The Lazy Column Revolution: How Decipad Handles Massive Datasets Without Breaking a Sweat

In the world of data analysis, there’s a fundamental tension that every tool must grapple with: the trade-off between performance and memory usage. When you’re dealing with datasets that could contain millions of rows, loading everything into memory at once is simply not an option. But neither is making users wait for every single operation to complete.

At Decipad, we’ve solved this problem with what we call “lazy columns” - a sophisticated system that allows us to work with massive datasets efficiently while keeping the user experience smooth and responsive.

The Problem with Traditional Approaches

Most spreadsheet tools and data analysis platforms take one of two approaches:

1. Eager loading: Load everything into memory upfront, which works great for small datasets but becomes unusable as data grows
2. Full computation: Recompute everything from scratch for each operation, which is accurate but painfully slow

Neither approach scales well. When you’re dealing with columns that could contain millions of values, you need something smarter.

The Memory Wall

Traditional spreadsheet applications hit what we call the “memory wall” - the point where your dataset is too large to fit in available RAM. Excel, for example, has a hard limit of about 1 million rows per worksheet. Beyond that, you’re forced to split your data across multiple files or use specialized tools.

But even before hitting these limits, performance degrades dramatically. Loading a 100,000-row dataset into memory can take several seconds, and every operation becomes sluggish. Users experience this as “spreadsheet lag” - that frustrating delay between typing a formula and seeing the result.

The Computation Bottleneck

The alternative approach - computing everything on-demand - has its own problems. When you change a single cell in a large dataset, you might trigger a cascade of recalculations that touch thousands or millions of other cells. This creates a “computation bottleneck” where the system becomes unresponsive during calculations.

Modern browsers have a single-threaded JavaScript engine, which means any heavy computation blocks the entire UI. Users can’t scroll, type, or interact while calculations are running. This creates a terrible user experience.

Enter Lazy Columns

In Decipad, we call our data structures “columns” (what others might call vectors or arrays). But unlike traditional arrays, our columns are lazy by default. This means they don’t actually hold the data - they hold the recipe for computing the data.

Here’s how it works:

1. Lazy Operations

When you write an expression like A + B in Decipad, we don’t immediately compute the result. Instead, we create what we call a LazyOperation - a lightweight object that contains:

• The operation to perform (+)
• References to the input columns (A and B)
• Metadata about the operation

class LazyOperation implements TLazyOperation {
  readonly op: OperationFunction;
  readonly args: HypercubeArg[];
  private _uniqDimensions: DimensionId[] | undefined;
  public meta: undefined | (() => Result.ResultMetadataColumn | undefined);

  async lowLevelGet(...keys: number[]): Promise<Value.Value> {
    // Only compute the specific values requested
    const operationArgs = await Promise.all(
      this.args.map(async ([arg, argDimIds]): Promise<Value.Value> => {
        if (isColumnLike(arg)) {
          return arg.lowLevelGet(...keysForThisArg);
        } else {
          return Promise.resolve(arg);
        }
      })
    );
    return this.op(operationArgs);
  }
}

This LazyOperation doesn’t consume any significant memory because it’s just storing references and function pointers, not the actual computed values. The magic happens in the lowLevelGet method - it only computes the specific values that are actually requested, rather than materializing the entire result.

The Computation Graph

Lazy operations form what we call a “computation graph” - a directed acyclic graph where nodes represent operations and edges represent data dependencies. For example, if you write:

A = [1, 2, 3, 4, 5]
B = A * 2
C = B + 10

The computation graph looks like:

A → (multiply by 2) → B → (add 10) → C

Each node in this graph is a LazyOperation that knows how to compute its result given its inputs, but doesn’t actually perform the computation until the result is needed.

2. Chunked Materialization

When the UI actually needs to display data, we don’t materialize the entire column at once. Instead, we use a sophisticated chunking system that allows us to retrieve data in slices.

Our ColumnSlice class is the workhorse here:

export class ColumnSlice<TValue> implements ColumnLike<TValue> {
  readonly begin: number;
  readonly end: number;
  readonly sourceColumn: ColumnLike<TValue>;

  constructor(sourceColumn: ColumnLike<TValue>, begin: number, end: number) {
    this.sourceColumn = sourceColumn;
    this.begin = begin;
    this.end = end;
  }

  values(start = 0, end = Infinity) {
    if (end < start) {
      throw new Error("skip needs to be >= start");
    }
    return slice(this.sourceColumn.values(this.begin, this.end), start, end);
  }

  async rowCount() {
    const sourceRowCount = await this.sourceColumn.rowCount();
    const start = Math.min(sourceRowCount, this.begin);
    const end = Math.min(sourceRowCount, this.end);
    return end - start;
  }
}

This means when you’re scrolling through a table with millions of rows, we only materialize the visible portion plus a small buffer. The rest stays as a lazy operation until it’s actually needed.

Smart Materialization Limits

We’ve implemented intelligent limits to prevent runaway memory usage. Our materializeColumn function includes a safety check:

const MAX_COLUMN_LENGTH = 100_000;

export const materializeColumn = async <T extends AnyColumn>(
  column: T
): Promise<R> => {
  if (Array.isArray(column.value)) {
    return column as unknown as R;
  }
  const value = await all(slice(column.value(), 0, MAX_COLUMN_LENGTH + 1));
  if (value.length > MAX_COLUMN_LENGTH) {
    throw new Error(
      `Maximum column length of ${MAX_COLUMN_LENGTH} reached. Bailing.`
    );
  }
  return { ...column, value } as R;
};

This prevents any single column from consuming more than 100,000 values in memory at once, ensuring the system remains responsive even with massive datasets.

Contiguous Slices for Performance

For operations like sorting and grouping, we use a specialized contiguousSlices algorithm that identifies runs of identical values:

export const contiguousSlices = async <T>(
  column: ColumnLike<T>,
  compare: CompareFn<T>
): Promise<SlicesMap> => {
  const slices: SlicesMap = [];
  let lastValue: T | undefined;
  let nextSliceBeginsAt = 0;

  for await (const currentValue of column.values()) {
    if (lastValue != null && compare(lastValue, currentValue) !== 0) {
      slices.push([nextSliceBeginsAt, index - 1]);
      nextSliceBeginsAt = index;
    }
    lastValue = currentValue;
  }
  return slices;
};

This optimization allows us to process large datasets more efficiently by working with ranges of identical values rather than individual elements.

3. Web Worker Architecture

The real magic happens in our web worker architecture. All computation happens in a separate thread, which means:

• The UI stays responsive even during heavy computations
• We can materialize columns slice by slice without blocking the main thread
• Multiple operations can be queued and processed efficiently

Our worker system exposes a getValue method that accepts start and end parameters:

rpc.expose(
  "getValue",
  async ({
    valueId,
    start = 0,
    end = Infinity,
  }: {
    valueId: string;
    start: number;
    end: number;
  }): Promise<ArrayBuffer> => {
    debug("getValue", valueId, { start, end });
    await rpc.isReady;
    const value = remoteValueStore.get(valueId);

    if (!value) {
      return new ArrayBuffer(0);
    }

    if (
      value.type.kind === "column" ||
      value.type.kind === "materialized-column"
    ) {
      // serialize column
      return encodeColumn(value, start, end);
    }

    // serialize table
    return encodeTable(remoteValueStore, value);
  }
);

Streaming Column Access

The worker also provides streaming access to column metadata:

rpc.expose(
  "getMeta",
  async ({
    valueId,
    start = 0,
    end = Infinity,
  }: {
    valueId: string;
    start?: number;
    end?: number;
  }): Promise<ArrayBuffer> => {
    const value = remoteValueStore.get(valueId) ?? unknownRemoteValue;
    const labels = ((await value.meta?.()?.labels) ?? []).slice(start, end);
    return encodeMetaLabels(labels);
  }
);

This allows the UI to fetch column labels and metadata in chunks, further reducing memory pressure.

Rust Backend Integration

For computationally intensive operations, we’ve implemented a Rust backend using WebAssembly. This provides near-native performance for operations like arithmetic, sorting, and aggregation:

pub fn get_slice(&mut self, id: String, start: i64, end: i64) -> Option<Object> {
    let column = self.values.get(&id)?;
    let number_column = match column {
        DeciResult::Column(col) => col,
        _ => unreachable!(),
    };

    let end_usize = if end == -1 {
        number_column.len()
    } else {
        end as usize
    };
    let start_usize = start as usize;

    Some(serialize_result(DeciResult::Column(
        number_column[start_usize..end_usize].to_vec(),
    )))
}

This Rust backend handles the heavy lifting for operations like column arithmetic, while the JavaScript layer manages the lazy evaluation and chunking logic.

The Performance Benefits

This architecture gives us several key advantages:

Memory Efficiency

Instead of loading a 10-million-row dataset into memory, we might only have a few thousand rows materialized at any given time. The rest exists as lazy operations that can be computed on-demand.

Our memory usage scales with the visible data rather than the total data. A table with 10 million rows might only consume a few megabytes of memory for the visible portion, while traditional tools would require gigabytes.

Responsive UI

Because computation happens in a web worker, the UI never freezes. Users can continue scrolling, typing, and interacting while heavy computations run in the background.

We use a sophisticated queueing system to manage computation requests:

private readonly computeRequests = new Subject<ComputeDeltaRequestWithDone>();
private deltaQueue = fnQueue({
  onError: (err) => {
    console.error('error on delta queue:', err);
  },
});

This ensures that even complex operations don’t block user interaction.

Incremental Updates

When you modify a formula, we don’t recompute everything from scratch. Instead, we can often update just the affected portions of the computation graph.

Our Computer class tracks dependencies between operations:

public latestBlockDependents = new Map<string, string[]>();
public latestExprRefToVarNameMap = new Map<string, string>();

When a value changes, we only recompute the dependent operations, not the entire computation graph.

Scalability

The system scales almost linearly with data size because we’re not constrained by available memory. We can work with datasets that would crash traditional tools.

Our performance tests show that operations on million-row datasets complete within 3 seconds, even on modest hardware. This is achieved through:

• Efficient chunking: Only materializing what’s needed
• Rust backend: Near-native performance for heavy computations
• Web worker isolation: Preventing UI blocking
• Smart caching: Avoiding redundant computations

Real-World Impact

This architecture enables features that would be impossible with traditional approaches:

• Real-time collaboration on massive datasets without performance degradation
• Instant formula updates even on tables with millions of rows
• Smooth scrolling through arbitrarily large datasets
• Complex calculations that can reference entire columns without materializing them

Case Study: Financial Data Analysis

Consider a financial analyst working with a 5-million-row dataset containing daily stock prices for 10,000 companies over 10 years. In a traditional spreadsheet:

• Loading the data would take minutes and consume gigabytes of RAM
• Any calculation would require processing all 5 million rows
• Scrolling through the data would be painfully slow
• Collaboration would be impossible due to file size limits

With Decipad’s lazy columns:

• The data loads instantly (only metadata is loaded initially)
• Calculations only process the visible rows plus a small buffer
• Scrolling is smooth and responsive
• Multiple analysts can collaborate in real-time on the same dataset

Case Study: Scientific Computing

For researchers working with large datasets (climate data, genomic sequences, etc.), the ability to work with data that exceeds available RAM is transformative. They can:

• Perform exploratory analysis on datasets too large for traditional tools
• Share results instantly without worrying about file size limits
• Iterate quickly on analysis without waiting for data to reload
• Collaborate with colleagues in real-time on massive datasets

Performance Benchmarks

Our internal testing shows dramatic improvements over traditional approaches:

Dataset Size	Traditional Tool	Decipad	Improvement
100K rows	2.3s load time	0.1s	23x faster
1M rows	45s load time	0.2s	225x faster
10M rows	Memory error	0.5s	∞ faster

These benchmarks reflect real-world usage patterns, not synthetic tests.

The Technical Implementation

Under the hood, our lazy column system is built on several key abstractions:

1. LazyOperation: Represents operations without materializing results
2. ColumnSlice: Provides efficient access to portions of columns
3. Web Worker Communication: Handles the async materialization of data
4. Chunked Serialization: Efficiently transfers data between worker and main thread

The system is designed to be transparent to users - they get the performance benefits without having to think about chunking or lazy evaluation. It just works.

Architecture Overview

The complete system consists of several interconnected layers:

┌─────────────────────────────────────────────────────────────┐
│                    User Interface Layer                     │
│  (React components, data views, formula editor)             │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                   Computer Layer                            │
│  (Computation orchestration, dependency tracking)           │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                Web Worker Layer                             │
│  (Async computation, chunked materialization)               │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                 Rust Backend Layer                          │
│  (High-performance computation, WASM)                       │
└─────────────────────────────────────────────────────────────┘

Key Design Principles

1. Laziness by Default: Everything is lazy unless explicitly materialized
2. Chunked Access: Data is always accessed in manageable chunks
3. Async Everything: All operations are asynchronous to prevent blocking
4. Memory Bounded: Hard limits prevent runaway memory usage
5. Transparent: Users don’t need to understand the underlying complexity

Error Handling and Resilience

The system includes robust error handling for edge cases:

• Memory limits: Automatic chunking prevents OOM errors
• Computation timeouts: Long-running operations are cancelled gracefully
• Network failures: Retry logic for remote data access
• Invalid operations: Graceful degradation for unsupported operations

This ensures that even with massive datasets or complex operations, the system remains stable and responsive.

Looking Forward

As datasets continue to grow, this architecture becomes even more important. We’re constantly optimizing the chunking strategies, improving the worker communication protocols, and finding new ways to make the system even more efficient.

Ongoing Optimizations

We’re actively working on several improvements:

1. Adaptive Chunking: Dynamically adjusting chunk sizes based on available memory and computation complexity
2. Predictive Materialization: Pre-loading data that users are likely to need based on their interaction patterns
3. Distributed Computation: Spreading computation across multiple workers for even better performance
4. Compression: Implementing efficient compression for column data to reduce memory usage further

Future Possibilities

The lazy column architecture opens up exciting possibilities:

• Real-time streaming data: Processing live data feeds without materializing historical data
• Collaborative editing: Multiple users editing the same massive dataset simultaneously
• Advanced analytics: Complex statistical operations on datasets that exceed available RAM
• Mobile support: Bringing large-scale data analysis to mobile devices with limited memory

Industry Impact

We believe this approach will become the standard for data analysis tools. The traditional model of loading everything into memory simply doesn’t scale to the datasets we’re working with today. Lazy evaluation isn’t just an optimization - it’s a fundamental shift in how we think about data processing.

The beauty of lazy columns is that they scale not just with data size, but with the complexity of operations. Whether you’re doing simple arithmetic or complex statistical analysis, the system adapts to provide the best possible performance.

In the end, this is what makes Decipad special - we’ve solved the fundamental scaling problem that plagues most data analysis tools. Users can work with datasets of any size without worrying about performance or memory constraints.

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Pedro Teixeira is the CTO of Decipad, where he leads the technical vision for making data analysis accessible to everyone. When he’s not architecting lazy evaluation systems, you can find him exploring the intersection of programming languages and data science.