Databases on IT Comparison

Serializable vs Weaker Isolation: Strict Ordering vs Controlled Anomalies

Sun, 06 Sep 2026 09:47:51 +0900

Overview

Serializable isolation guarantees that concurrent transactions produce a result equivalent to some serial order, eliminating every possible race condition. Weaker isolation levels like Read Committed or Snapshot trade that guarantee for higher throughput, deliberately allowing certain read/write anomalies that application code must tolerate or guard against.

Comparison Diagram

Comparison Table

Aspect	Serializable	Weaker Isolation
Isolation guarantee	Result equivalent to some serial execution of all transactions	Allows specific interleavings; guarantee varies by level (Read Committed, Repeatable Read, Snapshot)
Concurrency control	Full conflict serializability via strict two-phase locking, serializable snapshot isolation (SSI), or predicate locks	Row-level locks or MVCC snapshots that only block on narrower conflict sets (e.g. write-write)
Anomalies prevented	Dirty reads, non-repeatable reads, phantom reads, and write skew all eliminated	Only a subset prevented; phantoms and write skew commonly remain possible
Contention behavior	Higher rate of lock waits, deadlocks, or serialization-failure aborts under concurrent access	Readers rarely block writers (MVCC) or lower lock scope, so contention is reduced
Throughput impact	Lower throughput and higher latency as concurrency increases, especially with hot rows	Higher throughput and better scalability under contention
Application responsibility	Application can assume correctness; only needs to retry on serialization-failure errors	Application must reason about anomalies and add explicit checks (e.g. version columns, SELECT FOR UPDATE)
Conflict detection timing	Detected either at lock-acquisition time or at commit time (optimistic serializable schemes)	Detected only for the narrower conflicts the level covers, often just at commit for MVCC writes
Default in major databases	Rarely the default; must be explicitly requested (e.g. SET TRANSACTION ISOLATION LEVEL SERIALIZABLE)	Default in most systems out of the box (PostgreSQL/Oracle default to Read Committed, MySQL InnoDB to Repeatable Read)

Key Differences

Serializable enforces true serial equivalence; weaker levels only rule out a defined subset of anomalies.
Weaker isolation relies on MVCC snapshots or narrow locks, cutting contention compared to serializable’s broader locking or SSI conflict tracking.
Under Serializable, correctness bugs shift into retry loops on abort; under weaker levels, they shift into missed application-level checks.
Write skew is the classic anomaly Serializable closes that even Snapshot Isolation leaves open.
Choosing a level is a runtime trade-off between guaranteed correctness and throughput, not a one-time schema decision.

When to Use Each

Serializable

B-Tree vs LSM Tree: In-Place Updates vs Log-Structured Merges

Sun, 06 Sep 2026 09:42:56 +0900

Overview

B-Trees and LSM Trees are the two dominant on-disk index structures used by databases, and they diverge on how they handle writes. A B-Tree performs in-place updates on a balanced page structure to keep reads fast, while an LSM Tree buffers writes in memory and reconciles them later through background compaction, trading read simplicity for write throughput.

Comparison Diagram

Comparison Table

Aspect	B-Tree	LSM Tree
Write path	Traverses the tree to locate the target page and updates it in place, splitting nodes as needed	Appends the entry to an in-memory memtable plus a write-ahead log; no seek to the record’s final location
Read path	Single root-to-leaf traversal, O(log n) page reads from one location	Checks the memtable then potentially multiple SSTables across levels, often aided by bloom filters
Update/Delete handling	Overwrites the existing value directly at its page	Writes a new version or a tombstone; the old entry is only removed later during compaction
On-disk structure	One mutable, balanced tree of fixed-size pages, always sorted	Immutable sorted SSTable files organized into levels of increasing size
Background maintenance	Node splits and merges happen incrementally as part of each write	Periodic compaction merges SSTables across levels and drops stale versions
Write amplification	Low to moderate; occasional page rewrites and splits	Higher; the same record can be rewritten multiple times as it moves through levels
Read amplification & space reclaim	Minimal read amplification; deleted space is reclaimed immediately	Higher read amplification from scanning multiple levels; space reclaimed only after compaction
Range scans	Efficient via sorted leaf pages linked in order	Efficient within a level but requires merging sorted runs across levels

Key Differences

A B-Tree updates data in place, while an LSM Tree defers changes through append-only writes to a memtable
LSM Trees gain higher write throughput by avoiding random disk seeks, at the cost of ongoing compaction
B-Trees give more predictable read latency since each key lives in exactly one place
LSM read and space overhead comes from having to consult multiple SSTable levels
Deletes in an LSM Tree are recorded as tombstones rather than removed immediately

When to Use Each

B-Tree

SQL vs NoSQL: Relational Tables vs Flexible Documents

Sun, 02 Aug 2026 07:42:50 +0900

Overview

SQL (relational) databases organize data into fixed-schema tables linked by foreign keys and queried with a standardized language, prioritizing consistency and structured relationships. NoSQL (non-relational) databases store data as documents, key-value pairs, wide columns, or graphs with flexible or absent schemas, prioritizing horizontal scale and adaptability. The right choice depends on how relational your data is and whether you need strict consistency or elastic scale.

Comparison Diagram

Comparison Table

Aspect	SQL (Relational)	NoSQL (Non-Relational)
Data model	Tables with fixed rows/columns, normalized via foreign keys	Documents, key-value pairs, wide-column, or graph structures with per-record flexibility
Schema	Schema-on-write, enforced by the engine (types, constraints, CREATE TABLE)	Schema-on-read; little to no enforcement, validation left to the application
Query language	Standardized SQL (SELECT, JOIN, WHERE)	Varies by product — Mongo query API, CQL, Gremlin, or simple key lookups
Consistency model	Strong ACID transactions across tables by default	Often eventual/tunable consistency (BASE); some now offer document-level ACID
Scaling approach	Vertical scaling first; horizontal sharding possible but complex	Built for horizontal scaling/sharding across commodity nodes
Relationships	Modeled via joins and foreign keys	Modeled via embedding (denormalization) or manual reference resolution
Typical examples	PostgreSQL, MySQL, SQL Server, Oracle	MongoDB, Cassandra, DynamoDB, Redis, Neo4j
Best fit workload	Complex multi-entity queries, reporting, transactional integrity	High-volume writes, evolving schemas, massive horizontal scale

Key Differences

SQL normalizes data into related tables with a fixed schema enforced at write time; NoSQL stores flexible, often denormalized records with schema left to the application.
SQL guarantees ACID transactions across tables by default; most NoSQL stores trade strict consistency for availability/partition tolerance (BASE).
SQL relationships require JOINs across tables; NoSQL typically embeds related data in one document to avoid joins, or resolves references manually.
SQL databases scale vertically first and shard with effort; NoSQL databases are architected from the start for horizontal, distributed scaling.
Changing a SQL schema requires a migration; NoSQL documents can differ in shape from record to record with no migration needed.

When to Use Each

SQL (Relational)