Building a full-text search engine in TypeScript

Michele Riva

Michele Riva

Senior Software Architect @NearForm

Google Developer Expert

Microsoft MVP

MicheleRivaCode

MicheleRivaCode

Why?

MicheleRivaCode

MicheleRivaCode

What I cannot create, I do not understand

Richard Feynman

MicheleRivaCode

A journey through algorithms and data structures

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

There's no slow programming language, just bad DSA design

MicheleRivaCode

What is "full-text" search?

sybase.com

Full-text search is a more advanced way to search a database.

Full-text search quickly finds all instances of a term (word) in a table without having to scan rows and without having to know which column a term is stored in.

Full-text search works by using text indexes.

A text index stores positional information for all terms found in the columns you create the text index on.

MicheleRivaCode

What is "full-text" search?

sybase.com

Full-text search is a more advanced way to search a database.

Full-text search quickly finds all instances of a term (word) in a table without having to scan rows and without having to know which column a term is stored in.

Full-text search works by using text indexes.

A text index stores positional information for all terms found in the columns you create the text index on.

MicheleRivaCode

What is "full-text" search?

sybase.com

Full-text search is a more advanced way to search a database.

Full-text search quickly finds all instances of a term (word) in a table without having to scan rows and without having to know which column a term is stored in.

Full-text search works by using text indexes.

A text index stores positional information for all terms found in the columns you create the text index on.

MicheleRivaCode

What is "full-text" search?

sybase.com

Full-text search is a more advanced way to search a database.

Full-text search quickly finds all instances of a term (word) in a table without having to scan rows and without having to know which column a term is stored in.

Full-text search works by using text indexes.

A text index stores positional information for all terms found in the columns you create the text index on.

Popular full-text search engines

MicheleRivaCode

"New generation" full-text search engines

MicheleRivaCode

Sonic

Meilisearch

JavaScript-based full-text search engines

MicheleRivaCode

Lunr.js

MiniSearch

Fuse.js

MicheleRivaCode

Where to start?

MicheleRivaCode

Understand what kind of data we want to store and retrieve

MicheleRivaCode

[
  {
    "id": 1,
    "quote": "It's alive! It's alive!",
    "movie": "Frankenstein",
    "year": 1931
  },
  {
    "id": 2,
    "quote": "You've got to ask yourself one question: 'Do I feel lucky?' Well, do ya, punk?",
    "movie": "Dirty Harry",
    "year": 1971
  },
  {
    "id": 3,
    "quote": "Mama always said life was like a box of chocolates. You never know what you're gonna get.",
    "movie": "Forrest Gump",
    "year": 1994
  }
]

Example documents

MicheleRivaCode

// "It's alive! It's alive!"
["Its", "alive", "Its", "alive"]

// "You've got to ask yourself one question: 'Do I feel lucky?' Well, do ya, punk?"
[
  "Youve", "got", "to", "ask", "yourself", "one", "question",
  "Do", "I", "feel", "lucky", "Well", "do", "ya", "punk"
]

// "Mama always said life was like a box of chocolates. You never know what you're gonna get."
[
  "Mama", "always", "said", "life", "was", "like", "a", "box", "of", 
  "chocolates", "You", "never", "know", "what", "youre", "gonna", "get"
]

Tokenizer

Break the sentences into individual tokens

MicheleRivaCode

// "It's alive! It's alive!"
["its", "alive", "its", "alive"]

// "You've got to ask yourself one question: 'Do I feel lucky?' Well, do ya, punk?"
[
  "youve", "got", "to", "ask", "yourself", "one", "question",
  "do", "i", "feel", "lucky", "well", "do", "ya", "punk"
]

// "Mama always said life was like a box of chocolates. You never know what you're gonna get."
[
  "mama", "always", "said", "life", "was", "like", "a", "box", "of", 
  "chocolates", "you", "never", "know", "what", "youre", "gonna", "get"
]

Tokenizer

Lowercase all tokens

MicheleRivaCode

// "It's alive! It's alive!"
["its", "alive"]

// "You've got to ask yourself one question: 'Do I feel lucky?' Well, do ya, punk?"
[
  "youve", "got", "to", "ask", "yourself", "one", "question",
  "do", "i", "feel", "lucky", "well", "ya", "punk"
]

// "Mama always said life was like a box of chocolates. You never know what you're gonna get."
[
  "mama", "always", "said", "life", "was", "like", "a", "box", "of", 
  "chocolates", "you", "never", "know", "what", "youre", "gonna", "get"
]

Tokenizer

Remove duplicates

MicheleRivaCode

// "It's alive! It's alive!"
["alive"]

// "You've got to ask yourself one question: 'Do I feel lucky?' Well, do ya, punk?"
[
  "youve", /* "got", */ /* "to", */ "ask", "yourself", "one", "question",
  /* "do", */ /* "i", */ "feel", "lucky", "well", "ya", "punk"
]

// "Mama always said life was like a box of chocolates. You never know what you're gonna get."
[
  "mama", "always", "said", "life", /* "was", */, "like", /* "a", */ "box", /* "of", */
  "chocolates", "you", "never", "know", /* "what", */ "youre", /* "gonna", */ "get"
]

Tokenizer

Remove stop-words*

MicheleRivaCode

What is a stop word?

Stop words are a set of commonly used words in a language. Examples of stop words in English are “a”, “the”, “is”, “are” and etc. Stop words are commonly used in Text Mining and Natural Language Processing (NLP) to eliminate words that are so commonly used that they carry very little useful information.

https://www.opinosis-analytics.com/knowledge-base/stop-words-explained/

MicheleRivaCode

// "It's alive! It's alive!"
["alive"]

// "You've got to ask yourself one question: 'Do I feel lucky?' Well, do ya, punk?"
[
  "youve", /* "got", */ /* "to", */ "ask", "yourself", "one", "question",
  /* "do", */ /* "i", */ "feel", "lucky", "well", "ya", "punk"
]

// "Mama always said life was like a box of chocolates. You never know what you're gonna get."
[
  "mama", "always", "said", "life", /* "was", */, "like", /* "a", */ "box", /* "of", */
  "chocolates", "you", "never", "know", /* "what", */ "youre", /* "gonna", */ "get"
]

Tokenizer

Remove stop-words*

MicheleRivaCode

// "It's alive! It's alive!"
["alive"]

// "You've got to ask yourself one question: 'Do I feel lucky?' Well, do ya, punk?"
[
  "you" /* was "youve" */, "ask", "yourself", "one", "question",
  "feel", "luck" /* was "lucky" */, "well", /* "ya" becomes "you", duplicate */ "punk"
]

// "Mama always said life was like a box of chocolates. You never know what you're gonna get."
[
  "mom" /* was "mama" */, "always", "say" /* was "said" */, "life", "like", "box", 
  "chocolate" /* was "chocolates" */, "you", "never", "know", /*"you", was "youre", duplicate */, "get"
]

Tokenizer

Stemming*

MicheleRivaCode

Snowball

https://snowballstem.org

MicheleRivaCode

English 🇺🇸🇬🇧🇦🇺

http://snowball.tartarus.org/algorithms/english/stemmer.html

MicheleRivaCode

German 🇩🇪

http://snowball.tartarus.org/algorithms/german/stemmer.html

MicheleRivaCode

Italian 🇮🇹

http://snowball.tartarus.org/algorithms/italian/stemmer.html

MicheleRivaCode

Finnish 🇫🇮

http://snowball.tartarus.org/algorithms/finnish/stemmer.html

MicheleRivaCode

MicheleRivaCode

[
  {
    "id": 1,
    "quote": ["alive"],
    ...
  },
  {
    "id": 2,
    "quote": ["you", "ask", "yourself", "one", "question", "feel", "luck", "well", "punk"],
    ...
  },
  {
    "id": 3,
    "quote": ["mom", "always", "say", "life", "like", "box", "chocolate", "you", "never", "know", "get"],
    ...
  }
]

Final Result

Remaining tokens

MicheleRivaCode

How do we want to store this data?

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

Find document containing the word "chocolate" in linear time

MicheleRivaCode

Find document containing the word "chocolate" in linear time

MicheleRivaCode

Find document containing the word "chocolate" in linear time

MicheleRivaCode

Find document containing the word "chocolate" in linear time

MicheleRivaCode

Find document containing the word "chocolate" in linear time

MicheleRivaCode

Find document containing the word "chocolate" in linear time

MicheleRivaCode

Find document containing the word "chocolate" in linear time

MicheleRivaCode

Time complexity is O(n)

MicheleRivaCode

MicheleRivaCode

animal   = dog
book     = algorithms to live by
color    = green
language = javascript
city     = florence
food     = chocolate

HashMaps are used to store data in key-value pairs

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

function hash(key: string, size: number): number {
  let hash = 0;
  
  for (let i = 0; i < key.length; i++) {
    let char = key[i];
    hash = (hash << 5) + char.charCodeAt(0);
    hash = (hash & hash) % size;
  }
  
  return hash;
}

Example of an hashing algorithm

MicheleRivaCode

function hash(key: string, size: number): number {
  let hash = 0;
  
  for (let i = 0; i < key.length; i++) {
    let char = key[i];
    hash = (hash << 5) + char.charCodeAt(0);
    hash = (hash & hash) % size;
  }
  
  return hash;
}

const size = 10;

hash("food", size);           // => 2
hash("book", size);           // => 7
hash("hello, Berlin!", size); // => 9

Example of an hashing algorithm

MicheleRivaCode

When asking for a key, we know the exact position of its value inside of the array.

Hence, time complexity is O(1)

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

But that's not enough to find "chocolate" inside of our array of documents in O(1)

MicheleRivaCode

We need an inverted index

MicheleRivaCode

{
  1 => ["alive"],
  2 => ["you", "ask", "yourself", "one", "question", "feel", "luck", "well", "punk"],
  3 => ["mom", "always", "say", "life", "like", "box", "chocolate", "you", "never", "know", "get"],
}

Regular HashMap

MicheleRivaCode

{
  "alive"     => [1],
  "you"       => [2, 3],
  "ask"       => [1],
  "yourself"  => [2],
  "chocolate" => [3],
  "punk"      => [2],
  "one"       => [2],
  "question"  => [2],
  "feel"      => [2],
  "mom"       => [3],
  "always"    => [3],
  "say"       => [3],
  "know"      => [3],
  "luck"      => [2],
  "life"      => [3],
  "like"      => [3],
  "well"      => [2],
  "box"       => [3],
  "never"     => [3],
  "get"       => [3]
}

Inverted Index

MicheleRivaCode

Optimizing space

MicheleRivaCode

{
  "intersect"         => [10,32,12,2,3],
  "interstellar"      => [2,6,20,23,42],
  "intergalactic"     => [12,3,54,29,32],
  "international"     => [32,12,34,64,2],
  "intervene"         => [92,12,42,54,6],
  "internal"          => [102,32,543,6,1],

  "telecommunication" => [91,2,4,23],
  "television"        => [10,8,6,15,3,2],
  "telephone"         => [1,85,14,54,76]
}

Many tokens are sharing a common prefix

MicheleRivaCode

Trees to the rescue!

MicheleRivaCode

MicheleRivaCode

Prefix tree

MicheleRivaCode

Private

Primark

Prime

Primate

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

MicheleRivaCode

We can use a prefix tree as an "inverted index" to store the reference of a token with the document

MicheleRivaCode

"primark" => [1, 3]
"primate" => [2, 4]
"prime"   => [1, 5]
"private" => [2, 6]
"art"     => [4, 5]
"artist"  => [4, 7]

MicheleRivaCode

"primark" => [1, 3]
"primate" => [2, 4]
"prime"   => [1, 5]
"private" => [2, 6]
"art"     => [4, 5]
"artist"  => [4, 7]

MicheleRivaCode

"Talk is cheap! Show me the code!"

MicheleRivaCode

type Nullable<T> = T | null;

type Children = Map<string, TrieNode>;

type Docs = Set<string>;

type NodeContent = [string, Docs];

interface ITrieNode {
  key:       string;
  parent:    Nullable<TrieNode>;
  children:  Nullable<Children>;
  docs:      Docs;
  end:       boolean;
  
  getWord:   ()           => NodeContent;
  removeDoc: (id: string) => boolean;
}

trieNode.ts

MicheleRivaCode

type FindResult = {
  [key: string]: Set<string>; 
}

interface ITrie {
  root:            TrieNode;
  
  insert:          (word: string, docId: string) => void;
  contains:        (word: string)                => boolean;
  find:            (prefix: string)              => FindResult;
  removeDocByWord: (word: string, docId: string) => boolean;
  remove:          (word: string)                => boolean;
}

trie.ts

MicheleRivaCode

class TrieNode implements ITrieNode {
  public key;
  public parent   = null;
  public children = new Map();
  public docs     = new Set();
  public end      = false;
}

trieNode.ts

MicheleRivaCode

class TrieNode implements ITrieNode {
  public key;
  public parent   = null;
  public children = {};
  public docs     = new Set();
  public end      = false;
  
  constructor(key: string) {
    this.key = key;
  }
}

trieNode.ts

MicheleRivaCode

class TrieNode implements ITrieNode {
  public key;
  public parent   = null;
  public children = {};
  public docs     = new Set();
  public end      = false;
  
  constructor(key: string) {
    this.key = key;
  }
  
  getWord(): NodeContent {
    let node: TrieNode = this;
    let output = "";

    while (node !== null) {
      output = node.key + output;
      node = node.parent!;
    }

    return [output, this.docs];
  }
}

trieNode.ts

MicheleRivaCode

MicheleRivaCode

class TrieNode implements ITrieNode {
  public key;
  public parent   = null;
  public children = {};
  public docs     = new Set();
  public end      = false;
  
  constructor(key: string) {
    this.key = key;
  }
  
  getWord() {
    let output = "";
    let node = this;
    
    while (node !== null) {
      output = node.key + output;
      node = node.parent!;
    }
    
    return [output, this.docs];
  }
  
  removeDoc(docID: string): boolean {
    return this.docs.delete(docID);
  }
}

trieNode.ts

MicheleRivaCode

MicheleRivaCode

TC39 has standardized TCE

(tail-call elimination) with ES6

MicheleRivaCode

MicheleRivaCode

class Trie implements ITrie {
  private root = new TrieNode("");
}

trie.ts

MicheleRivaCode

insert(word: string, docId: string): void {
  const wordLength = word.length;
  let node = this.root;

  for (let i = 0; i < wordLength; i++) {
    const char = word[i];

    if (!node.children?.has(char)) {
      const newTrieNode = new TrieNode(char);
      newTrieNode.setParent(node);
      node.children!.set(char, newTrieNode);
    }

    node = node.children!.get(char)!;

    if (i === wordLength - 1) {
      node.setEnd(true);
      node.docs.add(docId);
    }
  }
}

trie.ts

  find(prefix: string): FindResult {
    let node = this.root;
    const output: FindResult = {};

    for (const char of prefix) {
      if (node?.children?.has(char)) {
        node = node.children.get(char)!;
      } else {
        return output;
      }
    }

    findAllWords(node, output);

    function findAllWords(_node: TrieNode, _output: FindResult) {
      if (_node.end) {
        const [word, docIDs] = _node.getWord();

        if (!(word in _output)) {
          _output[word] = new Set();
        }

        if (docIDs?.size) {
          for (const doc of docIDs) {
            _output[word].add(doc);
          }
        }
      }

      for (const childNode of _node.children?.values() ?? []) {
        findAllWords(childNode, _output);
      }
    }

    return output;
  }

MicheleRivaCode

 Tokenizer

 Prefix-tree

❌ Typo-tolerance

MicheleRivaCode

trie.find("wrld");

// Resuls:

[
  {
    id: 1,
    quote: "Hello, World!"
  },
  {
    id: 2,
    quote: "What a wonderful world"
  }
]

Dynamic programming

MicheleRivaCode

Dynamic Programming

An algorithmic technique for solving an optimization problem by breaking it down into simpler subproblems and utilizing the fact that the optimal solution to the overall problem depends upon the optimal solution to its subproblems.

https://educative.io

MicheleRivaCode

Levenshtein distance

MicheleRivaCode

Levenshtein distance

MicheleRivaCode

The Levenshtein algorithm calculates the least number of edit operations that are necessary to modify one string to obtain another string.

MicheleRivaCode

const word1 = "moon";
const word2 = "lions";

levenshtein(word1, word2); // => 3

MicheleRivaCode

Allowed operations

Insert

Delete

Replace

MicheleRivaCode

Edit distance of "Moon" and "Lions"

1)

MOON

LIONS

REPLACE

MicheleRivaCode

Edit distance of "Moon" and "Lions"

1)

MOON

LIONS

REPLACE

2)

LOON

LIONS

REPLACE

MicheleRivaCode

Edit distance of "Moon" and "Lions"

1)

MOON

LIONS

REPLACE

2)

LOON

LIONS

REPLACE

3)

LION

LIONS

INSERT

MicheleRivaCode

Λ L I O N S
Λ
M
O
O
N

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ
M
O
O
N

Insert

Delete

Replace

MO -> L

1

2

3

4

5

6

2

3

4

1

D(2,2)

MicheleRivaCode

Λ L I O N S
Λ
M
O
O
N

MOO -> O

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

D(4,3)

MicheleRivaCode

Λ L I O N S
Λ 0
M
O
O
N

"" -> ""

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ 0 1
M
O
O
N

"" -> "L"

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ 0 1 2
M
O
O
N

"" -> "LI"

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3
M
O
O
N

"" -> "LIO"

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4
M
O
O
N

"" -> "LION"

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M
O
O
N

"" -> "LIONS"

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1
O
O
N

"M" -> ""

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1
O 2
O
N

"MO" -> ""

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1
O 2
O 3
N

"MOO" -> ""

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1
O 2
O 3
N 4

"MOON" -> ""

Insert

Delete

Replace

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1
O 2
O 3
N 4

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

D(2,1)

MicheleRivaCode

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1
O 2
O 3
N 4

+1

2

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

D(2,1)

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1
O 2
O 3
N 4

+1

2

+1

2

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

D(2,1)

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1
O 2
O 3
N 4

+1

2

+1

2

1

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

D(2,1)

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1 1
O 2
O 3
N 4

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1 1
O 2
O 3
N 4

+1

2

+1

2

2

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

D(3,1)

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1 1 2 3 4 5
O 2
O 3
N 4

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1 1 2 3 4 5
O 2 2 2
O 3
N 4

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

D(4,2)

MicheleRivaCode

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1 1 2 3 4 5
O 2 2 2
O 3
N 4

2

2

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

D(4,2) = D(3,1)

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1 1 2 3 4 5
O 2 2 2 2 3 4
O 3 3 3 2 3 4
N 4 4 4 3 2 3

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1 1 2 3 4 5
O 2 2 2 2 3 4
O 3 3 3 2 3 4
N 4 4 4 3 2

3

Insert

Delete

Replace

1

2

3

4

5

6

2

3

4

1

MicheleRivaCode

Λ L I O N S
Λ 0 1 2 3 4 5
M 1 1 2 3 4 5
O 2 2 2 2 3 4
O 3 3 3 2 3 4
N 4 4 4 3 2

0

Insert

Delete

Replace

1

2

2

2

3

1

2

3

4

5

6

2

3

4

1

MicheleRivaCode

Edit distance of "Moon" and "Lions"

1)

MOON

LIONS

REPLACE

2)

LOON

LIONS

REPLACE

3)

LION

LIONS

INSERT

MicheleRivaCode

Λ P O S E R
Λ 0 1 2 3 4 5
H 1 1 2 3 4 5
O 2 2 1 2 3 4
R 3 3 2 2 3 3
S 4 4 3 2 3 4
E 5 5 4 3 2

Levenshtein distance of Horse - Poser

3

MicheleRivaCode

Levenshtein distance of Race - Raise

Λ R A I S E
Λ 0 1 2 3 4 5
R 1 0 1 2 3 4
I 2 1 0 1 2 3
C 3 2 1 1 2 3
E 4 3 2 2 2

2

MicheleRivaCode

export function levenshtein(a: string, b: string): number {
  if (!a.length) return b.length;
  if (!b.length) return a.length;

  let tmp;

  if (a.length > b.length) {
    tmp = a;
    a = b;
    b = tmp;
  }

  const row = Array.from({ length: a.length + 1 }, (_, i) => i);
  let val = 0;

  for (let i = 1; i <= b.length; i++) {
    let prev = i;

    for (let j = 1; j <= a.length; j++) {
      if (b[i - 1] === a[j - 1]) {
        val = row[j - 1];
      } else {
        val = Math.min(row[j - 1] + 1, Math.min(prev + 1, row[j] + 1));
      }

      row[j - 1] = prev;
      prev = val;
    }
    row[a.length] = prev;
  }

  return row[a.length];
}

We can perform these operations on both strings and trees

MicheleRivaCode

Tree Edit Distance (and Levenshtein Distance)

Simple fast algorithms for the editing distance between trees and related problems

Kaizhong Zhang and Dennis Shasha

https://shorturl.at/otBMY

MicheleRivaCode

MicheleRivaCode

import { Lyra } from '@nearform/lyra';

const db = new Lyra({
  schema: {
    author: 'string',
    quote: 'string'
  }
});

MicheleRivaCode

await db.insert({
  quote: 'It is during our darkest moments that we must focus to see the light.',
  author: 'Aristotle'
});

await db.insert({
  quote: 'If you really look closely, most overnight successes took a long time.',
  author: 'Steve Jobs'
});

await db.insert({
  quote: 'If you are not willing to risk the usual, you will have to settle for the ordinary.',
  author: 'Jim Rohn'
});

await db.insert({
  quote: 'You miss 100% of the shots you don\'t take',
  author: 'Wayne Gretzky - Michael Scott'
});

MicheleRivaCode

const searchResult = await db.search({
  term: 'if',
  properties: ['quote']
});

// Result

{
  elapsed: '99μs',
  hits: [
    {
      id: 'ckAOPGTA5qLXx0MgNr1Zy',
      quote: 'If you really look closely, most overnight successes took a long time.',
      author: 'Steve Jobs'
    },
    {
      id: 'fyl-_1veP78IO-wszP86Z',
      quote: 'If you are not willing to risk the usual, you will have to settle for the ordinary.',
      author: 'Jim Rohn'
    }
  ],
  count: 2
}

MicheleRivaCode

const searchResult = await db.search({
  term: 'Michael',
  properties: '*'
});

// Result

{
  elapsed: '111μs',
  hits: [
    {
      id: 'L1tpqQxc0c2djrSN2a6TJ',
      quote: "You miss 100% of the shots you don't take",
      author: 'Wayne Gretzky - Michael Scott'
    }
  ],
  count: 1
}

MicheleRivaCode

MicheleRivaCode

npm i @nearform/lyra

MicheleRivaCode

MicheleRivaCode

Real-World Next.js

Build scalable, high performances and modern web applications using Next.js, the React framework for production

MicheleRivaCode

MicheleRivaCode

@MicheleRiva

@MicheleRivaCode

/in/MicheleRiva95

www.micheleriva.dev

Made with Slides.com