Get all data from 7 tables over 2 hops in the intranet
Get All Data from 7 Tables Over 2 Hops in the Intranet
In the world of software development, especially when it comes to designing and optimizing database interactions, it’s imperative to scrutinize every architectural decision. Recently, I found myself in a design discussion at my reputable company regarding a hierarchical NoSQL database structure consisting of a parent table and six child tables. The query design allowed us to fetch all child and parent data in a single call using the parent’s primary key. While this might seem efficient at first glance, it raised some critical questions about payload size, network congestion, and overall system performance.
The Challenge of Excessive Data Retrieval
During the discussion, I expressed my concerns about the necessity of retrieving all data when we only needed a single column. The response I received was dismissive, labeling my concerns as trivial and suggesting I was overthinking the issue. This reaction left me perplexed, particularly given the scale of our operations, which amount to approximately 50 billion requests per day.
To put this in perspective, if each request retrieves about 1KB of data, we’re looking at a staggering total of 10 terabytes of data being transferred daily. This is not just a technical detail; it’s a significant concern that could lead to network congestion and increased latency across our systems.
The Importance of Data-Driven Decisions
One critical takeaway from this experience is the need for data-driven metrics. As a tech lead, I believe that if you want results, you must present compelling evidence that supports your perspective. I would recommend producing metrics that demonstrate how optimizing the data retrieval process could lead to cost savings for the company—both in terms of infrastructure and operational efficiency.
For instance, switching to a more optimized protocol like gRPC could substantially reduce the payload size and improve the speed of data transfer. If you can quantify these potential savings, it might sway opinions and foster a more open discussion about architectural decisions.
Understanding the Bigger Picture
While 50 billion requests per day is indeed a daunting figure, it’s also essential to consider the systems in place designed to manage such scale. Some developers might feel they can afford to overlook certain performance considerations due to the robustness of their infrastructure. However, I argue that even at this scale, trivializing the issue of data size is a missed opportunity for efficiency.
To further understand why my colleague felt unconcerned, I would suggest measuring the time it takes for the database to respond and the subsequent code to parse the data. At an average of 579,000 calls per second, this is a critical metric that can reveal whether the current system is truly optimized for performance.
Exploring Caching Solutions
Another avenue worth exploring is the implementation of caching mechanisms for frequently accessed data. If the column in question changes infrequently, caching it could free up substantial resources and reduce the need for redundant database hits. This strategy could also alleviate some of the network load, especially in a high-traffic environment.
Conclusion: A Call for Continuous Improvement
In conclusion, the discussion surrounding data retrieval practices in a high-traffic environment is not just an academic exercise; it’s a crucial aspect of system design that can have far-reaching implications. As software developers and tech leads, we must advocate for best practices in our teams, ensuring that we consider the scalability, performance, and cost implications of our architectural decisions.
While it can be tempting to overlook certain concerns in the name of expediency, a proactive approach can lead to significant improvements in both system performance and business outcomes. Let’s continue to foster an environment where all voices are heard, and data-driven decisions guide our actions.