Phoenix LiveView Race Conditions: Fix Payment System Bugs

Race conditions in Phoenix LiveView occur when the timing of events, user interactions, WebSocket messages, or server responses, creates unexpected state conflicts. For teams building high-throughput, real-time systems with Erlang/Elixir, these timing issues can manifest as cleared form inputs, stale data overwrites, or inconsistent multi-user states. While LiveView's single-process architecture handles events sequentially, the asynchronous nature of WebSocket communication, DOM patching, and concurrent user actions creates scenarios where logical races emerge despite sequential processing.

Understanding and preventing these race conditions is critical for enterprises running distributed payment systems, collaborative platforms, or any real-time application where state consistency directly impacts user trust and system reliability.

Understanding Race Conditions in LiveView's Architecture

Phoenix LiveView operates as a stateful Elixir process that maintains UI state in socket assigns and processes events through handle_event, handle_info, and related callbacks. Each connected client has its own LiveView process, and events arrive as messages over a persistent WebSocket connection.

The race conditions emerge from three architectural realities:

Event Ordering Across Network Boundaries: Client-side events fire rapidly, but each travels over the WebSocket and may arrive at the server with slight timing variations. A user typing in a debounced search field followed by an immediate form submission creates two events that race to complete processing.

Asynchronous DOM Patching: The client receives server diffs and applies them asynchronously. When multiple patches arrive in quick succession, the order of application isn't guaranteed to match the order they were generated on the server.

Concurrent Multi-User Updates: In collaborative applications, multiple LiveView processes may update shared resources simultaneously. Without coordination, these concurrent writes create classic distributed system race conditions.

Common Race Condition Patterns

1. Input Clearing During Rapid Form Interactions

A user fills multiple form fields in quick succession. When they complete the third field, the first field's value disappears from the client—even though the server's socket assigns still contain the correct value.

Root Cause: The rapid succession of phx-change events triggers overlapping DOM patches. An earlier patch, delayed in processing or transmission, arrives after a later one and overwrites the client-side DOM with stale data.

Real Impact: This commonly occurs in checkout flows with multiple interconnected fields (billing address, shipping address, payment method). Users perceive the form as "broken" and abandon the transaction, directly impacting conversion rates in payment systems.

2. Stale Assigns Overwriting Fresh State

A user types in a debounced search input (triggering background phx-change events) and immediately clicks a "Submit" button. The submit handler completes first, setting socket.assigns.query to the final value. Moments later, the delayed phx-change event finishes processing and overwrites the query with an intermediate value.

Result: The server processes a search for incomplete input rather than the user's intended final query.

3. Out-of-Order Animation and State Updates

Using JS.show() with CSS transitions, a modal appears instantly without the smooth fade-in animation. The element becomes visible before the transition class applies, creating a jarring "pop-in" effect.

Root Cause: The display: block change happens before the browser applies the animation class. This is a race between DOM modification, CSS rendering, and JavaScript execution timing.

4. Multi-User State Conflicts

In a collaborative document editor, User A and User B both edit the same paragraph simultaneously. User A's edit completes first and broadcasts via PubSub. User B's edit completes moments later, also broadcasting an update. Both LiveView processes receive both broadcasts.

Without Coordination: User A sees B's changes overwrite theirs. User B sees A's changes overwrite theirs. The document enters an inconsistent state where both users see different content.

Technical Implementation: Prevention Strategies

1. Event Versioning for Ordering Guarantees

Track event sequence with explicit version numbers to discard out-of-order updates:

Client-Side Implementation:

Each keystroke includes a monotonically increasing timestamp. The server ignores events with versions older than the current state.

2. Temporary Assigns for Ephemeral State

Prevent stale assigns from persisting across renders:

The logs list resets after each render. New log entries append without risk of stale data lingering from previous renders. This pattern is critical for streaming data where only the latest batch matters.

3. UI State Locking During Operations

Prevent overlapping events by disabling UI elements during processing:

Combined with server-side state tracking:

This prevents duplicate payment submissions while maintaining responsive UI feedback.

4. Optimistic Updates with Rollback

For latency-sensitive operations, update the UI optimistically while validating in the background:

5. Coordinated Multi-User State with GenServer

For shared state across multiple users, centralize coordination in a GenServer:

LiveViews interact with this central authority:

Race Conditions in Payment Orchestration Systems

Payment orchestration engines face particularly severe consequences from race conditions. When routing transactions across multiple payment service providers (PSPs), event ordering determines whether payments succeed, fail, or duplicate.

Consider a payment orchestration flow where a transaction is first attempted through PSP A. The attempt fails due to a timeout. The orchestration engine immediately routes the payment to PSP B as a fallback. PSP B successfully processes the payment. However, moments later, a delayed success response arrives from PSP A—they actually processed the transaction, just slowly.

Without proper event versioning, the orchestration engine might process both responses, leading to:

• Duplicate charges to the customer

• Incorrect transaction status (marked as failed when actually succeeded)

• Failed reconciliation between merchant records and PSP settlement data

The solution combines event versioning with idempotency keys:

This pattern prevents duplicate payment processing and ensures the orchestration engine always operates on current state, not stale PSP responses. For teams building payment orchestration engines, this event versioning approach isn't optional—it's the foundation of reliable multi-PSP routing.

Real-Time Fraud Detection and Event Ordering

Fraud detection systems must process transaction events in the correct sequence to maintain accurate risk scoring. A race condition in fraud monitoring can allow fraudulent transactions through or create false positives that block legitimate customers.

Consider this scenario: A customer initiates a $500 payment. The fraud detection system begins analyzing the transaction, pulling the customer's recent history and calculating a risk score. While this analysis is running, the customer's device fingerprint service returns additional context indicating this is a trusted device. However, if the initial fraud score calculation completes and triggers a block decision before the device trust update arrives, the system blocks a legitimate transaction.

The event ordering problem becomes critical:

This coordination ensures fraud decisions account for all available data, not just whichever check completes first. The pattern prevents premature blocking while maintaining sub-second decision latency—critical for real-time fraud monitoring systems that can't afford to delay checkout flows.

Debugging Race Conditions

Detection Techniques

1. Concurrent Test Scenarios

Simulate race conditions by spawning multiple LiveView connections:

2. Event Logging and Tracing

Add comprehensive logging to track event processing order:

3. LiveView Connection State Indicators

Use .phx-connected and .phx-loading classes to identify timing issues:

Performance Considerations

Race condition prevention mechanisms must not introduce unacceptable latency. For enterprise payment systems processing thousands of transactions per second, every millisecond matters.

Latency Budget for Event Processing:

• Event validation and versioning: < 5ms

• Optimistic UI updates: < 10ms

• GenServer coordination: < 20ms

• Total P95 event handling: < 50ms

Optimization Strategies:

1. ETS for High-Frequency State: Store version counters and fast-changing state in ETS rather than GenServer state to avoid serialization bottlenecks.

2. Selective Broadcasting: Don't broadcast every state change. Batch updates or use debouncing for high-frequency events.

3. Process Pooling: For coordinated state (like DocumentServer), use process pools to distribute load across multiple GenServer instances.

When Race Conditions Indicate Deeper Architecture Problems

Race conditions often aren’t just bugs, they can be symptoms of deeper architectural issues. Identifying the patterns can help guide structural improvements.

1. Frequent Version Conflicts Requiring Retries

• Symptom: Users experience repeated version conflicts, causing operations to fail or require multiple retries.

• Root Cause: Excessive shared mutable state across processes or modules.

• Solution: Decompose your system into smaller, independently owned state boundaries. Each component should manage its own state to reduce contention.

2. UI Flicker and Inconsistent Rendering

• Symptom: The interface frequently flickers or renders inconsistently.

• Root Cause: Mixing client-side JavaScript with LiveView’s server-driven DOM patching creates conflicts in rendering.

• Solution: Standardize logic: either fully server-side with LiveView or fully client-side with hooks. Avoid hybrid approaches that mix the two.

3. Multi-User Conflicts Despite Coordination

• Symptom: Simultaneous edits from multiple users cause conflicts, even when coordination mechanisms are in place.

• Root Cause: Attempting real-time collaboration without using Operational Transforms or CRDTs (Conflict-free Replicated Data Types).

• Solution: Implement proper conflict-free data structures to ensure safe concurrent edits in collaborative features.

4. Payment Failures with Unclear Causes

• Symptom: Transactions fail with vague “system errors” or transient issues.

• Root Cause: Race conditions in payment processing lead to inconsistent state or double processing attempts.

• Solution: Carefully review event ordering in payment flows, and implement idempotency and versioning to guarantee consistent outcomes.

Conclusion

Race conditions in Phoenix LiveView emerge from the asynchronous nature of WebSocket communication and concurrent state updates. Event versioning handles out-of-order messages. Temporary assigns prevent stale state persistence. UI locking stops overlapping operations. GenServer coordination serializes multi-user updates.

These patterns matter particularly for payment systems, fraud detection, and collaborative platforms where state consistency directly affects reliability. When building high-throughput fintech applications on Erlang/Elixir, addressing race conditions early prevents production issues that are harder to diagnose under load.

For teams facing complex race condition challenges in distributed payment architectures or real-time collaboration systems, Crafting Software, one of the top Erlang and Elixir consultancies and development companies in the world, specializes in building fault-tolerant, high-concurrency payment platforms. With deep expertise in functional programming, distributed systems architecture, payment orchestration engines, real-time fraud monitoring, and PSD2-compliant authentication systems, Crafting Software helps enterprises architect and scale production-grade fintech platforms that handle millions of concurrent transactions while maintaining state consistency across distributed infrastructure.

Phoenix LiveView Race Conditions FAQ

1. What's the best way to implement event versioning in LiveView to prevent out-of-order payment processing?

Event versioning requires tracking sequence numbers for each transaction flow and discarding stale updates. The implementation involves adding version metadata to client events, validating version order in handle_event/3, and using ETS or GenServer state to maintain version counters. For payment systems, this prevents scenarios where a delayed PSP response overwrites a successful retry attempt. The pattern extends to fraud detection systems where risk scores must process in correct order. When implementing across multiple payment flows with varying latency requirements, architectural guidance helps avoid the common pitfall of version counter overflow or improper version comparison logic that can still allow race conditions through.

2. How do you prevent duplicate payment charges in LiveView when users double-click the submit button?

Preventing duplicate charges requires three layers: client-side UI locking with phx-disable-with, server-side processing state flags, and idempotency keys sent to payment processors. The server checks socket.assigns.processing before initiating payment and rejects duplicate events. However, this alone doesn't handle network retries or race conditions between LiveView process restarts. Production implementations need coordination with payment orchestration engines to track attempt IDs and ensure PSP-level idempotency. Complex scenarios involving multiple PSPs, automatic failover, and retry logic require deeper architectural patterns that synchronize state across the entire payment stack.

3. What GenServer patterns prevent race conditions in multi-user LiveView applications with shared payment data?

GenServer coordination centralizes shared state and serializes conflicting updates. For payment systems, this means a dedicated process per transaction that validates version conflicts before applying updates from multiple administrators or fraud analysts. The GenServer maintains authoritative state while LiveView processes subscribe to updates. Implementation challenges include handling GenServer crashes without losing transaction state, managing process registry for thousands of concurrent transactions, and optimizing for sub-50ms response times required by real-time payment flows. Production architectures often need process pooling, ETS caching layers, and sophisticated supervision trees that standard tutorials don't cover.

4. How do you debug intermittent race conditions in LiveView that only appear under production load?

Debugging production race conditions requires structured logging with microsecond timestamps, correlation IDs across distributed processes, and load testing that replicates concurrent user patterns. The challenge is that race conditions often don't reproduce in development environments with single-user testing. Tools include concurrent LiveView connection spawning in tests, event replay mechanisms for captured production sequences, and telemetry instrumentation that tracks event processing order across WebSocket boundaries. When race conditions affect payment flows specifically—causing failed transactions or incorrect fraud scores—the debugging approach needs integration with PSP sandbox environments and realistic traffic simulation that matches production concurrency patterns.

5. What architecture prevents race conditions between LiveView fraud detection and payment authorization in real-time systems?

Real-time fraud detection must complete before payment authorization, but both operate asynchronously with variable latency. The architecture requires coordinating multiple async checks (device fingerprinting, velocity rules, ML model scoring) and making decisions only when all checks complete. Implementation uses task supervision with timeout handling, result aggregation patterns, and fallback logic when checks exceed latency budgets. The complexity increases when fraud systems need sub-100ms response times while querying external services, maintaining feature store state, and handling PSP-specific risk thresholds. Production systems need sophisticated orchestration between fraud detection GenServers, LiveView processes, and payment routing logic that prevents premature authorization while maintaining checkout performance.