What are the data structures used to link phone numbers to user accounts?

[muskanhossain]

WhatsApp likely employs a combination of data structures to efficiently and securely link phone numbers to user accounts across its massive and distributed systems. Given the need for rapid lookups, strong consistency for core account information, and scalability for billions of users, a multi-layered approach is probable. Here's a breakdown of potential data structures:

1. Hash Tables (Dictionaries or Maps):

Primary Key Lookup: The most fundamental data structure for linking phone numbers to user accounts is likely a highly optimized hash table or dictionary. The phone number itself would serve as australia whatsapp number data the unique key, and the value associated with this key would be a pointer or reference to the user's complete account data.
Fast Retrieval: Hash tables offer an average time complexity of O(1) for lookups, insertions, and deletions, making them ideal for quickly retrieving a user's account information based on their phone number during login, message routing, and other core operations.
Distribution and Sharding: To handle the scale of WhatsApp, this primary phone number-to-account mapping would likely be sharded (partitioned) across numerous servers. Consistent hashing or range-based partitioning could be used to determine which server holds the data for a specific phone number.
2. Relational Database Tables:

Structured User Data: While hash tables excel at key-value lookups, relational databases (like MySQL or PostgreSQL, which WhatsApp might use for structured data) are likely employed to store the more complex and structured information associated with user accounts.
users Table: A central users table would likely exist, with a primary key column for a unique User ID and a column storing the user's phone number. Other columns would store profile information, settings, registration details, etc.
Secondary Indexes: To further optimize queries based on phone numbers (even if the User ID is the primary key), secondary indexes would be created on the phone number column. This allows the database to quickly locate user records using their phone number without having to scan the entire table.
3. Inverted Indexes (for Search):

For features like searching for contacts by phone number, an inverted index might be used. This structure maps keywords (in this case, digits or partial phone numbers) to the User IDs or phone numbers that contain them. This allows for efficient prefix or substring searches on phone numbers.
4. Trie (Prefix Tree) (for Autocomplete and Range Queries):

For features like suggesting contacts as a user types a phone number, a Trie data structure could be beneficial. Tries allow for efficient prefix-based searching. As a user enters digits, the Trie can quickly traverse the branches corresponding to those digits and suggest matching phone numbers or associated contacts.
5. Graph Databases (for Social Connections):

While not directly linking phone numbers to accounts, graph databases (like those potentially used to manage social connections and group memberships) might store phone numbers as nodes or properties of nodes, with edges representing relationships between users. This allows for efficient traversal of the social graph based on phone numbers.
Key Considerations:

Uniqueness: The phone number must serve as a unique identifier for each account within the system.
Performance: The data structures must support high read and write throughput with low latency.
Scalability: The system must be able to handle billions of user accounts and scale horizontally as the user base grows.
Security: The storage of phone numbers must be secure, with appropriate encryption and access controls.
In conclusion, WhatsApp likely utilizes a combination of hash tables for fast primary lookups based on phone numbers, relational databases with secondary indexes for structured user data, inverted indexes and Tries for efficient searching and autocomplete, and potentially graph databases for managing social connections. This multi-faceted approach allows them to manage the vast amounts of user data associated with phone numbers while ensuring performance, scalability, and security.