Hash Tables

Group by Key

Build buckets keyed by a shared field, preserving the first-seen key order.

Algorithm

Canonical pairs (a,1), (b,2), (a,3), (c,4), (b,5) print {a: [1, 3], b: [2, 5], c: [4]}. The replay uses the same input in every language, so this Go DSA implementation can be compared directly with the rest of the DSA track.

bucket map Each key owns a list. A new key creates a bucket; a repeated key appends to the existing bucket.

Basic Implementation

basic.go
Replay: real traced execution (multi-file project)
package main

import (
    "fmt"
    "strings"
)

type pair struct {
    key string
    value int
}

func main() {
    pairs := []pair{{"a", 1}, {"b", 2}, {"a", 3}, {"c", 4}, {"b", 5}}
    groups := map[string][]int{}
    order := []string{}
    for _, item := range pairs {
        if _, ok := groups[item.key]; !ok {
            order = append(order, item.key)
        }
        groups[item.key] = append(groups[item.key], item.value)
    }
    parts := []string{}
    for _, key := range order {
        values := groups[key]
        renderedValues := []string{}
        for _, value := range values {
            renderedValues = append(renderedValues, fmt.Sprintf("%d", value))
        }
        parts = append(parts, fmt.Sprintf("%s: [%s]", key, strings.Join(renderedValues, ", ")))
    }
    fmt.Println("{" + strings.Join(parts, ", ") + "}")
}

  1. pairs ← [(a, 1), (b, 2), (a, 3), (c, 4), (b, 5)]

    1package main
    values this step[(a, 1), (b, 2), (a, 3), (c, 4), (b, 5)]pairs

  2. groups ← {}

    14pairs := []pair{{"a", 1}, {"b", 2}, {"a", 3}, {"c", 4}, {"b", 5}}15groups := map[string][]int{}16order := []string{}
    values this step{}groups

  3. groups ← {a: [1]}

    14pairs := []pair{{"a", 1}, {"b", 2}, {"a", 3}, {"c", 4}, {"b", 5}}15groups := map[string][]int{}16order := []string{}
    values this step{} → {a: [1]}groupsakey1value

  4. groups ← {a: [1], b: [2]}

    14pairs := []pair{{"a", 1}, {"b", 2}, {"a", 3}, {"c", 4}, {"b", 5}}15groups := map[string][]int{}16order := []string{}
    values this step{a: [1]} → {a: [1], b: [2]}groupsbkey2value

  5. groups ← {a: [1, 3], b: [2]}

    14pairs := []pair{{"a", 1}, {"b", 2}, {"a", 3}, {"c", 4}, {"b", 5}}15groups := map[string][]int{}16order := []string{}
    values this step{a: [1], b: [2]} → {a: [1, 3], b: [2]}groupsakey3value

  6. groups ← {a: [1, 3], b: [2], c: [4]}

    14pairs := []pair{{"a", 1}, {"b", 2}, {"a", 3}, {"c", 4}, {"b", 5}}15groups := map[string][]int{}16order := []string{}
    values this step{a: [1, 3], b: [2]} → {a: [1, 3], b: [2], c: [4]}groupsckey4value

  7. groups ← {a: [1, 3], b: [2, 5], c: [4]}

    14pairs := []pair{{"a", 1}, {"b", 2}, {"a", 3}, {"c", 4}, {"b", 5}}15groups := map[string][]int{}16order := []string{}
    values this step{a: [1, 3], b: [2], c: [4]} → {a: [1, 3], b: [2, 5], c: [4]}groupsbkey5value

  8. stdout ← {a: [1, 3], b: [2, 5], c: [4]}

    27for _, value := range values {28    renderedValues = append(renderedValues, fmt.Sprintf("%d", value))29}
    values this step{a: [1, 3], b: [2, 5], c: [4]}stdout{a: [1, 3], b: [2, 5], c: [4]}groups

  9. bucket 1 after collision ← c -> a, degradation risk ← long chains can degrade lookup toward O(n)

    27for _, value := range values {28    renderedValues = append(renderedValues, fmt.Sprintf("%d", value))29}
    values this stepa → c -> abucket 1 after collisionlong chains can degrade lookup toward O(n)degradation riskresize or rehash when load factor growsmitigationcnew key

Complexity

  • Time: O(n) average
  • Space: O(k + n) for buckets and values

Implementation notes

  • Keep output formatting deterministic. Do not rely on unordered hash-map printing when the lesson needs cross-language comparison.
  • The trace highlights the hash table state after each write and includes a collision contrast where one bucket chain grows, showing why long chains can degrade lookup and why real tables resize or rehash.