Expecting a system to offer instant and flawless consistency, always and everywhere, for everyone, is, in my opinion, one of the biggest misconceptions in the tech world. One of the most fundamental lessons I’ve learned over years of building systems, developing software, and managing networks is that reality rarely meets this expectation. The “eventual consistency” state we encounter, especially in distributed systems, has come to feel to me not just like a technical concept, but an inevitable reality of life and how we do business.
This situation means that when data is updated at one point, this change isn’t immediately reflected across all replicas in the system, but rather, they all become consistent after a certain period (eventually). I call this the “it’ll be fine” principle; that is, accepting that the system will ultimately reach the desired state, rather than expecting everything to be instant and perfect. This perspective, over time, has become significant not only in lines of code but also in my career and my outlook on life.
Distributed Systems and the End of Instant Consistency Expectations
In my early twenties, when I was setting up my first serious systems, I was searching for every piece of data to appear the same everywhere, instantly, as if it were a holy grail. When a record was created, I wanted to see all reports and all user screens updated within a millisecond. However, field experience showed me how difficult and costly this ideal was in practice. Especially in large-scale and geographically distributed systems, this was an almost impossible expectation.
A case we experienced while developing an internal platform for a bank clearly illustrated this situation. After a user performed a transaction, the relevant balance information wasn’t updated instantly; some reports experienced a delay of 3-5 seconds. While this was initially perceived as a major problem by the client, the additional load and potential performance bottlenecks we would introduce for instant synchronization showed that a 3-5 second delay was an acceptable trade-off. In fact, after accepting this, the system’s overall performance significantly improved.
The Technical Side of Eventual Consistency: CAP Theorem and Realities
The concept of eventual consistency is often associated with the CAP Theorem. This theorem states that a distributed system can only guarantee two out of three properties simultaneously: Consistency, Availability, and Partition Tolerance. Since we generally consider Partition Tolerance (resilience to network failures) indispensable, we are forced to choose between Consistency and Availability. At this point, eventual consistency is an approach that prioritizes Availability over Consistency.
For example, in a project where I used PostgreSQL’s logical replication mechanism, writes to the primary database might not be instantly reflected on the replicas. As I observed from pg_replication_slots and pg_stat_replication tables, under heavy write loads, replication lag could reach seconds, sometimes even minutes. This meant that, especially when read operations were performed from replicas, the user had to wait a short period to read a change they had just made. This delay was acceptable for most business scenarios because it was more critical for the system to always be available and continue responding even under heavy load. In such situations, I tried to manage this delay by optimizing settings like wal_sender_timeout and wal_receiver_timeout, but reducing it to zero was not always possible.
Living with Eventual Consistency in Workflows
This technical reality was also with us when designing our workflows. While working in the ERP of a manufacturing company, critical business processes like “purchase, produce, ship, invoice” actually operated according to an eventual consistency model in the background. For instance, when raw materials were received, even if stock records were updated instantly, it could take a few seconds for this information to be reflected in the production planning module or on operator screens.
This situation sometimes led to minor disruptions. An operator, thinking that newly arrived material wasn’t visible in stock, would manually check, only to realize that the system hadn’t completed replication yet. In a production tracking application I developed, due to the material stock quantity not updating instantly on operator screens, manual checks were required an average of 2-3 times a day. This resulted in a total delay of 5-10 minutes on the production line. As a solution, I displayed a warning on operator screens like “data last updated X seconds ago,” trying to manage expectations correctly. In scenarios like these, sitting down with business units to find a clear answer to the question “what delay is acceptable?” plays a key role in system design.
Life’s Own Eventual Consistency
Over time, I realized that this eventual consistency was not just a part of software systems, but also a part of life itself. In my career, I often expected immediate returns for the effort I invested in a project. Sometimes, after months of intense work, I was disappointed when I saw no tangible results. It was just like the replication delay of a distributed system; I put in the effort, but the result wasn’t immediately visible.
In one of my side products, while developing an Android spam blocker application, I gained almost no users for the first 6 months. I spent days writing code, fixing bugs, and adding new features; but download numbers remained stagnant. It was as if I was waiting for data in a remote data center to synchronize. Then, in the 7th month, unexpectedly, a technology blog mentioned my application, and download numbers increased by 300%. This situation, just like in systems, showed that efforts cumulatively reached an “eventually consistent” result at some point. In life, expecting instant consistency is naive; what matters is moving steadily in the right direction and trusting that the result will come sooner or later.
Managing Expectations and Patience: An Engineer’s Perspective
As I understood eventual consistency, I learned better how to manage my expectations and be patient. In engineering, trying to make everything perfect instantly often leads to bigger problems. Last month, on a client project, I forced immediate consistency for a critical reporting service with every data write. In the PostgreSQL database behind the service, I locked rows using FOR UPDATE locks for every transaction. Result: the service deadlocked 15-20 times a day, and we experienced a 4-hour outage.
This mistake cost me dearly. Afterward, we accepted a 20-second delay for reports and reverted to an eventual consistency model. We processed data asynchronously using a message queue (Redis Streams) in the background and updated the reporting database on a separate replica. The service stabilized, deadlocks disappeared, and overall performance significantly improved. This incident showed me once again that, in some cases, embracing “good enough” eventual consistency instead of chasing perfect consistency is more beneficial for both systems and our mental health. This was also part of the ability to accept problems and find practical solutions, as I mentioned in my my debugging experience in distributed systems article.
A Look to the Future: Eventual Consistency and Adaptation
In the future, with edge computing, IoT, and increasingly distributed architectures, the role of eventual consistency in our lives will grow even further. Expecting data from every sensor, every device, to synchronize instantly with a central system is unrealistic. This situation requires us to adapt both our technical systems and our own life strategies to this reality.
I apply this principle when designing the backend for my personal finance applications as well. For example, when a transaction is made, the balance is updated instantly, but it might take a few seconds for different reporting tables or statistics to reach their final state. For me, what’s important is that the main transaction is completed correctly and quickly; the consistency of other auxiliary data is “eventually” ensured. This approach is valid not only in engineering but also in career planning: accepting short-term fluctuations and taking steady steps towards my long-term goals. In my my articles on ERP architecture articles, I also emphasized the flexibility and adaptation of business processes with a similar perspective.
Eventual consistency, rather than a flaw, is a natural characteristic of complex and distributed systems. This reality helps us design more robust, scalable systems and ground our personal expectations in a more realistic foundation. Instead of seeking instant perfection, belief in ultimate consistency offers a more peaceful and productive journey, both in the world of software and in life itself.