As web applications grow in users and complexity, scaling the infrastructure and architecture becomes critical to ensure high availability, performance, and efficiency. Adopting cloud services and DevOps principles and patterns can help tackle the challenges of scaling. This post will explore some key strategies and solutions.

Load Balancing Application Servers

For web applications built on platforms like Java and Tomcat, load balancing is essential to distribute user requests across multiple application server instances. This provides redundancy in case of server failures and allows horizontal scaling.

Some issues that arise with traditional load balancing approaches:

• Session data is lost when servers restart or failover occurs
• Tomcat clustering has limitations in scalability due to all-to-all replication
• Sticky sessions can cause imbalanced loads

A robust solution is to use memcached session manager (MSM) to store and replicate session data across memcached servers. This prevents session loss during restarts or failovers. The application servers connect to the memcached cluster to retrieve and store session data.

With MSM, you can run multiple Tomcat instances behind a load balancer like Amazon's Elastic Load Balancing or HAProxy. The load balancer can distribute requests in a round-robin fashion without sticky sessions. This enables easy horizontal scaling and redundancy.

Key benefits include:

• Prevent session loss during failures and deployments
• Scale horizontally without session replication limits
• No stickiness requirements for load balancer
• Faster serialization with Kryo vs Java serialization
• Concurrent requests can be served as locking handled in MSM

Adopting this pattern enables high availability and performance for large user bases.

Cloud Storage for Large Files

Serving large files like documents, images, and videos from application servers can degrade performance and availability. Instead, leveraging cloud storage services like Amazon S3, Google Cloud Storage, or Azure Blob Storage is more efficient.

Benefits of using cloud storage include:

• Remove large data from databases for better performance
• Leverage high durability and availability of cloud storage
• Virtually unlimited, inexpensive storage capacity
• Easy integration via REST APIs and client libraries
• Built-in redundancy, backup, and global distribution

For example, storing product images in S3 instead of a database can significantly reduce database size and improve response times. The high throughput and global edge caching of S3 can efficiently deliver images to users.

Cloud storage usage patterns:

• User file uploads directly to cloud storage
• Application backups sent to cloud storage
• Static assets stored in cloud storage and CDN for fast delivery
• Big data sets archived in cloud storage, accessed via APIs when needed

By leveraging cloud storage, you can reduce infrastructure costs and improve scalability for large files.

Asynchronous Task Processing

Web applications often need to perform tasks like sending emails, image processing, or data analytics that can take from a few seconds to hours. Executing these synchronously degrades user experience. A queue-based asynchronous processing pattern is optimal.

The workflow consists of:

1. Application adds tasks to message queue such as Amazon SQS
2. Worker nodes poll queue and consume tasks
3. Workers process tasks asynchronously
4. Results are stored in database or sent via callbacks

Benefits of asynchronous task processing:

• Users get immediate response instead of waiting for tasks
• Long-running tasks don't make application unresponsive
• Easy to scale workers horizontally to match load
• Retry failed tasks with exponential backoff
• Use different worker types optimized for task types
• Logs and metrics for task monitoring

Other use cases include order processing, image thumbnail generation, transcoding videos, sending push notifications, and regularly scheduled jobs.

Overall, a queue-based asynchronous architecture enhances scalability and performance.

Leveraging Platform-as-a-Service

When architecting complex, large-scale web applications, considerable effort goes into provisioning and managing infrastructure. To focus engineering effort on application code instead of infrastructure, Platform-as-a-Service (PaaS) providers like Heroku, Google App Engine, and Elastic Beanstalk can be leveraged.

Benefits of using PaaS:

Benefits of using PaaS:

• Eliminates infrastructure provisioning and configuration
• Auto-scaling of compute resources based on load
• Services like databases and caching without ops work
• Built-in support for redundancy and failover
• Managed deployments and rollbacks
• Integration with analytics, monitoring, and logging

This shifts operational responsibilities to the PaaS provider:

• Managing operating systems and capacity
• Database administration
• Load balancing and autoscaling
• Monitoring infrastructure health

Engineers are freed to focus on product innovation and features. However, some limitations exist around flexibility in software stack and dependencies.

Overall PaaS solutions significantly reduce ops overhead for cloud-native applications built within the guardrails of the platform.

Centralized Logging

Aggregating logs across many servers and environments is essential for holistic monitoring and troubleshooting. This can be challenging with dispersed infrastructure. A centralized logging pipeline is critical for efficiency.

An effective stack:

• Servers stream logs to central aggregator like Splunk or Loggly
• Forwarders preprocess and format logs before transmission
• Can filter noise before transmission based on log level
• Powerful search and analytics capabilities forLogs stored in cloud reduce management overhead
• Dashboards provide visibility into log data trends
• Alerts based on saved searches for proactive monitoring

Benefits include:

• No need to log into individual servers to access logs
• Analyze trends across servers like error rates
• Correlate logs with infrastructure and application metrics
• Pivot quickly during incidents to find root cause
• Proactively monitor for anomalies and alerts

With terabytes of log data across servers, centralized logging and analysis is indispensable.

Real-time User Monitoring

While infrastructure and application monitoring provide vital telemetry, tracking business-level metrics reflects real user experiences. Real-time user monitoring (RUM) helps gauge customer-impacting issues proactively.

Some metrics to track in real-time with RUM:

• User signups and logins
• Page views and click paths
• Orders placed and revenue
• Errors encountered by users
• Application exceptions and traces
• 3rd party service API usage
• Client-side performance data

Tooling like New Relic, DataDog, and Dynatrace provide RUM capabilities such as:

• Ingesting web, mobile, and server-side telemetry
• Tracking user journeys across apps
• Performing root cause analysis of errors and slowdowns
• Visualizing business KPIs on dashboards
• Anomaly detection in usage trends
• Real-time application monitoring and profiling

RUM is vital for measuring real customer experience and business operations. It surfaces issues directly impacting customers that may be unseen otherwise.

Cloud-based Email Delivery

Reliably sending a high volume of email like registration confirmations, receipts, and promotions is challenging. Building in-house email infrastructure is complex. Cloud-based email services provide easy scalability.

Services like SendGrid, Mailgun, and Amazon SES provide:

• Simple SMTP or REST API based email sending
• Built-in scalability and redundancy
• Authentication and sender reputation management
• Email templates and custom branding
• Delivery tracking and metrics
• Bounce and unsubscribe handling
• DKIM and SPF support to avoid spam

Key considerations for selection:

• Pricing model based on volume and features
• Integration libraries for platforms and languages
• Flexible whitelabeling of sender details
• Scalability to support future growth
• Retention and analytics around emails
• SMTP service for legacy application compatibility

For fast integration and delivery at scale, cloud email services are ideal for transactional and marketing email.

Scaling for Performance and Resiliency

Several other important considerations exist for scaling web applications:

Caching - Adding a Redis or Memcached caching layer reduces database and backend loads. This improves response times and scalability. Caching common queries, expensive computations, and session data is impactful.

CDN for Assets - Serving static assets like images, CSS and JavaScript via content delivery networks improves performance by caching assets closer to users. Tools like Cloudflare and CloudFront optimize delivery.

Async and Workers - Node.js and similar frameworks shine for I/O heavy apps by enabling asynchronous, non-blocking operations. Workers and background threads help isolate expensive processing.

Stream Processing - For high throughput data streams, using Kafka, Kinesis or Azure Event Hubs allows scaling data ingestion and parallel event processing.

Containers and Microservices - As monoliths grow, breaking into containerized microservices makes scaling engineering teams and systems easier. Kubernetes has become the de facto orchestration platform.

SQL Replication - Scaling databases can be done through master-slave replication and sharding. Solutions like Vitess provide automated sharding. Replication improves performance and redundancy.

NoSQL Databases- Popular NoSQL databases like MongoDB, Cassandra, and DynamoDB provide in-built scalability through horizontal partitioning and replication.

By combining these scalability patterns, large-scale web apps can achieve tremendous growth.