postgres mongo dynamo

This article offers a practical and concise comparison of three major databases (PostgreSQL, MongoDB, and DynamoDB) exploring their strengths, limitations, and real-world usage patterns. It is designed for developers and architects who need to make informed decisions about which database to use for their applications. The article covers core data models, querying capabilities, similarities and differences, use cases, serverless and scaling considerations, pricing, performance, availability, developer experience, ecosystem, common pitfalls, and anti-patterns.

1. Introduction

Let’s first start remembering what are these three databases and why are often compared. What kind of applications and companies use them today.

1.1 PosgreSQL

PostgreSQL is a powerful, open-source relational database management system (RDBMS) known for its robustness, extensibility, and standards compliance. Originally developed at the University of California, Berkeley, PostgreSQL has evolved over decades to become one of the most advanced databases available, supporting both SQL (relational) and JSON (non-relational) querying. It is fully ACID-compliant, ensuring reliable transactions and data integrity, and supports complex queries, joins, indexing, and advanced data types such as arrays, hstore, and geometric types.

One of PostgreSQL’s standout features is its extensibility: users can define custom data types, operators, and functions, and leverage a rich ecosystem of extensions like PostGIS for geospatial data or TimescaleDB for time-series workloads. PostgreSQL is widely used in industries ranging from finance to web development, powering everything from small applications to large-scale enterprise systems. Its active community, frequent updates, and compatibility with major cloud providers make it a popular choice for developers seeking a reliable, feature-rich database solution.

1.2 MongoDB

MongoDB is a popular open-source, document-oriented NoSQL database designed for flexibility, scalability, and ease of development. Instead of storing data in tables and rows as in traditional relational databases, MongoDB uses collections and documents, where each document is a JSON-like object (BSON) that can have varying structures. This schema-less approach allows developers to store complex, hierarchical data and evolve application requirements without the need for costly schema migrations. MongoDB supports powerful querying and indexing capabilities, including aggregation pipelines, geospatial queries, and full-text search, making it suitable for a wide range of use cases.

MongoDB is widely adopted for modern web and mobile applications, especially where rapid development and iteration are important. Its horizontal scaling features, such as sharding and replica sets, enable it to handle large volumes of data and high-throughput workloads across distributed environments. The database integrates seamlessly with popular programming languages and frameworks, and its managed cloud offering, MongoDB Atlas, simplifies deployment, scaling, and maintenance. MongoDB’s flexibility, developer-friendly APIs, and strong community support have made it a go-to choice for startups and enterprises building content management systems, analytics platforms, IoT solutions, and more.

1.3 DynamoDB

DynamoDB is a fully managed NoSQL database service provided by Amazon Web Services (AWS), designed for high performance, scalability, and seamless integration with the AWS ecosystem. It uses a key-value and document data model, allowing developers to store and retrieve any amount of data with predictable low latency. DynamoDB automatically handles data partitioning and replication across multiple availability zones, ensuring high availability and durability without manual intervention. Its flexible schema design enables rapid iteration and adaptation to changing application requirements, making it well-suited for dynamic and large-scale workloads.

One of DynamoDB’s core strengths is its ability to scale horizontally to support millions of requests per second, making it a popular choice for serverless architectures, IoT backends, gaming platforms, and real-time analytics. The service offers features such as on-demand and provisioned capacity modes, automatic scaling, global tables for multi-region replication, and fine-grained access control. DynamoDB integrates tightly with other AWS services like Lambda, AppSync, and CloudWatch, enabling developers to build highly responsive, event-driven applications. Its pay-per-use pricing model and managed infrastructure reduce operational overhead, allowing teams to focus on application logic rather than database management.

2. Core Data Model

  • PostgreSQL: Relational, strongly typed schemas, ACID-compliant.
  • MongoDB: Document-oriented, flexible JSON-like schema.
  • DynamoDB: Key-value and document model optimized for high-throughput.

2.1 PosgreSQL

PosgreSQL uses a relational data model with strongly typed schemas, where data is organized into tables with predefined columns and relationships enforced through foreign keys. This structure supports complex queries, joins, and transactional integrity, making it ideal for applications requiring consistency and structured data.

Example: An e-commerce platform with customers, orders, products, and inventory, where enforcing relationships and constraints is essential.

2.2 MongoDB

MongoDB employs a document-oriented model, storing data as flexible, JSON-like documents within collections. Each document can have a different structure, allowing for easy representation of hierarchical or evolving data. This schema-less approach is well-suited for applications that need to handle diverse or rapidly changing data formats.

Example: A blogging platform where each post can have a different structure, embedded comments, and metadata that may change over time.

2.3 DynamoDB

DynamoDB utilizes a key-value and document data model optimized for high-throughput and scalability. Data is stored in tables as items, each identified by a primary key, and attributes can vary between items. This flexible model enables efficient access patterns and horizontal scaling, making it a strong choice for large-scale, distributed workloads.

Example: A real-time leaderboard for a mobile game, where each player’s score is updated and retrieved frequently, and ultra-fast response times are required.

3. Querying Capabilities

  • PostgreSQL: SQL with joins, aggregates, CTEs, JSONB.
  • MongoDB: Mongo Query Language for aggregation pipelines, indexes.
  • DynamoDB: key-based access patterns, secondary indexes.

3.1 PosgreSQL

PosgreSQL offers a rich SQL query language that supports complex joins, aggregations, subqueries, window functions, and advanced features like Common Table Expressions (CTEs) and JSONB operations. This makes it ideal for scenarios where you need to combine data from multiple tables, perform analytics, or enforce business logic at the database level.

Example: Retrieving all orders for a customer in a specific date range, including product details and total amounts, using multi-table joins and aggregate functions.

3.2 MongoDB

MongoDB uses a flexible query language that allows for powerful document-based queries, including filtering, projection, and updates on nested fields. Its aggregation pipeline enables complex data transformations, grouping, and calculations within collections.

Example: Aggregating blog posts by author to count the number of posts per user, filter by tags, and sort by creation date, all within a single aggregation pipeline.

3.3 DynamoDB

DynamoDB is optimized for simple, high-speed key-value access patterns, but also supports more advanced queries using secondary indexes and conditional expressions. While it doesn’t support joins or multi-item transactions as easily as relational databases, it excels at retrieving items by primary key or performing range queries on sorted keys.

Example: Fetching all leaderboard entries for a specific game level, sorted by score, using a global secondary index and a single query operation.

4. Similarities and Differences

When comparing PostgreSQL, MongoDB, and DynamoDB, it’s essential to consider their core features, strengths, and trade-offs. Here’s a detailed comparison table highlighting key aspects: This table summarizes the main differences and similarities between PostgreSQL, MongoDB, and DynamoDB across various dimensions. It helps to quickly identify which database might be the best fit for specific use cases based on factors like data model, query capabilities, transactions, indexing, replication, consistency, scaling, cloud offerings, and common pitfalls.

Feature PostgreSQL MongoDB DynamoDB
Data Model Relational, strongly typed schemas Document-oriented, flexible schema (BSON) Key-value & document, flexible attributes
Schema Schema-based Schema-less Schema-less
Query Language SQL (rich, supports joins, CTEs, aggregates) MongoDB Query Language, Aggregation Pipeline Key-based queries, limited expressions
Transactions Full ACID, multi-row, multi-table Multi-document (since v4.0), ACID for single doc ACID for single-table, limited multi-table
Indexing B-tree, GIN, GiST, full-text, custom Single field, compound, geospatial, text Primary key, secondary indexes
Replication Synchronous/asynchronous, streaming, logical Replica sets, sharding Multi-AZ, global tables
Consistency Strong (default) Tunable (strong/eventual) Tunable (eventual/strong per operation)
Scaling Vertical (primary), some horizontal (shards) Horizontal (sharding, replica sets) Horizontal (automatic partitioning)
Cloud Offerings AWS RDS, Azure, GCP, Neon, Supabase MongoDB Atlas, AWS/Azure integrations Native to AWS
Best For Complex queries, analytics, structured data Flexible, evolving data, rapid dev, JSON High-throughput, serverless, IoT, gaming
Ecosystem Mature, many extensions, strong community Growing, strong integrations, active community AWS-centric, integrates with AWS services
Common Pitfalls Over-indexing, schema migrations Unbounded docs, lack of normalization Poor key design, overuse of scans

5. Use Cases

  • PostgreSQL: Analytics platforms, financial apps, monoliths, legacy systems.
  • MongoDB: Content management, mobile backends, prototyping.
  • DynamoDB: High-scale serverless apps, IoT, gaming backends.
use cases

5.1 PosgreSQL

  • Financial applications: Strong ACID compliance and transactional integrity make it ideal for banking, accounting, and payment systems.
  • Analytics platforms: Advanced SQL, complex joins, and aggregations support data warehousing and business intelligence workloads.
  • Legacy or monolithic systems: Mature ecosystem and structured schemas fit traditional enterprise applications.
  • Geospatial applications: Extensions like PostGIS enable powerful location-based queries.

5.2 MongoDB

  • Content management systems: Flexible schema supports evolving content types and metadata.
  • Mobile and web backends: Rapid development and easy handling of nested, user-generated data.
  • IoT and sensor data: Handles diverse, semi-structured data from devices with varying formats.
  • Prototyping and MVPs: Schema-less design allows fast iteration as requirements change.

5.3 DynamoDB

  • High-scale serverless apps: Seamless scaling and managed infrastructure suit event-driven, cloud-native workloads.
  • IoT telemetry: Handles massive streams of time-series data with predictable low latency.
  • Gaming leaderboards: Optimized for fast, frequent updates and queries by primary key.
  • E-commerce carts and sessions: High-throughput, low-latency access for user sessions and shopping carts.

6. Serverless & Scaling

All of them have some autoscaling and global distribution. Also multi-region and read replicas. We will review the limitations in serverless environments.

6.1 PostgreSQL

  • Autoscaling: Traditional PostgreSQL on RDS requires manual scaling (vertical or read replicas). Aurora PostgreSQL supports auto-scaling of compute and storage, but scaling is not instant and may cause brief interruptions.
  • Global Distribution: Aurora Global Databases allow for cross-region read replicas with low-latency global reads and disaster recovery. Standard RDS supports read replicas in other regions, but with higher replication lag.
  • Multi-region & Read Replicas: Both RDS and Aurora support multiple read replicas (within and across regions), but only one writable primary per cluster.
  • Serverless Limitations: Aurora Serverless v2 auto-scales compute, but has cold start latency and limited support for some advanced PostgreSQL features. Not ideal for ultra-low-latency or bursty workloads.
  • Scaling Caveats: Write scaling is limited (single writer), and horizontal scaling is mostly for reads. High write throughput or global write workloads require careful design or are better suited to other databases.

6.2 MongoDB

  • Autoscaling: MongoDB Atlas provides auto-scaling for clusters (RAM, CPU, storage) and can automatically add shards as data grows. Manual sharding is possible for self-hosted setups.
  • Global Distribution: Atlas Global Clusters allow for geographically distributed data, routing users to the nearest region. Sharding enables horizontal scaling across multiple nodes and regions.
  • Multi-region & Read Replicas: Replica sets support multiple read replicas, which can be distributed across regions for high availability and disaster recovery.
  • Serverless Limitations: Atlas Serverless is designed for variable workloads but may have cold starts and limited configuration options. Some advanced features (e.g., custom sharding) are not available.
  • Scaling Caveats: Sharding adds complexity to queries and data modeling. Write scaling is possible but requires careful shard key selection. Cross-region writes can introduce latency and consistency trade-offs.

6.3 DynamoDB

  • Autoscaling: DynamoDB natively supports auto-scaling for read/write throughput and storage. It can instantly scale to handle millions of requests per second.
  • Global Distribution: Global Tables replicate data across multiple AWS regions with multi-active writes, providing low-latency access and disaster recovery.
  • Multi-region & Read Replicas: Global Tables provide multi-region, multi-master replication. All regions can accept writes and reads.
  • Serverless Limitations: DynamoDB is inherently serverless and fully managed, but costs can spike with inefficient access patterns (e.g., scans). Some features (e.g., transactions) have limits on item size and throughput.
  • Scaling Caveats: Partition key design is critical for even data distribution and avoiding throttling. Hot partitions or poorly chosen keys can limit scalability and increase costs.

7. Pricing and Cloud Offerings

Pricing varies significantly across PostgreSQL, MongoDB, and DynamoDB, depending on the cloud provider, instance type, storage, and usage patterns. Below is a summary of typical costs for each database in popular cloud environments.

PostgreSQL (AWS RDS & Aurora)

  • Hosted: AWS RDS, Azure Database for PostgreSQL, Google Cloud SQL.
  • Serverless: Neon, Supabase Edge Functions.
  • RDS PostgreSQL (On-Demand):
    • db.t3.medium (2 vCPU, 4GB RAM): $0.067/hour ($48/month)
    • Storage: ~$0.115/GB/month (General Purpose SSD)
    • Multi-AZ: Doubles instance cost
  • Aurora PostgreSQL (Serverless v2):
    • ~$0.12 per Aurora Capacity Unit (ACU)-hour (billed per second)
    • Storage: ~$0.10/GB/month
    • Example: Small serverless workload (2 ACUs, 20GB): ~$18/month + storage

MongoDB (Atlas on AWS)

  • Hosted: MongoDB Atlas, AWS/Azure integrations.
  • Serverless: MongoDB Atlas Serverless.
  • Shared Cluster (M0 Free Tier):
    • 512MB storage, shared RAM, limited throughput: $0
  • Dedicated Cluster (M10, 2 vCPU, 8GB RAM):
    • $0.08/hour ($60/month)
    • Storage: ~$0.25/GB/month
  • Serverless Instance:
    • ~$0.30/million reads, ~$1.25/million writes, ~$0.25/GB/month storage
    • Example: 1M reads, 0.5M writes, 10GB storage: ~$2/month

DynamoDB

  • Native to AWS.
  • On-demand and provisioned capacity pricing.
  • On-Demand Mode:
    • ~$1.25 per million write request units
    • ~$0.25 per million read request units
    • Storage: ~$0.25/GB/month
    • Example: 5M writes, 10M reads, 20GB storage: ~$13/month
  • Provisioned Mode:
    • 10 write units + 50 read units: ~$7/month
    • Storage: ~$0.25/GB/month
  • Free Tier: 25GB storage, 25 RCU/WCU, 2.5M reads/writes per month (first 12 months)

8. Performance and Concurrency

Performance and concurrency are critical factors when choosing a database, as they directly impact application responsiveness, scalability, and reliability under load. Each database—PostgreSQL, MongoDB, and DynamoDB—employs different mechanisms for handling concurrent operations, isolation, and throughput. Understanding these differences helps ensure your application remains performant and consistent as usage grows.

  • PostgreSQL: MVCC and locking behavior.
  • MongoDB: document-level locking and write isolation.
  • DynamoDB: throughput model, partitioning, and throttling.
Performance

8.1 PosgreSQL

PostgreSQL uses Multi-Version Concurrency Control (MVCC) to handle concurrent transactions. MVCC allows readers and writers to operate without blocking each other by keeping multiple versions of data rows. This means that readers see a consistent snapshot of the database, even as writes occur. However, certain operations (like updates to the same row) can still result in row-level locks, and long-running transactions may lead to table bloat or increased vacuuming overhead. PostgreSQL also supports explicit locking (row, table) for advanced use cases.

  • Pros: Excellent for mixed read/write workloads, strong consistency, and complex transactional logic. Readers are rarely blocked by writers.
  • Cons: Write-heavy workloads can lead to increased vacuum activity and potential contention on hot rows. Large transactions may impact performance due to bloat.
  • Write-Heavy Good, but may require tuning vacuum and indexing; row-level locks can be a bottleneck on hot rows.
  • Read-Heavy Excellent, especially with proper indexing and read replicas; supports complex queries.
  • Mixed Strong transactional guarantees and concurrency; suitable for most OLTP workloads.
  • How to overcome possible issues:
    • Tune autovacuum settings and monitor regularly.
    • Use partitioning to spread writes across tables.
    • Optimize indexes and avoid unnecessary ones.
    • Batch writes and keep transactions short.
    • For hot rows, consider application-level sharding or redesigning data access patterns.

8.2 MongoDB

MongoDB uses document-level locking, meaning only the specific document being written to is locked, not the entire collection or database. This allows for high concurrency and throughput, especially for workloads with many small, independent writes. MongoDB provides atomic operations at the document level, ensuring that updates to a single document are isolated and consistent. However, multi-document transactions (introduced in v4.0) are more expensive and can impact performance.

  • Pros: High concurrency for workloads with many independent documents. Efficient for read-heavy and write-heavy workloads where operations are mostly on single documents.
  • Cons: Multi-document transactions are slower and less efficient. Large or unbounded documents can cause performance issues.
  • Write-Heavy Very good for independent document writes; document-level locking minimizes contention.
  • Read-Heavy Very good, especially for simple queries and indexed fields; scales well with replica sets.
  • Mixed Performs well for workloads with mostly single-document operations; multi-document transactions less efficient.
  • How to Overcome possible issues:
    • Design schemas to keep most operations within a single document.
    • Use capped collections or TTL indexes to control document growth.
    • Avoid frequent multi-document transactions; denormalize data where appropriate.
    • Monitor and split large collections with sharding as needed.

8.3 DynamoDB

DynamoDB is designed for massive horizontal scalability using a partitioned, distributed architecture. You provision (or use on-demand) read and write throughput, and DynamoDB automatically partitions data based on your primary key. If a partition receives more traffic than its throughput allows, requests are throttled (rate-limited). Proper key design is critical to avoid “hot partitions” that can bottleneck performance. DynamoDB provides eventual or strong consistency per operation and supports transactions with some limitations.

  • Pros: Extremely high throughput and low latency for well-designed access patterns. Scales seamlessly for write-heavy, read-heavy, or mixed workloads.
  • Cons: Poor key design can lead to throttling and uneven performance. Complex queries (e.g., joins, aggregations) are not supported natively.
  • Write-Heavy Excellent if partition keys are well-designed; can handle massive write throughput.
  • Read-Heavy Excellent for key-based lookups; scales horizontally; limited for complex queries.
  • Mixed Handles mixed workloads well if access patterns are predictable and keys are well-designed.
  • How to Overcome possible issues:
    • Carefully design partition keys to distribute load evenly.
    • Use secondary indexes for alternative access patterns.
    • Avoid full-table scans; use queries and indexes instead.
    • Enable auto-scaling and monitor CloudWatch metrics for throttling.
    • Use DynamoDB Streams and AWS Lambda for offloading heavy processing.

9. Availability and Durability

Availability and durability are crucial for ensuring your data remains safe and accessible, even in the face of hardware failures, network issues, or regional outages. Each database offers different replication strategies, failover mechanisms, and guarantees for data protection and uptime.

9.1 PostgreSQL

  • Replication Models: Supports both synchronous and asynchronous replication. Synchronous replication ensures no data loss but may impact write latency; asynchronous replication is faster but risks some data loss during failover.
  • Automatic Failover & Backups: Managed services like AWS RDS and Aurora provide automatic failover to replicas and automated backups with point-in-time recovery. Manual setup is required for self-hosted deployments.
  • Multi-region Capabilities & SLAs: Aurora Global Databases enable cross-region replication with low-latency reads and disaster recovery. Standard RDS supports cross-region read replicas, but with higher lag. SLAs depend on the cloud provider (e.g., AWS RDS offers up to 99.99% availability).
  • Region Outage/Partition: In a region outage, failover to a cross-region replica is possible with Aurora Global, but there may be some replication lag and potential data loss if using async replication. Network partitions can cause split-brain scenarios if not managed carefully.

9.2 MongoDB

  • Replication Models: Uses replica sets with primary-secondary (single-writer) architecture. Replication is asynchronous by default, but can be tuned for stricter write concerns.
  • Automatic Failover & Backups: Replica sets provide automatic failover—if the primary goes down, a secondary is elected as the new primary. Backups are supported via snapshots and point-in-time recovery in managed services like MongoDB Atlas.
  • Multi-region Capabilities & SLAs: Atlas Global Clusters allow for multi-region deployments, distributing data closer to users. SLAs for Atlas can reach 99.995% availability.
  • Region Outage/Partition: If a region fails, another region’s replica can be promoted to primary, but there may be some data loss if writes haven’t replicated. Network partitions can temporarily split the cluster, but automatic reconciliation occurs once connectivity is restored.

9.3 DynamoDB

  • Replication Models: DynamoDB automatically replicates data across multiple Availability Zones (AZs) within a region for high durability. Global Tables provide multi-region, multi-active replication.
  • Automatic Failover & Backups: Built-in automatic failover within a region; no manual intervention needed. Continuous backups, point-in-time recovery, and on-demand backups are available.
  • Multi-region Capabilities & SLAs: Global Tables enable active-active replication across regions with low-latency global access. AWS offers a 99.999% availability SLA for DynamoDB.
  • Region Outage/Partition: If a region goes down, applications can fail over to another region with a Global Table. Writes may temporarily conflict or be delayed, but DynamoDB resolves conflicts automatically. Data durability is extremely high, but cross-region replication lag can cause minimal data loss in rare cases.

10. Developer Experience

The developer experience encompasses the tools, workflows, and resources available for building, testing, and maintaining applications with each database. Here’s how PostgreSQL, MongoDB, and DynamoDB compare in terms of tooling, local development, and schema management.

10.1 PosgreSQL

PostgreSQL offers a mature and robust developer experience with a wide range of tools, including the psql CLI, graphical clients (pgAdmin, DBeaver), and extensive SDK support for most programming languages. Local development is straightforward with Docker images or native packages, and test containers are widely available. Schema management and migrations are well-supported by tools like Flyway, Liquibase, and various ORMs. Its strong standards compliance and rich ecosystem make it pleasant to build, test, and evolve applications, especially for teams familiar with SQL.

10.2 MongoDB

MongoDB provides a modern developer experience with a powerful CLI (mongosh), GUI tools (MongoDB Compass), and official drivers for all major languages. Local development is easy using Docker or the community server, and Atlas offers a free cloud tier for quick prototyping. Schema migrations are less rigid due to its schema-less nature, but tools like MongoDB Migrate and Mongoose (for Node.js) help manage changes. Its flexible data model and developer-friendly APIs make it fast and enjoyable to iterate on new features.

10.3 DynamoDB

DynamoDB integrates tightly with AWS tooling, offering the AWS CLI, SDKs for all major languages, and a web console for management. Local development is possible with DynamoDB Local (a downloadable version), but some features differ from the cloud service. Schema management is minimal due to its schema-less design, but modeling access patterns requires up-front planning. Migrations are mostly handled at the application level. While highly scalable, DynamoDB’s developer experience can be less intuitive for those new to NoSQL or AWS, but it excels for serverless and event-driven apps.

11. Ecosystem and Community

A vibrant ecosystem and active community can greatly enhance the usability, extensibility, and long-term viability of a database platform. Let’s examine how PostgreSQL, MongoDB, and DynamoDB stack up in terms of integrations, tooling, and community support.

  • Postgre: Extensions (e.g. PostGIS, TimescaleDB), ORMs (e.g. Prisma, Sequelize, etc).
  • MongoDB: integrations with MEAN/MERN stack.
  • DynamoDB: with AWS Lambda, AppSync, etc.
Community and Ecosystem

11.1 PosgreSQL

PostgreSQL boasts a mature and vibrant ecosystem with a wide range of extensions such as PostGIS (for geospatial queries), TimescaleDB (for time-series data), and pg_partman (for partitioning). It is supported by many popular ORMs, including Prisma, Sequelize, SQLAlchemy, and Hibernate, making integration with most programming languages straightforward. The PostgreSQL community is large, active, and well-established, offering extensive documentation, tutorials, and long-term support. Frequent updates and a strong focus on standards ensure continued innovation and reliability.

11.2 MongoDB

MongoDB is well-integrated with modern development stacks, especially the MEAN/MERN stack (MongoDB, Express, Angular/React, Node.js), making it a popular choice for full-stack JavaScript applications. It offers official drivers for all major languages and frameworks, and has a growing ecosystem of tools for analytics, search, and data visualization. The community is active, with plenty of learning resources, online courses, and forums. MongoDB Inc. provides long-term support for enterprise users and regular updates for the open-source version.

11.3 DynamoDB

DynamoDB is deeply integrated into the AWS ecosystem, working seamlessly with AWS Lambda (for serverless compute), AppSync (GraphQL APIs), Step Functions, and other AWS services. It is supported by the AWS SDKs for all major languages and has a growing set of third-party tools for data modeling and migrations. While the open-source community is smaller compared to PostgreSQL and MongoDB, AWS provides extensive documentation, workshops, and long-term support. DynamoDB is a top choice for teams building serverless, event-driven, or cloud-native applications on AWS.

12. Common Pitfalls and Anti-Patterns

Every database has its own set of common mistakes and anti-patterns that can lead to performance, scalability, or maintainability issues. Understanding these pitfalls—and how to avoid or fix them—will help you build more robust and efficient systems.

  • PostgreSQL: over-indexing, improper schema changes.
  • MongoDB: unbounded document growth, lack of normalization.
  • DynamoDB: bad key design, over-reliance on scans.

12.1 PostgreSQL

  • Over-Indexing

    • Problem: Creating too many indexes can slow down write operations and increase storage usage.
    • Example: Adding indexes on every column in a frequently updated table.
    • How to Avoid/Fix: Only index columns that are frequently queried or used in JOIN/WHERE clauses. Regularly review and drop unused indexes using tools like pg_stat_user_indexes.

  • Improper Schema Changes

    • Problem: Making frequent or unplanned schema changes (e.g., adding columns, changing types) can lock tables and impact availability.
    • Example: Running ALTER TABLE on a large production table during peak hours.
    • How to Avoid/Fix: Schedule schema changes during maintenance windows. Use tools like pg_repack or perform changes in small, incremental steps. For large tables, consider creating new tables and migrating data gradually.

  • Ignoring Vacuum and Bloat

    • Problem: Not tuning autovacuum or ignoring table bloat can degrade performance over time.
    • Example: Long-running transactions prevent vacuum from reclaiming space, leading to slow queries.
    • How to Avoid/Fix: Monitor autovacuum activity, tune thresholds, and avoid long transactions. Use VACUUM and ANALYZE regularly.

12.2 MongoDB

  • Unbounded Document Growth

    • Problem: Documents that grow indefinitely (e.g., appending to arrays) can exceed the 16MB document size limit and slow down updates.
    • Example: Storing all user activity logs in a single user document.
    • How to Avoid/Fix: Split large arrays into separate collections (e.g., one document per log entry). Use capped collections or TTL indexes for time-based data.

  • Lack of Normalization

    • Problem: Over-embedding or duplicating data can lead to inconsistencies and large, unwieldy documents.
    • Example: Embedding all order details and customer info in every order document.
    • How to Avoid/Fix: Use references for data that changes frequently or is shared across documents. Balance embedding and referencing based on access patterns.

  • Ignoring Indexes

    • Problem: Not creating indexes on frequently queried fields leads to slow queries and collection scans.
    • Example: Querying on an unindexed field in a large collection.
    • How to Avoid/Fix: Analyze query patterns and create appropriate single-field or compound indexes. Use the explain() method to check query plans.

12.3 DynamoDB

  • Bad Key Design

    • Problem: Poorly chosen partition keys can create “hot partitions,” leading to throttling and uneven performance.
    • Example: Using a timestamp or a non-unique value as the partition key for all writes.
    • How to Avoid/Fix: Choose partition keys that evenly distribute traffic. For time-series data, use composite keys or add random suffixes to spread writes.

  • Over-Reliance on Scans

    • Problem: Using Scan operations for most queries is inefficient and expensive at scale.
    • Example: Scanning the entire table to find items by a non-key attribute.
    • How to Avoid/Fix: Use Query operations with well-designed keys and secondary indexes. Only use Scan for infrequent, administrative tasks.

  • Ignoring Provisioned Throughput and Throttling

    • Problem: Not monitoring or adjusting read/write capacity can lead to throttling and failed requests.
    • Example: Sudden traffic spikes exceed provisioned capacity, causing errors.
    • How to Avoid/Fix: Enable auto-scaling for read/write capacity. Monitor CloudWatch metrics and set alarms for throttling events.

13. Conclusion and Recommendation Matrix

Choosing the right database depends on your application’s requirements, team expertise, and future growth plans. PostgreSQL, MongoDB, and DynamoDB each excel in different scenarios, and understanding their strengths and trade-offs is key to making an informed decision.

Decision Checklist

  • Choose PostgreSQL if:

    • You need strong consistency, complex queries, or transactional integrity.
    • Your data is highly structured and relationships matter.
    • You want a mature ecosystem and broad cloud support.

  • Choose MongoDB if:

    • Your data model is flexible or evolving.
    • You need to iterate quickly, especially for web/mobile backends.
    • You want easy horizontal scaling and JSON-like document storage.

  • Choose DynamoDB if:

    • You require seamless serverless integration and massive, predictable scale.
    • Your access patterns are simple and key-based.
    • You want fully managed, global, and highly available infrastructure.

Final Thoughts:
No single database is universally optimal for every scenario. It is essential to evaluate your application’s requirements, anticipated scale, and team expertise when selecting a solution. Begin with the database that aligns best with your current needs, but remain open to evolving your architecture as your product matures.

In large-scale projects, it is common to leverage multiple databases—such as PostgreSQL, MongoDB, DynamoDB or Elasticsearch, SQL Server Redis. Assigning each to the part of the system where it delivers the greatest value.