DuckDB is a lightweight, single-machine analytical database optimized for OLAP queries on local data with sub-second response times, while Apache Spark is a distributed computing framework designed for large-scale data processing across clusters with support for batch, streaming, and ML workloads. DuckDB excels at interactive analytics on datasets under 100GB, while Spark handles petabyte-scale distributed processing.
DuckDB vs Apache Spark — Side-by-side comparison on A Versus B
Embedded in-process OLAP database optimized for analytical queries on local datasets
Distributed computing framework for large-scale data processing and machine learning
Architecture. Query Latency (typical OLAP query). Maximum Data Scale. Setup Complexity. Memory Usage (typical). Supported Data Formats. Learning Curve
Choose DuckDB if you're performing interactive analytics on local or cloud-connected datasets under 1TB, need fast query responses, and want zero operational overhead. Choose Apache Spark if you need to process petabyte-scale data across distributed clusters, require streaming capabilities, or need production-grade fault tolerance and multi-tenancy support.
DuckDB: 1 (single machine) | Apache Spark: N/A
DuckDB: 10-50 million rows/sec | Apache Spark: N/A
DuckDB: 95% standard SQL | Apache Spark: N/A
DuckDB: 18,500+ | Apache Spark: 35,900 stars
DuckDB: 0.5-2 seconds | Apache Spark: N/A
DuckDB: Partial (batch insert-optimized) | Apache Spark: N/A
DuckDB: Single-threaded writes only | Apache Spark: N/A
DuckDB: 5 years (since 2019) | Apache Spark: N/A
DuckDB: Parquet, CSV, JSON, Iceberg, Hugging Face | Apache Spark: N/A
DuckDB: Growing (100K+) | Apache Spark: N/A
Query Latency (100M rows, simple aggregation)
DuckDB: 12+ formats | Apache Spark: Parquet, ORC, JSON, CSV, Avro, Delta (via library)
DuckDB: 50-200ms | Apache Spark: 2000-5000ms
DuckDB: 1,000 | Apache Spark: 100,000+ (petabyte scale)
DuckDB: 10-50MB | Apache Spark: 500-2000MB
DuckDB: 1-5 minutes | Apache Spark: 30-120 minutes
DuckDB: Python, SQL, C++, R, Julia, Node.js | Apache Spark: Python, Scala, SQL, R, Java
DuckDB: 2-5 (local install) | Apache Spark: 60-120 (cluster setup)
DuckDB: Not supported | Apache Spark: Native support
DuckDB: No (single machine) | Apache Spark: Yes (RDD lineage-based)
DuckDB: Fully supported | Apache Spark: N/A
DuckDB: C++ (Rust bindings available) | Apache Spark: N/A
DuckDB: 5 (Python, R, Java, Node.js, Go) | Apache Spark: N/A
DuckDB: v0.10.0 (2024) | Apache Spark: N/A
Total Cost of Ownership (Annual, 100TB dataset)
Query Latency (10GB dataset, simple aggregate)
Query Latency (1TB dataset, complex join)
DuckDB: 2 TB (local) | Apache Spark: N/A
DuckDB: 1-5 simultaneous | Apache Spark: N/A
DuckDB: No (requires external integration) | Apache Spark: N/A
DuckDB: Batch-focused only | Apache Spark: N/A
DuckDB: Limited by single machine | Apache Spark: N/A
DuckDB: Python, R, Java, C++, Node.js, Go | Apache Spark: Python, Scala, Java, R, SQL
Annual Infrastructure Cost (1TB dataset)
Query Performance on 10GB Parquet File (GROUP BY aggregation)
DuckDB: 2.1 GB (with compression) | Apache Spark: N/A
DuckDB: 45ms (lightweight binary) | Apache Spark: N/A
Number of Built-in Data Transformation Methods
DuckDB: 65 SQL functions + standard | Apache Spark: N/A
DuckDB: 8.2K questions | Apache Spark: N/A
Maximum Dataset Size (without disk streaming)
DuckDB: 1000+ GB (out-of-core) | Apache Spark: N/A
DuckDB: Yes (ROW_NUMBER, LAG, LEAD, RANK, etc.) | Apache Spark: N/A
DuckDB: Unlimited (disk-dependent) | Apache Spark: N/A
DuckDB: None (local only) | Apache Spark: N/A
DuckDB: Not supported | Apache Spark: N/A
DuckDB: 7 (Python, Node.js, Go, Rust, R, Java, C++) | Apache Spark: 5 (Scala, Python, Java, R, SQL)
DuckDB: No (embedded library) | Apache Spark: N/A
Setup Time (fresh install to first query)
DuckDB: 2 minutes | Apache Spark: 60-120 minutes
DuckDB: 0.1GB | Apache Spark: 2GB (per executor)
DuckDB: 0.3 seconds | Apache Spark: 8 seconds
DuckDB: 1TB | Apache Spark: 1000TB+ (petabytes)
DuckDB: 280+ contributors | Apache Spark: 1500+ contributors
DuckDB: 22000 stars | Apache Spark: 40000 stars
DuckDB: 4 hours | Apache Spark: 40 hours
DuckDB: 2-64 (varies) | Apache Spark: 8-12GB (caching overhead)
DuckDB: No (single-node only) | Apache Spark: N/A
DuckDB: No (batch only) | Apache Spark: N/A
DuckDB: 200 MB (compressed) | Apache Spark: N/A
DuckDB: Full ANSI SQL + extensions | Apache Spark: N/A
DuckDB: 7 (requires SQL) | Apache Spark: N/A
DuckDB: 512MB standalone | Apache Spark: 8GB (per node, 3+ nodes recommended)
DuckDB: 0.1-0.5 seconds | Apache Spark: 5-30 seconds
DuckDB: 2-5 (download and run) | Apache Spark: 45-120 (including cluster setup)
DuckDB: 5 (SQL, Python, R, C++, Go) | Apache Spark: 6 (Scala, Python, SQL, R, Java, Go)
DuckDB: 10-100 (vectorized) | Apache Spark: 1-10 (cluster dependent)
DuckDB: ~1 TB (single machine) | Apache Spark: N/A
Query Latency (1B rows, COUNT aggregation)
DuckDB: 4-8x compression | Apache Spark: N/A
DuckDB: 20,000+ stars | Apache Spark: N/A
DuckDB: MIT - No restrictions | Apache Spark: N/A
DuckDB: N/A | Apache Spark: 40-80 hours (dedicated DevOps engineer)
SQL Query Performance (TPC-DS Benchmark)
DuckDB: N/A | Apache Spark: 45-120 seconds
DuckDB: N/A | Apache Spark: 1-5 (via Jupyter sharing)
DuckDB: N/A | Apache Spark: 0 (manual implementation required)
DuckDB: N/A | Apache Spark: 25,000+ questions
DuckDB: N/A | Apache Spark: $1,500-$5,000+
DuckDB: N/A | Apache Spark: 5-15 minutes
DuckDB: N/A | Apache Spark: Hadoop, Kubernetes, cloud object storage (3+ classes)
DuckDB: N/A | Apache Spark: 0 (requires external frameworks)
DuckDB: N/A | Apache Spark: 30-120 seconds
DuckDB: N/A | Apache Spark: Python, Scala, Java, SQL, R
DuckDB: N/A | Apache Spark: Unlimited (depends on storage)
DuckDB: N/A | Apache Spark: 4+ (AWS, Azure, GCP, on-prem)
DuckDB: N/A | Apache Spark: 50+ (MLlib + custom models)
DuckDB: N/A | Apache Spark: 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON)
DuckDB: N/A | Apache Spark: 30-60 seconds (in-memory)
DuckDB: N/A | Apache Spark: 10-60 seconds
DuckDB: N/A | Apache Spark: 500-5000 ms micro-batches
DuckDB: N/A | Apache Spark: Lineage-based recovery (RDD parents)
DuckDB: N/A | Apache Spark: 200+ via ecosystem integrations
DuckDB: N/A | Apache Spark: 65% (Databricks, AWS, Google, Meta deployments)
DuckDB: N/A | Apache Spark: ~400-800MB (GC overhead)
DuckDB: N/A | Apache Spark: Partial in Structured Streaming, limited out-of-order support
DuckDB: N/A | Apache Spark: ~500K-1M events/sec
DuckDB: N/A | Apache Spark: Unified via Structured Streaming/Dataset API
DuckDB: N/A | Apache Spark: 0.5-2 seconds
DuckDB: N/A | Apache Spark: 8-64 GB per node
DuckDB: N/A | Apache Spark: 100-500 ms (micro-batch)
DuckDB: N/A | Apache Spark: 12 years (since 2013)
DuckDB: N/A | Apache Spark: 74% currently use
DuckDB: N/A | Apache Spark: Full MLlib with algorithms, pipelines
DuckDB: N/A | Apache Spark: Depends on underlying storage
DuckDB: N/A | Apache Spark: 100-2,000ms (Spark Streaming micro-batch interval)
DuckDB: N/A | Apache Spark: 500,000 - 2,000,000 (batch-optimized)
DuckDB: N/A | Apache Spark: 7,200,000 (StackOverflow, job postings 2024)
DuckDB: N/A | Apache Spark: 14,000+ (Spark packages, MLlib, SQL, GraphX, etc.)
DuckDB: N/A | Apache Spark: 2-4 weeks (larger talent pool, more examples)
DuckDB: N/A | Apache Spark: 80-150 lines (custom state handling needed)
DuckDB: N/A | Apache Spark: 2 types (RDD state, DataFrame state)
DuckDB: N/A | Apache Spark: 45,000-55,000
Typical Processing Speed (iterative queries)
DuckDB: N/A | Apache Spark: 16-64 GB (in-memory caching)
DuckDB: N/A | Apache Spark: Native (Spark Streaming, Structured Streaming)
DuckDB: N/A | Apache Spark: 450,000+ globally
DuckDB: N/A | Apache Spark: Highly active (Apache Foundation priority)
Approximate Learning Time for Developers
How to choose between DuckDB and Apache Spark
DuckDB vs Apache Spark — Structured Comparison Data
Machine-readable comparison dataset for DuckDB vs Apache Spark. Contains 170 attribute comparisons, 5 FAQ pairs.
DuckDB vs Apache Spark — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for DuckDB vs Apache Spark.
When should I use DuckDB instead of Spark?
Use DuckDB when your dataset is under 1TB, you need interactive query response times (sub-second), you're working locally or with a single machine, and you want zero infrastructure overhead. DuckDB is ideal for data exploration, ad-hoc analytics, and notebooks. Spark is overkill for these use cases and adds unnecessary complexity.
Can DuckDB handle streaming data like Spark?
No, DuckDB is designed for analytical SQL on static/batch data. It does not natively support streaming ingestion. Apache Spark has Structured Streaming for real-time data processing. If you need streaming, Spark is the right choice, or consider complementing DuckDB with a separate streaming system like Kafka.
Yes, DuckDB 1.0 reached production stability in 2024. However, its production use case is limited to single-machine deployments. Multi-node clustering (Motherduck) is in beta. For mission-critical distributed systems, Spark has more mature production deployments across thousands of organizations.
What's the cost difference between DuckDB and Spark?
DuckDB is open-source and free. Apache Spark is also open-source and free, but requires infrastructure costs (servers, clusters, cloud resources). Motherduck (DuckDB's cloud offering) costs $0.005 per GB scanned. Databricks (Spark managed service) costs significantly more. For pure software, both are free; costs come from infrastructure.
Can I migrate from Spark to DuckDB or vice versa?
Partial migration is possible. DuckDB uses standard SQL, so Spark SQL queries often work directly. However, Spark's distributed execution model, RDDs, and complex transformations don't translate to DuckDB. You can use DuckDB for small datasets, then scale to Spark if needed. The reverse (Spark to DuckDB) is not practical for large-scale systems.
How to Choose Between DuckDB and Apache Spark
Architecture: DuckDB has Single-machine in-process database, while Apache Spark has Distributed cluster computing framework.
Consider: Query Latency (typical OLAP query)
Query Latency (typical OLAP query): DuckDB has 50-500ms, while Apache Spark has 5-30 seconds.
Maximum Data Scale: DuckDB has 100GB-1TB (practical limit), while Apache Spark has Multiple petabytes.
Setup Complexity: DuckDB has Zero configuration, pip install duckdb, while Apache Spark has Requires cluster setup, YARN/Kubernetes.
Memory Usage (typical): DuckDB has 100MB-2GB for operations, while Apache Spark has 2GB+ per executor minimum.
Supported Data Formats: DuckDB has Parquet, CSV, JSON, Arrow, SQLite, while Apache Spark has Parquet, ORC, CSV, Avro, Delta Lake, Iceberg.
Learning Curve: DuckDB has Minimal - standard SQL, Python/R integration, while Apache Spark has Steep - requires understanding of RDDs, DAGs, cluster concepts.