Implementing Custom Data Compression and Serialization for WebSocket Feeds

Beyond JSON: The Need for Speed in Data Transmission

In the world of high-frequency trading, the standard tools of web development often fall short. While JSON has become the ubiquitous data interchange format for web APIs, its verbosity and parsing overhead make it a suboptimal choice for the most latency-sensitive trading applications. For a high-frequency trader, every byte and every microsecond counts. The time it takes to send a message over the network and the time it takes to parse that message on the other end can be the difference between a profitable trade and a missed opportunity. This is why many HFT firms have abandoned JSON in favor of custom binary protocols for their internal WebSocket feeds.

A custom binary protocol allows for a much more compact and efficient representation of data than JSON. By eliminating the overhead of field names and other human-readable formatting, a binary protocol can significantly reduce the size of messages, which in turn reduces network latency. Furthermore, a well-designed binary protocol can be parsed much more quickly than JSON, as the parser can read the data directly into memory without having to perform complex string manipulations. This can result in a significant reduction in CPU usage and a corresponding increase in throughput.

Designing a Custom Binary Protocol: A Balancing Act

The design of a custom binary protocol is a careful balancing act between a number of competing goals. The protocol must be compact, to minimize network latency. It must be fast to parse, to minimize CPU overhead. And it must be flexible enough to accommodate future changes and extensions. Achieving all of these goals is a challenging task that requires a deep understanding of both the data being transmitted and the underlying hardware and software.

One common approach is to use a fixed-length message header that contains the message type and length, followed by a variable-length payload. The header allows the parser to quickly identify the type of message and to allocate the correct amount of memory for the payload. The payload can then be structured using a combination of fixed-size fields for common data types (e.g., integers, floats) and variable-size fields for less common or optional data. This approach provides a good balance of performance and flexibility.

Another important consideration is the endianness of the data. Endianness refers to the order in which bytes are stored in memory. Some systems store the most significant byte first (big-endian), while others store the least significant byte first (little-endian). To ensure that the protocol is interoperable between different systems, it is important to choose a consistent endianness and to convert the data to that endianness before sending it over the network.

Data Compression Techniques for Market Data

In addition to using a compact binary protocol, many HFT firms also use data compression to further reduce the size of their messages. There are a number of compression techniques that are well-suited for market data, including delta compression, run-length encoding, and dictionary compression.

Delta compression is a technique that involves sending only the difference between the current value and the previous value of a field. This can be very effective for fields that change frequently but by small amounts, such as the price of a stock. For example, instead of sending the full price of a stock in every message, you can send only the change in price since the last message. This can result in a significant reduction in message size.

Run-length encoding is a technique that involves replacing a sequence of identical values with a single value and a count. This can be very effective for fields that have a small number of possible values, such as the side of an order (buy or sell). For example, instead of sending a sequence of 10 buy orders, you can send a single message that indicates that there are 10 buy orders.

Dictionary compression is a technique that involves building a dictionary of common values and then replacing those values with a shorter code. This can be very effective for fields that have a large but finite number of possible values, such as the symbol of a stock. For example, instead of sending the full symbol of a stock in every message, you can send a shorter code that corresponds to that symbol in the dictionary.

Serialization and Deserialization Performance: The Final Frontier

The final piece of the puzzle is the performance of the serialization and deserialization code. This is the code that converts the data from its in-memory representation to the binary protocol format, and vice versa. To achieve the lowest possible latency, this code must be highly optimized.

One common technique is to use code generation to create the serialization and deserialization code. This involves writing a program that takes a definition of the protocol as input and then generates the C++ or Java code that is needed to serialize and deserialize the data. This can result in very efficient code, as the generator can take advantage of low-level language features and compiler optimizations.

Another important consideration is memory management. The serialization and deserialization code should be designed to minimize memory allocations and other operations that can introduce latency. This can involve using techniques like object pooling and pre-allocation to avoid the overhead of creating and destroying objects on the fly.

The Trade-offs: Is It Worth It?

The decision to implement a custom binary protocol is not one to be taken lightly. It is a significant undertaking that requires a substantial investment in time and resources. For many firms, the benefits of using a standard protocol like JSON outweigh the performance gains of a custom solution. However, for firms that are operating at the cutting edge of HFT, a custom binary protocol is not a luxury; it is a necessity. In a world where every microsecond counts, the performance gains of a custom protocol can be the difference between success and failure.