Trino vs Presto vs StarRocks: Best Distributed SQL Query Engine 2026

Why Self-Host a Distributed SQL Query Engine?

Modern data teams run analytics across diverse storage systems — S3 buckets, Parquet files on HDFS, MySQL replicas, Elasticsearch clusters, and Kafka streams. Moving all this data into a single warehouse is expensive, slow, and creates stale copies. Distributed SQL query engines solve this by querying data where it lives, using standard SQL, without ETL pipelines or data duplication.

Self-hosting gives you full control over compute scaling, connector configurations, and data governance. You avoid per-query cloud costs and keep sensitive data on your own infrastructure. Whether you’re running sub-second dashboards, ad-hoc exploration, or batch reporting, a self-hosted query engine eliminates vendor lock-in while maintaining enterprise-grade performance.

In this guide, we compare the three leading open-source options: Trino, Presto, and StarRocks — examining their architectures, features, deployment models, and real-world performance to help you pick the right engine for your stack.

Trino: The Community-Driven Standard

Trino (formerly PrestoSQL) is the most actively developed distributed SQL engine in the open-source ecosystem. After a 2018 fork from PrestoDB, the Trino community has grown rapidly, with 12,700+ GitHub stars and contributions from hundreds of organizations.

Architecture

Trino uses a coordinator-worker architecture:

• Coordinator — parses SQL, generates query plans, schedules work across workers, and returns results
• Workers — execute query tasks (scan, filter, join, aggregate) and read data from connectors
• Connectors — pluggable data source adapters (Hive, MySQL, PostgreSQL, Kafka, Iceberg, Delta Lake, and 30+)

Trino does not store data. It reads directly from external storage systems, making it a pure compute layer that sits on top of your data lakehouse.

Key Strengths

• 30+ connectors — query Hive, MySQL, PostgreSQL, MongoDB, Kafka, Elasticsearch, and more in a single query
• Iceberg/Delta Lake/Hudi support — native table format integration for ACID operations on data lakes
• Cost-based optimizer — uses table statistics for efficient join ordering and partition pruning
• Row-level security — fine-grained access control with system-level and catalog-level policies
• Multi-engine federation — join data from MySQL and Kafka in the same query without moving data

Quick Start with docker

Trino provides an official Docker image that runs a single-node cluster for development:

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docker run -d \
  --name trino \
  -p 8080:8080 \
  trinodb/trino:latest

For a production-like multi-node setup, create a docker-compose.yml:

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version: "3.8"
services:
  trino-coordinator:
    image: trinodb/trino:latest
    container_name: trino-coordinator
    ports:
      - "8080:8080"
    volumes:
      - ./trino/etc:/etc/trino:ro
    deploy:
      resources:
        limits:
          memory: 4G
          cpus: "2"

  trino-worker:
    image: trinodb/trino:latest
    container_name: trino-worker
    volumes:
      - ./trino/etc:/etc/trino:ro
    depends_on:
      - trino-coordinator
    deploy:
      replicas: 2
      resources:
        limits:
          memory: 4G
          cpus: "2"

Configuration files in ./trino/etc/:

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# config.properties (coordinator)
coordinator=true
node-scheduler.include-coordinator=false
http-server.http.port=8080
discovery.uri=http://trino-coordinator:8080
query.max-memory=8GB
query.max-memory-per-node=4GB

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# config.properties (worker)
coordinator=false
http-server.http.port=8080
discovery.uri=http://trino-coordinator:8080
query.max-memory=8GB
query.max-memory-per-node=4GB

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# catalog/hive.properties
connector.name=hive
hive.metastore.uri=thrift://hive-metastore:9083
hive.parquet.use-column-names=true

Presto: The Facebook Original

PrestoDB is the original distributed SQL query engine, created by Meta (Facebook) and now maintained by the Linux Foundation’s Presto Foundation. With 16,700+ GitHub stars, it has the largest user base and longest track record of any open-source SQL engine.

Architecture

Presto shares a coordinator-worker design similar to Trino (since they share lineage). Key differences:

• Memory management — Presto uses a fixed-memory pool model with spill-to-disk support for large queries
• Plugin system — extensible connectors with a mature plugin ecosystem
• Presto-on-Spark — experimental Spark-based execution engine for very large workloads
• Resource groups — built-in query queuing and resource allocation for multi-tenant environments

Key Strengths

• Mature ecosystem — 10+ years of production use at scale (Meta, Uber, Twitter)
• Presto-on-Spark — handle massive workloads that exceed memory by spilling to Spark
• Resource groups — fine-grained query scheduling and queue management
• Wide connector support — Hive, RaptorX, MySQL, PostgreSQL, Redis, Cassandra, Kafka
• Linux Foundation backing — neutral governance with corporate sponsorship

Quick Start with Docker

Presto does not provide an official single-container image, but you can build one:

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version: "3.8"
services:
  presto-coordinator:
    image: prestodb/presto:0.290
    container_name: presto-coordinator
    ports:
      - "8080:8080"
    volumes:
      - ./presto/etc:/opt/presto/etc:ro
    command: ["--server", "coordinator"]

  presto-worker:
    image: prestodb/presto:0.290
    container_name: presto-worker
    volumes:
      - ./presto/etc:/opt/presto/etc:ro
    command: ["--server", "worker"]
    depends_on:
      - presto-coordinator

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# config.properties (coordinator)
coordinator=true
node-scheduler.include-coordinator=false
http-server.http.port=8080
discovery.uri=http://presto-coordinator:8080
query.max-memory=12GB
query.max-memory-per-node=4GB

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# catalog/hive.properties
connector.name=hive-hadoop2
hive.metastore.uri=thrift://hive-metastore:9083

StarRocks: The Speed-Optimized Contender

StarRocks is the newest entrant and the only engine that combines compute with its own MPP (Massively Parallel Processing) storage engine. As a Linux Foundation project with 11,500+ GitHub stars, it is purpose-built for sub-second analytics on both data lakehouse and local storage.

Architecture

Unlike Trino and Presto, StarRocks has its own storage layer:

• Frontend (FE) — handles metadata management, query parsing, and query planning. Similar to Trino’s coordinator.
• Backend (BE) — stores data in columnar format and executes query tasks. Can run as compute-only (external tables) or compute+storage (internal tables).
• Primary Key index — enables real-time UPSERT/DELETE operations on local tables
• Smart materialized views — automatic query rewrite to transparently use pre-computed results

StarRocks can query external data sources (like Trino/Presto) via its external table feature, but achieves peak performance when data is loaded into its own columnar storage.

Key Strengths

• Sub-second query latency — consistently benchmarks faster than Trino/Presto on local data
• Primary Key model — real-time data updates with UPSERT/DELETE support
• Native C++ engine — vectorized execution, SIMD optimizations, and compiled expressions
• Materialized views — automatic query rewrite for transparent acceleration
• Lakehouse mode — query Iceberg, Hudi, and Delta Lake tables without data ingestion
• MySQL wire protocol — drop-in MySQL client compatibility (no special JDBC driver needed)

Quick Start with Docker

StarRocks provides a single-container all-in-one image:

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docker run -d \
  --name starrocks \
  -p 9030:9030 \
  -p 8030:8030 \
  -p 8040:8040 \
  starrocks/allin1-ubuntu:latest

Connect using any MySQL client:

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mysql -h 127.0.0.1 -P 9030 -u root

For a production multi-node cluster:

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version: "3.8"
services:
  fe-leader:
    image: starrocks/fe-ubuntu:3.3-latest
    container_name: fe-leader
    ports:
      - "8030:8030"
      - "9020:9020"
      - "9030:9030"
    volumes:
      - ./fe/conf:/opt/starrocks/fe/conf:ro
      - fe-data:/opt/starrocks/fe/meta
    command: ["--host_type", "FOLLOWER"]

  be-1:
    image: starrocks/be-ubuntu:3.3-latest
    container_name: be-1
    ports:
      - "8040:8040"
    volumes:
      - be1-data:/opt/starrocks/be/storage
    environment:
      FE_HOST: fe-leader
    depends_on:
      - fe-leader

  be-2:
    image: starrocks/be-ubuntu:3.3-latest
    container_name: be-2
    volumes:
      - be2-data:/opt/starrocks/be/storage
    environment:
      FE_HOST: fe-leader
    depends_on:
      - fe-leader

volumes:
  fe-data:
  be1-data:
  be2-data:

Head-to-Head Comparison

When to Choose Each Engine

Choose Trino if:

• You need to query data across 10+ different systems in a single SQL query
• Your primary workload is federated analytics across a data lakehouse
• You want the most active community with the fastest release cycle
• You need deep Iceberg/Delta Lake/Hudi integration with ACID operations
• Your team is already familiar with the Presto/Trino ecosystem

Choose Presto if:

• You have an existing Presto deployment and want stability over new features
• You need Presto-on-Spark for workloads exceeding memory capacity
• You require mature resource group configurations for multi-tenant environments
• Your organization is already invested in the Presto Foundation ecosystem

Choose StarRocks if:

• Sub-second query latency is your top priority
• You need real-time data ingestion with UPSERT/DELETE operations
• You want automatic query acceleration via materialized views
• Your team prefers MySQL client compatibility (no JDBC needed)
• You run high-concurrency dashboard workloads with many concurrent users

Performance Considerations

Benchmark results vary by workload, but general patterns emerge:

• Ad-hoc queries on data lakes: Trino and Presto perform similarly (both use the same lineage of optimizer). Trino’s cost-based optimizer often generates more efficient plans for complex joins.
• Real-time dashboards on local data: StarRocks consistently outperforms both Trino and Presto by 3-10x due to its native columnar storage and vectorized C++ execution.
• Concurrent queries: StarRocks handles 100+ concurrent queries better due to its resource group and workload group management. Trino and Presto can saturate worker memory under high concurrency.
• Data ingestion: Only StarRocks supports direct data ingestion with real-time UPSERT. Trino and Presto require external systems (Kafka Connect, Spark) for data loading.

For organizations running both interactive dashboards and federated lake queries, a common architecture pairs StarRocks for hot-path analytics with Trino for cold-path federation, reading the same Iceberg tables from object storage.

For related reading, see our ClickHouse vs Druid vs Pinot analytics comparison for alternative real-time analytics engines, and our open data lakehouse formats guide to understand the table formats these engines query. If you’re building dashboards on top of your query engine, our BI dashboard comparison covers the visualization layer.

FAQ

Is Trino the same as Presto?

No. Trino was forked from Presto in 2018 by the original Presto co-founders (Martin Traverso, Dain Sundstrom, and David Phillips) after Meta changed the project’s governance model. Both projects share a common codebase ancestry but have diverged significantly. Trino has a more active development community and faster release cycle, while PrestoDB maintains stability and the Presto-on-Spark feature.

Can Trino and Presto store data?

Neither Trino nor Presto stores data natively. Both are compute-only engines that query data where it lives — in Hive warehouses, object storage (S3, GCS), or external databases via connectors. StarRocks, by contrast, includes its own columnar MPP storage engine for sub-second query performance on ingested data.

Which engine supports real-time data updates?

StarRocks supports real-time UPSERT and DELETE operations through its Primary Key data model. Data can be ingested via Stream Load, Routine Load (Kafka), or INSERT statements, with queryable results within seconds. Trino and Presto are read-only engines — they query external data sources but cannot modify or ingest data directly.

How do I connect BI tools to these engines?

StarRocks speaks the MySQL wire protocol natively, so any MySQL-compatible BI tool (Superset, Metabase, Tableau, Power BI) connects out of the box. Trino and Presto provide JDBC and ODBC drivers, plus ODBC bridges for broader compatibility. Most modern BI tools have native Trino/kubernetesnectors.

Can I run these engines on Kubernetes?

All three support Kubernetes deployment. Trino and Presto use Helm charts from their respective projects. StarRocks provides a Kubernetes operator for automated cluster management, including automatic BE scaling and FE leader election.

What are the minimum hardware requirements?

For a development setup, any engine runs on 4 CPU cores and 8GB RAM. For production: Trino/Presto recommend 8+ cores and 32GB RAM per worker with fast local SSD for spill. StarRocks recommends 8+ cores and 32GB RAM per BE node with NVMe storage for optimal columnar read performance.