Twitter
LinkedIn
Facebook
EmailTrino 475+
High-performance distributed SQL query engine with advanced DML, time travel, and native Iceberg optimization for interactive analytics
Key Features
Multi-Catalog Support
hive_metastore, glue, jdbc, rest, nessie, or snowflake catalogs; each exposes same tables once configured in catalog properties
Advanced SQL Analytics
Ad-hoc SQL reads with filter, projection, and partition pruning; writes via INSERT, CREATE TABLE AS, CREATE OR REPLACE TABLE, INSERT OVERWRITE
Complete DML Operations
UPDATE, DELETE, and MERGE INTO supported, emitting position/equality delete files instead of rewriting entire partitions when possible
Intelligent Storage Strategy
Default Merge-on-Read for row-level DML (compact delete files, merge on-the-fly). CTAS/INSERT OVERWRITE follow Copy-on-Write semantics
No Streaming Support
Trino is batch/interactive only; happily reads Iceberg tables updated by streaming engines, but does not run continuous ingestion jobs
Legacy Format Support
Not yet GA for spec v3; currently supports only spec v1/v2; deletion vectors & row lineage planned but not available
Advanced Time Travel
Automatic hidden partition pruning; time travel via FOR VERSION AS OF and FOR TIMESTAMP AS OF (also to branches/tags)
Schema Evolution & Metadata
ALTER TABLE add/drop/rename columns; metadata tables ($history, $snapshots, $files) queryable; system.table_changes() for row-level change streams
Enterprise Security
Delegates ACLs to underlying catalog (Hive Ranger, AWS IAM, Nessie policies); supports snapshot isolation; commit metadata visible for audit
Advanced Maintenance
Built-in maintenance procedures (optimize, expire_snapshots, remove_orphan_files), metadata caching, bucket-aware execution, fault-tolerant execution
Trino Iceberg Feature Matrix
Comprehensive breakdown of Iceberg capabilities in Trino 475+
Dimension | Support Level | Implementation Details | Min Version |
|---|---|---|---|
Catalog Types | FullMulti-Catalog | hive_metastore, glue, jdbc, rest, nessie, snowflake - unified access via catalog properties | 414+ |
SQL Analytics | FullInteractive | Ad-hoc SQL with pushdown optimizations; INSERT, CREATE TABLE AS, CREATE OR REPLACE TABLE | 414+ |
DML Operations | FullRow-Level | UPDATE, DELETE, MERGE INTO with position/equality delete files for efficiency | 414+ |
Storage Strategy | FullAdaptive | Default MoR for DML (delete files), CoW for CTAS/INSERT OVERWRITE | 414+ |
Streaming Support | NoneBatch/Interactive | No streaming capabilities; reads tables updated by streaming engines | N/A |
Format Support | Limitedv1/v2 Only | Spec v1/v2 support; v3 (deletion vectors, row lineage) not yet GA | 414+ |
Time Travel | FullSQL Native | FOR VERSION AS OF and FOR TIMESTAMP AS OF; branch/tag navigation | 414+ |
Schema Evolution | FullComplete DDL | ALTER TABLE add/drop/rename; metadata tables; system.table_changes() streams | 414+ |
Security & Governance | FullDelegated | Delegates to catalog ACLs (Ranger, IAM, Nessie); snapshot isolation | 414+ |
Maintenance Procedures | FullBuilt-in | optimize, expire_snapshots, remove_orphan_files via ALTER TABLE EXECUTE | 414+ |
Performance Features | FullAdvanced | Metadata caching, bucket-aware execution, fault-tolerant execution | 414+ |
Known Limitations | MinorManageable | Small file proliferation impacts performance; static catalog configuration | 414+ |
Showing 12 entries
For issues, click here (GitHub)Use Cases
Interactive Data Analytics
High-performance ad-hoc queries and data exploration
- Real-world example: A data science team at a SaaS company uses Trino to explore 50TB of customer behavior data stored in Iceberg tables. Analysts write ad-hoc SQL queries in their notebooks, getting results in seconds thanks to Trino's distributed query engine and partition pruning. They can quickly test hypotheses and build dashboards without waiting for batch jobs
- Business intelligence and reporting dashboards with sub-second response times
- Data science and ML feature engineering with interactive exploration
- Complex analytical queries across large datasets with automatic optimizations
Multi-Catalog Data Federation
Unified access to data across heterogeneous systems
- Real-world example: A retail conglomerate has data spread across AWS Glue, on-premises Hive Metastore, and Nessie catalogs. Using Trino, their analysts write a single SQL query that joins customer data from Glue, product data from Hive, and real-time inventory from Nessie - all without moving or replicating data. Trino federates across all three catalogs seamlessly
- Cross-cloud data lake analytics without data movement or replication
- Legacy system integration with modern cloud catalogs for unified access
- Federated queries across different storage systems and metadata stores
Lambda Architecture Query Layer
Batch processing and serving layer for real-time architectures
- Real-world example: An IoT platform uses Apache Flink to write real-time sensor data to Iceberg tables. Trino serves as the query layer, allowing business analysts to run complex aggregations and historical analysis on the data that Flink continuously updates. Trino handles batch analytical queries while Flink manages real-time stream processing
- Analytical queries on streaming-updated tables with batch processing power
- Historical analysis complementing real-time views from streaming engines
- Batch aggregation and reporting workflows for operational analytics
Enterprise Data Warehouse
Modern cloud-native data warehouse with ACID compliance
- Real-world example: A healthcare provider modernizes their legacy Oracle data warehouse by migrating to Iceberg tables queried with Trino. They use time travel queries to audit patient record changes for compliance, UPDATE operations to correct data quality issues, and schema evolution to add new fields as healthcare regulations change. Trino provides warehouse capabilities on open formats at fraction of the cost
- Traditional data warehouse modernization with open table formats
- Time travel for data auditing and compliance with regulatory requirements
- Row-level data corrections and updates with ACID transaction guarantees
Resources & Documentation
Official Documentation
Complete API reference and guides
Getting Started Guide
Quick start tutorials and examples
Trino
Documentation
Iceberg Connector Documentation
Documentation
Trino Performance Tuning
Documentation
Catalog Configuration Guide
Documentation
Table Maintenance Procedures
Documentation
Security Configuration
Documentation
Metadata Tables Reference
Documentation
Time Travel Syntax
Documentation