The world of trading moves faster than ever, with millions of transactions occurring in fractions of a second. The entry of retail investors has added volatility to the markets, placing enormous demands on trading platforms to deliver real-time data access, process vast quantities of transactions, and provide uninterrupted service. During events like the GameStop frenzy of January 2021, platforms that could not meet this demand faltered, resulting in delayed portfolio updates, investor frustration, and lost trust.

While Redis Enterprise has been used to meet some real-time challenges, it falls short when platforms need high scalability, low-latency performance, and dynamic, complex data models. GigaSpaces’ Data Hub and XAP offer a superior alternative, combining a space-based architecture with in-memory data processing to ensure both technical excellence and business impact. Let’s explore why GigaSpaces is the better choice for building a resilient and high-performance trading platform.

Meeting Market Volatility and User Expectations

Modern trading platforms face twin challenges: high market volatility and evolving user expectations. When stock prices change every second and investors flood the platform, slowdowns or outages can erode trust and result in significant financial losses.

Investors expect real-time access to portfolio data, including:

• Instant Portfolio Valuation: Reflecting up-to-the-second changes in security prices.
• Real-Time Gain/Loss Calculations: Providing accurate insights into performance for timely decision-making.

To meet these expectations, platforms must handle:

1. Continuous streams of real-time data from exchanges with high-speed updates.
2. Concurrent access by millions of users checking and trading across diverse portfolios simultaneously.

Redis, a popular in-memory data store, is often promoted as a solution for real-time workloads. However, it struggles to manage the scale and complexity of modern trading applications:

• Flat Data Models: Redis uses basic key-value, hash, and set data structures, limiting the ability to model complex financial relationships, such as nested portfolios or multiple trades within a security.
• Manual Sharding and Scaling: Redis requires careful planning to manage sharding and partitioning, which becomes cumbersome as data volumes grow.
• Network Overheads: Redis systems often involve multiple network hops for data retrieval, impacting latency in high-frequency trading environments.

Let’s take a look at how a basic portfolio model would be implemented in Redis and compare it to the same model in GigaSpaces. In Redis, a portfolio with multiple securities must be split across several keys, each representing different attributes. For example:

# Adding securities to a portfolio
SADD portfolio:123 lot1 lot2

# Creating hashes for each lot
HMSET lot:lot1 ticker AAPL quantity 100 price 145.30
HMSET lot:lot2 ticker TSLA quantity 50 price 800.10

# Fetching all securities in the portfolio
SMEMBERS portfolio:123

# Retrieving details of each lot
HGETALL lot:lot1
HGETALL lot:lot2

This approach works but introduces several challenges:

1. Multiple network hops: Fetching all lots and their attributes requires several operations.
2. Data inconsistency risks: Operations across multiple keys can result in inconsistencies.
3. Performance bottlenecks: Redis struggles with large-scale queries that span many keys.

With GigaSpaces, we can model the portfolio holdings with complex objects, meaning that the same portfolio can be modeled as a single object stored within the in-memory data grid. Here’s how you can implement it:

@SpaceClass
public class Portfolio {
    @SpaceId(autoGenerate = true)
    private String portfolioId;

    private List holdings;

    // Constructors, getters, and setters
}

@SpaceClass
public class Security {
    private String ticker;
    private int quantity;
    private double price;

    // Constructors, getters, and setters
}

// Storing a portfolio in GigaSpaces
Portfolio portfolio = new Portfolio();
portfolio.setHoldings(List.of(
    new Security("AAPL", 100, 145.30),
    new Security("TSLA", 50, 800.10)
));
space.write(portfolio);

// Querying a portfolio from GigaSpaces
Portfolio result = space.readById(Portfolio.class, "123");

Advantages of GigaSpaces:

• Reduced Complexity: All holdings are stored as part of a single portfolio object, eliminating the need for multiple keys and reducing query complexity.
• Co-located Processing: Data resides where it’s processed, reducing network latency and increasing query performance.

Consistency Guaranteed: GigaSpaces ensures atomic writes and updates, avoiding inconsistency risks found in Redis.

Real-Time Event Processing with GigaSpaces vs. Redis

Redis Streams can be used to handle real-time price updates:

# Adding a price update to the stream
XADD prices:stream * ticker AAPL price 145.50

# Reading from the stream
XREAD COUNT 1 STREAMS prices:stream 0

While Redis Streams offer basic event handling, scaling this model across multiple users and securities becomes complex, with network bottlenecks and manual sharding. Redis Streams also lack native support for complex processing logic.

In GigaSpaces, event-driven architecture is built-in, or native, making it easy to trigger real-time updates:

@EventDriven
public class PriceUpdateListener {
    @SpaceDataEvent
    public void onPriceUpdate(Security security) {
        System.out.println("Price updated: " + security.getTicker() + " - $" + security.getPrice());
    }
}

GigaSpaces Advantages:

• Native Event Handling: Built-in support for distributed events simplifies real-time processing.
• Co-located Data and Logic: Events are processed where the data resides, reducing latency.
• Automatic Scaling: As more data flows through the system, GigaSpaces scales dynamically without manual intervention

Handling Market Surges: Why GigaSpaces is Ready for the Next GameStop

In an era where milliseconds make the difference between profit and loss, trading platforms cannot afford to rely on solutions that introduce latency, complexity, and inconsistency. Redis may work for simple caching use cases, but it falls short in the demanding, high-performance environment of real-time trading.

Imagine another sudden market surge. With Redis, manual intervention may be required to scale the system, risking service degradation. GigaSpaces, however, provides automated failover, self-healing capabilities, and dynamic scaling to handle such events seamlessly.

• Tens of thousands of updates per second: GigaSpaces processes market feeds with minimal latency.
• Uninterrupted service: High availability ensures platforms remain operational, protecting both investor trust and revenue.
• Effortless scaling: Nodes can be added on the fly to meet demand, without manual configuration or downtime.

GigaSpaces, with its space-based architecture, in-memory data grid, and native event-driven processing, provides a superior alternative. By reducing operational complexity, ensuring seamless scalability, and delivering lightning-fast performance, GigaSpaces empowers trading platforms to meet the demands of today’s markets and prepare for tomorrow’s challenges.