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Learn open addressing in C: linear probing, quadratic probing, and double hashing. Master collision handling, tombstones, rehashing, and performance.

Open Addressing in Hashing in C: Complete Guide

Open Addressing Collision Handling Technique in Hashing in C: A Comprehensive Tutorial

Open Addressing is a collision handling technique where all elements are stored directly in the hash table itself. When a collision occurs, the algorithm probes for the next available slot in the table. Unlike chaining, no linked lists or extra nodes are used—everything lives in one contiguous array, giving excellent cache performance.

This tutorial provides an in-depth exploration of Open Addressing in C: fundamental concepts, three probing strategies (linear, quadratic, double hashing), full implementations, comparison with chaining, complexity analysis, common pitfalls, and best practices.


Conceptual Introduction & Fundamentals

What is Open Addressing?

Open Addressing (also called closed hashing) stores all elements in the hash table itself. When a collision occurs, we probe for the next slot using a probe sequence.

Key properties:

  • Table size m must be ≥ number of keys n.
  • No pointers or linked lists—everything in the table.
  • Probing finds alternate slots on collision.
  • Requires tombstones for deletion.

Why Open Addressing?

  • Cache-friendly: Everything stored contiguously.
  • Memory efficient: No pointer overhead.
  • Simple deletion: With tombstones.
  • Good for known workloads: When number of keys is predictable.

Core Operations

OperationDescription
Insert(k)Probe until an empty slot is found; insert.
Search(k)Probe until key found or empty slot reached.
Delete(k)Mark slot as DELETED (tombstone); search continues past it.

Probe Function Types

ProbingProbe SequenceClustering
Linear Probing(h(k) + i) % mPrimary
Quadratic Probing(h(k) + i²) % mSecondary
Double Hashing(h(k) + i × h2(k)) % mMinimal

Linear Probing

Sequential probing: (h(k) + 1) % m, (h(k) + 2) % m, …

  • Best cache performance.
  • Suffers from primary clustering: long contiguous runs of occupied slots.

Quadratic Probing

Probes: (h(k) + 1²) % m, (h(k) + 2²) % m, …

  • Reduces primary clustering.
  • Retains secondary clustering: keys with same initial hash follow identical probes.

Double Hashing

Probes: (h(k) + 1 × h2(k)) % m, (h(k) + 2 × h2(k)) % m, …

  • Best distribution — reduces both primary and secondary clustering.
  • Poor cache performance — jumps unpredictable.
  • Requires a second hash function that never returns 0.

Performance Analysis

For load factor α = n / m (α < 1):

OperationExpected Time
Search< 1 / (1 - α)
Insert< 1 / (1 - α)
Delete< 1 / (1 - α)

At α = 0.5, expected probes ≈ 2; at α = 0.9, expected probes ≈ 10.

Open Addressing vs. Separate Chaining

AspectOpen AddressingSeparate Chaining
Table sizeMust be ≥ nAny
CacheExcellentPoor
ClusteringYesNo
InsertSlight computationO(1) prepend
DeleteTombstones neededDirect removal
Load factorα < 1Any
Pointer overheadNonePer node
Best forKnown workloadsDynamic workloads

Syntax & Core Mechanics

Slot States

c

#define EMPTY (-1) #define DELETED (-2)

Sentinel keys mark empty and deleted slots.

Basic Structure

c

typedef struct { int *keys; int *values; int capacity; int size; int deletedCount; } HashTable;

Probe Sequences

c

/* Linear Probing */ pos = (base + i) % capacity; /* Quadratic Probing */ pos = (base + i * i) % capacity; /* Double Hashing */ pos = (base + i * h2(key)) % capacity;

Second Hash Function (Double Hashing)

Must never return 0:

c

int h2(int key, int m) { return 1 + (key % (m - 1)); /* Ensures non-zero step */ }

Load Factor Threshold

Keep α < 0.7 for good performance; rehash when exceeded.

c

double lf = (double)(size + deletedCount) / capacity; if (lf > 0.7) rehash();

Full Practical Code Example with Explanation

This program demonstrates all three probing strategies with a unified hash table.

c

/** * open_addressing.c * Complete hash table using open addressing. * Supports linear, quadratic, and double hashing. */ #include <stdio.h> #include <stdlib.h> #include <string.h> #include <errno.h> #define MAX_LINE_LEN 100 #define EMPTY (-1) #define DELETED (-2) #define REHASH_THRESHOLD 0.7 typedef enum { PROBE_LINEAR, PROBE_QUADRATIC, PROBE_DOUBLE } ProbeType; typedef struct { int *keys; int *values; int capacity; int size; int deletedCount; ProbeType probe; } HashTable; /* Next prime >= n */ static int nextPrime(int n) { if (n <= 2) return 2; if (n % 2 == 0) n++; while (1) { int isPrime = 1; for (int i = 3; i * i <= n; i += 2) { if (n % i == 0) { isPrime = 0; break; } } if (isPrime) return n; n += 2; } } /* Primary hash */ static unsigned int h1(int key, int m) { return ((unsigned int)key) % (unsigned int)m; } /* Secondary hash for double hashing (must never be 0) */ static unsigned int h2(int key, int m) { return 1u + ((unsigned int)key % (unsigned int)(m - 1)); } /* Compute probe index for iteration i */ static int probeAt(int base, int i, int key, int m, ProbeType type) { unsigned int idx; switch (type) { case PROBE_LINEAR: idx = ((unsigned int)base + (unsigned int)i) % (unsigned int)m; break; case PROBE_QUADRATIC: idx = ((unsigned int)base + (unsigned int)(i * i)) % (unsigned int)m; break; case PROBE_DOUBLE: idx = ((unsigned int)base + (unsigned int)i * h2(key, m)) % (unsigned int)m; break; default: return -1; } return (int)idx; } HashTable *createHashTable(int initialCapacity, ProbeType probe) { HashTable *ht = malloc(sizeof(HashTable)); if (ht == NULL) return NULL; int capacity = nextPrime(initialCapacity); ht->keys = malloc(capacity * sizeof(int)); ht->values = malloc(capacity * sizeof(int)); if (ht->keys == NULL || ht->values == NULL) { free(ht->keys); free(ht->values); free(ht); return NULL; } for (int i = 0; i < capacity; i++) { ht->keys[i] = EMPTY; ht->values[i] = 0; } ht->capacity = capacity; ht->size = 0; ht->deletedCount = 0; ht->probe = probe; return ht; } void freeHashTable(HashTable *ht) { if (ht == NULL) return; free(ht->keys); free(ht->values); free(ht); } /* Rehash: double capacity and reinsert all live entries */ static int rehash(HashTable *ht) { int oldCap = ht->capacity; int *oldKeys = ht->keys; int *oldValues = ht->values; int newCap = nextPrime(oldCap * 2); int *newKeys = malloc(newCap * sizeof(int)); int *newValues = malloc(newCap * sizeof(int)); if (newKeys == NULL || newValues == NULL) { free(newKeys); free(newValues); return -1; } for (int i = 0; i < newCap; i++) { newKeys[i] = EMPTY; newValues[i] = 0; } ht->keys = newKeys; ht->values = newValues; ht->capacity = newCap; ht->size = 0; ht->deletedCount = 0; for (int i = 0; i < oldCap; i++) { if (oldKeys[i] != EMPTY && oldKeys[i] != DELETED) { int key = oldKeys[i]; int value = oldValues[i]; int base = (int)h1(key, newCap); for (int j = 0; j < newCap; j++) { int pos = probeAt(base, j, key, newCap, ht->probe); if (newKeys[pos] == EMPTY) { newKeys[pos] = key; newValues[pos] = value; ht->size++; break; } } } } free(oldKeys); free(oldValues); return 0; } int insert(HashTable *ht, int key, int value) { double lf = (double)(ht->size + ht->deletedCount) / ht->capacity; if (lf > REHASH_THRESHOLD) { if (rehash(ht) != 0) return -1; } int base = (int)h1(key, ht->capacity); for (int i = 0; i < ht->capacity; i++) { int pos = probeAt(base, i, key, ht->capacity, ht->probe); if (ht->keys[pos] == key) { ht->values[pos] = value; return 0; } if (ht->keys[pos] == EMPTY || ht->keys[pos] == DELETED) { if (ht->keys[pos] == DELETED) ht->deletedCount--; ht->keys[pos] = key; ht->values[pos] = value; ht->size++; return 0; } } return -1; } int search(HashTable *ht, int key, int *outValue) { int base = (int)h1(key, ht->capacity); for (int i = 0; i < ht->capacity; i++) { int pos = probeAt(base, i, key, ht->capacity, ht->probe); if (ht->keys[pos] == EMPTY) return 0; if (ht->keys[pos] == key) { *outValue = ht->values[pos]; return 1; } } return 0; } int deleteKey(HashTable *ht, int key) { int base = (int)h1(key, ht->capacity); for (int i = 0; i < ht->capacity; i++) { int pos = probeAt(base, i, key, ht->capacity, ht->probe); if (ht->keys[pos] == EMPTY) return -1; if (ht->keys[pos] == key) { ht->keys[pos] = DELETED; ht->deletedCount++; ht->size--; return 0; } } return -1; } static const char *probeName(ProbeType t) { switch (t) { case PROBE_LINEAR: return "linear"; case PROBE_QUADRATIC: return "quadratic"; case PROBE_DOUBLE: return "double"; } return "unknown"; } void display(const HashTable *ht) { double lf = (double)ht->size / ht->capacity; printf("Hash Table (%s, size=%d, capacity=%d, deleted=%d, load=%.2f):\n", probeName(ht->probe), ht->size, ht->capacity, ht->deletedCount, lf); for (int i = 0; i < ht->capacity; i++) { printf(" [%2d]: ", i); if (ht->keys[i] == EMPTY) printf("EMPTY\n"); else if (ht->keys[i] == DELETED) printf("DELETED\n"); else printf("(%d → %d)\n", ht->keys[i], ht->values[i]); } } /* ============ Demos ============ */ static void demoLinear(void) { printf("\n=== Linear Probing ===\n"); HashTable *ht = createHashTable(7, PROBE_LINEAR); if (ht == NULL) return; int keys[] = {50, 70, 76, 85, 93}; for (int i = 0; i < 5; i++) insert(ht, keys[i], keys[i]); display(ht); int v; printf("\nSearch 76: "); if (search(ht, 76, &v)) printf("%d\n", v); else printf("not found\n"); printf("Delete 70...\n"); deleteKey(ht, 70); display(ht); freeHashTable(ht); } static void demoQuadratic(void) { printf("\n=== Quadratic Probing ===\n"); HashTable *ht = createHashTable(7, PROBE_QUADRATIC); if (ht == NULL) return; int keys[] = {22, 30, 50}; for (int i = 0; i < 3; i++) insert(ht, keys[i], keys[i]); display(ht); freeHashTable(ht); } static void demoDouble(void) { printf("\n=== Double Hashing ===\n"); HashTable *ht = createHashTable(7, PROBE_DOUBLE); if (ht == NULL) return; int keys[] = {27, 43, 692, 72}; for (int i = 0; i < 4; i++) insert(ht, keys[i], keys[i]); display(ht); freeHashTable(ht); } static void demoComparison(void) { printf("\n=== Probe Sequence Comparison ===\n"); int keys[] = {50, 70, 76, 85, 93}; HashTable *lin = createHashTable(11, PROBE_LINEAR); HashTable *quad = createHashTable(11, PROBE_QUADRATIC); HashTable *dbl = createHashTable(11, PROBE_DOUBLE); if (!lin || !quad || !dbl) { freeHashTable(lin); freeHashTable(quad); freeHashTable(dbl); return; } for (int i = 0; i < 5; i++) { insert(lin, keys[i], keys[i]); insert(quad, keys[i], keys[i]); insert(dbl, keys[i], keys[i]); } printf("Linear table:\n"); for (int i = 0; i < lin->capacity; i++) printf("%4d ", lin->keys[i]); printf("\n\nQuadratic table:\n"); for (int i = 0; i < quad->capacity; i++) printf("%4d ", quad->keys[i]); printf("\n\nDouble hashing table:\n"); for (int i = 0; i < dbl->capacity; i++) printf("%4d ", dbl->keys[i]); printf("\n"); freeHashTable(lin); freeHashTable(quad); freeHashTable(dbl); } static int readInteger(const char *prompt, int *outValue) { char buffer[MAX_LINE_LEN]; char *endPtr; long val; printf("%s", prompt); fflush(stdout); if (fgets(buffer, sizeof(buffer), stdin) == NULL) return -1; errno = 0; val = strtol(buffer, &endPtr, 10); if (endPtr == buffer || (*endPtr != '\n' && *endPtr != '\0')) return -1; if (errno == ERANGE || val < -2147483647 - 1 || val > 2147483647) return -1; *outValue = (int)val; return 0; } int main(void) { int choice; char buffer[MAX_LINE_LEN]; printf("=== Open Addressing Demo ===\n"); while (1) { printf("\n--- Menu ---\n"); printf("1. Linear Probing\n"); printf("2. Quadratic Probing\n"); printf("3. Double Hashing\n"); printf("4. Compare All Three\n"); printf("5. Interactive (Linear Probing)\n"); printf("6. Exit\n"); printf("Enter choice: "); fflush(stdout); if (fgets(buffer, sizeof(buffer), stdin) == NULL) { fprintf(stderr, "Error reading choice.\n"); return EXIT_FAILURE; } choice = atoi(buffer); if (choice == 1) { demoLinear(); } else if (choice == 2) { demoQuadratic(); } else if (choice == 3) { demoDouble(); } else if (choice == 4) { demoComparison(); } else if (choice == 5) { HashTable *ht = createHashTable(11, PROBE_LINEAR); if (ht == NULL) continue; int opt; while (1) { printf("\n--- Interactive Hash Table ---\n"); printf("1. Insert\n2. Search\n3. Delete\n4. Display\n5. Back\n"); printf("Enter choice: "); fflush(stdout); if (fgets(buffer, sizeof(buffer), stdin) == NULL) break; opt = atoi(buffer); if (opt == 1) { int k, v; if (readInteger("Key: ", &k) == 0 && readInteger("Value: ", &v) == 0) { insert(ht, k, v); printf("Inserted (%d → %d)\n", k, v); } } else if (opt == 2) { int k, v; if (readInteger("Key: ", &k) == 0) { if (search(ht, k, &v)) printf("Found: %d → %d\n", k, v); else printf("Key %d not found\n", k); } } else if (opt == 3) { int k; if (readInteger("Key: ", &k) == 0) { if (deleteKey(ht, k) == 0) printf("Deleted %d\n", k); else printf("Key %d not found\n", k); } } else if (opt == 4) { display(ht); } else if (opt == 5) { break; } } freeHashTable(ht); } else if (choice == 6) { printf("Exiting.\n"); return EXIT_SUCCESS; } else { printf("Invalid choice.\n"); } } return EXIT_SUCCESS; }

Explanation of the Code

  1. HashTable — Stores keys, values, capacity, size, deleted count, and probe type.
  2. h1 — Primary division hash.
  3. h2 — Secondary hash for double hashing; returns 1..m-1.
  4. probeAt — Computes index for iteration i based on probe type.
  5. insert — Probes until key matched or EMPTY/DELETED found.
  6. search — Probes until key found or EMPTY encountered.
  7. deleteKey — Replaces key with DELETED tombstone.
  8. rehash — Grows capacity and reinserts live entries.

Expected Output (sample run)

text

=== Linear Probing === Hash Table (linear, size=5, capacity=7, deleted=0, load=0.71): [ 0]: (70 → 70) [ 1]: (50 → 50) [ 2]: (76 → 76) [ 3]: (85 → 85) [ 4]: (93 → 93) [ 5]: EMPTY [ 6]: EMPTY Search 76: 76 Delete 70... Hash Table (linear, size=4, capacity=7, deleted=1, load=0.57): [ 0]: DELETED [ 1]: (50 → 50) [ 2]: (76 → 76) [ 3]: (85 → 85) [ 4]: (93 → 93) [ 5]: EMPTY [ 6]: EMPTY === Quadratic Probing === Hash Table (quadratic, size=3, capacity=7, deleted=0, load=0.43): [ 0]: EMPTY [ 1]: (22 → 22) [ 2]: (30 → 30) [ 3]: EMPTY [ 4]: EMPTY [ 5]: (50 → 50) [ 6]: EMPTY === Double Hashing === Hash Table (double, size=4, capacity=7, deleted=0, load=0.57): ... === Probe Sequence Comparison === Linear table: 70 50 76 85 93 -1 -1 ... Quadratic table: -1 50 70 93 -1 76 85 ... Double hashing table: ...

Common Pitfalls, Edge Cases & Memory Considerations

1. Infinite Probe Loop

Unbounded probing can loop forever if no empty slot exists.

Solution: Bound loop by capacity.

2. Quadratic Probing Not Visiting All Slots

Standard quadratic probing may miss slots if m is not prime and load is high.

Solution: Use prime table sizes; or use the modified formula from the previous tutorial.

3. Double Hashing with h2(k) = 0

If h2(k) returns 0, probing stalls.

Solution: Ensure h2(k) ∈ [1, m-1].

4. Forgetting Tombstones

Deleting by clearing breaks probe chains.

Solution: Use DELETED marker.

5. Load Factor Reaching 1

Table becomes full; insert fails.

Solution: Rehash when α > 0.7.

6. Primary Clustering (Linear)

Runs of occupied slots slow lookups.

Solution: Use quadratic or double hashing.

7. Secondary Clustering (Quadratic)

Keys with same initial hash follow identical probes.

Solution: Use double hashing.

8. Rehash Double-Counting

Not updating size and deletedCount correctly during rehash.

Solution: Reset counters at rehash start, then count entries as they are reinserted.

9. Negative Keys

key % m for negative keys yields negative indices.

Solution: Cast to unsigned int.

10. Memory Considerations

  • Keys + values arrays: capacity × (sizeof(int) × 2).
  • No pointers per entry — memory efficient.
  • Rehash doubles memory temporarily.
  • Free all arrays on cleanup.

Summary & Best Practices

Open Addressing is a memory-efficient, cache-friendly collision resolution strategy. Choosing the right probe sequence (linear, quadratic, double hashing) determines performance and clustering behavior.

Key Takeaways

  • All entries stored in the table itself.
  • Three probe strategies: linear, quadratic, double.
  • Linear: best cache, primary clustering.
  • Quadratic: middle ground, secondary clustering.
  • Double: best distribution, poor cache.
  • Deletion uses tombstones.
  • Load factor α = n/m; keep below 0.7.
  • Expected operations: O(1 / (1 - α)).

Best Practices

  1. Use prime table sizes for better distribution.
  2. Keep α ≤ 0.7; rehash proactively.
  3. Use tombstones for deletion.
  4. Bound probe loops by capacity.
  5. Choose double hashing for minimal clustering.
  6. Choose linear probing for cache-sensitive workloads.
  7. Ensure h2(k) is never 0 in double hashing.
  8. Track deletedCount to trigger rehash.
  9. Cast keys to unsigned before modulo.
  10. Profile with real workloads to pick the best strategy.

By mastering open addressing, you’ll implement memory-efficient, high-performance hash tables suitable for embedded systems, real-time applications, and cache-sensitive workloads.

Learn separate chaining in C hashing: linked list buckets, insert/search/delete, rehashing, performance analysis, and implementation.

Separate Chaining in Hashing in C: Complete Guide

Separate Chaining Collision Handling Technique in Hashing in C: A Comprehensive Tutorial

Separate Chaining is one of the most popular and commonly used techniques to handle collisions in hashing. Instead of probing for an alternative slot, each bucket holds a chain (typically a linked list) of all entries that hash to the same index. This makes chaining simple, robust, and capable of handling arbitrarily high load factors.

This tutorial provides an in-depth exploration of Separate Chaining in C: fundamental concepts, data structures for chains, implementation, performance analysis, advantages and disadvantages, common pitfalls, and best practices.


Conceptual Introduction & Fundamentals

What is Separate Chaining?

Separate Chaining resolves collisions by maintaining a chain at each slot of the hash table. When multiple keys hash to the same index, they are stored in the same chain (a linked list, dynamic array, or balanced BST).

Hash function example: h(k) = k % 5

Keys [12, 22, 15, 25]:

text

12 % 5 = 2 22 % 5 = 2 (collision with 12 → add to chain) 15 % 5 = 0 25 % 5 = 0 (collision with 15 → add to chain)

Resulting table:

text

[0]: 15 → 25 [1]: (empty) [2]: 12 → 22 [3]: (empty) [4]: (empty)

Why Separate Chaining?

  • Simple to implement: Just linked lists in buckets.
  • Never fills up: Chains grow as needed.
  • Robust to bad hash functions: Even poor distribution degrades gracefully.
  • Handles dynamic workloads: Ideal when the number of keys is unknown.
  • Easy deletion: No tombstones needed.

Two Main Collision Handling Methods

MethodDescription
Separate ChainingEach slot holds a chain of entries
Open AddressingAll entries stored in the table itself with probing

Performance Analysis

Under simple uniform hashing:

MetricFormula
Table slotsm
Number of keysn
Load factorα = n / m
Expected search timeO(1 + α)
Expected delete timeO(1 + α)
Insert timeO(1)

If α = O(1), all operations are O(1) average.


Syntax & Core Mechanics

Data Structures for Chains

StructureSearchDeleteInsertCache
Linked ListO(len)O(len)O(1)Poor
Dynamic ArrayO(len)O(len)O(1)*Good
Self-Balancing BSTO(log len)O(log len)O(log len)Poor

*Amortized; resizing may be O(len).

Java 8+ uses a Red-Black Tree for chains longer than 8 entries to bound worst-case lookup time.

Linked List Node

c

typedef struct Entry { int key; int value; struct Entry *next; } Entry;

Hash Table Structure

c

typedef struct { Entry **buckets; int bucketCount; int size; } HashTable;

Hash Function (Division Method)

c

static unsigned int hashIndex(int key, int bucketCount) { return ((unsigned int)key) % (unsigned int)bucketCount; }

Insertion Algorithm

  1. Compute idx = hash(key).
  2. Traverse bucket; if key found, update value.
  3. Otherwise, prepend new node to bucket.

Search Algorithm

  1. Compute idx = hash(key).
  2. Traverse bucket list; return value if found.
  3. Return “not found” if chain exhausted.

Deletion Algorithm

  1. Compute idx = hash(key).
  2. Traverse bucket list tracking the previous node.
  3. Unlink the matching node; free it.

Full Practical Code Example with Explanation

This program implements a complete hash map using separate chaining with linked lists, supporting insert, search, delete, display, and rehashing.

c

/** * separate_chaining.c * Complete hash table using separate chaining. * Supports insert, search, delete, display, and rehashing. */ #include <stdio.h> #include <stdlib.h> #include <string.h> #include <errno.h> #define MAX_LINE_LEN 100 #define DEFAULT_BUCKETS 7 #define REHASH_THRESHOLD 1.0 /* Node in a chain */ typedef struct Entry { int key; int value; struct Entry *next; } Entry; /* Hash table with separate chaining */ typedef struct { Entry **buckets; int bucketCount; int size; int useRehashing; } HashTable; /* Division-method hash with unsigned cast */ static unsigned int hashIndex(int key, int bucketCount) { return ((unsigned int)key) % (unsigned int)bucketCount; } /* Find the next prime >= n */ static int nextPrime(int n) { if (n <= 2) return 2; if (n % 2 == 0) n++; while (1) { int isPrime = 1; for (int i = 3; i * i <= n; i += 2) { if (n % i == 0) { isPrime = 0; break; } } if (isPrime) return n; n += 2; } } /* Create a hash table */ HashTable *createHashTable(int bucketCount, int useRehashing) { HashTable *ht = malloc(sizeof(HashTable)); if (ht == NULL) return NULL; ht->buckets = calloc(bucketCount, sizeof(Entry *)); if (ht->buckets == NULL) { free(ht); return NULL; } ht->bucketCount = bucketCount; ht->size = 0; ht->useRehashing = useRehashing; return ht; } /* Free all entries and the table */ void freeHashTable(HashTable *ht) { if (ht == NULL) return; for (int i = 0; i < ht->bucketCount; i++) { Entry *cur = ht->buckets[i]; while (cur != NULL) { Entry *next = cur->next; free(cur); cur = next; } } free(ht->buckets); free(ht); } /* Rehash: grow bucket count and reinsert all entries */ static int rehash(HashTable *ht) { int newBucketCount = nextPrime(ht->bucketCount * 2); Entry **newBuckets = calloc(newBucketCount, sizeof(Entry *)); if (newBuckets == NULL) return -1; for (int i = 0; i < ht->bucketCount; i++) { Entry *cur = ht->buckets[i]; while (cur != NULL) { Entry *next = cur->next; unsigned int idx = hashIndex(cur->key, newBucketCount); cur->next = newBuckets[idx]; newBuckets[idx] = cur; cur = next; } } free(ht->buckets); ht->buckets = newBuckets; ht->bucketCount = newBucketCount; return 0; } /* Insert or update a key-value pair */ int insert(HashTable *ht, int key, int value) { if (ht->useRehashing) { double lf = (double)(ht->size + 1) / ht->bucketCount; if (lf > REHASH_THRESHOLD) { if (rehash(ht) != 0) return -1; } } unsigned int idx = hashIndex(key, ht->bucketCount); /* Update if key exists */ Entry *cur = ht->buckets[idx]; while (cur != NULL) { if (cur->key == key) { cur->value = value; return 0; } cur = cur->next; } /* Prepend new entry */ Entry *newEntry = malloc(sizeof(Entry)); if (newEntry == NULL) return -1; newEntry->key = key; newEntry->value = value; newEntry->next = ht->buckets[idx]; ht->buckets[idx] = newEntry; ht->size++; return 0; } /* Search for a key; returns 1 if found */ int search(const HashTable *ht, int key, int *outValue) { unsigned int idx = hashIndex(key, ht->bucketCount); Entry *cur = ht->buckets[idx]; while (cur != NULL) { if (cur->key == key) { *outValue = cur->value; return 1; } cur = cur->next; } return 0; } /* Delete a key */ int deleteKey(HashTable *ht, int key) { unsigned int idx = hashIndex(key, ht->bucketCount); Entry *cur = ht->buckets[idx]; Entry *prev = NULL; while (cur != NULL) { if (cur->key == key) { if (prev == NULL) ht->buckets[idx] = cur->next; else prev->next = cur->next; free(cur); ht->size--; return 0; } prev = cur; cur = cur->next; } return -1; } /* Return chain length at a given bucket */ int chainLength(const HashTable *ht, int idx) { int count = 0; Entry *cur = ht->buckets[idx]; while (cur) { count++; cur = cur->next; } return count; } /* Find maximum chain length (worst-case lookup) */ int maxChainLength(const HashTable *ht) { int max = 0; for (int i = 0; i < ht->bucketCount; i++) { int len = chainLength(ht, i); if (len > max) max = len; } return max; } /* Display the table */ void display(const HashTable *ht) { double lf = (double)ht->size / ht->bucketCount; printf("Hash Table (size=%d, buckets=%d, load=%.2f, maxChain=%d):\n", ht->size, ht->bucketCount, lf, maxChainLength(ht)); for (int i = 0; i < ht->bucketCount; i++) { printf(" [%2d]:", i); Entry *cur = ht->buckets[i]; while (cur != NULL) { printf(" (%d→%d)", cur->key, cur->value); cur = cur->next; } printf("\n"); } } /* ============ Demos ============ */ static void demoBasicChaining(void) { printf("\n=== Basic Separate Chaining ===\n"); HashTable *ht = createHashTable(5, 0); if (ht == NULL) return; int keys[] = {12, 22, 15, 25, 5, 17}; int n = sizeof(keys) / sizeof(keys[0]); for (int i = 0; i < n; i++) { insert(ht, keys[i], keys[i] * 10); } display(ht); int v; printf("\nSearch 22: "); if (search(ht, 22, &v)) printf("%d\n", v); else printf("not found\n"); printf("Search 100: "); if (search(ht, 100, &v)) printf("%d\n", v); else printf("not found\n"); printf("\nDelete 15...\n"); deleteKey(ht, 15); display(ht); freeHashTable(ht); } static void demoRehashing(void) { printf("\n=== Chaining with Rehashing ===\n"); HashTable *ht = createHashTable(5, 1); if (ht == NULL) return; for (int i = 0; i < 20; i++) { int key = (i * 13 + 7) % 100; insert(ht, key, key * 10); if (i == 4 || i == 9 || i == 19) { printf("\nAfter %d inserts:\n", i + 1); display(ht); } } freeHashTable(ht); } /* Read integer safely */ static int readInteger(const char *prompt, int *outValue) { char buffer[MAX_LINE_LEN]; char *endPtr; long val; printf("%s", prompt); fflush(stdout); if (fgets(buffer, sizeof(buffer), stdin) == NULL) return -1; errno = 0; val = strtol(buffer, &endPtr, 10); if (endPtr == buffer || (*endPtr != '\n' && *endPtr != '\0')) return -1; if (errno == ERANGE || val < -2147483647 - 1 || val > 2147483647) return -1; *outValue = (int)val; return 0; } int main(void) { int choice; char buffer[MAX_LINE_LEN]; printf("=== Separate Chaining Demo ===\n"); while (1) { printf("\n--- Menu ---\n"); printf("1. Basic Chaining Demo\n"); printf("2. Chaining with Rehashing Demo\n"); printf("3. Interactive Hash Table\n"); printf("4. Exit\n"); printf("Enter choice: "); fflush(stdout); if (fgets(buffer, sizeof(buffer), stdin) == NULL) { fprintf(stderr, "Error reading choice.\n"); return EXIT_FAILURE; } choice = atoi(buffer); if (choice == 1) { demoBasicChaining(); } else if (choice == 2) { demoRehashing(); } else if (choice == 3) { HashTable *ht = createHashTable(DEFAULT_BUCKETS, 1); if (ht == NULL) continue; int opt; while (1) { printf("\n--- Interactive Hash Table ---\n"); printf("1. Insert\n2. Search\n3. Delete\n4. Display\n5. Back\n"); printf("Enter choice: "); fflush(stdout); if (fgets(buffer, sizeof(buffer), stdin) == NULL) break; opt = atoi(buffer); if (opt == 1) { int k, v; if (readInteger("Key: ", &k) == 0 && readInteger("Value: ", &v) == 0) { insert(ht, k, v); printf("Inserted (%d → %d)\n", k, v); } } else if (opt == 2) { int k, v; if (readInteger("Key: ", &k) == 0) { if (search(ht, k, &v)) printf("Found: %d → %d\n", k, v); else printf("Key %d not found\n", k); } } else if (opt == 3) { int k; if (readInteger("Key: ", &k) == 0) { if (deleteKey(ht, k) == 0) printf("Deleted %d\n", k); else printf("Key %d not found\n", k); } } else if (opt == 4) { display(ht); } else if (opt == 5) { break; } } freeHashTable(ht); } else if (choice == 4) { printf("Exiting.\n"); return EXIT_SUCCESS; } else { printf("Invalid choice.\n"); } } return EXIT_SUCCESS; }

Explanation of the Code

  1. Entry — Node in a chain storing key, value, next.
  2. HashTable — Bucket array, bucket count, size, rehashing flag.
  3. hashIndex — Division-method hash with unsigned cast.
  4. nextPrime — Chooses prime bucket sizes.
  5. createHashTable — Allocates bucket array.
  6. insert — Prepends new node; updates existing key; optionally rehashes.
  7. search — Traverses chain linearly.
  8. deleteKey — Unlinks and frees matching node.
  9. rehash — Doubles bucket count and reinserts all entries.
  10. chainLength, maxChainLength — Helpers for diagnostics.

Expected Output (sample run)

text

=== Basic Separate Chaining === Hash Table (size=6, buckets=5, load=1.20, maxChain=3): [ 0]: (25→250) (15→150) [ 1]: (17→170) [ 2]: (22→220) (12→120) [ 3]: [ 4]: (5→50) Search 22: 220 Search 100: not found Delete 15... Hash Table (size=5, buckets=5, load=1.00, maxChain=2): [ 0]: (25→250) [ 1]: (17→170) [ 2]: (22→220) (12→120) [ 3]: [ 4]: (5→50)

Common Pitfalls, Edge Cases & Memory Considerations

1. Forgetting to Free Nodes

Each malloc must be paired with free.

c

free(cur); /* Required for every node */

2. Not Updating Duplicate Keys

Insert should update existing key, not append duplicates.

3. Integer Overflow in Hash

key * multiplier can overflow.

Solution: Cast to unsigned int before modulo.

4. Poor Cache Performance

Linked lists scatter memory.

Solution: Use dynamic arrays for chains (cache-friendly) or Red-Black Trees for large chains.

5. Long Chains

Worst-case search becomes O(n).

Solution: Rehash when load factor is high; consider RB-trees for large chains.

6. Empty Buckets Wasting Memory

Bucket array itself wastes space.

Solution: Size buckets based on expected n and target load factor.

7. Unbounded Growth

No rehashing means chains grow arbitrarily.

Solution: Use useRehashing = 1 and rehash when α > 1.

8. Unsigned vs. Signed Keys

Negative keys need careful handling.

Solution: Cast keys to unsigned int.

9. Thread Safety

Concurrent access to chains corrupts data.

Solution: Use locks per bucket or a global mutex for multi-threaded use.

10. Memory Considerations

  • Bucket array: bucketCount × sizeof(Entry *).
  • Each entry: sizeof(Entry) + malloc overhead.
  • Chains use more memory than open addressing.
  • Free all entries and the bucket array on destruction.

Summary & Best Practices

Separate Chaining is a robust, easy-to-implement collision resolution strategy with excellent behavior for dynamic workloads. It trades memory and cache efficiency for simplicity and worst-case robustness.

Key Takeaways

  • Each bucket holds a chain of entries.
  • Insert at head: O(1).
  • Search/delete: O(1 + α) average.
  • Linked lists: O(1) insert, poor cache.
  • Dynamic arrays: cache-friendly.
  • RB-trees (Java 8+): O(log len) worst-case.
  • Never fills up; robust to load factors.

Best Practices

  1. Use prime bucket sizes.
  2. Prepend for O(1) insert.
  3. Update existing keys rather than duplicating.
  4. Rehash when load factor > 1.
  5. Free every entry and the bucket array.
  6. Cast keys to unsigned int.
  7. Consider dynamic arrays for cache efficiency.
  8. Consider RB-trees for long chains (Java-style).
  9. Use djb2 or similar good hash functions.
  10. Profile for large datasets.

By mastering separate chaining, you’ll implement hash tables that are simple, scalable, and robust to collision-heavy workloads.