software
MySQL vs MongoDB 2026: Database Comparison
MySQL is a relational database using structured SQL queries and ACID transactions, while MongoDB is a NoSQL document database using flexible JSON-like documents. MySQL excels for structured data with complex relationships, while MongoDB prioritizes scalability and flexible schemas for unstructured data.
MySQL
Open-source relational SQL database with ACID compliance and mature ecosystem
Financial institutions, healthcare systems, e-commerce platforms with complex inventory, and applications requiring strict data consistency
Score63%
VS
M
MongoDB
Open-source NoSQL document database with flexible schemas and native horizontal scaling
Content management systems, real-time analytics platforms, IoT applications with high write volumes, and startups with evolving product requirements
Score63%
146 attributes7 differences16 pros/cons
Quick Answer
AI SummaryMySQL is a relational database using structured SQL queries and ACID transactions, while MongoDB is a NoSQL document database using flexible JSON-like documents. MySQL excels for structured data with complex relationships, while MongoDB prioritizes scalability and flexible schemas for unstructured data.
Our Verdict
AI-assistedChoose MySQL if you have structured data with complex relationships, require strict ACID compliance across transactions, or need established enterprise support with minimal learning curve. Choose MongoDB if you need rapid scaling, have semi-structured or frequently evolving data schemas, or prioritize developer flexibility with JSON-like document handling.
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MySQL
9.3/10
MongoDB
5.7/10
M
Choose MySQL if
Best pickFinancial institutions, healthcare systems, e-commerce platforms with complex inventory, and applications requiring strict data consistency
M
Choose MongoDB if
Content management systems, real-time analytics platforms, IoT applications with high write volumes, and startups with evolving product requirements
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Key Differences at a Glance
- Data Model:Relational (tables, rows, columns) vs Document-based (JSON/BSON documents)
- Query Language:SQL (Structured Query Language) vs MongoDB Query Language (MQL)
- Schema Flexibility:✓ MongoDB wins(Dynamic schema (schema-less) vs Fixed schema required)
Key Facts & Figures
88 numeric metrics compared
| Metric | MySQL | MongoDB | Ratio |
|---|---|---|---|
| Simple Query Throughput(queries/sec) | ~28,000 (MySQL 8.4) | — | — |
| Complex Join Performance(ms response time) | ~450 (5-table join) | — | — |
| Base Memory Footprint(MB) | ~80 (minimal config) | — | — |
| AWS RDS Managed Cost($/month (db.t3.micro, single-AZ)) | $13.50 (MySQL 8.4) | — | — |
| Supported Versions (2026)(major versions) | 5 (8.0-8.4 active) | — | — |
| Replication Lag (typical)(ms) | 5-50 (binary log) | — | — |
| Concurrent Connections(connections) | 151 (default, configurable) | — | — |
| Max Database Size(TB) | 140 TB per table | — | — |
| Memory Footprint(MB) | 150-500 MB | — | — |
| Typical Query Response Time(milliseconds) | 5-50 ms (indexed queries) | — | — |
| Average Query Latency (structured data)(ms) | 3-5ms for simple queries | 8-12ms for equivalent queries | |
| Memory Usage (100GB dataset)(GB) | 8-12GB working set | 24-36GB working set | |
| Years in Production(years) | 25+ years (MySQL 1.0 released 1995) | 19 years (MongoDB 1.0 released 2009) | |
| Write Throughput (single server)(operations/second) | 10,000-50,000 ops/sec | 100,000-500,000 ops/sec | |
| Community Popularity(% of developers) | 46% of web applications (Stack Overflow 2024) | 28% of web applications (Stack Overflow 2024) | |
| Monthly Cost (10GB, 100K requests)(USD) | $50-200 (self-hosted) | — | — |
| Community Size (Stack Overflow Tags)(questions) | 600+ thousand | — | — |
| Read Throughput Improvement(x multiplier) | 1x baseline | — | — |
| Cost (On-Demand, Single Instance/Month)(USD) | $0 (open-source) | — | — |
| Deployment Platforms Supported(count) | 5+ (on-prem, cloud, edge, containers, hybrid) | — | — |
| Read Query Performance (SELECT 1M rows)(seconds) | 2.8s (MySQL 8.0) | — | — |
| Write Performance (INSERT 100K rows)(seconds) | 4.2s (MySQL 8.0) | — | — |
| Storage Engines Available(count) | 2 (InnoDB, MyISAM) | — | — |
| Memory Footprint (Idle instance)(MB) | 145MB | — | — |
| Enterprise Support Cost(USD/year) | $2,500-$50,000 (Oracle) | — | — |
| GitHub Community Activity(stars) | ~4,400 stars | — | — |
| Simple SELECT Query Speed(milliseconds (relative)) | 100ms (baseline) | — | — |
| Complex Analytical Query Speed (with JOINs + aggregations)(milliseconds (relative)) | 850ms (without window functions) | — | — |
| Default Memory Usage(MB) | 150MB | — | — |
| Annual Enterprise Support Cost (per server)(USD) | $3,500 | — | — |
| Major Release Frequency(years) | 2.0 | — | — |
| Available Storage Engines(count) | 5 | — | — |
| Maximum JSON Document Size(GB) | 64MB (practical limit) | — | — |
| Full-Text Search Languages Supported(languages) | 1-2 (English mainly) | — | — |
| Hosting Provider Compatibility(percent) | 99% of providers | — | — |
| Simple Query Speed (1M rows, SELECT *)(milliseconds) | 45ms (MySQL) | — | — |
| Complex Analytical Query Speed (Aggregate + Join)(milliseconds) | 1,200ms (MySQL) | — | — |
| Minimum Memory Requirement(GB) | 50MB | — | — |
| Maximum Connection Limit (Default)(connections) | 151 | — | — |
| Developer Preference (2024 Survey)(%) | 53% | — | — |
| Memory Footprint (Running Instance)(MB) | 300-500 MB | — | — |
| Concurrent User Capacity(users) | 100+ simultaneous users | — | — |
| Maximum Database Size(TB) | 140 TB theoretical | — | — |
| Typical Query Speed (Small Dataset)(ms) | 5-15 ms (network latency included) | — | — |
| Global Market Share(%) | 46% | — | — |
| Average Query Optimization Rules(count) | ~15 core rules | — | — |
| Major Release Cycle(months) | ~3 months (quarterly) | — | — |
| Storage Engine Options(count) | 1 (InnoDB primary) | — | — |
| Stack Overflow Questions(questions) | 450K+ | — | — |
| Read Throughput Performance Improvement(multiplier) | 1x (baseline) | — | — |
| High Availability Replication Factor(copies) | 1 (self-managed: 2-3 typical) | — | — |
| Minimum Monthly Operational Cost (production-ready)(USD) | $0 (software) + infrastructure costs | — | — |
| Backup Retention Window (default)(days) | 0 (manual only) | — | — |
| Storage Scalability Limit(TB) | Depends on hardware (typically 1-10TB) | — | — |
| Setup & Deployment Time(hours) | 4-24 (server provisioning + config) | — | — |
| Write Latency (single insert)(ms) | 2-5 ms | 3-8 ms | |
| Max Recommended Dataset Size(GB) | 1-10 TB (single server) | Petabytes (with sharding) | — |
| Memory Overhead per Connection(MB) | 0.5-1 MB | 2-5 MB | |
| Write Throughput (bulk inserts)(documents/second) | 50,000-100,000 docs/sec | 50,000-100,000 docs/sec | |
| Query Language Complexity (learning curve)(months) | 2-4 weeks (MongoDB Query Language simpler syntax) | 2-4 weeks (MongoDB Query Language simpler syntax) | |
| Memory Usage (100K documents)(MB) | 250-350 MB | 250-350 MB | |
| Index Types Supported(count) | 10+ (single field, compound, geospatial, text, wildcard) | 10+ (single field, compound, geospatial, text, wildcard) | |
| Free Tier Storage(GB) | 0 GB (Atlas free tier discontinued) | 0 GB (Atlas free tier discontinued) | |
| Minimum Production Tier Cost(USD/month) | $57 | $57 | |
| Annual npm Downloads (Drivers)(millions) | 21.7M | 21.7M | |
| Baseline Latency(milliseconds) | 5-10ms (cluster dependent) | 5-10ms (cluster dependent) | |
| Minimum Monthly Cost (Production)(USD) | $57 (M10 Atlas cluster) | $57 (M10 Atlas cluster) | |
| Read Throughput (1TB dataset)(ops/second) | 10,000-50,000 ops/sec | 10,000-50,000 ops/sec | |
| Document Size Limit(MB) | 16 MB | 16 MB | |
| Minimum Monthly Cost (Managed)(USD) | $57 | $57 | |
| Time to Production Deployment(days) | 40-80 hours | 40-80 hours | |
| Query Complexity (Aggregation Capability)(rating 1-10) | 9 (full aggregation pipeline) | 9 (full aggregation pipeline) | |
| Setup DevOps Requirement(hours/month) | 20-40 hours | 20-40 hours | |
| Real-time Sync Implementation Effort(days) | 5-10 days (custom code) | 5-10 days (custom code) | |
| Startup Monthly Cost(USD) | $57/month | $57/month | |
| Average Query Latency(milliseconds) | 10-50 ms | 10-50 ms | |
| Free Tier Database Storage(GB) | 512 MB | 512 MB | |
| Monthly Cost (Entry Paid Tier)(USD) | $57 | $57 | |
| Query Response Time (Median)(ms) | 38 ms (indexed query) | 38 ms (indexed query) | |
| Maximum Database Size (Free Tier)(GB) | 0.512 GB | 0.512 GB | |
| Max Write Throughput (Optimized)(writes/second) | ~50,000 (sharded cluster) | ~50,000 (sharded cluster) | |
| Storage Overhead (vs Equivalent Relational)(multiple) | 2-3x larger (denormalization) | 2-3x larger (denormalization) | |
| Community Size & Ecosystem(relative rank) | ~25k stars | ~25k stars | |
| Typical Setup Complexity(time to production (hours)) | 4-8 hours (managed cloud option available) | 4-8 hours (managed cloud option available) | |
| Starting Managed Hosting Cost(USD/month) | $57 (Atlas M10) | $57 (Atlas M10) | |
| Storage Efficiency(% overhead) | 25-40% (document format, field duplication) | 25-40% (document format, field duplication) | |
| Join Performance on 1M+ rows(seconds) | 8-15+ seconds ($lookup) | 8-15+ seconds ($lookup) | |
| Extension Ecosystem(available extensions) | Limited (core features only) | Limited (core features only) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
Evenly matched3 ties
M
2MongoDBMySQL
Attribute
MongoDB
- Relational (tables, rows, columns)Data ModelDocument-based (JSON/BSON documents)
- SQL (Structured Query Language)Query LanguageMongoDB Query Language (MQL)
- Fixed schema requiredSchema FlexibilityDynamic schema (schema-less)(winner)
- Complex sharding, limited native supportHorizontal ScalabilityBuilt-in sharding, native horizontal scaling(winner)
- Full ACID support across tables(winner)ACID TransactionsMulti-document ACID (MongoDB 4.0+)
- Easier for developers familiar with SQLLearning CurveEasier for developers familiar with JSON/JavaScript
- Highly optimized for structured queries(winner)Read Performance (simple queries)Good for nested document retrieval
- Data Model
MySQL
Relational (tables, rows, columns)
MongoDB
Document-based (JSON/BSON documents)
- Query Language
MySQL
SQL (Structured Query Language)
MongoDB
MongoDB Query Language (MQL)
- Schema Flexibility
MySQL
Fixed schema required
MongoDB
Dynamic schema (schema-less)(winner)
- Horizontal Scalability
MySQL
Complex sharding, limited native support
MongoDB
Built-in sharding, native horizontal scaling(winner)
- ACID Transactions
MySQL
Full ACID support across tables(winner)
MongoDB
Multi-document ACID (MongoDB 4.0+)
- Learning Curve
MySQL
Easier for developers familiar with SQL
MongoDB
Easier for developers familiar with JSON/JavaScript
- Read Performance (simple queries)
MySQL
Highly optimized for structured queries(winner)
MongoDB
Good for nested document retrieval
Full Comparison
| Attribute | MySQL | MongoDB |
|---|---|---|
| ACID Compliance Level | Partial (InnoDB only) | — |
| Uptime SLA(percent) | User-dependent (no guarantee) | — |
| ACID Compliance | Full ACID support | — |
| High Availability Replication Factor(copies) | 1 (self-managed: 2-3 typical) | — |
| SLA Uptime Guarantee(percent) | None (depends on your infrastructure) | — |
Show 2 more attributesMulti-row/Document Transactions(null) ACID from v4.0 (slower than PostgreSQL) — ACID Compliance Scope Multi-document transactions (4.0+) — | ||
| Simple Query Throughput(queries/sec) | ~28,000 (MySQL 8.4) | — |
| Complex Join Performance(ms response time) | ~450 (5-table join) | — |
| Replication Lag (typical)(ms) | 5-50 (binary log) | — |
| Concurrent Connections(connections) | 151 (default, configurable) | — |
| Memory Footprint(MB) | 150-500 MB | — |
Show 27 more attributesTypical Query Response Time(milliseconds) 5-50 ms (indexed queries) — Average Query Latency (structured data)(ms) 3-5ms for simple queries 8-12ms for equivalent queries Write Throughput (single server)(operations/second) 10,000-50,000 ops/sec 100,000-500,000 ops/sec Read Throughput Improvement(x multiplier) 1x baseline — Read Query Performance (SELECT 1M rows)(seconds) 2.8s (MySQL 8.0) — Write Performance (INSERT 100K rows)(seconds) 4.2s (MySQL 8.0) — Simple SELECT Query Speed(milliseconds (relative)) 100ms (baseline) — Complex Analytical Query Speed (with JOINs + aggregations)(milliseconds (relative)) 850ms (without window functions) — Simple Query Speed (1M rows, SELECT *)(milliseconds) 45ms (MySQL) — Complex Analytical Query Speed (Aggregate + Join)(milliseconds) 1,200ms (MySQL) — Memory Footprint (Running Instance)(MB) 300-500 MB — Typical Query Speed (Small Dataset)(ms) 5-15 ms (network latency included) — Average Query Optimization Rules(count) ~15 core rules — Read Throughput Performance Improvement(multiplier) 1x (baseline) — Write Latency (single insert)(ms) 2-5 ms 3-8 ms Memory Overhead per Connection(MB) 0.5-1 MB 2-5 MB Write Throughput (bulk inserts)(documents/second) 50,000-100,000 docs/sec — Memory Usage (100K documents)(MB) 250-350 MB — Baseline Latency(milliseconds) 5-10ms (cluster dependent) — Read Throughput (1TB dataset)(ops/second) 10,000-50,000 ops/sec — Maximum Concurrent Connections(connections) Unlimited (scales with cluster) — Real-time Capabilities(ms latency) Change Streams (enterprise feature) — Average Query Latency(milliseconds) 10-50 ms — Query Response Time (Median)(ms) 38 ms (indexed query) — Max Write Throughput (Optimized)(writes/second) ~50,000 (sharded cluster) — Storage Efficiency(% overhead) 25-40% (document format, field duplication) — Join Performance on 1M+ rows(seconds) 8-15+ seconds ($lookup) — | ||
| JSON Query Capability | JSON functions only | — |
| Full-Text Search | Limited, basic support | — |
| Time-Series Optimization | Standard table partitioning | — |
| Transaction Support(consistency level) | Full ACID across multiple tables (since v5.7) | Multi-document ACID (since v4.0, snapshot isolation) |
| Schema Flexibility | Fixed schema, requires migration for changes | Schema-less (dynamic fields) |
Show 14 more attributesStorage Engines Available(count) 2 (InnoDB, MyISAM) — Available Storage Engines(count) 5 — Full-Text Search Languages Supported(languages) 1-2 (English mainly) — Built-in JSON Support Yes, since 5.7 (basic operations only) — Full-Text Search Capability Basic (limited language support, no stemming) — Window Functions Support Since 8.0 (limited implementation) — Storage Engine Options(count) 1 (InnoDB primary) — Multi-Document ACID Support(null) Native since v5.0 (2008) Added in v4.0 (2018) Index Types Supported(count) 10+ (single field, compound, geospatial, text, wildcard) — Query Language Standard MongoDB Query Language (MQL) — Built-in Authentication No (third-party only) — Auto-generated API Manual API layer required — Query Language Complexity Support(capability level) Aggregation pipeline (moderate for complex queries) — Extension Ecosystem(available extensions) Limited (core features only) — | ||
| Base Memory Footprint(MB) | ~80 (minimal config) | — |
| Default Memory Usage(MB) | 150MB | — |
| Minimum Memory Requirement(GB) | 50MB | — |
| Vector Similarity Support | Via third-party extensions | — |
| AWS RDS Managed Cost($/month (db.t3.micro, single-AZ)) | $13.50 (MySQL 8.4) | — |
| Enterprise Support Cost(USD/year) | $2,500-$50,000 (Oracle) | — |
| Minimum Monthly Operational Cost (production-ready)(USD) | $0 (software) + infrastructure costs | — |
| Starting Managed Hosting Cost(USD/month) | $57 (Atlas M10) | — |
| Supported Versions (2026)(major versions) | 5 (8.0-8.4 active) | — |
| Enterprise Support Availability | Oracle, multiple vendors | — |
| Max Database Size(TB) | 140 TB per table | — |
| Horizontal Scalability(text) | Manual sharding (theoretical unlimited) | Native automatic sharding across nodes |
| Auto-Scaling Capability | Manual configuration required | — |
| Maximum Storage Capacity(TB) | 64TB (hardware dependent) | — |
| Maximum Read Replicas(replicas) | Unlimited (with sharding complexity) | — |
Show 8 more attributesMaximum Connection Limit (Default)(connections) 151 — Concurrent User Capacity(users) 100+ simultaneous users — Storage Scalability Limit(TB) Depends on hardware (typically 1-10TB) — Max Recommended Dataset Size(GB) 1-10 TB (single server) Petabytes (with sharding) Horizontal Scaling Manual sharding configuration required — Max Horizontal Scalability Unlimited (sharding across clusters) — Native Horizontal Scaling Yes (automatic sharding) — Sharding (Horizontal Scaling) Native, automatic shard distribution — | ||
| Built-in Replication | Yes - master-slave, group replication | — |
| Memory Usage (100GB dataset)(GB) | 8-12GB working set(winner) | 24-36GB working set |
| Years in Production(years) | 25+ years (MySQL 1.0 released 1995)(winner) | 19 years (MongoDB 1.0 released 2009) |
| Community Popularity(% of developers) | 46% of web applications (Stack Overflow 2024)(winner) | 28% of web applications (Stack Overflow 2024) |
| Developer Preference (2024 Survey)(%) | 53% | — |
| Monthly Cost (10GB, 100K requests)(USD) | $50-200 (self-hosted) | — |
| Cost (On-Demand, Single Instance/Month)(USD) | $0 (open-source) | — |
| Annual Enterprise Support Cost (per server)(USD) | $3,500 | — |
| Free Tier Storage(GB) | 0 GB (Atlas free tier discontinued) | — |
| Minimum Monthly Cost (Production)(USD) | $57 (M10 Atlas cluster) | — |
Show 3 more attributesMinimum Monthly Cost (Managed)(USD) $57 — Startup Monthly Cost(USD) $57/month — Free Tier Database Storage(GB) 512 MB — | ||
| Maximum Storage per Database(TB) | Unlimited | — |
| Maximum Database Size(TB) | 140 TB theoretical | — |
| Maximum Database Size (Free Tier)(GB) | 0.512 GB | — |
| Database Branching Support | Third-party tools only | — |
| Community Size (Stack Overflow Tags)(questions) | 600+ thousand | — |
| Backup Automation | Manual configuration required | — |
| Setup & Deployment Time(hours) | 4-24 (server provisioning + config) | — |
| Operational Complexity(setup hours) | Moderate to high (backups, sharding, monitoring) | — |
| Setup DevOps Requirement(hours/month) | 20-40 hours | — |
| Deployment Platforms Supported(count) | 5+ (on-prem, cloud, edge, containers, hybrid) | — |
| Latest Stable Version (2026) | MySQL 8.4 LTS | — |
| Memory Footprint (Idle instance)(MB) | 145MB | — |
| GitHub Community Activity(stars) | ~4,400 stars | — |
| Stack Overflow Questions(questions) | 450K+ | — |
| Community Size & Ecosystem(relative rank) | ~25k stars | — |
| ACID Compliance by Default | No (conditional) | — |
| Transaction ACID Support | Multi-document (v4.0+) | — |
| Foreign Key Support | Not enforced natively | — |
| Major Release Frequency(years) | 2.0 | — |
| Major Release Cycle(months) | ~3 months (quarterly) | — |
| Maximum JSON Document Size(GB) | 64MB (practical limit) | — |
| Hosting Provider Compatibility(percent) | 99% of providers | — |
| Window Functions Availability | No (until 8.0) | — |
| Setup Time(minutes) | 15-30 minutes (server setup required) | — |
| Network Access | Remote access via TCP/IP | — |
| Horizontal Scaling Method | Manual sharding + Atlas auto-scaling | — |
| User Authentication & Permissions | Role-based access control (RBAC) with 5+ permission levels | — |
| Global Market Share(%) | 46% | — |
| Windows Native Support(quality level) | Full MSI installer, native support | — |
| Binary Compatibility with MySQL(version range) | Reference implementation | — |
| Default GTID Replication Configuration(boolean) | Requires manual setup | — |
| Automatic Failover Time(seconds) | Manual (30-60+ minutes) | — |
| Backup Retention Window (default)(days) | 0 (manual only) | — |
| Query Language Complexity (learning curve)(months) | 2-4 weeks (MongoDB Query Language simpler syntax) | — |
| Enterprise Support | MongoDB Atlas managed cloud (SaaS); community edition free | — |
| Minimum Production Tier Cost(USD/month) | $57 | — |
| Maximum Connections Per Database(connections) | Unlimited (sharding dependent) | — |
| Annual npm Downloads (Drivers)(millions) | 21.7M | — |
| Query Complexity Support | 200+ query operators, joins, aggregation pipeline | — |
| Transaction Scope | Multi-document ACID across collections | — |
| Document Size Limit(MB) | 16 MB | — |
| Query Complexity (Aggregation Capability)(rating 1-10) | 9 (full aggregation pipeline) | — |
| Time to Production Deployment(days) | 40-80 hours | — |
| Read Operations (Free Tier Monthly)(operations) | No free tier | — |
| Real-time Sync Implementation Effort(days) | 5-10 days (custom code) | — |
| Required DevOps Knowledge | High (infrastructure management needed) | — |
| Monthly Cost (Entry Paid Tier)(USD) | $57 | — |
| ACID Transaction Support(boolean) | Multi-document (v4.0+, with limitations) | — |
| Storage Overhead (vs Equivalent Relational)(multiple) | 2-3x larger (denormalization) | — |
| Schema Enforcement | Optional/flexible (schema-on-read) | — |
| Typical Setup Complexity(time to production (hours)) | 4-8 hours (managed cloud option available) | — |
| Query Language Complexity | MQL (simpler for simple queries, steeper for complex joins) | — |
MySQLMMongoDB
ACID Compliance Level
Partial (InnoDB only)
—
Uptime SLA(percent)
User-dependent (no guarantee)
—
ACID Compliance
Full ACID support
—
High Availability Replication Factor(copies)
1 (self-managed: 2-3 typical)
—
SLA Uptime Guarantee(percent)
None (depends on your infrastructure)
—
Show 2 more attributes
Multi-row/Document Transactions(null)
ACID from v4.0 (slower than PostgreSQL)
—
ACID Compliance Scope
Multi-document transactions (4.0+)
—
Simple Query Throughput(queries/sec)
~28,000 (MySQL 8.4)
—
Complex Join Performance(ms response time)
~450 (5-table join)
—
Replication Lag (typical)(ms)
5-50 (binary log)
—
Concurrent Connections(connections)
151 (default, configurable)
—
Memory Footprint(MB)
150-500 MB
—
Show 27 more attributes
Typical Query Response Time(milliseconds)
5-50 ms (indexed queries)
—
Average Query Latency (structured data)(ms)
3-5ms for simple queries
8-12ms for equivalent queries
Write Throughput (single server)(operations/second)
10,000-50,000 ops/sec
100,000-500,000 ops/sec
Read Throughput Improvement(x multiplier)
1x baseline
—
Read Query Performance (SELECT 1M rows)(seconds)
2.8s (MySQL 8.0)
—
Write Performance (INSERT 100K rows)(seconds)
4.2s (MySQL 8.0)
—
Simple SELECT Query Speed(milliseconds (relative))
100ms (baseline)
—
Complex Analytical Query Speed (with JOINs + aggregations)(milliseconds (relative))
850ms (without window functions)
—
Simple Query Speed (1M rows, SELECT *)(milliseconds)
45ms (MySQL)
—
Complex Analytical Query Speed (Aggregate + Join)(milliseconds)
1,200ms (MySQL)
—
Memory Footprint (Running Instance)(MB)
300-500 MB
—
Typical Query Speed (Small Dataset)(ms)
5-15 ms (network latency included)
—
Average Query Optimization Rules(count)
~15 core rules
—
Read Throughput Performance Improvement(multiplier)
1x (baseline)
—
Write Latency (single insert)(ms)
2-5 ms
3-8 ms
Memory Overhead per Connection(MB)
0.5-1 MB
2-5 MB
Write Throughput (bulk inserts)(documents/second)
50,000-100,000 docs/sec
—
Memory Usage (100K documents)(MB)
250-350 MB
—
Baseline Latency(milliseconds)
5-10ms (cluster dependent)
—
Read Throughput (1TB dataset)(ops/second)
10,000-50,000 ops/sec
—
Maximum Concurrent Connections(connections)
Unlimited (scales with cluster)
—
Real-time Capabilities(ms latency)
Change Streams (enterprise feature)
—
Average Query Latency(milliseconds)
10-50 ms
—
Query Response Time (Median)(ms)
38 ms (indexed query)
—
Max Write Throughput (Optimized)(writes/second)
~50,000 (sharded cluster)
—
Storage Efficiency(% overhead)
25-40% (document format, field duplication)
—
Join Performance on 1M+ rows(seconds)
8-15+ seconds ($lookup)
—
JSON Query Capability
JSON functions only
—
Full-Text Search
Limited, basic support
—
Time-Series Optimization
Standard table partitioning
—
Transaction Support(consistency level)
Full ACID across multiple tables (since v5.7)
Multi-document ACID (since v4.0, snapshot isolation)
Schema Flexibility
Fixed schema, requires migration for changes
Schema-less (dynamic fields)
Show 14 more attributes
Storage Engines Available(count)
2 (InnoDB, MyISAM)
—
Available Storage Engines(count)
5
—
Full-Text Search Languages Supported(languages)
1-2 (English mainly)
—
Built-in JSON Support
Yes, since 5.7 (basic operations only)
—
Full-Text Search Capability
Basic (limited language support, no stemming)
—
Window Functions Support
Since 8.0 (limited implementation)
—
Storage Engine Options(count)
1 (InnoDB primary)
—
Multi-Document ACID Support(null)
Native since v5.0 (2008)
Added in v4.0 (2018)
Index Types Supported(count)
10+ (single field, compound, geospatial, text, wildcard)
—
Query Language Standard
MongoDB Query Language (MQL)
—
Built-in Authentication
No (third-party only)
—
Auto-generated API
Manual API layer required
—
Query Language Complexity Support(capability level)
Aggregation pipeline (moderate for complex queries)
—
Extension Ecosystem(available extensions)
Limited (core features only)
—
Base Memory Footprint(MB)
~80 (minimal config)
—
Default Memory Usage(MB)
150MB
—
Minimum Memory Requirement(GB)
50MB
—
Vector Similarity Support
Via third-party extensions
—
AWS RDS Managed Cost($/month (db.t3.micro, single-AZ))
$13.50 (MySQL 8.4)
—
Enterprise Support Cost(USD/year)
$2,500-$50,000 (Oracle)
—
Minimum Monthly Operational Cost (production-ready)(USD)
$0 (software) + infrastructure costs
—
Starting Managed Hosting Cost(USD/month)
$57 (Atlas M10)
—
Supported Versions (2026)(major versions)
5 (8.0-8.4 active)
—
Enterprise Support Availability
Oracle, multiple vendors
—
Max Database Size(TB)
140 TB per table
—
Horizontal Scalability(text)
Manual sharding (theoretical unlimited)
Native automatic sharding across nodes
Auto-Scaling Capability
Manual configuration required
—
Maximum Storage Capacity(TB)
64TB (hardware dependent)
—
Maximum Read Replicas(replicas)
Unlimited (with sharding complexity)
—
Show 8 more attributes
Maximum Connection Limit (Default)(connections)
151
—
Concurrent User Capacity(users)
100+ simultaneous users
—
Storage Scalability Limit(TB)
Depends on hardware (typically 1-10TB)
—
Max Recommended Dataset Size(GB)
1-10 TB (single server)
Petabytes (with sharding)
Horizontal Scaling
Manual sharding configuration required
—
Max Horizontal Scalability
Unlimited (sharding across clusters)
—
Native Horizontal Scaling
Yes (automatic sharding)
—
Sharding (Horizontal Scaling)
Native, automatic shard distribution
—
Built-in Replication
Yes - master-slave, group replication
—
Memory Usage (100GB dataset)(GB)
8-12GB working set
24-36GB working set
Years in Production(years)
25+ years (MySQL 1.0 released 1995)
19 years (MongoDB 1.0 released 2009)
Community Popularity(% of developers)
46% of web applications (Stack Overflow 2024)
28% of web applications (Stack Overflow 2024)
Developer Preference (2024 Survey)(%)
53%
—
Monthly Cost (10GB, 100K requests)(USD)
$50-200 (self-hosted)
—
Cost (On-Demand, Single Instance/Month)(USD)
$0 (open-source)
—
Annual Enterprise Support Cost (per server)(USD)
$3,500
—
Free Tier Storage(GB)
0 GB (Atlas free tier discontinued)
—
Minimum Monthly Cost (Production)(USD)
$57 (M10 Atlas cluster)
—
Show 3 more attributes
Minimum Monthly Cost (Managed)(USD)
$57
—
Startup Monthly Cost(USD)
$57/month
—
Free Tier Database Storage(GB)
512 MB
—
Maximum Storage per Database(TB)
Unlimited
—
Maximum Database Size(TB)
140 TB theoretical
—
Maximum Database Size (Free Tier)(GB)
0.512 GB
—
Database Branching Support
Third-party tools only
—
Community Size (Stack Overflow Tags)(questions)
600+ thousand
—
Backup Automation
Manual configuration required
—
Setup & Deployment Time(hours)
4-24 (server provisioning + config)
—
Operational Complexity(setup hours)
Moderate to high (backups, sharding, monitoring)
—
Setup DevOps Requirement(hours/month)
20-40 hours
—
Deployment Platforms Supported(count)
5+ (on-prem, cloud, edge, containers, hybrid)
—
Latest Stable Version (2026)
MySQL 8.4 LTS
—
Memory Footprint (Idle instance)(MB)
145MB
—
GitHub Community Activity(stars)
~4,400 stars
—
Stack Overflow Questions(questions)
450K+
—
Community Size & Ecosystem(relative rank)
~25k stars
—
ACID Compliance by Default
No (conditional)
—
Transaction ACID Support
Multi-document (v4.0+)
—
Foreign Key Support
Not enforced natively
—
Major Release Frequency(years)
2.0
—
Major Release Cycle(months)
~3 months (quarterly)
—
Maximum JSON Document Size(GB)
64MB (practical limit)
—
Hosting Provider Compatibility(percent)
99% of providers
—
Window Functions Availability
No (until 8.0)
—
Setup Time(minutes)
15-30 minutes (server setup required)
—
Network Access
Remote access via TCP/IP
—
Horizontal Scaling Method
Manual sharding + Atlas auto-scaling
—
User Authentication & Permissions
Role-based access control (RBAC) with 5+ permission levels
—
Global Market Share(%)
46%
—
Windows Native Support(quality level)
Full MSI installer, native support
—
Binary Compatibility with MySQL(version range)
Reference implementation
—
Default GTID Replication Configuration(boolean)
Requires manual setup
—
Automatic Failover Time(seconds)
Manual (30-60+ minutes)
—
Backup Retention Window (default)(days)
0 (manual only)
—
Query Language Complexity (learning curve)(months)
2-4 weeks (MongoDB Query Language simpler syntax)
—
Enterprise Support
MongoDB Atlas managed cloud (SaaS); community edition free
—
Minimum Production Tier Cost(USD/month)
$57
—
Maximum Connections Per Database(connections)
Unlimited (sharding dependent)
—
Annual npm Downloads (Drivers)(millions)
21.7M
—
Query Complexity Support
200+ query operators, joins, aggregation pipeline
—
Transaction Scope
Multi-document ACID across collections
—
Document Size Limit(MB)
16 MB
—
Query Complexity (Aggregation Capability)(rating 1-10)
9 (full aggregation pipeline)
—
Time to Production Deployment(days)
40-80 hours
—
Read Operations (Free Tier Monthly)(operations)
No free tier
—
Real-time Sync Implementation Effort(days)
5-10 days (custom code)
—
Required DevOps Knowledge
High (infrastructure management needed)
—
Monthly Cost (Entry Paid Tier)(USD)
$57
—
ACID Transaction Support(boolean)
Multi-document (v4.0+, with limitations)
—
Storage Overhead (vs Equivalent Relational)(multiple)
2-3x larger (denormalization)
—
Schema Enforcement
Optional/flexible (schema-on-read)
—
Typical Setup Complexity(time to production (hours))
4-8 hours (managed cloud option available)
—
Query Language Complexity
MQL (simpler for simple queries, steeper for complex joins)
—
Pros & Cons
10 pros·6 cons across both
M
MySQL
+5-3
Pros
- Full ACID compliance across multiple tables ensures data integrity for financial and transactional systems
- Mature ecosystem with 25+ years of development, extensive tooling, and comprehensive documentation
- Superior performance for complex JOIN operations on structured data (typically 30-40% faster than document queries)
- Proven at scale: powers Facebook, Twitter, and Wikipedia with petabyte-scale deployments
- Low resource footprint: runs efficiently on modest hardware with minimal memory overhead
Cons
- Vertical scaling limitation: sharding is complex and requires significant architectural planning
- Schema inflexibility: schema changes require migrations that can lock large tables during production updates
- Poor fit for semi-structured data: storing nested objects requires extensive normalization or JSON columns
M
MongoDB
+5-3
Pros
- Native horizontal scalability: automatic sharding distributes data seamlessly across servers without complex configuration
- Schema flexibility: add/modify fields without downtime or schema migrations, ideal for rapidly evolving applications
- Developer-friendly: JSON-like BSON format aligns naturally with JavaScript/Python object models, reducing impedance mismatch
- Embedded documents: stores related data together, eliminating costly JOINs for hierarchical/nested structures
- High throughput: handles 1M+ operations/second on standard hardware for write-heavy workloads
Cons
- Memory overhead: typical deployments consume 2-3x more RAM than equivalent MySQL setups due to BSON overhead
- Eventual consistency: distributed transactions have latency costs and eventual consistency windows increase complexity
- Join penalties: queries across multiple collections are significantly slower (5-10x) than native SQL JOINs
Frequently Asked Questions
5 questions
Use MySQL if you have well-defined, structured data with clear relationships (e.g., user accounts, orders, inventory). Use MongoDB if your schema is evolving, you need rapid horizontal scaling, or you're building real-time applications with high write volumes. MongoDB has faster time-to-market; MySQL has better long-term data integrity guarantees.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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