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Presto 0.288+

Distributed SQL query engine with REST/Nessie catalogs, row-level DELETE, time travel, and configurable MoR/CoW modes for interactive analytics

Key Features

100
REST/Nessie + OAuth2

Comprehensive Catalog Support

Hive Metastore, AWS Glue, REST/Nessie (0.277+ with OAuth2), Hadoop (file-based); JDBC possible via same properties

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85
Java + C++ (Velox)

Dual Engine Architecture

Java: full read/write with INSERT, CTAS, DELETE. C++ (Velox): high-performance read-only execution with same read path capabilities

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70
DELETE βœ“ UPDATE ⚠️ MERGE βœ—

Evolving DML Capabilities

INSERT, CTAS, DELETE (row-level, partition) available; UPDATE experimental; MERGE not yet supported. C++ engine has no DML

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100
Table-Level Control

Configurable Storage Strategy

Table props write.delete.mode/write.update.mode choose COPY_ON_WRITE or MERGE_ON_READ; readers honor MoR with enable-merge-on-read-mode=true

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0
Batch Only

No Streaming Support

No streaming capabilities - batch queries only; external engines handle CDC and streaming ingestion

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20
Post-0.295 Planned

Format V3 Roadmap

Roadmap: read Deletion Vectors & Row Lineage after Iceberg 1.8 libraries land; write DV planned post-0.295. Currently supports v1/v2 only

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100
AS OF Syntax

Advanced Time Travel

AS OF TIMESTAMP / @snapshot_id=... syntax (0.282+). Snapshot procedures: rollback, expire, remove orphan files

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95
System Tables

Rich Metadata Ecosystem

Add/drop/rename/widen columns; $snapshots, $history, $manifests, $partitions, $files, $entries, $refs, $properties, $changelog metadata tables

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100
Delegated ACLs

Enterprise Security

Relies on Hive/Glue/Nessie ACLs; Presto logs for audit; iceberg.security configuration for authorization

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90
Caching + Tuning

Performance Optimizations

In-memory manifest cache (0.282+); Parquet/ORC footer cache; dynamic filtering; split-thread tuning; optional Alluxio/file-stripe cache

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Presto Iceberg Feature Matrix

Comprehensive breakdown of Iceberg capabilities in Presto 0.288+

Dimension
Support Level
Implementation Details
Since Version
Catalog Types
FullREST/Nessie + OAuth2
Hive Metastore, AWS Glue, REST/Nessie (OAuth2), Hadoop file-based, JDBC via properties
0.277+
SQL Analytics
PartialJava Full, C++ Read
Java: full read/write (INSERT, CTAS, DELETE); C++ (Velox): read-only high performance
0.277+
DML Operations
PartialDELETE βœ“ UPDATE ⚠️ MERGE βœ—
INSERT, CTAS, DELETE available; UPDATE experimental; MERGE not yet supported
0.277+
Storage Strategy
FullConfigurable
Table-level write.delete.mode/write.update.mode control; MoR reader optimization
0.277+
Streaming Support
NoneBatch Only
No streaming capabilities; batch queries only; external engines handle CDC
N/A
Format Support
Limitedv1/v2 Only
v1/v2 support; v3 roadmap: read DV/lineage post-1.8, write DV post-0.295
0.277+
Time Travel
FullAS OF Syntax
AS OF TIMESTAMP/@snapshot_id syntax; snapshot procedures (rollback, expire, cleanup)
0.282+
Schema Evolution
FullRich Metadata
Add/drop/rename/widen columns; $snapshots, $history, $manifests, $changelog tables
0.277+
Security & Governance
FullCatalog ACLs
Hive/Glue/Nessie ACLs integration; Presto audit logs; iceberg.security config
0.277+
Performance Features
FullMulti-layer Cache
Manifest cache (0.282+), footer cache, dynamic filtering, optional Alluxio/stripe cache
0.282+
Engine Variants
DualJava + C++ (Velox)
Java: full capabilities; C++ (Velox): high-performance reads with same optimizations
0.277+
Known Limitations
MinorMERGE Missing
No MERGE; UPDATE experimental; C++ read-only; limited cost-based optimization
0.277+

Showing 12 entries

Use Cases

Interactive Data Analytics

High-performance interactive queries on Iceberg data lakes

  • Business intelligence and dashboard queries
  • Ad-hoc data exploration and analysis
  • Interactive reporting with time travel
  • Complex analytical queries with joins

Multi-Catalog Data Federation

Unified access across diverse catalog systems

  • Cross-catalog analytical queries
  • REST/Nessie catalog integration
  • Hybrid cloud data lake access
  • Legacy Hive to modern catalog migration

Data Modification Workflows

Row-level data corrections and maintenance

  • Data quality correction workflows
  • GDPR compliance data deletion
  • Partition-level data maintenance
  • Append-heavy workloads with corrections

Performance-Critical Analytics

High-performance deployments with advanced optimization

  • Large-scale analytical workloads requiring caching
  • C++ (Velox) deployments for read-heavy workloads
  • Performance-tuned environments with Alluxio
  • Complex queries requiring advanced optimization


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