Abstract Algorithms

Databases Mental Model Tradeoffs Failure Modes Interview Reasoning ACID Properties Replication Consensus

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HyperLogLog Cardinality Estimation

Hash values route into registers, leading-zero runs update maxima, and the harmonic mean estimates unique cardinality with bounded error.

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1ACID Properties Explained: How SQL Databases Guarantee Atomicity, Consistency, Isolation, and DurabilityTLDR: ACID is four orthogonal guarantees that every SQL transaction must provide. Atomicity says all-or-nothing: PostgreSQL implements it via WAL rollback; MySQL InnoDB via undo logs. Consistency says38 min 2Isolation Levels in Databases: Read Committed, Repeatable Read, Snapshot, and Serializable ExplainedTLDR: Isolation levels control which concurrency anomalies a transaction can see. Read Committed (PostgreSQL and Oracle's default) prevents dirty reads but still silently allows non-repeatable reads, 28 min 3How CDC Works Across Databases: PostgreSQL, MySQL, MongoDB, and BeyondA data engineering team at a fintech company built what they believed was a robust Change Data Capture pipeline: three source databases (PostgreSQL, MongoDB, and Cassandra), Debezium connectors wired 37 min 4NoSQL Partitioning: How Cassandra, DynamoDB, and MongoDB Split DataTLDR: Every NoSQL database hides a partitioning engine behind a deceptively simple API. Cassandra uses a consistent hashing ring where a Murmur3 hash of your partition key selects a node — virtual nod24 min 5SQL Partitioning: Range, Hash, List, and Composite Strategies ExplainedTLDR: SQL partitioning divides one logical table into smaller physical child tables, all accessed through the parent table name. The query optimizer skips irrelevant child tables entirely — a process 25 min 6Compare-and-Swap and Optimistic Locking: How Every Database Implements ItTLDR: Compare-and-Swap (CAS) is the CPU-level atomic instruction that makes lock-free concurrency possible. Optimistic locking builds on it at the database layer: read freely, compute locally, write o34 min 7Partitioning Approaches in SQL and NoSQL: Horizontal, Vertical, Range, Hash, and List PartitioningTLDR: Partitioning splits one logical table into smaller physical pieces. The database skips irrelevant pieces entirely — turning a 30-second full-table scan into a sub-second single-partition read. S12 min 8Dirty Write Explained: When Uncommitted Data Gets OverwrittenTLDR: A dirty write occurs when Transaction B overwrites data that Transaction A has written but not yet committed. The result is not a rollback or an error — it is silently inconsistent committed dat28 min 9Read Skew Explained: Inconsistent Snapshots Across Multiple ObjectsTLDR: Read skew occurs when a transaction reads two logically related objects at different points in time — one before and one after a concurrent transaction commits — producing a view that never exis34 min 10Lost Update Explained: When Two Writes Become OneTLDR: A lost update occurs when two concurrent read-modify-write transactions both read the same committed value, both compute a new value from it, and both write back — with the second write silently38 min 11Phantom Read Explained: When New Rows Appear Mid-TransactionTLDR: A phantom read occurs when a transaction runs the same range query twice and gets a different set of rows — because a concurrent transaction inserted or deleted matching rows and committed in be32 min 12Write Skew Explained: The Anomaly That Requires Serializable IsolationTLDR: Write skew is the hardest concurrency anomaly to reason about: two concurrent transactions each read a shared condition, decide they can safely proceed, and then write to different rows. No indi23 min

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