When building distributed systems, we often unknowingly find ourselves pushed towards an "eventual consistency" model. This means that data will eventually be consistent, but not necessarily consistent at every given moment. While it initially sounds like a logical and scalable solution, over the years, I've repeatedly experienced how deeply this model impacts the developer's mindset and the overall complexity of the system. This post describes the challenges brought by this shift in mindset and how I cope with them.
When tracking inventory in an ERP system, we expect the stock to decrease immediately when a product is shipped out. However, if this operation is part of a distributed architecture and the stock update goes through an asynchronous queue, a user might see the old value if they check the stock status a second later. This situation raises the question, "What just happened?", and as developers, we must learn to live with this "time-delayed consistency." This learning process goes beyond a mere technical issue; it fundamentally changes our approach to problem-solving and system design.
The Promise of Eventual Consistency and Unexpected Realities
Eventual consistency is a model we frequently prefer in distributed systems to achieve high availability and performance. Especially in microservice architectures or geographically distributed systems, performing every operation instantly with full consistency might be impractical due to network latencies and resource locking. Therefore, it's assumed that data replicas might differ for a while but will eventually converge to the same state. This promise seems like a cornerstone for building scalable systems.
However, reality shows how fine a line this promise walks. In an ERP system for a manufacturing company, we had a scenario where we performed a stock check at the time of order creation and then decreased the stock when a shipping order was issued. The shipping order was put into an asynchronous message queue, and the actual stock deduction occurred a few seconds later. If a user tried to create a second order simultaneously, and the system hadn't yet processed the stock deduction from the first order, we risked creating an order for an out-of-stock product. This situation demonstrated how critical the "eventual" timeframe could be, showing that even a few seconds could disrupt the business workflow. As a solution, we introduced the concept of "reserved stock" at the time of order and used this reservation until the actual stock deduction occurred. This, however, added another layer of complexity.
⚠️ The Eventual Consistency Trap
Eventual consistency is often marketed as an "acceptable trade-off for performance and scalability." However, the cost of this trade-off comes back as increased mental load for the developer, more complex code, and extended debugging time. It is vital to clearly define the data consistency expectations of each system component and act accordingly.
The Additional Burden on the Developer Mindset
For a developer accustomed to a strong consistency model, transitioning to eventual consistency requires a significant mental shift. The simplicity of "I saved the data, now I can read it" disappears. Instead, a much more complex thought process emerges: "I saved the data; it will be readable after a while, but the old data might be visible in the meantime." This brings up the question, "Is this data up-to-date right now?" with every query, every data read. This skepticism slows down the development process and increases the likelihood of errors.
While developing the backend for my own side project, I had a situation where I wanted users to see immediate results after completing a specific operation. The operation was running asynchronously in the background. It was easy to show the user a message like "Your request has been received, it will be completed shortly," but the real challenge was when the user later tried to view the result of this operation on another screen, and the data hadn't synchronized yet. As a solution, I started maintaining an "operation status" (processing, completed, failed) for each operation and updated the user interface based on this status. This wasn't just about adding a database column; it involved additional work like setting up polling mechanisms in the UI, sending notifications via WebSocket, and clearly indicating this transition to the user. This kind of approach requires the developer not just to write code but to think much more deeply about the user experience and system behavior.
# Asynchronous processing of an incoming order
def process_order_async(order_id):
# Mark the order status as "processing"
db.orders.update_one({"_id": order_id}, {"$set": {"status": "processing"}})
# Send to message queue
message_queue.publish("order_processing_queue", {"order_id": order_id})
# Return an immediate response to the user
return {"message": "Your order has been received and is being processed.", "order_id": order_id, "status": "processing"}
# Background worker
def worker_process_order(message):
order_id = message["order_id"]
# Actual stock deduction and other business logic
success = perform_stock_deduction(order_id)
if success:
db.orders.update_one({"_id": order_id}, {"$set": {"status": "completed", "completed_at": datetime.now()}})
else:
db.orders.update_one({"_id": order_id}, {"$set": {"status": "failed", "failed_at": datetime.now()}})
# User querying order status
def get_order_status(order_id):
order = db.orders.find_one({"_id": order_id})
return {"order_id": order_id, "status": order["status"], "details": order.get("details")}
Even the simple example above demonstrates the necessity of the status field and a background worker. This creates an additional burden on the developer's mind by splitting a situation that could be handled with a single atomic operation in strong consistency into two different stages.
Transaction Outbox and Idempotency: Inevitable Patterns
When working with eventual consistency, certain architectural patterns become almost inevitable for managing system complexity. Foremost among these are "Transaction Outbox" and "Idempotency." When an operation atomically requires both writing to a database and sending a message to a message queue, the Transaction Outbox pattern comes into play. This ensures that even if the database operation and message sending operation are not part of the "same transaction," the message will definitely be sent after the database operation.
In a manufacturing ERP, when a production order was completed, we needed to record stock movements and send an event to the relevant finance module. Initially, we performed these two operations separately, but sometimes messages failed to be sent to the message queue, leading to inconsistencies between stock and financial records. As a solution, we created an outbox table. When stock movement was recorded in the database, a record was also added to the outbox table within the same transaction. A separate service continuously scanned this outbox table and sent messages to the queue. Once a message was successfully sent, the outbox record was deleted. This ensured that the finance module always received accurate and complete information.
-- Example table for Transaction Outbox pattern in PostgreSQL
CREATE TABLE outbox_messages (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
aggregate_type VARCHAR(255) NOT NULL,
aggregate_id UUID NOT NULL,
event_type VARCHAR(255) NOT NULL,
payload JSONB NOT NULL,
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
processed_at TIMESTAMP WITH TIME ZONE,
is_processed BOOLEAN DEFAULT FALSE
);
-- An example transaction
BEGIN;
-- Core business logic
INSERT INTO stock_movements (product_id, quantity, type) VALUES ('prod123', 10, 'OUT');
-- Add message to Outbox
INSERT INTO outbox_messages (aggregate_type, aggregate_id, event_type, payload)
VALUES ('stock_movement', 'prod123', 'stock_deducted', '{"product_id": "prod123", "quantity": 10}');
COMMIT;
Using this pattern shifts the developer's mindset from "I can only perform an operation once" to "I can perform an operation multiple times, but I must ensure the result is always the same," which is "idempotency." Because messages in message queues can sometimes be sent multiple times (at-least-once delivery), the receiving end must handle this idempotently. If we receive an operation twice, the second time it arrives, we should not repeat the effects of the first operation. For example, an operation to add 10 TL to a user's balance should only increase the balance by 10 TL, not 20 TL, even if the message arrives twice. This is achieved by maintaining a unique transaction ID for each operation and checking this ID. I delved into this topic in more detail in my post [related: idempotent operations in distributed systems].
Consistency Models and Trade-offs: Reasons for Choice
When designing systems, choosing which consistency model to adopt is a trade-off between business requirements and technical constraints. Strong consistency guarantees that once an operation is completed, its effects are immediately visible across the entire system. This is usually achieved with a single database or distributed transaction managers, but it can be costly in terms of scalability and availability. Eventual consistency, on the other hand, guarantees that data will eventually be consistent, but inconsistencies may occur during this period. This model is more suitable for systems requiring high scalability and availability.
In my experience, when making this decision, I always consider the criticality level of the business workflow. For example, in financial transactions like banking, strong consistency is an absolute requirement. A transfer being incomplete or displaying incorrect information is unacceptable. In such systems, we accept the performance degradation or complexity introduced by distributed transactions. However, in cases where momentary inconsistency does not lead to significant business loss, such as the number of likes on a social media application, eventual consistency is more sensible. Even if a user doesn't see a like immediately, seeing it a few seconds later doesn't cause a major problem and helps the system serve billions of users.
ℹ️ The CAP Theorem and the Real World
The CAP theorem states that a distributed system can provide at most two of three properties simultaneously: Consistency, Availability, and Partition Tolerance. Eventual consistency is a type of Consistency that often emerges when we choose Availability and Partition Tolerance. Most often, I've encountered scenarios where network partitions are inevitable, and high availability is critical for the business. In these situations, we are forced to sacrifice some consistency.
In a client project, we were redesigning the inventory management for a global e-commerce platform. Instantly synchronizing stock information from warehouses in different parts of the world was impossible due to network latencies. In this scenario, each region managed its own stock but sent updated information to the main center at specific intervals (e.g., every 5 minutes). This meant we had strong consistency regionally but eventual consistency globally. If stock ran out in one region, users in other regions might not see it immediately, but this was determined to be an acceptable trade-off in terms of customer satisfaction.
Challenges and Solutions I Observed in Production Environments
One of the biggest challenges with eventual consistency is detecting and debugging unexpected situations that arise in a production environment. Since data inconsistencies are not instantaneous, problems often accumulate and manifest as a larger business issue. For example, small stock discrepancies might accumulate over a month and lead to a significant difference during the end-of-month inventory check. These are among the "insidious" problems.
Once, while examining the flow from production to shipment in a manufacturing company's ERP, I noticed consistent shortages of 1-2 units in shipment reports for a specific product group. Initially, it was overlooked, but even this 0.01% difference in monthly shipments of 20,000 units could lead to serious losses. After approximately 3 days of detailed investigation, I found that the problem stemmed from a "final check" step performed immediately after the shipment order was created but before the stock was updated. This final check overlooked products that were reserved but not yet reflected as deducted from stock, due to the asynchronous stock deduction not yet being processed. As a solution, we redesigned the final check step to run after the stock deduction was completed, closing the inconsistency window.
To detect such issues, investing in observability tools is crucial. Not just logs and metrics, but also distributed tracing is critical for understanding eventual consistency problems. Visualizing how an operation propagates across different services and when data becomes consistent is invaluable for finding the root cause of the problem. In my production systems, I use OpenTelemetry-based traces to monitor the time taken from the start to the end of an operation and which services were affected in what order. Sometimes, I even add custom metrics that indicate when specific data becomes "up-to-date." I touched on this topic in my post [related: hunting system issues with observability].
💡 Observability in Eventual Consistency
Observability in systems working with eventual consistency is many times more important than in strong consistency systems. Tracking each operation with a unique trace ID, understanding the internal states of different services, and when these states converge, accelerates debugging processes. Otherwise, you might struggle for hours or even days with "lost data" or "incorrect reports."
My Approach and Developer Maturity
Living with eventual consistency and successfully implementing this model requires a certain level of maturity as a developer. This encompasses not only technical knowledge but also a deep understanding of business workflows and the ability to foresee potential error scenarios. My approach to this topic is based on a few key principles:
- Defining Consistency Boundaries: I always ask, "How long can this data remain inconsistent, and how much will this situation affect the business?" I define these boundaries clearly with the business units. Different tolerances might apply: 0 seconds for financial data, 5 minutes for reporting, and 1-2 seconds for the user interface.
- Explicit Design: I explicitly state the existence of eventual consistency at every layer of the system. This reflects the situation everywhere, from API documentation to user interface messages. Messages like "Your operation is currently being processed, results will be visible shortly" are important for managing user expectations.
- Compensation & Rollback: One of the biggest risks introduced by eventual consistency is how the system will revert to a previous consistent state in case of an error. Therefore, I design a compensation mechanism or rollback strategy for every distributed operation. For example, if a stock deduction fails, the stock is automatically increased back, and relevant parties are notified.
- Monitoring and Alerting: I actively monitor inconsistency windows. If the number of pending messages in a message queue exceeds a certain threshold or if an operation's completion time becomes abnormally long, I receive an immediate alert. This helps me detect potential inconsistency issues early.
- Testing Strategies: I don't just perform unit and integration tests; I also use "chaos engineering" approaches to test how the system behaves under inconsistent conditions. I simulate scenarios like network latencies and message queue outages to measure the system's resilience.
Last month, on an internal banking platform, when users completed a specific operation, a report in another system needed to be updated. Initially, this was solved with a simple event dispatch, but the report update sometimes took up to 1 minute. During this 1 minute, if the user checked the report, they saw old data. This brought up the question again: "How eventual is 'eventual'?" As a solution, we also sent a transaction_id to the reporting system and ensured the report only showed its latest updated state with that transaction_id. Thus, the user saw a "data is updating" message instead of old data, even knowing the report wasn't current. This was a good example of how a developer not only solves a technical problem but also needs to manage the user experience and business expectations.
