When to use Druid vs Postgres?

Druid and Postgres overlap a little, but they are built for very different use cases. The right choice depends mainly on what kind of queries you expect and how much data you’re dealing with.

---

Apache Druid

Best for:

• Real-time analytics and dashboards (sub-second query latency at scale)

• Large-scale event/stream data (billions of rows)

• OLAP (Online Analytical Processing) workloads

When to use Druid:

• You’re ingesting high-velocity streaming data (e.g., Kafka, Kinesis) and need queries on it in real-time.

• You need fast aggregations (e.g., counts, sums, averages, group-bys) over very large datasets.

• You want to serve interactive dashboards (e.g., Grafana, Superset, Looker) with response times <1s.

• Use cases:

  • Product analytics (user clicks, app events)

  • Fraud detection (real-time anomaly detection)

  • Monitoring metrics/logs (observability at scale)

Strengths:

• Optimized columnar storage → fast scans

• Bitmap indexing & segment pruning → very fast filters

• Scales horizontally → handle TB–PB data

Weaknesses:

• Not great for transactional (OLTP) workloads

• Limited support for complex joins

• Requires more infra complexity (brokers, historicals, middle managers, etc.)

---

Postgres

Best for:

• Traditional relational workloads (CRUD apps, OLTP)

• Small to medium-scale analytics (data marts, BI queries)

• Complex queries with joins, constraints, transactions

When to use Postgres:

• You need a general-purpose database that balances transactions and analytics.

• Your dataset fits on a single server (hundreds of GB to low TBs).

• You need strong ACID guarantees (banking, e-commerce orders, user accounts).

• Use cases:

  • Application backend databases

  • Business reporting with moderate data volumes

  • Analytics on small/mid-sized datasets

Strengths:

• Mature SQL support (joins, subqueries, CTEs)

• Extensions (PostGIS, TimescaleDB, etc.)

• Easy ops and lower infra overhead

Weaknesses:

• Analytics performance degrades with billions of rows

• Limited real-time ingestion (batch ETL is common)

• Scaling beyond a single node requires external tooling (Citus, sharding)

---

Rule of Thumb

• Use Postgres if you need transactions, rich SQL, and smaller-scale analytics.

• Use Druid if you need real-time, sub-second analytics on streaming or very large datasets.

---

Druid vs Postgres – Direct Comparison

Feature / Dimension	Apache Druid	Postgres
Primary Use Case	Real-time analytics, OLAP, dashboards	OLTP (transactions), general-purpose RDBMS, smaller-scale analytics
Query Latency	Sub-second (ms–100s ms) even at TB scale	ms for OLTP; seconds–minutes for complex analytics on large datasets
Data Ingestion	Real-time streaming (Kafka, Kinesis) + batch	Batch inserts/ETL, not optimized for streaming
Scale	Horizontally scalable (billions–trillions of rows)	Vertical scaling (single node) by default; horizontal scaling via Citus/sharding
Storage Model	Column-oriented, compressed, distributed segments	Row-oriented (with some columnar support via extensions like Citus or TimescaleDB)
Joins Support	Limited (best with star-schema style denormalization)	Full SQL joins, subqueries, foreign keys, constraints
Transactions	Not ACID; eventually consistent for ingestion	Fully ACID compliant, strong consistency
Cost	Higher infra cost (needs multiple services: brokers, historicals, middle managers)	Lower infra cost; runs on a single VM or small cluster
Complexity (Ops)	Complex (cluster setup, tuning, segment management)	Simpler; widely known, many tools and hosting options
Data Volume Sweet Spot	100s GB → PB scale	MB → low TB scale (before performance degrades)
Extensibility	Focused on OLAP (limited extensibility)	Rich ecosystem: PostGIS, TimescaleDB, full SQL features
Typical Use Cases	Real-time dashboards, product analytics, fraud detection, log/metrics analytics	Application backend, transactional workloads, moderate BI queries, geospatial apps

---

Rule of thumb:

• Use Postgres if you need transactions, joins, or manageable data volumes (<1–2TB active data).

• Use Druid if you need sub-second aggregations over massive, fast-changing datasets (10s–100s TBs or more).