comparison of declarative loop and imperative loop

Question

comparison of declarative loop and imperative loop

In my journey to transition from an imperative programming style to a declarative one, I've encountered a challenge related to performance when dealing with loops. Specifically, I have a set of original DATA that I need to manipulate in order to achieve 3 desired outcomes: itemsHash, namesHash, rangeItemsHash

// initial data

const DATA = [
  {id: 1, name: 'Alan', date: '2021-01-01', age: 0},
  {id: 2, name: 'Ben', date: '1980-02-02', age: 41},
  {id: 3, name: 'Clara', date: '1959-03-03', age: 61},
]

...

// desired outcome

// itemsHash => {
//   1: {id: 1, name: 'Alan', date: '2021-01-01', age: 0},
//   2: {id: 2, name: 'Ben', date: '1980-02-02', age: 41},
//   3: {id: 3, name: 'Clara', date: '1959-03-03', age: 61},
// }

// namesHash => {1: 'Alan', 2: 'Ben', 3: 'Clara'}

// rangeItemsHash => {
//   minor: [{id: 1, name: 'Alan', date: '2021-01-01', age: 0}],
//   junior: [{id: 2, name: 'Ben', date: '1980-02-02', age: 41}],
//   senior: [{id: 3, name: 'Clara', date: '1959-03-03', age: 61}],
// }

// imperative approach

const itemsHash = {}
const namesHash = {}
const rangeItemsHash = {}

DATA.forEach(person => {
  itemsHash[person.id] = person;
  namesHash[person.id] = person.name;
  if (person.age > 60){
    if (typeof rangeItemsHash['senior'] === 'undefined'){
      rangeItemsHash['senior'] = []
    }
    rangeItemsHash['senior'].push(person)
  }
  else if (person.age > 21){
    if (typeof rangeItemsHash['junior'] === 'undefined'){
      rangeItemsHash['junior'] = []
    }
    rangeItemsHash['junior'].push(person)
  }
  else {
    if (typeof rangeItemsHash['minor'] === 'undefined'){
      rangeItemsHash['minor'] = []
    }
    rangeItemsHash['minor'].push(person)
  }
})

// declarative approach

const itemsHash = R.indexBy(R.prop('id'))(DATA);
const namesHash = R.compose(R.map(R.prop('name')),R.indexBy(R.prop('id')))(DATA);

const gt21 = R.gt(R.__, 21);
const lt60 = R.lte(R.__, 60);
const isMinor = R.lt(R.__, 21);
const isJunior = R.both(gt21, lt60);
const isSenior = R.gt(R.__, 60);


const groups = {minor: isMinor, junior: isJunior, senior: isSenior };

const rangeItemsHash = R.map((method => R.filter(R.compose(method, R.prop('age')))(DATA)))(groups)

In order to achieve the desired outcome, the imperative approach only requires looping through the data once, whereas the declarative approach involves at least 3 loops (itemsHash, namesHash , rangeItemsHash ) Which method is more efficient? Are there any performance trade-offs to consider?

javascript functional-programming ramda.js declarative-programming

Answer 1

Answer №1

I have a few thoughts on this topic.

First and foremost, it's essential to determine if performance is truly an issue. It's common for developers to optimize code prematurely without identifying the actual bottlenecks in their application. My approach is to prioritize writing clear and simple code initially, with performance considerations in mind but not as the primary focus. If performance becomes a concern, I recommend benchmarking the application to pinpoint the areas that need optimization. In my experience, the optimizations needed are rarely as straightforward as optimizing a loop from three iterations to one.

If optimization within a single loop is necessary, leveraging a reduce call can be effective. Here's an example:

// A snippet demonstrating the use of reduce
const ageGroup = ({age}) => age > 60 ? 'senior' : age > 21 ? 'junior' : 'minor'

const convert = (people) =>
  people.reduce(({itemsHash, namesHash , rangeItemsHash}, person, _, __, group = ageGroup(person)) => ({
    itemsHash: {...itemsHash, [person.id]: person},
    namesHash: {...namesHash, [person.id]: person.name},
    rangeItemsHash: {...rangeItemsHash, [group]: [...(rangeItemsHash[group] || []), person]}
  }), {itemsHash: {}, namesHash: {}, rangeItemsHash: {}})

// Sample data
const data = [{id: 1, name: 'Alan', date: '2021-01-01', age: 0}, {id: 2, name: 'Ben', date: '1980-02-02', age: 41}, {id: 3, name: 'Clara', date: '1959-03-03', age: 61}]

// Demo
console.log(JSON.stringify(
  convert(data)
, null, 4))

(prevent JSON.stringify call to display shared references across output hashes)

From here, there are two potential strategies to enhance this code further.

The first option is to incorporate Ramda, which offers functions to streamline certain aspects of the code. By using R.reduce and functions like evolve, assoc, and over, we can achieve a more declarative style of coding. Here's a modified version:

// Using Ramda for optimization
const ageGroup = ({age}) => age > 60 ? 'senior' : age > 21 ? 'junior' : 'minor'

const convert = (people) =>
  R.reduce(
    (acc, person, group = ageGroup(person)) => evolve({
      itemsHash: assoc(person.id, person),
      namesHash: assoc(person.id, person.name),
      rangeItemsHash: over(lensProp(group), append(person))
    })(acc), {itemsHash: {}, namesHash: {}, rangeItemsHash: {minor: [], junior: [], senior: []}}, 
    people
  )

// Sample data
const data = [{id: 1, name: 'Alan', date: '2021-01-01', age: 0}, {id: 2, name: 'Ben', date: '1980-02-02', age: 41}, {id: 3, name: 'Clara', date: '1959-03-03', age: 61}]

// Demo
console.log(JSON.stringify(
  convert(data)
, null, 4))

(include scripts from Ramda library for above functionality)

Another angle to consider is addressing the performance issue known as "the reduce ({...spread}) anti-pattern." This involves modifying the accumulator object within the reduce callback function. While Ramda may not directly assist with this problem due to its functional nature, custom helper functions can offer a solution while improving code readability. Here's an alternative implementation:

// Enhancing performance via custom utility functions
const push = (x, xs) => ((xs.push(x)), x)
const put = (k, v, o) => ((o[k] = v), o)
const appendTo = (k, v, o) => put(k, push(v, o[k] || []), o)

const ageGroup = ({age}) => age > 60 ? 'senior' : age > 21 ? 'junior' : 'minor'

const convert = (people) =>
  people.reduce(({itemsHash, namesHash, rangeItemsHash}, person, _, __, group = ageGroup(person)) => ({
    itemsHash: put(person.id, person, itemsHash),
    namesHash: put(person.id, person.name, namesHash),
    rangeItemsHash: appendTo(group, person, rangeItemsHash)
  }), {itemsHash: {}, namesHash: {}, rangeItemsHash: {}})

// Sample data
const data = [{id: 1, name: 'Alan', date: '2021-01-01', age: 0}, {id: 2, name: 'Ben', date: '1980-02-02', age: 41}, {id: 3, name: 'Clara', date: '1959-03-03', age: 61}]

// Demo
console.log(JSON.stringify(
  convert(data)
, null, 4))

In conclusion, unless performance issues are clearly identified, extensive optimizations may not be necessary. Prioritizing clean and understandable code is crucial. Utilizing tools like Ramda can simplify complex operations and facilitate code maintenance without sacrificing clarity.

Answer 2

I have a few thoughts on this topic.

First and foremost, it's essential to determine if performance is truly an issue. It's common for developers to optimize code prematurely without identifying the actual bottlenecks in their application. My approach is to prioritize writing clear and simple code initially, with performance considerations in mind but not as the primary focus. If performance becomes a concern, I recommend benchmarking the application to pinpoint the areas that need optimization. In my experience, the optimizations needed are rarely as straightforward as optimizing a loop from three iterations to one.

If optimization within a single loop is necessary, leveraging a reduce call can be effective. Here's an example:

// A snippet demonstrating the use of reduce
const ageGroup = ({age}) => age > 60 ? 'senior' : age > 21 ? 'junior' : 'minor'

const convert = (people) =>
  people.reduce(({itemsHash, namesHash , rangeItemsHash}, person, _, __, group = ageGroup(person)) => ({
    itemsHash: {...itemsHash, [person.id]: person},
    namesHash: {...namesHash, [person.id]: person.name},
    rangeItemsHash: {...rangeItemsHash, [group]: [...(rangeItemsHash[group] || []), person]}
  }), {itemsHash: {}, namesHash: {}, rangeItemsHash: {}})

// Sample data
const data = [{id: 1, name: 'Alan', date: '2021-01-01', age: 0}, {id: 2, name: 'Ben', date: '1980-02-02', age: 41}, {id: 3, name: 'Clara', date: '1959-03-03', age: 61}]

// Demo
console.log(JSON.stringify(
  convert(data)
, null, 4))

(prevent JSON.stringify call to display shared references across output hashes)

From here, there are two potential strategies to enhance this code further.

The first option is to incorporate Ramda, which offers functions to streamline certain aspects of the code. By using R.reduce and functions like evolve, assoc, and over, we can achieve a more declarative style of coding. Here's a modified version:

// Using Ramda for optimization
const ageGroup = ({age}) => age > 60 ? 'senior' : age > 21 ? 'junior' : 'minor'

const convert = (people) =>
  R.reduce(
    (acc, person, group = ageGroup(person)) => evolve({
      itemsHash: assoc(person.id, person),
      namesHash: assoc(person.id, person.name),
      rangeItemsHash: over(lensProp(group), append(person))
    })(acc), {itemsHash: {}, namesHash: {}, rangeItemsHash: {minor: [], junior: [], senior: []}}, 
    people
  )

// Sample data
const data = [{id: 1, name: 'Alan', date: '2021-01-01', age: 0}, {id: 2, name: 'Ben', date: '1980-02-02', age: 41}, {id: 3, name: 'Clara', date: '1959-03-03', age: 61}]

// Demo
console.log(JSON.stringify(
  convert(data)
, null, 4))

(include scripts from Ramda library for above functionality)

Another angle to consider is addressing the performance issue known as "the reduce ({...spread}) anti-pattern." This involves modifying the accumulator object within the reduce callback function. While Ramda may not directly assist with this problem due to its functional nature, custom helper functions can offer a solution while improving code readability. Here's an alternative implementation:

// Enhancing performance via custom utility functions
const push = (x, xs) => ((xs.push(x)), x)
const put = (k, v, o) => ((o[k] = v), o)
const appendTo = (k, v, o) => put(k, push(v, o[k] || []), o)

const ageGroup = ({age}) => age > 60 ? 'senior' : age > 21 ? 'junior' : 'minor'

const convert = (people) =>
  people.reduce(({itemsHash, namesHash, rangeItemsHash}, person, _, __, group = ageGroup(person)) => ({
    itemsHash: put(person.id, person, itemsHash),
    namesHash: put(person.id, person.name, namesHash),
    rangeItemsHash: appendTo(group, person, rangeItemsHash)
  }), {itemsHash: {}, namesHash: {}, rangeItemsHash: {}})

// Sample data
const data = [{id: 1, name: 'Alan', date: '2021-01-01', age: 0}, {id: 2, name: 'Ben', date: '1980-02-02', age: 41}, {id: 3, name: 'Clara', date: '1959-03-03', age: 61}]

// Demo
console.log(JSON.stringify(
  convert(data)
, null, 4))

In conclusion, unless performance issues are clearly identified, extensive optimizations may not be necessary. Prioritizing clean and understandable code is crucial. Utilizing tools like Ramda can simplify complex operations and facilitate code maintenance without sacrificing clarity.

Answer 3

Answer №2

Just like how you can compose functions with

.map(f).map(g) == .map(compose(g, f))

, you can also compose reducers to ensure all results are obtained in a single pass.

The decision to loop once or multiple times when writing declarative code is not necessarily related to the simplicity of the code.

// Implementing reducer logic for all 3 values of interest
// ID: person
const idIndexReducer = (idIndex, p) => 
  ({ ...idIndex, [p.id]: p });

// ID: name
const idNameIndexReducer = (idNameIndex, p) => 
  ({ ...idNameIndex, [p.id]: p.name });
  
// Age
const ageLabel = ({ age }) => age > 60 ? "senior" : age > 40 ? "medior" : "junior";
const ageGroupReducer = (ageGroups, p) => {
  const ageKey = ageLabel(p);
  
  return {
    ...ageGroups,
    [ageKey]: (ageGroups[ageKey] || []).concat(p)
  }
}

// Combining the reducers
const seed = { idIndex: {}, idNameIndex: {}, ageGroups: {} };
const reducer = ({ idIndex, idNameIndex, ageGroups }, p) => ({
  idIndex: idIndexReducer(idIndex, p),
  idNameIndex: idNameIndexReducer(idNameIndex, p),
  ageGroups: ageGroupReducer(ageGroups, p)
})

const DATA = [
  {id: 1, name: 'Alan', date: '2021-01-01', age: 0},
  {id: 2, name: 'Ben', date: '1980-02-02', age: 41},
  {id: 3, name: 'Clara', date: '1959-03-03', age: 61},
]

// Single loop processing
console.log(
  JSON.stringify(DATA.reduce(reducer, seed), null, 2)
);

Regarding the subjectivity of whether it's worthwhile, simplicity and performance play key roles. In my experience, transitioning from 1 to 3 loops for small datasets often yields negligible impact.

Therefore, if leveraging Ramda, I would adhere to:

const { prop, indexBy, map, groupBy, pipe } = R;

const DATA = [
  {id: 1, name: 'Alan', date: '2021-01-01', age: 0},
  {id: 2, name: 'Ben', date: '1980-02-02', age: 41},
  {id: 3, name: 'Clara', date: '1959-03-03', age: 61},
];

const byId = indexBy(prop("id"), DATA);
const nameById = map(prop("name"), byId);
const ageGroups = groupBy(
  pipe(
    prop("age"), 
    age => age > 60 ? "senior" : age > 40 ? "medior" : "junior"
  ),
  DATA
);

console.log(JSON.stringify({ byId, nameById, ageGroups }, null, 2))

<script src="https://cdn.jsdelivr.net/npm/<a href="/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="6e1c0f030a0f2e5e405c59405f">[email protected]</a>/dist/ramda.min.js"></script>

Answer 4

Just like how you can compose functions with

.map(f).map(g) == .map(compose(g, f))

, you can also compose reducers to ensure all results are obtained in a single pass.

The decision to loop once or multiple times when writing declarative code is not necessarily related to the simplicity of the code.

// Implementing reducer logic for all 3 values of interest
// ID: person
const idIndexReducer = (idIndex, p) => 
  ({ ...idIndex, [p.id]: p });

// ID: name
const idNameIndexReducer = (idNameIndex, p) => 
  ({ ...idNameIndex, [p.id]: p.name });
  
// Age
const ageLabel = ({ age }) => age > 60 ? "senior" : age > 40 ? "medior" : "junior";
const ageGroupReducer = (ageGroups, p) => {
  const ageKey = ageLabel(p);
  
  return {
    ...ageGroups,
    [ageKey]: (ageGroups[ageKey] || []).concat(p)
  }
}

// Combining the reducers
const seed = { idIndex: {}, idNameIndex: {}, ageGroups: {} };
const reducer = ({ idIndex, idNameIndex, ageGroups }, p) => ({
  idIndex: idIndexReducer(idIndex, p),
  idNameIndex: idNameIndexReducer(idNameIndex, p),
  ageGroups: ageGroupReducer(ageGroups, p)
})

const DATA = [
  {id: 1, name: 'Alan', date: '2021-01-01', age: 0},
  {id: 2, name: 'Ben', date: '1980-02-02', age: 41},
  {id: 3, name: 'Clara', date: '1959-03-03', age: 61},
]

// Single loop processing
console.log(
  JSON.stringify(DATA.reduce(reducer, seed), null, 2)
);

Regarding the subjectivity of whether it's worthwhile, simplicity and performance play key roles. In my experience, transitioning from 1 to 3 loops for small datasets often yields negligible impact.

Therefore, if leveraging Ramda, I would adhere to:

const { prop, indexBy, map, groupBy, pipe } = R;

const DATA = [
  {id: 1, name: 'Alan', date: '2021-01-01', age: 0},
  {id: 2, name: 'Ben', date: '1980-02-02', age: 41},
  {id: 3, name: 'Clara', date: '1959-03-03', age: 61},
];

const byId = indexBy(prop("id"), DATA);
const nameById = map(prop("name"), byId);
const ageGroups = groupBy(
  pipe(
    prop("age"), 
    age => age > 60 ? "senior" : age > 40 ? "medior" : "junior"
  ),
  DATA
);

console.log(JSON.stringify({ byId, nameById, ageGroups }, null, 2))

<script src="https://cdn.jsdelivr.net/npm/<a href="/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="6e1c0f030a0f2e5e405c59405f">[email protected]</a>/dist/ramda.min.js"></script>

comparison of declarative loop and imperative loop

Answer №1

Answer №2

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