Druid is a real-time OLAP database optimized for time-series analytics with sub-second query latency, while Pinot is a columnar OLAP datastore designed for real-time analytics at scale with higher throughput on aggregation queries. Druid excels in streaming ingestion and time-series use cases, whereas Pinot performs better for high-cardinality dimension queries and massive fan-out aggregations.
Druid vs Pinot — Side-by-side comparison on A Versus B
Real-time OLAP database optimized for time-series analytics and streaming ingestion.
Real-time OLAP engine optimized for low-latency dimensional analytics on streaming data.
Query Latency (p99). Ingestion Rate. High-Cardinality Query Performance. Aggregation Throughput. Time-Series Optimization. Storage Compression Ratio. Operational Complexity
Choose Druid if you need real-time streaming analytics with sub-second latency on time-series data, monitoring dashboards, and can tolerate moderate operational overhead. Choose Pinot if you prioritize high-cardinality dimension analysis, massive query throughput at scale, and need better storage efficiency with simpler operational management.
Apache Druid: 50-150ms | Apache Pinot: 50-100ms
Apache Druid: 600-900MB | Apache Pinot: 150-300MB
Apache Druid: 100K-500K | Apache Pinot: 10K-50K
Apache Druid: Limited (requires denormalization) | Apache Pinot: Full support (INNER, LEFT, RIGHT, FULL)
Apache Druid: Custom JSON/Druid QL | Apache Pinot: PQL + Limited SQL (60% compatibility)
Apache Druid: 500+ (Airbnb, Netflix, etc) | Apache Pinot: 1000+ (LinkedIn, Uber, etc)
Apache Druid: 100-500ms | Apache Pinot: 500-2000ms
Apache Druid: 3-5 nodes | Apache Pinot: 5-8 nodes
Apache Druid: Druid SQL (Full) | Apache Pinot: PQL (Custom) + Presto Bridge
Apache Druid: 15,800 | Apache Pinot: 5,300
Apache Druid: Real-time metrics (specialized) | Apache Pinot: Ad-hoc queries (generalized)
Apache Druid: Limited (basic) | Apache Pinot: Full support
Apache Druid: 500,000 | Apache Pinot: 1,000,000-2,000,000 events/sec
Apache Druid: Basic (limited analyzers) | Apache Pinot: Limited (phrase queries, basic tokenization)
Apache Druid: 15,200 | Apache Pinot: N/A
Apache Druid: 500ms | Apache Pinot: 50-200ms
Apache Druid: Unlimited (distributed) | Apache Pinot: 1000+
Apache Druid: $0.10-$0.50 | Apache Pinot: N/A
Apache Druid: Time-window based (7 days to 3 years typical) | Apache Pinot: N/A
Apache Druid: ~60% ANSI SQL | Apache Pinot: 85% SQL coverage
Apache Druid: 16-64 | Apache Pinot: 16-32GB for analytics workload
Apache Druid: 10-100ms | Apache Pinot: N/A
Apache Druid: 1-5 seconds (streaming) | Apache Pinot: 0.1 (streaming)
Apache Druid: Included in storage/infrastructure | Apache Pinot: N/A
Apache Druid: Petabyte+ (with cluster scaling) | Apache Pinot: N/A
Apache Druid: Self-hosted, cloud, hybrid, open-source | Apache Pinot: On-prem, Kubernetes, multi-cloud
Apache Druid: High (cluster tuning, scaling, monitoring) | Apache Pinot: N/A
Apache Druid: 1,000,000+ | Apache Pinot: N/A
Apache Druid: $5 (self-hosted avg) | Apache Pinot: N/A
Apache Druid: Proprietary Druid SQL with extensions | Apache Pinot: N/A
Apache Druid: 45 (self-hosted setup) | Apache Pinot: 45-60 minutes
Apache Druid: 3 (recommended) | Apache Pinot: 3-5
Apache Druid: 50K-100K | Apache Pinot: 100K-500K
Apache Druid: 10:1 to 20:1 | Apache Pinot: 15:1 to 25:1
Apache Druid: 12+ (Coordinator, Broker, Historical, MiddleManager, Overlord, Router) | Apache Pinot: 8+ (Broker, Server, Controller, Minion)
Apache Druid: 1M-10M practical limit | Apache Pinot: 100M+ with dictionary encoding
Apache Druid: 100-500MB segments | Apache Pinot: 1-5GB segments
Apache Druid: 2-3 months | Apache Pinot: 1-2 months
P99 Query Latency (1B rows, aggregation)
Apache Druid: N/A | Apache Pinot: 100-500ms
Ingestion Latency (Kafka to query-ready)
Apache Druid: N/A | Apache Pinot: 0.5-2 seconds
Apache Druid: N/A | Apache Pinot: Standard ANSI SQL
Apache Druid: N/A | Apache Pinot: 0.5-2TB per node
Apache Druid: N/A | Apache Pinot: Kafka, Pulsar, Kinesis, S3
Apache Druid: N/A | Apache Pinot: 15,000+
Apache Druid: N/A | Apache Pinot: StarTree (24/7 SLA options)
Apache Druid: N/A | Apache Pinot: 3:1 to 10:1 (avg 6:1)
Apache Druid: N/A | Apache Pinot: 100-400MB
Apache Druid: N/A | Apache Pinot: Native tenant isolation
Apache Druid: N/A | Apache Pinot: $2000-3500
Apache Druid: N/A | Apache Pinot: $120-180
Apache Druid: N/A | Apache Pinot: 16-64GB
Apache Druid: N/A | Apache Pinot: 5-7 nodes (3 controllers + 2-4 brokers)
Apache Druid: N/A | Apache Pinot: 100-500TB (hot data)
Apache Druid: N/A | Apache Pinot: $0 (infrastructure only)
Apache Druid: N/A | Apache Pinot: 12 weeks
Apache Druid: N/A | Apache Pinot: 500,000/sec (streaming)
Apache Druid: N/A | Apache Pinot: $150,000
Apache Druid: N/A | Apache Pinot: 500+ (LinkedIn runs 1,000+)
Apache Druid: N/A | Apache Pinot: 14-30 days (requires schema design and tuning)
Apache Druid: N/A | Apache Pinot: Native SQL with PQL extensions (ANSI-compliant subset)
Apache Druid: N/A | Apache Pinot: 8,200+
Apache Druid: N/A | Apache Pinot: 240-480
Apache Druid: N/A | Apache Pinot: 100-500
Apache Druid: N/A | Apache Pinot: 100,000+
Apache Druid: N/A | Apache Pinot: Native (Kafka, S3, MQ)
Apache Druid: N/A | Apache Pinot: Java, Python (limited)
Annual Infrastructure Cost (1TB dataset)
Apache Druid: N/A | Apache Pinot: 50,000-150,000
Apache Druid: N/A | Apache Pinot: 100-500 typical
Apache Druid: N/A | Apache Pinot: <100ms
Apache Druid: N/A | Apache Pinot: Sub-second to 1 minute
Apache Druid: N/A | Apache Pinot: Apache 2.0 Open Source
Apache Druid: N/A | Apache Pinot: Kubernetes, on-premises, all cloud providers
Apache Druid: N/A | Apache Pinot: 1M+ eps native
Apache Druid: N/A | Apache Pinot: Self-hosted infrastructure costs (highly variable)
Apache Druid: N/A | Apache Pinot: <100 ms
Apache Druid: N/A | Apache Pinot: <1 second
Apache Druid: N/A | Apache Pinot: Free (self-hosted) / $0.10-0.50 (managed)
Apache Druid: N/A | Apache Pinot: Self-hosted (high DevOps)
Apache Druid: N/A | Apache Pinot: 10+ billion events/day (proven at LinkedIn, Airbnb)
Apache Druid: N/A | Apache Pinot: 40-80 hours (cluster provisioning, tuning)
Apache Druid: N/A | Apache Pinot: 100-500 (depends on cluster config)
Apache Druid: N/A | Apache Pinot: 1,000,000+
Apache Druid: N/A | Apache Pinot: 10,000+
Apache Druid: N/A | Apache Pinot: 50-200
Apache Druid: N/A | Apache Pinot: Yes (native distributed)
Typical Query Latency (1B rows, GROUP BY)
Apache Druid: N/A | Apache Pinot: 50-500ms
Apache Druid: N/A | Apache Pinot: 0.1-0.3x
Apache Druid: N/A | Apache Pinot: Not optimized (secondary feature)
Apache Druid: N/A | Apache Pinot: 9,200+
Apache Druid: N/A | Apache Pinot: Native (Kafka, S3, HDFS)
Apache Druid: N/A | Apache Pinot: Medium-High (columnar tuning)
Apache Druid: N/A | Apache Pinot: 10+ PB (proven at scale)
Apache Druid: N/A | Apache Pinot: Native PinotSQL (full ANSI compliance)
Apache Druid: N/A | Apache Pinot: 5-7 components
Query Latency (Typical Analytical Query)
Apache Druid: N/A | Apache Pinot: 1,000,000+ rows/sec
Apache Druid: N/A | Apache Pinot: $0 (open-source)
Apache Druid: N/A | Apache Pinot: 0 (requires external tools)
Apache Druid: N/A | Apache Pinot: Petabyte-scale (distributed)
How to choose between Apache Druid and Apache Pinot
Druid vs Pinot — Structured Comparison Data
Machine-readable comparison dataset for Druid vs Pinot. Contains 120 attribute comparisons, 5 FAQ pairs.
Druid vs Pinot — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Druid vs Pinot.
Which is better for real-time dashboards?
Druid is significantly better for real-time dashboards due to its sub-second query latency (p99: 100-500ms vs Pinot's 500-2000ms). If you need dashboard refreshes every 1-5 seconds with <500ms response times, Druid is the clear choice. Pinot's latency makes it unsuitable for interactive real-time use cases.
Which handles high-cardinality data better?
Pinot excels with high-cardinality dimensions, supporting 100M+ unique values through dictionary encoding and better compression. Druid struggles with high-cardinality data beyond 1-10M unique values. If your use case involves user IDs, session IDs, or product catalogs with millions of unique values, Pinot is the better choice.
What are typical ingestion rates and throughput?
Druid handles 1M+ events/second ingestion, while Pinot tops out around 500K events/second. However, Pinot compensates with 100K-500K QPS aggregation throughput compared to Druid's 50K-100K QPS. For write-heavy workloads, choose Druid; for read-heavy aggregation workloads, choose Pinot.
Which is easier to operate in production?
Pinot has a simpler operational model with fewer components (8+) compared to Druid (12+), resulting in lower DevOps overhead. Pinot can typically be production-ready in 1-2 months, while Druid requires 2-3 months of operationalization. Choose Pinot if you have limited DevOps resources.
Can both handle Kafka streaming natively?
Both support Kafka ingestion, but Druid has more mature streaming support with native Kafka indexing, automatic segment rollup, and simpler stream configuration. Pinot requires more manual configuration for streaming pipelines. For Kafka-first architectures, Druid is the more straightforward choice.
How to Choose Between Apache Druid and Apache Pinot
Query Latency (p99): Apache Druid has 100-500ms, while Apache Pinot has 500-2000ms.
Ingestion Rate: Apache Druid has 1M+ events/second, while Apache Pinot has 500K events/second.
Consider: High-Cardinality Query Performance
High-Cardinality Query Performance: Apache Druid has Moderate, while Apache Pinot has Excellent.
Aggregation Throughput: Apache Druid has 50K-100K QPS, while Apache Pinot has 100K-500K QPS.
Time-Series Optimization: Apache Druid has Native support with roll-up, while Apache Pinot has Good but secondary focus.
Storage Compression Ratio: Apache Druid has 10:1 to 20:1, while Apache Pinot has 15:1 to 25:1.
Operational Complexity: Apache Druid has High (12+ components), while Apache Pinot has Medium (8+ components).