In today's data-driven world, serialization plays a crucial role in ensuring data integrity and traceability across various industries. But beyond the initial software or hardware costs, there's a hidden iceberg of expenses that can significantly impact your bottom line. This article dives deep into the true cost of serialization and explores strategies to minimize these expenses.

The Value of Serialization & Deserialization

Data serialization and deserialization are fundamental concepts in programming/computer science that deal with converting complex data structures into transferable and storable data formats. Here's a breakdown:

Serialization:

Serialization is the process of converting an object's state to a byte stream. Imagine you have a well-organized desk with folders, notebooks, and pens (representing your program's data structures like objects). Serialization is the process of taking all that organized stuff on your desk and carefully packing it into a box (like a byte stream) for easy storage or transport. This box can be stored in a file, sent over a network, or saved for later use. Essentially:

• The program breaks down the data structure (your desk) into its basic building blocks (like variables and their values).
• These building blocks are then converted into a format that can be easily understood by different systems (like packing your notes and pens into a format suitable for shipping).
• This format is often a standardized format like JSON, XML, CSV, or a custom binary data format.

For example, you may have heard of Protocol Buffers, which are language and platform neutral mechanisms for serializing structured data.

Deserialization:

Once you have your box (serialized data) and want to use the stuff inside again, deserialization comes into play. It's like unpacking the box and neatly arranging everything back on your desk: 

• It takes the serialized object (the box) and interprets the format it's in.
• It then uses that information to recreate the original data structure (your desk) in memory.
• This allows your program to work with the data again, just as it was before it was serialized.
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Benefits of Serialization and Deserialization

• Data Persistence: Store program data (like user settings or game progress) in a file for later use.
• Data Transmission: Efficiently send complex data structures between programs or devices.
• Data Sharing: Facilitate sharing data in a standardized format across different systems.

In essence, serialization and deserialization are like packing and unpacking your data, making it transferable and storable while maintaining its integrity and functionality.

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Minimizing the Costs of Serialization

The process of serialization, while essential for tasks like data persistence and transmission across networks, can introduce significant hidden costs that erode performance and inflate operational expenses. Serialization overhead stems from two primary factors: marshaling and unmarshalling. Marshaling refers to the process of converting an object's state into a byte stream while unmarshalling reverses this process, recreating the original object from the serialized data. Both marshaling and unmarshalling require CPU cycles and can become bottlenecks in high-throughput systems.

5 Strategies to Reduce Serialization Overhead:

1. Choose Efficient Formats: While formats like JSON and XML are popular, they can be verbose and inefficient for data transfer. Consider alternative formats like Protocol Buffers, Apache Thrift, or MessagePack. These offer a compact binary representation that reduces the amount of data transmitted and processed during serialization/deserialization, leading to significant performance gains. Compare data serialization formats here.
2. Data Minimization: The more data you serialize, the greater the overhead. Analyze your data and identify unnecessary fields that can be excluded during serialization. This reduces the data footprint and streamlines the process. 
3. Lazy Loading: Don't serialize entire objects at once, especially if you only need specific fields. Implement lazy loading mechanisms to serialize data only when it's required, minimizing unnecessary processing.
4. Code Generation: Many serialization libraries offer code generation tools. These tools can automatically generate optimized code for serialization and deserialization tasks, reducing runtime overhead.
5. Deliver Services with an ITS or DSP: Integrated Technology Systems (ITSs) and their high-scale big brother, the Distributed Systems Platform (DSP), work by unifying backend components—databases, application servers, caching systems, and streaming services—into a single technology. This approach reduces serialization by reducing the need to transport information between various systems in order to deliver a response to a client. DSPs are very similar to ITSs with one critical difference, DSPs are able to synchronize data between geo-distributed nodes in real-time, allowing for low-latency global service fabrics to be created.

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How an ITS and DSP Remove Serialization

Imagine a bustling city with information flowing freely between buildings. Like busy citizens, data packets zip between offices (services) carrying crucial information. But there's a catch: every time they go between builds, they go through a lengthy security and packing process, packing their documents (serialization) and then unpacking them upon arrival (deserialization). This bureaucratic nightmare slows everyone down, creating bottlenecks and inefficiencies.

Now, what if there was a solution? What if instead of requiring people to secure, package, and un-package information several times to complete a single task, they only needed to go through this process upon entry to the city? This is essentially what an ITS and DSP achieves. Data packets are translated upon entry, allowing citizens to perform tasks freely until the information is packaged as a response to the client. This not only cuts down on paperwork (processing overhead) but also allows for a smoother flow of information, significantly improving the city's (system's) overall efficiency.

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The Power of a Single Serialization Per Response

A key advantage of leveraging an ITS for serialization lies in its ability to perform the process only once. Unlike traditional architectures where data might be serialized and deserialized multiple times (for example, consider a client-Apollo-API-database loop), an ITS can handle all processes internally. This significantly reduces the overhead of repeated marshaling and unmarshalling, leading to substantial performance gains.

CPU Cycles and Cost Implications

As we've discussed, serialization and deserialization are CPU-intensive tasks. Each time data is converted to a transferable format, and back, it consumes processing power. In high-throughput systems, these repeated cycles can become bottlenecks, limiting the system's overall capacity and driving up operational costs. However, by leveraging an ITS to minimize serialization events, you can significantly reduce the CPU usage associated with data processing. This translates to tangible cost savings and improved resource utilization, making a compelling case for adopting an ITS or DSP.

Latency and Throughput

Serialization and deserialization add latency to data processing. Each additional step in the data flow introduces a delay, which can accumulate and negatively impact your system's overall responsiveness. This becomes especially critical in real-time applications, where low latency is paramount. An ITS's ability to handle full-stack processes with a single technology allows your system to respond to each request faster and thus handle higher request volumes, ultimately increasing throughput per unit of RAM.

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Choosing the Best Route for Serialization Reduction

While efficient formats, data minimization, and other techniques play crucial roles in minimizing serialization costs, they cannot completely remove serialization steps. 

To remove serialization steps, an Integrated Technology System or Distributed Systems Platform is required. These technologies present a powerful, systemic solution for applications heavily reliant on data movement. However, ITSs and DSPs also require more significant structural change to how services operate and are thus are best coupled with new service creation or re-building existing services that are failing to meet requirements. 

For applications demanding peak performance, cost-effectiveness, and high scale, carefully evaluating the role a DSP can play is a must. This innovative new service delivery approach can be a game-changer, enabling you to achieve the optimal balance between data integrity, transferability, and cost for your high-throughput services.

Ready to explore the possibilities? Reach out to our Distributed Systems Architect at hello@harperdb.io.

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