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Modern databases rely on various data structures to optimize performance, storage, and retrieval. Below are eight critical data structures that power today’s databases:
- Skiplist: A probabilistic in-memory index structure used in databases like Redis for fast search, insertion, and deletion operations.
- Hash Index: A fundamental implementation of the “Map” data structure, enabling O(1) average-time complexity for lookups.
- SSTable (Sorted String Table): An immutable on-disk key-value storage format used in LSM trees.
- LSM Tree (Log-Structured Merge Tree): Combines a Skiplist (in-memory) with SSTables (on-disk) for high write throughput.
- B-Tree: A balanced tree structure optimized for disk storage, ensuring consistent read/write performance (used in PostgreSQL, MySQL).
- Inverted Index: Essential for full-text search engines like Apache Lucene (Elasticsearch).
- Suffix Tree: Efficiently handles string pattern matching and substring searches.
- R-Tree: Supports spatial indexing for multi-dimensional data (e.g., geolocation queries).
You Should Know:
Skiplist (Redis Example)
Redis CLI commands for sorted sets (using Skiplist internally) ZADD myzset 1 "one" 2 "two" 3 "three" ZRANGE myzset 0 -1 WITHSCORES
LSM Tree (RocksDB Example)
Basic RocksDB operations (C++ API snippet) include <rocksdb/db.h> rocksdb::DB db; rocksdb::Options options; options.create_if_missing = true; rocksdb::Status status = rocksdb::DB::Open(options, "/tmp/testdb", &db);
B-Tree (PostgreSQL Indexing)
-- Create a B-tree index in PostgreSQL CREATE INDEX idx_employee_name ON employees (name); EXPLAIN ANALYZE SELECT FROM employees WHERE name = 'John';
Inverted Index (Elasticsearch)
Create an index and search in Elasticsearch curl -X PUT "localhost:9200/my_index" curl -X POST "localhost:9200/my_index/_search" -H 'Content-Type: application/json' -d' { "query": { "match": { "text": "database" } } }'
R-Tree (Spatial Queries in SQLite)
-- Enable R-Tree in SQLite CREATE VIRTUAL TABLE spatial_index USING rtree(id, minX, maxX, minY, maxY); INSERT INTO spatial_index VALUES(1, 0, 10, 0, 10); SELECT id FROM spatial_index WHERE minX <= 5 AND maxX >= 5 AND minY <= 5 AND maxY >= 5;
What Undercode Say:
Understanding these data structures is crucial for database optimization, system design, and performance tuning. Here are some additional Linux/IT commands for deeper exploration:
- Redis Memory Analysis:
redis-cli --bigkeys redis-cli MEMORY USAGE <key>
LSM Tree Monitoring (RocksDB):
ldb --db=/tmp/rocksdb dump --hex
B-Tree Debugging (PostgreSQL):
SELECT pg_size_pretty(pg_indexes_size('idx_employee_name')); REINDEX INDEX idx_employee_name;
Spatial Data (PostGIS):
shp2pgsql -s 4326 shapefile.shp table_name | psql -d db_name
Full-Text Search (Linux grep):
grep -r "pattern" /path/to/files
Expected Output:
A structured breakdown of database data structures with practical commands for implementation and analysis.
URLs:
References:
Reported By: Alexxubyte Systemdesign – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅