11 Data Structures Powering Databases, Hashing, Skip List, B-Tree, Bloom Filter, MemTables, Bitmap

11 Essential Data Structures Powering Modern Databases #shorts #datastructures #algorithm #database

1. Hash Indexes
First up, we have Hash Indexes! These are used for quick lookups by mapping keys to their corresponding values using hash functions. Imagine them as the ultimate cheat sheet for finding data in milliseconds. Perfect for scenarios where exact matches are critical, like searching for a user by their ID.

2. Skip Lists
Next, Skip Lists! Think of these as linked lists on steroids, where you can skip over sections to find data faster. They’re often used in databases like Redis, offering a balance between simplicity and speed for range queries and indexing.

3. Inverted Indexes
If you’ve ever searched for a term on Google, you’ve benefited from Inverted Indexes! These data structures map words to the documents they appear in, making them essential for full-text search engines. Think of them as the brains behind rapid keyword searches.

4. B-Trees
Now, meet the legendary B-Trees! These are the workhorses for organizing and retrieving large amounts of sorted data. Most relational databases, like MySQL and PostgreSQL, use B-Trees for indexing. They’re optimized for disk storage, ensuring fast reads and writes.

5. Bloom Filters
Ever needed a quick way to check if something exists without storing the entire data? Enter Bloom Filters! They’re like digital gatekeepers—fast, memory-efficient, and capable of telling you if something might exist or definitely doesn’t. Perfect for reducing costly disk lookups.

6. MemTable
When databases need to handle high write throughput, they use MemTables. These in-memory structures temporarily store data before it’s written to disk. They’re a key component in databases like Cassandra and RocksDB, ensuring blazing-fast writes.

7. Bitmap Indexes
For analytical databases handling large datasets, Bitmap Indexes are a lifesaver. They represent data using bits, making it incredibly fast to perform operations like AND, OR, and NOT. Perfect for use cases like querying large data warehouses.

8. SSTable (Sorted String Table)
SSTables, or Sorted String Tables, are immutable data structures used in databases like LevelDB and Cassandra. They store sorted key-value pairs on disk, making reads super-efficient and writes simple when combined with MemTables.

9. Prefix Trees (Tries)
Want to store and search strings efficiently? Prefix Trees, or Tries, are your go-to. They’re widely used in autocomplete features and IP routing tables, allowing fast retrieval based on string prefixes.

10. WAL (Write-Ahead Log)
Databases need to be reliable, even in the event of a crash. That’s where Write-Ahead Logs (WAL) come in! They record all changes before applying them, ensuring data consistency and easy recovery. Think of WAL as the black box of a database.

11. Suffix Trees
Last but not least, we have Suffix Trees! These specialized trees are used for pattern matching and substring searches, making them essential in bioinformatics, text indexing, and data compression algorithms.

And there you have it—11 data structures that power the databases we rely on every day! From search engines to financial systems, these structures are the unsung heroes of modern computing. Which one fascinated you the most? Let me know in the comments below.

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