. | - **Complex API:** More complex to use compared to Logrus, especially when customizing logger behavior. - **Less Customizable:** Doesnât offer as much flexibility in log formatting compared to Logrus. |
-| **Zerolog** | - **Performance:** Designed for low-latency applications with zero allocations in logging calls, making it extremely fast. - **JSON-First:** Logs are structured as JSON by default, ideal for modern log management solutions. | - **Simplicity vs. Flexibility:** Focuses on performance and simplicity, which might limit customization options compared to Logrus. |
-| **Slog** | - **Structured and Contextual Logging:** Supports extensive contextual information logging. - **Scalability and Concurrency:** Suitable for logging from multiple goroutines without losing performance. - **Filters and Handlers:** Offers detailed control over log processing with flexible handlers and filters. | - **Less Popular:** Not as widely used or well-known as Logrus, Zap, or Zerolog, leading to fewer community resources and third-party integrations. - **Complexity:** API can be more complex due to its flexible and powerful filtering and handler mechanisms, leading to a steeper learning curve. |
+## Other Logging Libraries vs Logrus
+| Libraries | Advantages | Disadvantages |
+| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Logrus** | - **Structured Logging:** Supports logging in a JSON format, making it easier for machines to read and process logs. - **Extensibility:** Offers flexibility with custom formatters, levels, and hooks that integrate with various external systems. - **Contextual Information:** Easy to add contextual information to logs with fields, improving diagnosability of log entries. | - **Performance:** Not the fastest due to its use of reflection and more abstract design. - **API Stability:** Has had breaking changes in the past, though it has stabilized now. |
+| **Zap** | - **High Performance:** Avoids reflection and uses a zero-allocation philosophy, logging with minimal impact on application performance. - **Structured Logging:** Encodes logs in JSON and provides APIs for adding structured context. | - **Complex API:** More complex to use compared to Logrus, especially when customizing logger behavior. - **Less Customizable:** Doesnât offer as much flexibility in log formatting compared to Logrus. |
+| **Zerolog** | - **Performance:** Designed for low-latency applications with zero allocations in logging calls, making it extremely fast. - **JSON-First:** Logs are structured as JSON by default, ideal for modern log management solutions. | - **Simplicity vs. Flexibility:** Focuses on performance and simplicity, which might limit customization options compared to Logrus. |
+| **Slog** | - **Structured and Contextual Logging:** Supports extensive contextual information logging. - **Scalability and Concurrency:** Suitable for logging from multiple goroutines without losing performance. - **Filters and Handlers:** Offers detailed control over log processing with flexible handlers and filters. | - **Less Popular:** Not as widely used or well-known as Logrus, Zap, or Zerolog, leading to fewer community resources and third-party integrations. - **Complexity:** API can be more complex due to its flexible and powerful filtering and handler mechanisms, leading to a steeper learning curve. |
## Conclusion
@@ -747,9 +753,9 @@ Sensitive information may occasionally be accidentally included in logs, raising
### What is logrus?
-`logrus` is a popular organized logger designed for `Golang`. It is renowned for being compatible with the common library logger and having an easy-to-use API. The built-in log package in Go is expanded by Logrus to offer structured logging features.
+`logrus` is a popular organized logger designed for `Golang`. It is renowned for being compatible with the common library logger and having an easy-to-use API. The built-in log package in Go is expanded by Logrus to offer structured logging features.
-Compared to plain text logs, structured logging logs are easier to interpret programmatically because of their format. An overview of logrus's features and the reasons developers would like it over alternative logging frameworks are covered in the later sections of this guide.
+Compared to plain text logs, structured logging logs are easier to interpret programmatically because of their format. An overview of logrus's features and the reasons developers would like it over alternative logging frameworks are covered in the later sections of this guide.
### Is logrus deprecated?
@@ -763,12 +769,12 @@ Deprecation logging refers to the practice of logging warnings about features, f
### Which is better logrus vs zap?
-Choosing between Logrus and zap for logging in Go depends on specific project requirements. logrus is known for its flexibility and ease of use, with features like custom log levels and structured logging, making it suitable for applications where a straightforward and adaptable logging solution is favored.
+Choosing between Logrus and zap for logging in Go depends on specific project requirements. logrus is known for its flexibility and ease of use, with features like custom log levels and structured logging, making it suitable for applications where a straightforward and adaptable logging solution is favored.
On the other hand, zap shines in high-performance environments due to its zero-allocation design and efficient logging, ideal for high-load or low-latency systems where logging overhead must be minimized. Therefore, while logrus offers a great balance of features for general-purpose applications, zap is better suited for scenarios where performance is a critical factor. Assessing factors such as load, log volume, and system performance requirements will help determine the best choice for your specific needs.
### Which is better logrus vs zerolog?
-Choosing between logrus and zerolog for logging in Go applications hinges on balancing ease of use, flexibility, and performance. logrus excels with its user-friendly API, extensive customizability, and structured logging capabilities, making it suitable for general web applications where performance is not the primary concern.
+Choosing between logrus and zerolog for logging in Go applications hinges on balancing ease of use, flexibility, and performance. logrus excels with its user-friendly API, extensive customizability, and structured logging capabilities, making it suitable for general web applications where performance is not the primary concern.
In contrast, zerolog stands out in high-performance environments due to its focus on speed and efficiency, boasting zero allocation in many operations and optimal performance for high-throughput or low-latency systems. Thus, the decision should be based on whether developer convenience and flexibility (logrus) or logging performance with minimal system impact (zerolog) is more critical for your project's needs.
diff --git a/data/guides/python-logging-best-practices.mdx b/data/guides/python-logging-best-practices.mdx
index 98437c826..014114896 100644
--- a/data/guides/python-logging-best-practices.mdx
+++ b/data/guides/python-logging-best-practices.mdx
@@ -1,152 +1,167 @@
---
-
title: Python Logging Best Practices - Expert Tips with Practical Examples
slug: python-logging-best-practices
date: 2024-07-18
tags: [logging]
authors: [vivek_goswami]
description: Learn best practices for Python logging, including using appropriate log levels, custom loggers, formatting, handling exceptions, and centralizing log management using Signoz
-keywords: [Python logging, structured logs, logging levels, context, debugging, monitoring, best practices]
-
+keywords:
+ [Python logging, structured logs, logging levels, context, debugging, monitoring, best practices]
---
-
+ dangerouslySetInnerHTML={{
+ __html: JSON.stringify({
+ '@context': 'https://schema.org',
+ '@type': 'TechArticle',
+ headline: 'Python Logging Best Practices - Expert Tips with Practical Examples',
+ alternativeHeadline:
+ 'Learn best practices for Python logging, including using appropriate log levels, custom loggers, formatting, handling exceptions, and centralizing log management using SigNoz',
+ author: {
+ '@type': 'Person',
+ name: 'Vivek Goswami',
+ },
+ publisher: {
+ '@type': 'Organization',
+ name: 'SigNoz',
+ logo: {
+ '@type': 'ImageObject',
+ url: 'https://signoz.io/img/SigNozLogo-orange.svg',
+ },
+ },
+ datePublished: '2024-07-18T00:00:00Z',
+ dateModified: '2024-07-18T00:00:00Z',
+ mainEntityOfPage: {
+ '@type': 'WebPage',
+ '@id': 'https://signoz.io/guides/python-logging-best-practices/',
+ },
+ description:
+ 'Learn best practices for Python logging, including using appropriate log levels, custom loggers, formatting, handling exceptions, and centralizing log management using SigNoz',
+ keywords: [
+ 'Python logging',
+ 'structured logs',
+ 'logging levels',
+ 'context',
+ 'debugging',
+ 'monitoring',
+ 'best practices',
+ ],
+ articleSection: 'Technology',
+ inLanguage: 'en',
+ isPartOf: {
+ '@type': 'Blog',
+ name: 'SigNoz Blog',
+ url: 'https://signoz.io/blog/',
+ },
+ about: [
+ {
+ '@type': 'Thing',
+ name: 'Python Logging',
+ description:
+ 'Best practices for logging in Python, including using appropriate log levels, custom loggers, and structured logging.',
+ },
+ {
+ '@type': 'Thing',
+ name: 'Log Management',
+ description:
+ 'Techniques and tools for managing log data generated by software applications and systems.',
+ },
+ {
+ '@type': 'Thing',
+ name: 'Structured Logging',
+ description:
+ 'Logging data in a structured format like JSON or key-value pairs for easier analysis and monitoring.',
+ },
+ ],
+ mentions: [
+ {
+ '@type': 'SoftwareApplication',
+ name: 'SigNoz',
+ applicationCategory: 'Observability Platform',
+ },
+ ],
+ mainEntity: {
+ '@type': 'FAQPage',
+ mainEntity: [
+ {
+ '@type': 'Question',
+ name: 'What is the best way to log in Python?',
+ acceptedAnswer: {
+ '@type': 'Answer',
+ text: 'The best way to log in Python is by using the built-in logging module. It will log messages to the console with a timestamp, logger name, log level and the message.',
+ },
+ },
+ {
+ '@type': 'Question',
+ name: 'How to improve logging in Python?',
+ acceptedAnswer: {
+ '@type': 'Answer',
+ text: 'The guidelines to follow for improved logging in Python are as follows: 1. Use appropriate log levels 2. Add context 3. Use handlers 4. Format logs 5. Rotate log files 6. External Libraries',
+ },
+ },
+ {
+ '@type': 'Question',
+ name: 'What is the fastest way to log data in Python?',
+ acceptedAnswer: {
+ '@type': 'Answer',
+ text: "The fastest way to log data in Python is with the built-in 'logging' module with asynchronous logging.",
+ },
+ },
+ {
+ '@type': 'Question',
+ name: 'What is the lowest level of logging in Python?',
+ acceptedAnswer: {
+ '@type': 'Answer',
+ text: 'The lowest level of logging in Python is DEBUG.',
+ },
+ },
+ {
+ '@type': 'Question',
+ name: 'What is the highest logging level in Python?',
+ acceptedAnswer: {
+ '@type': 'Answer',
+ text: 'The highest logging level in Python is CRITICAL.',
+ },
+ },
+ {
+ '@type': 'Question',
+ name: 'What are the different types of logging in Python?',
+ acceptedAnswer: {
+ '@type': 'Answer',
+ text: 'There are five different types of logging in Python which are as follows: 1. Debug 2. Info 3. Warning 4. Error 5. Critical',
+ },
+ },
+ {
+ '@type': 'Question',
+ name: 'What is the most popular logger?',
+ acceptedAnswer: {
+ '@type': 'Answer',
+ text: "The most popular logger is the built-in logging module. It's extensively used due to its versatility, ease of use, and comprehensive features for logging messages across applications.",
+ },
+ },
+ {
+ '@type': 'Question',
+ name: 'Why do we use logging in Python?',
+ acceptedAnswer: {
+ '@type': 'Answer',
+ text: 'Logging is used in Python to record useful information, errors, and debugging messages during the execution of a program. It helps to understand the behavior of a program, diagnose issues, and monitor applications.',
+ },
+ },
+ {
+ '@type': 'Question',
+ name: 'How to write good logs?',
+ acceptedAnswer: {
+ '@type': 'Answer',
+ text: 'In order to write good logs one must ensure that the log messages are clear and concise, relevant, consistent, structured and handle errors gracefully.',
+ },
+ },
+ ],
+ },
+ }),
+ }}
+ />
Struggling with Python logging? You're not alone. Common headaches include:
@@ -157,6 +172,7 @@ Struggling with Python logging? You're not alone. Common headaches include:
- Security risks from logging sensitive data
Let's fix that. Here are the best practices we'll cover:
+
- Use appropriate log levels: Debug, Info, Warning, Error, Critical
- Create custom loggers: Tailor logging to your app's needs
- Format logs effectively: Make them readable and useful
@@ -244,11 +260,11 @@ same_logger.error("Error message from same_logger")
### Explanation:
1. Singleton Logger Setup:
- - `logging.getLogger('exampleLogger')` is called twice, but both `logger` and `same_logger` refer to the same logger instance due to the singleton nature.
+ - `logging.getLogger('exampleLogger')` is called twice, but both `logger` and `same_logger` refer to the same logger instance due to the singleton nature.
2. Configuration Consistency:
- - Both `logger` and `same_logger` share the same configuration. Adding handlers or changing log levels on one affects the other.
+ - Both `logger` and `same_logger` share the same configuration. Adding handlers or changing log levels on one affects the other.
3. Log Messages:
- - The log messages will be handled by the same set of handlers, ensuring consistent log output. Both messages will appear in the console and the `example.log` file with the same format.
+ - The log messages will be handled by the same set of handlers, ensuring consistent log output. Both messages will appear in the console and the `example.log` file with the same format.
### Output:
@@ -306,7 +322,7 @@ On running the above program the output will be:
2024-07-08 00:16:01,199 - CRITICAL - This is a critical message.
```
-Signoz is an open-source observability platform that enhances logging by effectively, enabling developers to quickly identify and address issues. By integrating with Signoz, teams can monitor logs in real-time, correlate them with system performance, and gain insights into application behavior, which streamlines troubleshooting and maintenance.
+Signoz is an open-source observability platform that enhances logging by effectively, enabling developers to quickly identify and address issues. By integrating with Signoz, teams can monitor logs in real-time, correlate them with system performance, and gain insights into application behavior, which streamlines troubleshooting and maintenance.
An elaborate step by step guide to send your python logs to Signoz by using OpenTelemetry can be found [here](https://signoz.io/opentelemetry/logging-in-python/).
@@ -442,28 +458,21 @@ In this example, you shall learn how to use a JSON configuration for logging:
```yaml
{
- "version": 1,
- "disable_existing_loggers": false,
- "formatters": {
- "standard": {
- "format": "%(asctime)s - %(levelname)s - %(name)s - %(message)s"
- }
- },
- "handlers": {
- "console": {
- "class": "logging.StreamHandler",
- "formatter": "standard",
- "level": "DEBUG",
- "stream": "ext://sys.stdout"
- }
+ 'version': 1,
+ 'disable_existing_loggers': false,
+ 'formatters':
+ { 'standard': { 'format': '%(asctime)s - %(levelname)s - %(name)s - %(message)s' } },
+ 'handlers':
+ {
+ 'console':
+ {
+ 'class': 'logging.StreamHandler',
+ 'formatter': 'standard',
+ 'level': 'DEBUG',
+ 'stream': 'ext://sys.stdout',
+ },
},
- "loggers": {
- "": {
- "handlers": ["console"],
- "level": "DEBUG",
- "propagate": true
- }
- }
+ 'loggers': { '': { 'handlers': ['console'], 'level': 'DEBUG', 'propagate': true } },
}
```
@@ -591,7 +600,7 @@ Output:
2024-07-12 09:37:06,037 - secureLogger - INFO - This is an info message without sensitive data.
```
-In this example, the `RedactingFormatter` replaces any instance of the password with asterisks.
+In this example, the `RedactingFormatter` replaces any instance of the password with asterisks.
### Conditional Logging
@@ -835,13 +844,13 @@ def check_inventory(item_id, quantity):
"""
Check the inventory for the given item and quantity.
Log a warning if the inventory is low, otherwise log an info message.
-
+
:param item_id: The ID of the item to check
:param quantity: The quantity of the item requested
"""
# Example inventory dictionary with item IDs and their available quantities
inventory = {"item1": 10, "item2": 5}
-
+
# Check if the requested quantity is greater than the available quantity in the inventory
if inventory.get(item_id, 0) < quantity:
# Log a warning if the inventory is low
@@ -979,98 +988,102 @@ Proper handling of exceptions is essential while logging in Python. It ensures t
### Best Practices for Handling Exceptions and Logging
1. Use Try-Except Blocks:
- - Wrap code likely to raise exceptions in `try-except` blocks.
- - Log exceptions with relevant context to aid in debugging.
-
- ```python
- import logging
-
- # Configure logging
- logging.basicConfig(level=logging.INFO)
- logger = logging.getLogger(__name__)
-
- try:
- # Code that may raise exceptions
- result = 10 / 0 # Example division by zero to trigger an exception
- except ZeroDivisionError as e:
- logger.error("Division by zero occurred", exc_info=True)
- # Optionally, handle the exception (e.g., notify, recover, fallback)
-
- ```
-
- Output :
-
- ```
- ERROR:__main__:Division by zero occurred
- Traceback (most recent call last):
- File "/Users/falcon/Desktop/signoz-python-logging/main.py", line 9, in
- result = 10 / 0 # Example division by zero to trigger an exception
- ZeroDivisionError: division by zero
-
- ```
-
+
+ - Wrap code likely to raise exceptions in `try-except` blocks.
+ - Log exceptions with relevant context to aid in debugging.
+
+ ```python
+ import logging
+
+ # Configure logging
+ logging.basicConfig(level=logging.INFO)
+ logger = logging.getLogger(__name__)
+
+ try:
+ # Code that may raise exceptions
+ result = 10 / 0 # Example division by zero to trigger an exception
+ except ZeroDivisionError as e:
+ logger.error("Division by zero occurred", exc_info=True)
+ # Optionally, handle the exception (e.g., notify, recover, fallback)
+
+ ```
+
+ Output :
+
+ ```
+ ERROR:__main__:Division by zero occurred
+ Traceback (most recent call last):
+ File "/Users/falcon/Desktop/signoz-python-logging/main.py", line 9, in
+ result = 10 / 0 # Example division by zero to trigger an exception
+ ZeroDivisionError: division by zero
+
+ ```
+
2. Log Contextual Information:
- - Include additional context in log messages, such as variable values or the current state of the application.
-
- ```python
- try:
- # Code that may raise exceptions
- item = inventory[item_id]
- except KeyError as e:
- logger.error(f"Item {item_id} not found in inventory", exc_info=True)
-
- ```
-
+
+ - Include additional context in log messages, such as variable values or the current state of the application.
+
+ ```python
+ try:
+ # Code that may raise exceptions
+ item = inventory[item_id]
+ except KeyError as e:
+ logger.error(f"Item {item_id} not found in inventory", exc_info=True)
+
+ ```
+
3. Use Exception Hierarchy:
- - Handle specific exceptions separately to provide tailored error messages and actions.
-
- ```python
- try:
- # Code that may raise exceptions
- file = open('file.txt', 'r')
- except FileNotFoundError as e:
- logger.error("File not found: file.txt", exc_info=True)
- except IOError as e:
- logger.error("IOError occurred while opening file", exc_info=True)
-
- ```
-
+
+ - Handle specific exceptions separately to provide tailored error messages and actions.
+
+ ```python
+ try:
+ # Code that may raise exceptions
+ file = open('file.txt', 'r')
+ except FileNotFoundError as e:
+ logger.error("File not found: file.txt", exc_info=True)
+ except IOError as e:
+ logger.error("IOError occurred while opening file", exc_info=True)
+
+ ```
+
4. Ensure Logging Configuration:
- - Configure logging settings, such as log level and output format, to suit your application's needs.
-
- ```python
- import logging
-
- logging.basicConfig(
- filename='app.log',
- format='%(asctime)s - %(levelname)s - %(message)s',
- level=logging.INFO
- )
-
- ```
-
+
+ - Configure logging settings, such as log level and output format, to suit your application's needs.
+
+ ```python
+ import logging
+
+ logging.basicConfig(
+ filename='app.log',
+ format='%(asctime)s - %(levelname)s - %(message)s',
+ level=logging.INFO
+ )
+
+ ```
+
5. Centralize Exception Handling:
- - Use a centralized mechanism for logging exceptions across your application to maintain consistency and simplify maintenance.
-
- ```python
- def process_data(data):
- try:
- # Code that may raise exceptions
- result = data / 0
- except Exception as e:
- logger.error("An error occurred during data processing", exc_info=True)
- # Optionally, raise or handle the exception appropriately
-
- def main():
- try:
- # Main application logic
- process_data(some_data)
- except Exception as e:
- logger.error("Unexpected error occurred", exc_info=True)
- # Optionally, handle or re-raise the exception
-
- ```
-
+
+ - Use a centralized mechanism for logging exceptions across your application to maintain consistency and simplify maintenance.
+
+ ```python
+ def process_data(data):
+ try:
+ # Code that may raise exceptions
+ result = data / 0
+ except Exception as e:
+ logger.error("An error occurred during data processing", exc_info=True)
+ # Optionally, raise or handle the exception appropriately
+
+ def main():
+ try:
+ # Main application logic
+ process_data(some_data)
+ except Exception as e:
+ logger.error("Unexpected error occurred", exc_info=True)
+ # Optionally, handle or re-raise the exception
+
+ ```
## Test and Monitor Logs
@@ -1079,58 +1092,60 @@ Testing and monitoring logs are important practices that ensure the working of l
### Testing Logs
1. Unit Testing:
- - Write unit tests to validate that log messages are generated correctly under different scenarios.
- - Use mocking to simulate log events and verify the behavior of loggers and handlers.
-
- ```python
- import unittest
- import logging
- from unittest.mock import patch
-
- class TestLogging(unittest.TestCase):
-
- def test_logging(self):
- with patch('logging.Logger.error') as mock_error:
- logging.error('Error message')
- mock_error.assert_called_once_with('Error message')
-
- ```
-
+
+ - Write unit tests to validate that log messages are generated correctly under different scenarios.
+ - Use mocking to simulate log events and verify the behavior of loggers and handlers.
+
+ ```python
+ import unittest
+ import logging
+ from unittest.mock import patch
+
+ class TestLogging(unittest.TestCase):
+
+ def test_logging(self):
+ with patch('logging.Logger.error') as mock_error:
+ logging.error('Error message')
+ mock_error.assert_called_once_with('Error message')
+
+ ```
+
2. Integration Testing:
- - Include tests that verify the integration of logging with other components of your application, such as error handling and data processing.
-
- ```python
- class TestIntegration(unittest.TestCase):
-
- def test_data_processing_with_logging(self):
- # Simulate data processing
- try:
- result = 10 / 0 # Example to trigger an exception
- except ZeroDivisionError:
- logging.error('Division by zero occurred')
- # Assert log message or expected behavior
- self.assertTrue(logging.error.called)
-
- ```
-
+
+ - Include tests that verify the integration of logging with other components of your application, such as error handling and data processing.
+
+ ```python
+ class TestIntegration(unittest.TestCase):
+
+ def test_data_processing_with_logging(self):
+ # Simulate data processing
+ try:
+ result = 10 / 0 # Example to trigger an exception
+ except ZeroDivisionError:
+ logging.error('Division by zero occurred')
+ # Assert log message or expected behavior
+ self.assertTrue(logging.error.called)
+
+ ```
+
3. Coverage:
- - Ensure that log messages cover various scenarios, including edge cases, error conditions, and expected outputs.
- - Use code coverage tools to verify that logging statements are executed during tests.
-
- ```python
- class TestCoverage(unittest.TestCase):
-
- def test_critical_error_handling(self):
- try:
- # Trigger critical error
- raise RuntimeError('Critical error')
- except RuntimeError as e:
- logging.critical('Critical error occurred: %s', e)
- # Assert log message
- self.assertTrue(logging.critical.called)
-
- ```
-
+
+ - Ensure that log messages cover various scenarios, including edge cases, error conditions, and expected outputs.
+ - Use code coverage tools to verify that logging statements are executed during tests.
+
+ ```python
+ class TestCoverage(unittest.TestCase):
+
+ def test_critical_error_handling(self):
+ try:
+ # Trigger critical error
+ raise RuntimeError('Critical error')
+ except RuntimeError as e:
+ logging.critical('Critical error occurred: %s', e)
+ # Assert log message
+ self.assertTrue(logging.critical.called)
+
+ ```
## Asynchronous Logging
@@ -1140,33 +1155,34 @@ Asynchronous logging is used to improve performance of logging operations in Pyt
The `aiologger` library provides a `JsonLogger` specifically designed for asynchronous logging using Python's asyncio framework.
-1. Installation:
-Install `aiologger` using pip:
-
- ```
+1. Installation:
+ Install `aiologger` using pip:
+
+ ````
pip install aiologger
-
- ```
-
-2. Example Implementation:
-
+
+ ```
+
+ ````
+
+2. Example Implementation:
+
```python
import asyncio
from aiologger.loggers.json import JsonLogger
-
+
async def main():
# Create a logger instance with default handlers
logger = JsonLogger.with_default_handlers(name="exampleLogger", level="DEBUG")
-
+
# Generate log messages asynchronously
for i in range(10):
await logger.debug(f"This is log message {i}")
-
+
if __name__ == "__main__":
asyncio.run(main())
-
+
```
-
Output:
@@ -1245,8 +1261,9 @@ One of the best practices for handling logs in complex applications is to use a
### Implementing SigNoz with Python and OpenTelemetry
To send Python logs to SigNoz:
+
1. SetUp SigNoz
-
+
2. Setup OpenTelemetry: Install the necessary libraries and configure the exporter.
3. Configure Logging: Use OpenTelemetry's Python SDK to set up logging.
4. Integrate with SigNoz: Configure the logging exporter to send logs to SigNoz's backend.
@@ -1260,49 +1277,16 @@ Let us look into some popular log tools and their features with the help of a ta
Here's a table comparing different logging tools in Python, including their features, strengths, and typical use cases:
-| Logging Tool | Features | Strengths | Typical Use Cases |
-| --- | --- | --- | --- |
-| logging (standard library) | - Standard library, no external dependencies - Configurable logging levels
-- Supports file and console handlers
-- Supports formatting and filters | - Widely used and well-documented
-- Flexible configuration - Suitable for most applications | - General-purpose logging
-- Simple to complex applications |
-| loguru | - Easy to use
-- Rich feature set
-- Supports structured logging
-- Asynchronous logging | - Simple API
-- Extensive formatting options
-- Built-in support for rotating files | - Simplifying logging setup
-- Projects requiring structured logging |
-| aiologger | - Asynchronous logging - JSON logging
-- Compatible with asyncio
-- Handlers for streams, files, and syslog | - Non-blocking logging - Suitable for asyncio applications
-- JSON logging for structured data | - Asyncio-based applications
-- High-performance logging |
-| structlog | - Structured logging
-- Integration with standard logging
-- Extensible processors - Rich formatting options | - Structured data output
-- Easily integrable with other tools
-- Customizable pipeline | - Microservices
-- Logging in JSON or other structured formats |
-| logbook | - Modern logging system
-- Context-sensitive logging
-- Thread and coroutine-safe
-- Flexible and powerful configuration | - Easier than the standard logging module
-- Contextual logging for more precise logs - Supports coroutines | - Complex applications needing contextual logging
- - Multi-threaded or async environments |
-| watchtower | - Logging to AWS CloudWatch
-- Integration with standard logging - Handles batch sending | - Direct integration with AWS
-- Simplifies logging setup for AWS services - Handles large volumes of logs | - Applications running on AWS
-- Cloud-based logging |
-| sentry_sdk | - Error tracking and monitoring
-- Integration with logging frameworks - Captures errors, performance issues | - Provides detailed error reports
-- Performance monitoring
-- Alerts and notifications | - Error monitoring in production
-- Performance tracking |
-| graypy | - Sends logs to Graylog - Integration with standard logging
-- Supports UDP and TCP | - Easy integration with Graylog
-- Real-time log analysis - Supports structured logging | - Centralized logging - Log aggregation and analysis using Graylog |
+| Logging Tool | Features | Strengths | Typical Use Cases |
+| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------- |
+| logging (standard library) | - Standard library, no external dependencies - Configurable logging levels - Supports file and console handlers - Supports formatting and filters | - Widely used and well-documented - Flexible configuration - Suitable for most applications | - General-purpose logging - Simple to complex applications |
+| loguru | - Easy to use - Rich feature set - Supports structured logging - Asynchronous logging | - Simple API - Extensive formatting options - Built-in support for rotating files | - Simplifying logging setup - Projects requiring structured logging |
+| aiologger | - Asynchronous logging - JSON logging - Compatible with asyncio - Handlers for streams, files, and syslog | - Non-blocking logging - Suitable for asyncio applications - JSON logging for structured data | - Asyncio-based applications - High-performance logging |
+| structlog | - Structured logging - Integration with standard logging - Extensible processors - Rich formatting options | - Structured data output - Easily integrable with other tools - Customizable pipeline | - Microservices - Logging in JSON or other structured formats |
+| logbook | - Modern logging system - Context-sensitive logging - Thread and coroutine-safe - Flexible and powerful configuration | - Easier than the standard logging module - Contextual logging for more precise logs - Supports coroutines | - Complex applications needing contextual logging - Multi-threaded or async environments |
+| watchtower | - Logging to AWS CloudWatch - Integration with standard logging - Handles batch sending | - Direct integration with AWS - Simplifies logging setup for AWS services - Handles large volumes of logs | - Applications running on AWS - Cloud-based logging |
+| sentry_sdk | - Error tracking and monitoring - Integration with logging frameworks - Captures errors, performance issues | - Provides detailed error reports - Performance monitoring - Alerts and notifications | - Error monitoring in production - Performance tracking |
+| graypy | - Sends logs to Graylog - Integration with standard logging - Supports UDP and TCP | - Easy integration with Graylog - Real-time log analysis - Supports structured logging | - Centralized logging - Log aggregation and analysis using Graylog |
## Conclusion
diff --git a/package.json b/package.json
index 6b5a96bcb..3da4062a3 100644
--- a/package.json
+++ b/package.json
@@ -105,7 +105,7 @@
"prettier --write"
]
},
- "packageManager": "yarn@4.3.1+sha224.934d21773e22af4b69a7032a2d3b4cb38c1f7c019624777cc9916b23",
+ "packageManager": "yarn@4.3.1+sha256.2535167e742927e1d2a1610d5004576e4e8f764d2ca5470d19536790f55cd6e2",
"eslintConfig": {
"extends": [
"plugin:storybook/recommended"