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date: 2026-01-28 title: 'Spice v1.11.0 (Jan 28, 2026)' type: blog authors: [peasee] tags: [release, cayenne, acceleration, datafusion, iceberg, arrow, snapshots, scylladb, data-connector, distributed-query, caching, dynamodb, llm-tool]

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Announcing the release of Spice v1.11.0-stable! ⚡

In Spice v1.11.0, Spice Cayenne reaches Beta status with acceleration snapshots, Key-based deletion vectors, and Amazon S3 Express One Zone support. DataFusion has been upgraded to v51 along with Arrow v57.2, and iceberg-rust v0.8.0. v1.11 adds several DynamoDB & DynamoDB Streams improvements such as JSON nesting, and adds significant improvements to Distributed Query with active-active schedulers and mTLS for enterprise-grade high-availability and secure cluster communication.

This release also adds new SMB, NFS, and ScyllaDB Data Connectors (Alpha), Prepared Statements with full SDK support (gospice, spice-rs, spice-dotnet, spice-java, spice.js, and spicepy), Google LLM Support for expanded AI inference capabilities, and significant improvements to caching, observability, and Hash Indexing for Arrow Acceleration.

What's New in v1.11.0

Spice Cayenne Accelerator Reaches Beta

Spice Cayenne has been promoted to Beta status with acceleration snapshots support and numerous performance and stability improvements.

Key Enhancements:

• Key-based Deletion Vectors: Improved deletion vector support using key-based lookups for more efficient data management and faster delete operations. Key-based deletion vectors are more memory-efficient than positional vectors for sparse deletions.
• S3 Express One Zone Support: Store Cayenne data files in S3 Express One Zone for single-digit millisecond latency, ideal for latency-sensitive query workloads that require persistence.

Improved Reliability:

• Resolved FuturesUnordered reentrant drop crashes
• Fixed memory growth issues related to Vortex metrics allocation
• Metadata catalog now properly respects cayenne_file_path location
• Added warnings for unparseable configuration values

For more details, refer to the Cayenne Documentation.

DataFusion v51 Upgrade

Apache DataFusion has been upgraded to v51, bringing significant performance improvements, new SQL features, and enhanced observability.

Performance Improvements:

• Faster CASE Expression Evaluation: Expressions now short-circuit earlier, reuse partial results, and avoid unnecessary scattering, speeding up common ETL patterns
• Better Defaults for Remote Parquet Reads: DataFusion now fetches the last 512KB of Parquet files by default, typically avoiding 2 I/O requests per file
• Faster Parquet Metadata Parsing: Leverages Arrow 57's new thrift metadata parser for up to 4x faster metadata parsing

New SQL Features:

• SQL Pipe Operators: Support for |> syntax for inline transforms
• DESCRIBE <query>: Returns the schema of any query without executing it
• Named Arguments in SQL Functions: PostgreSQL-style param => value syntax for scalar, aggregate, and window functions
• Decimal32/Decimal64 Support: New Arrow types supported including aggregations like SUM, AVG, and MIN/MAX

Example pipe operator:

Improved Observability:

• Improved EXPLAIN ANALYZE Metrics: New metrics including output_bytes, selectivity for filters, reduction_factor for aggregates, and detailed timing breakdowns

Arrow 57.2 Upgrade

Apache Arrow has been upgraded to v57.2, bringing major performance improvements and new capabilities.

Key Features:

• 4x Faster Parquet Metadata Parsing: A rewritten thrift metadata parser delivers up to 4x faster metadata parsing, especially beneficial for low-latency use cases and files with large amounts of metadata
• Parquet Variant Support: Experimental support for reading and writing the new Parquet Variant type for semi-structured data, including shredded variant values
• Parquet Geometry Support: Read and write support for Parquet Geometry types (GEOMETRY and GEOGRAPHY) with GeospatialStatistics
• New arrow-avro Crate: Efficient conversion between Apache Avro and Arrow RecordBatches with projection pushdown and vectorized execution support

DynamoDB Connector Enhancements

• Added JSON nesting for DynamoDB Streams
• Improved batch deletion handling

Distributed Query Improvements

High Availability Clusters: Spice now supports running multiple active schedulers in an active/active configuration for production deployments. This eliminates the scheduler as a single point of failure and enables graceful handling of node failures.

• Multiple schedulers run simultaneously, each capable of accepting queries
• Schedulers coordinate via a shared S3-compatible object store
• Executors discover all schedulers automatically
• A load balancer distributes client queries across schedulers

Example HA configuration:

mTLS Verification: Cluster communication between scheduler and executors now supports mutual TLS verification for enhanced security.

Credential Propagation: S3, ABFS, and GCS credentials are now automatically propagated to executors in cluster mode, enabling access to cloud storage across the distributed query cluster.

Improved Resilience:

• Exponential backoff for scheduler disconnection recovery
• Increased gRPC message size limit from 16MB to 100MB for large query plans
• HTTP health endpoint for cluster executors
• Automatic executor role inference when --scheduler-address is provided

For more details, refer to the Distributed Query Documentation.

iceberg-rust v0.8.0 Upgrade

Spice has been upgraded to iceberg-rust v0.8.0, bringing improved Iceberg table support.

Key Features:

• V3 Metadata Support: Full support for Iceberg V3 table metadata format
• INSERT INTO Partitioned Tables: DataFusion integration now supports inserting data into partitioned Iceberg tables
• Improved Delete File Handling: Better support for position and equality delete files, including shared delete file loading and caching
• SQL Catalog Updates: Implement update_table and register_table for SQL catalog
• S3 Tables Catalog: Implement update_table for S3 Tables catalog
• Enhanced Arrow Integration: Convert Arrow schema to Iceberg schema with auto-assigned field IDs, _file column support, and Date32 type support

Acceleration Snapshots

Acceleration snapshots enable point-in-time recovery and data versioning for accelerated datasets. Snapshots capture the state of accelerated data at specific points, allowing for fast bootstrap recovery and rollback capabilities.

Key Features:

• Flexible Triggers: Configure when snapshots are created based on time intervals or stream batch counts
• Automatic Compaction: Reduce storage overhead by compacting older snapshots (DuckDB only)
• Bootstrap Integration: Snapshots can reset cache expiry on load for seamless recovery (DuckDB with Caching refresh mode)
• Smart Creation Policies: Only create snapshots when data has actually changed

Example configuration:

Snapshots API and CLI: New API endpoints and CLI commands for managing snapshots programmatically.

CLI Commands:

HTTP API Endpoints:

Method	Endpoint	Description
GET	/v1/datasets/{dataset}/acceleration/snapshots	List all snapshots for a dataset
GET	/v1/datasets/{dataset}/acceleration/snapshots/{id}	Get details of a specific snapshot
POST	/v1/datasets/{dataset}/acceleration/snapshots/current	Set the current snapshot for rollback

For more details, refer to the Acceleration Snapshots Documentation.

Caching Acceleration Mode Improvements

The Caching Acceleration Mode introduced in v1.10.0 has received significant performance optimizations and reliability fixes in this release.

Performance Optimizations:

• Non-blocking Cache Writes: Cache misses no longer block query responses. Data is written to the cache asynchronously after the query returns, reducing query latency for cache miss scenarios.
• Batch Cache Writes: Multiple cache entries are now written in batches rather than individually, significantly improving write throughput for high-volume cache operations.

Reliability Fixes:

• Correct SWR Refresh Behavior: The stale-while-revalidate (SWR) pattern now correctly refreshes only the specific entries that were accessed instead of refreshing all stale rows in the dataset. This prevents unnecessary source queries and reduces load on upstream data sources.
• Deduplicated Refresh Requests: Fixed an issue where JSON array responses could trigger multiple redundant refresh operations. Refresh requests are now properly deduplicated.
• Fixed Cache Hit Detection: Resolved an issue where queries that didn't include fetched_at in their projection would always result in cache misses, even when cached data was available.
• Unfiltered Query Optimization: SELECT * queries without filters now return cached data directly without unnecessary filtering overhead.

For more details, refer to the Caching Acceleration Mode Documentation.

Prepared Statements

Improved Query Performance and Security: Spice now supports prepared statements, enabling parameterized queries that improve both performance through query plan caching and security by preventing SQL injection attacks.

Key Features:

• Query Plan Caching: Prepared statements cache query plans, reducing planning overhead for repeated queries
• SQL Injection Prevention: Parameters are safely bound, preventing SQL injection vulnerabilities
• Arrow Flight SQL Support: Full prepared statement support via Arrow Flight SQL protocol

SDK Support:

SDK	Support	Min Version	Method
gospice (Go)	✅ Full	v8.0.0+	SqlWithParams() with typed constructors (Int32Param, StringParam, TimestampParam, etc.)
spice-rs (Rust)	✅ Full	v3.0.0+	query_with_params() with RecordBatch parameters
spice-dotnet (.NET)	✅ Full	v0.3.0+	QueryWithParams() with typed parameter builders
spice-java (Java)	✅ Full	v0.5.0+	queryWithParams() with typed Param constructors (Param.int64(), Param.string(), etc.)
spice.js (JavaScript)

Example (Go):

Example (Java):

For more details, refer to the Parameterized Queries Documentation.

Spice Java SDK v0.5.0

Parameterized Query Support for Java: The Spice Java SDK v0.5.0 introduces parameterized queries using ADBC (Arrow Database Connectivity), providing a safer and more efficient way to execute queries with dynamic parameters.

Key Features:

• SQL Injection Prevention: Parameters are safely bound, preventing SQL injection vulnerabilities
• Automatic Type Inference: Java types are automatically mapped to Arrow types (e.g., double → Float64, String → Utf8)
• Explicit Type Control: Use the new Param class with typed factory methods (Param.int64(), Param.string(), Param.decimal128(), etc.) for precise control over Arrow types
• Updated Dependencies: Apache Arrow Flight SQL upgraded to 18.3.0, plus new ADBC driver support

Example:

Maven:

For more details, refer to the Spice Java SDK Repository.

Google LLM Support

Expanded AI Provider Support: Spice now supports Google embedding and chat models via the Google AI provider, expanding the available LLM options for AI inference workloads alongside existing providers like OpenAI, Anthropic, and AWS Bedrock.

Key Features:

• Google Chat Models: Access Google's Gemini models for chat completions
• Google Embeddings: Generate embeddings using Google's text embedding models
• Unified API: Use the same OpenAI-compatible API endpoints for all LLM providers

Example spicepod.yaml configuration:

For more details, refer to the Google LLM Documentation (see docs PR #1286).

URL Tables

Query data sources directly via URL in SQL without prior dataset registration. Supports S3, Azure Blob Storage, and HTTP/HTTPS URLs with automatic format detection and partition inference.

Supported Patterns:

• Single files: SELECT * FROM 's3://bucket/data.parquet'
• Directories/prefixes: SELECT * FROM 's3://bucket/data/'
• Glob patterns: SELECT * FROM 's3://bucket/year=*/month=*/data.parquet'

Key Features:

• Automatic file format detection (Parquet, CSV, JSON, etc.)
• Hive-style partition inference with filter pushdown
• Schema inference from files
• Works with both SQL and DataFrame APIs

Example with hive partitioning:

Enable via spicepod.yml:

Cluster Mode Async Query APIs (experimental)

New asynchronous query APIs for long-running queries in cluster mode:

• /v1/queries endpoint: Submit queries and retrieve results asynchronously

OpenTelemetry Improvements

Unified Telemetry Endpoint: OTel metrics ingestion has been consolidated to the Flight port (50051), simplifying deployment by removing the separate OTel port (50052). The push-based metrics exporter continues to support integration with OpenTelemetry collectors.

Note: This is a breaking change. Update your configurations if you were using the dedicated OTel port 50052. Internal cluster communication now uses port 50052 exclusively.

Observability Improvements

Enhanced Dashboards: Updated Grafana and Datadog example dashboards with:

• Snapshot monitoring widgets
• Improved accelerated datasets section
• Renamed ingestion lag charts for clarity

Additional Histogram Buckets: Added more buckets to histogram metrics for better latency distribution visibility.

For more details, refer to the Monitoring Documentation.

Hash Indexing for Arrow Acceleration (experimental)

Arrow-based accelerations now support hash indexing for faster point lookups on equality predicates. Hash indexes provide O(1) average-case lookup performance for columns with high cardinality.

Features:

• Primary key hash index support
• Secondary index support for non-primary key columns
• Composite key support with proper null value handling

Example configuration:

For more details, refer to the Hash Index Documentation.

SMB and NFS Data Connectors

Network-Attached Storage Connectors: New data connectors for SMB (Server Message Block) and NFS (Network File System) protocols enable direct federated queries against network-attached storage without requiring data movement to cloud object stores.

Key Features:

• SMB Protocol Support: Connect to Windows file shares and Samba servers with authentication support
• NFS Protocol Support: Connect to Unix/Linux NFS exports for direct data access
• Federated Queries: Query Parquet, CSV, JSON, and other file formats directly from network storage with full SQL support
• Acceleration Support: Accelerate data from SMB/NFS sources using DuckDB, Spice Cayenne, or other accelerators

Example spicepod.yaml configuration:

For more details, refer to the Data Connectors Documentation.

ScyllaDB Data Connector

A new data connector for ScyllaDB, the high-performance NoSQL database compatible with Apache Cassandra. Query ScyllaDB tables directly or accelerate them for faster analytics.

Example configuration:

For more details, refer to the ScyllaDB Data Connector Documentation.

Flight SQL TLS Connection Fixes

TLS Connection Support: Fixed TLS connection issues when using grpc+tls:// scheme with Flight SQL endpoints. Added support for custom CA certificate files via the new flightsql_tls_ca_certificate_file parameter.

Developer Experience Improvements

• Turso v0.3.2 Upgrade: Upgraded Turso accelerator for improved performance and reliability
• Rust 1.91 Upgrade: Updated to Rust 1.91 for latest language features and performance improvements
• Spice Cloud CLI: Added spice cloud CLI commands for cloud deployment management
• Improved Spicepod Schema: Improved JSON schema generation for better IDE support and validation
• Acceleration Snapshots: Added configurable snapshots_create_interval for periodic acceleration snapshots independent of refresh cycles
• Tiered Caching with Localpod: The Localpod connector now supports caching refresh mode, enabling multi-layer acceleration where a persistent cache feeds a fast in-memory cache
• GitHub Data Connector: Added workflows and workflow runs support for GitHub repositories
• NDJSON/LDJSON Support: Added support for Newline Delimited JSON and Line Delimited JSON file formats

Additional Improvements & Bug Fixes

• Model Listing: New functionality to list available models across multiple AI providers
• DuckDB Partitioned Tables: Primary key constraints now supported in partitioned DuckDB table mode
• Post-refresh Sorting: New on_refresh_sort_columns parameter for DuckDB enables data ordering after writes
• Improved Install Scripts: Removed jq dependency and improved cross-platform compatibility
• Better Error Messages: Improved error messaging for bucket UDF arguments and deprecated OpenAI parameters
• Reliability: Fixed DynamoDB IAM role authentication with new dynamodb_auth: iam_role parameter
• Reliability: Fixed cluster executors to use scheduler's temp_directory parameter for shuffle files
• Reliability: Initialize secrets before object stores in cluster executor mode
• Reliability: Added page-level retry with backoff for transient GitHub GraphQL errors
• Performance: Improved statistics for rewritten DistributeFileScanOptimizer plans
• Developer Experience: Added max_message_size configuration for Flight service

Contributors

• @ewgenius
• @Jeadie
• @krinart
• @lukekim
• @mach-kernel
• @peasee
• @phillipleblanc
• @sgrebnov

Breaking Changes

OTel Ingestion Port Change

OTel ingestion has been moved to the Flight port (50051), removing the separate OTel port 50052. Port 50052 is now used exclusively for internal cluster communication. Update your configurations if you were using the dedicated OTel port.

Distributed Query Cluster Mode Requires mTLS

Distributed query cluster mode now requires mTLS for secure communication between cluster nodes. This is a security enhancement to prevent unauthorized nodes from joining the cluster and accessing secrets.

Migration Steps:

1. Generate certificates using spice cluster tls init and spice cluster tls add
2. Update scheduler and executor startup commands with --node-mtls-* arguments
3. For development/testing, use --allow-insecure-connections to opt out of mTLS

Renamed CLI Arguments:

Old Name	New Name
--cluster-mode	--role
--cluster-ca-certificate-file	--node-mtls-ca-certificate-file
--cluster-certificate-file	--node-mtls-certificate-file
--cluster-key-file	--node-mtls-key-file
--cluster-address	--node-bind-address
--cluster-advertise-address	--node-advertise-address
--cluster-scheduler-url	--scheduler-address

Removed CLI Arguments:

• --cluster-api-key: Replaced by mTLS authentication

Cookbook Updates

New ScyllaDB Data Connector Recipe: New recipe demonstrating how to use the ScyllaDB Data Connector. See ScyllaDB Data Connector Recipe for details.

New SMB Data Connector Recipe: New recipe demonstrating how to use the SMB Data Connector. See SMB Data Connector Recipe for details.

The Spice Cookbook includes 86 recipes to help you get started with Spice quickly and easily.

Upgrading

To upgrade to v1.11.0, use one of the following methods:

CLI:

Homebrew:

Docker:

Pull the spiceai/spiceai:1.11.0 image:

For available tags, see DockerHub.

Helm:

AWS Marketplace:

Spice is available in the AWS Marketplace.

Dependencies

• DataFusion: Upgraded to v51 (release notes)
• Arrow: Upgraded to v57.2 (release notes)
• iceberg-rust: Upgraded to v0.8.0 (release notes)

What's Changed

Changelog

• OTel exporter for push metrics by @lukekim in #8442
• fix: Update benchmark snapshots by @app/github-actions in #8448
• Add TPCH append tests to scheduled dispatch workflow by @sgrebnov in #8451
• Add snapshot creation logging by @krinart in #8469
• Fix PeriodicReader panic by @krinart in #8471
• Benchmarks: increase readiness timeout for turso acceleration (TPC-H) by @sgrebnov in #8470
• fix: Pin CUDA build actions to commits by @peasee in #8477
• Add Criterion benchmarking to chunking crate. by @Jeadie in #8431
• DuckDB agg pushdown: gate behind accelerator parameter by @mach-kernel in