Univer Sheet
Architecture of Formula Engine

Architecture of Formula Engine

📊📝📽️ Univer General

Chapter outline discussing Univer's formula engine architecture, it is recommended to read the "Overall Architecture" chapter first.

The primary objectives in designing the formula engine are:

  1. Support Univer's various document types and related functionalities to connect to formulas
  2. Provide a smooth user experience, supporting computation on web worker and server side
  3. Support advanced formula capabilities, aligned with features provided by Microsoft Office 365, including but not limited to:
    1. Sense formula execution status, support for stopping formula execution, support for loop reference detection and iteration calculation execution limit setting
    2. Let / lambda and other custom functions
    3. Supporting supertables, formulae, and naming ranges.

Overall Architecture

The architecture diagram is presented as follows:

formula engine architecture

  1. Model: Stores the initial formula data, such as location and formula string.
  2. Engine: Responsible for syntactic and semantic analysis of the formula string, analyzing the dependencies between formulas, and more.
  3. Service: Provides a formula computation environment, supports registering functions, and offers custom name, supertable, and formula scheduling services.
  4. Command and Controller: Control the formula module coordination.
  5. Function: Function implementation.


The Engine is the core of the formula engine, providing the following capabilities:

  • Dependency analysis, determining the order of execution for a set of formulas.
  • Syntax and lexical analysis of each formula string, generating a syntax tree.
  • Performing computation through the syntax tree.
  • Providing basic operations for functions, including addition, subtraction, multiplication, and division, string concatenation, trigonometric functions, etc.

The Engine architecture is presented in the following figure:



The Lexer is responsible for the lexical analysis of the formula string. It matches tokens defined by the engine and generates nodes based on the rules, constructing a tree of LexerNode nodes. For example, A1, B10, SUM will all be recognized as LexerNode. The recognition of node types will be handled by the Parser. For instance:

=(sum(sum(A1:B10), E10, 100) + 5) * 6 - 1

The transformed LexerNode tree looks as follows:


After generating the LexerNode tree, the engine will call the conversion method, using postfix notation (opens in a new tab) to replace the original infix expression, eliminating the parentheses in the computation. For example:

( 3 + 4 ) * 5 - 6

Will be converted to:

3 4 + 5 × 6 -


The Parser's main responsibilities are to transform the LexerNode tree generated by the Lexer as follows, creating an AstNode tree:

  1. Convert node names containing functions like SUM to FunctionNode formula nodes, and references like E10 to ReferenceNode reference nodes, and operators like + to OperatorNode operation nodes.
  2. Other node types include:
    1. LambdaNode, specific to lambda functions, for parametrization and wrapping as lambda-value-object
    2. UnionNode, merging A1:B10 as RangeReference
    3. PrefixNode, recognizing - as a negative number and compatibility with @ in older versions
    4. SuffixNode, recognizing % as percentage and # as shorthand for dynamic array formula range
    5. ValueNode, recognizing text, number, and logical value as the three basic types
  3. Convert let to lambda execution
  4. Inject parameters for lambda

The illustration of the transformed LexerNode tree to AstNode tree is as follows:



Responsible for executing a single expression, recursively obtaining the return value by invoking the methods of the AST node, with the following main responsibilities:

  1. Converting operators to meta functions and executing them, primarily including arithmetic and comparison operators
  2. Instantiating characters, numbers, and Boolean values as ValueObject, and instantiating arrays as ArrayValueObject
  3. Instantiating references as ReferenceObject, and calling the internal method to convert them to ArrayValueObject
  4. Invoking specific functions to start the calculation, with the values received by the function being the base class BaseValueObject, and returning ReferenceObject | BaseValueObject | AsyncValueObject
  5. Asynchronous calculations will be awaited for results in the upper layer and passed to the lower layer, so there is no need to write asynchronous methods in the function, just passing the Promise as a parameter to AsyncValueObject and returning
  6. For INDIRECT, OFFSET, and other reference functions, return ReferenceObject.

BaseValueObject is a crucial operation type in the formula engine computation, with the following inheritance types:


Representing a null value, treated as false or 0 when calculated with other value types. An ErrorValueObject is returned directly if unable to calculate.


Representing an error, similar to Excel's #VALUE!, #NAME!, #REF!, etc. An ErrorValueObject can be returned directly in the function to represent a calculation error.


Comprised of three basic value types: NumberValueObject, StringValueObject, and BooleanValueObject, each implementing their respective numeric calculation methods. The precision of the underlying computation is handled by big.js (opens in a new tab).


Passing lambda functions as arguments to lower-level functions allows them to be applied in functions such as MAKEARRAY (opens in a new tab) and REDUCE (opens in a new tab).


This is the core of matrix computation and can be computed with any PrimitiveValueObject or another ArrayValueObject. The calculation is demonstrated in the following figure:


It also supports calculations such as sum, average, min, max, std, var, power, and more. Furthermore, it has implemented capabilities like Numpy's slice (opens in a new tab) and filter (opens in a new tab) for functions such as vlookup, xlookup, match, and others.

export class Vlookup extends BaseFunction {
  override calculate(
    lookupValue: BaseValueObject,
    tableArray: BaseValueObject,
    colIndexNum: BaseValueObject,
    rangeLookup: BaseValueObject = new PrimitiveValueObject(false)
  ) {
    const colIndexNumValue = this.getIndexNumValue(colIndexNum);
    // Extract the first column of the tableArray
    const searchArray = (tableArray as ArrayValueObject).slice(0, 1);
    // Extract the column specified by colIndexNumValue
    const resultArray = (tableArray as ArrayValueObject).slice(colIndexNumValue - 1, colIndexNumValue);
    // Use the pick method of the resultArray to filter the matrix based on the lookupValue
    // Return the first result value found
    return resultArray.pick((searchArray as ArrayValueObject).isEqual(lookupValue) as ArrayValueObject).getFirstCell();

In situations with a high number of formulas, ArrayValueObject implements a reverse index on the list to enhance iteration performance.



Responsible for formula dependency analysis, marking formulas that need to be calculated and outputting a queue of the marked formulas for execution.


As shown in the above image, when the content of cell A1 changes, the formulas in A2, A3, A4, A5 will be marked as dirty, and the marked formulas will undergo dependency analysis, resulting in the final output order of A2 -> A3 -> A5 -> A4.

If INDIRECT / OFFSET (opens in a new tab) -like reference functions are encountered, the Dependency module will call Lexer and Parser to perform pre-computation. This allows for the advance calculation of the reference range of these types of functions, which is then used to calculate their dependencies.


Provides various services for the computation process. Here are a few of the key services:

IFormulaCurrentConfigService and IFormulaRuntimeService

Used for loading Univer data and storing temporary data for the formula execution process. The runtime returns all calculation results once the formula execution is complete.


Used for registering functions and their descriptions. Users can also register quick, custom functions through uniscript.


Registers features in the sheet domain, such as pivot tables, conditional formatting, and data validation. For example, pivot tables:

  1. Pivot tables can register a dependency range and a getDirtyData method.
  2. After the dependency range is marked as dirty, the getDirtyData method is executed, implementing the calculation logic within the pivot table.
  3. The getDirtyData method can return a dirty area and temporary data for the dirty area, which are used for further calculation dependent on the pivot table results. The final, correct result is obtained.


Registers formulas for doc and slide, which are non-table domains. These formulas are not dependent on sheet formulas, so they do not need to return dirty areas and temporary data.


The core method for triggering formula calculation, which provides the following functions:

  1. Circular dependency execution
  2. Return of runtime status, including the total number of formulas executed and the number of completed formulas.
  3. Secondary marking of array formulas as dirty, and their execution after the results are returned.
  4. Uses requestImmediateMacroTask to avoid the 4ms limit of setTimeout, enabling formula execution in macro tasks and supporting termination of formula execution.
  5. Formula execution time statistics.


Implemented with matrix calculation, reducing code size and centralizing core logic. This leads to more standardized function implementation and improved accuracy and quality.

The formula engine references Numpy's matrix operation concept, reducing the amount of code needed for function implementation and centralizing numerical computation, trigonometric function computation, and other core logic in BaseValueObject. This lays the groundwork for standardizing function implementation.

The following is an example of a Sum formula implementation:

export class Sum extends BaseFunction {
  override calculate(...variants: BaseValueObject[]) {
    // Initialize an accumulator variable for summing
    let accumulatorAll: BaseValueObject = new NumberValueObject(0);
    // The number of parameters for the Sum function is determined by the user. The following loop obtains and calculates.
    for (let i = 0; i < variants.length; i++) {
      let variant = variants[i];
      // If the input is a reference range like A1:B10, the upper layer will automatically convert it to an ArrayValueObject.
      if (variant.isArray()) {
        // Call the sum function on ArrayValueObject to sum the value of all ValueObjects
        variant = (variant as ArrayValueObject).sum();
      // Call the numerical computation function Plus on ValueObject to perform the sum
      accumulatorAll = accumulatorAll.plus(variant);
    return accumulatorAll;

Copyright © 2021-2024 DreamNum Co,Ltd. All Rights Reserved.