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Spice v1.11.0 (Jan 28, 2026)

· 58 min read
William Croxson
William Croxson
Senior Software Engineer at Spice AI

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.

DataFusion v51 ClickBench Performance

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:

SELECT * FROM t
|> WHERE a > 10
|> ORDER BY b
|> LIMIT 5;

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.

Arrow 57 Parquet Metadata Parsing Performance

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:

runtime:
  scheduler:
    state_location: s3://my-bucket/spice-cluster
    params:
      region: us-east-1

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:

datasets:
  - from: s3://my-bucket/data.parquet
    name: my_dataset
    acceleration:
      enabled: true
      engine: cayenne
      mode: file
      snapshots: enabled
      snapshots_trigger: time_interval
      snapshots_trigger_threshold: 1h
      snapshots_creation_policy: on_changed

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

CLI Commands:

# List all snapshots for a dataset
spice acceleration snapshots taxi_trips

# Get details of a specific snapshot
spice acceleration snapshot taxi_trips 3

# Set the current snapshot for rollback (requires runtime restart)
spice acceleration set-snapshot taxi_trips 2

HTTP API Endpoints:

MethodEndpointDescription
GET/v1/datasets/{dataset}/acceleration/snapshotsList all snapshots for a dataset
GET/v1/datasets/{dataset}/acceleration/snapshots/{id}Get details of a specific snapshot
POST/v1/datasets/{dataset}/acceleration/snapshots/currentSet 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:

SDKSupportMin VersionMethod
gospice (Go)✅ Fullv8.0.0+SqlWithParams() with typed constructors (Int32Param, StringParam, TimestampParam, etc.)
spice-rs (Rust)✅ Fullv3.0.0+query_with_params() with RecordBatch parameters
spice-dotnet (.NET)✅ Fullv0.3.0+QueryWithParams() with typed parameter builders
spice-java (Java)✅ Fullv0.5.0+queryWithParams() with typed Param constructors (Param.int64(), Param.string(), etc.)
spice.js (JavaScript)✅ Fullv3.1.0+query() with parameterized query support
spicepy (Python)✅ Fullv3.1.0+query() with parameterized query support

Example (Go):

import "github.com/spiceai/gospice/v8"

client, _ := spice.NewClient()
defer client.Close()

// Parameterized query with typed parameters
results, _ := client.SqlWithParams(ctx,
    "SELECT * FROM products WHERE price > $1 AND category = $2",
    spice.Float64Param(10.0),
    spice.StringParam("electronics"),
)

Example (Java):

import ai.spice.SpiceClient;
import ai.spice.Param;
import org.apache.arrow.adbc.core.ArrowReader;

try (SpiceClient client = new SpiceClient()) {
    // With automatic type inference
    ArrowReader reader = client.queryWithParams(
        "SELECT * FROM products WHERE price > $1 AND category = $2",
        10.0, "electronics");

    // With explicit typed parameters
    ArrowReader reader = client.queryWithParams(
        "SELECT * FROM products WHERE price > $1 AND category = $2",
        Param.float64(10.0),
        Param.string("electronics"));
}

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:

import ai.spice.SpiceClient;
import ai.spice.Param;

try (SpiceClient client = new SpiceClient()) {
    // With automatic type inference
    ArrowReader reader = client.queryWithParams(
        "SELECT * FROM taxi_trips WHERE trip_distance > $1 LIMIT 10",
        5.0);

    // With explicit typed parameters for precise control
    ArrowReader reader = client.queryWithParams(
        "SELECT * FROM orders WHERE order_id = $1 AND amount >= $2",
        Param.int64(12345),
        Param.decimal128(new BigDecimal("99.99"), 10, 2));
}

Maven:

<dependency>
  <groupId>ai.spice</groupId>
  <artifactId>spiceai</artifactId>
  <version>0.5.0</version>
</dependency>

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:

models:
  - from: google:gemini-2.0-flash
    name: gemini
    params:
      google_api_key: ${secrets:GOOGLE_API_KEY}

embeddings:
  - from: google:text-embedding-004
    name: google_embeddings
    params:
      google_api_key: ${secrets:GOOGLE_API_KEY}

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:

-- Partitions are automatically inferred from paths
SELECT * FROM 's3://bucket/data/' WHERE year = '2024' AND month = '01'

Enable via spicepod.yml:

runtime:
  params:
    url_tables: enabled

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:

datasets:
  - from: postgres:users
    name: users
    acceleration:
      enabled: true
      engine: arrow
      primary_key: user_id
      indexes:
        '(tenant_id, user_id)': unique  # Composite hash index

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:

datasets:
  # SMB share
  - from: smb://fileserver/share/data.parquet
    name: smb_data
    params:
      smb_username: ${secrets:SMB_USER}
      smb_password: ${secrets:SMB_PASS}

  # NFS export
  - from: nfs://nfsserver/export/data.parquet
    name: nfs_data

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:

datasets:
  - from: scylladb:my_keyspace.my_table
    name: scylla_data
    acceleration:
      enabled: true
      engine: duckdb

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​

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 NameNew 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:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.11.0 image:

docker pull spiceai/spiceai:1.11.0

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 1.11.0

AWS Marketplace:

Spice is available in the AWS Marketplace.

Dependencies​

What's Changed​

Changelog​

Spice v1.11.0-rc.2 (Jan 22, 2026)

· 24 min read
Viktor Yershov
Viktor Yershov
Senior Software Engineer at Spice AI

Announcing the release of Spice v1.11.0-rc.2! ⭐

v1.11.0-rc.2 is the second release candidate for advanced test of v1.11. It brings Spice Cayenne to Beta status with acceleration snapshots support, a new ScyllaDB Data Connector, upgrades to DataFusion v51, Arrow 57.2, and iceberg-rust v0.8.0. It includes significant improvements to distributed query, caching, and observability.

What's New in v1.11.0-rc.2​

Spice Cayenne Accelerator Reaches Beta​

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

Improved Reliability:

  • Fixed timezone database issues in Docker images that caused acceleration panics
  • 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

Example configuration with snapshots:

datasets:
  - from: s3://my-bucket/data.parquet
    name: my_dataset
    acceleration:
      enabled: true
      engine: cayenne
      mode: file

DataFusion v51 Upgrade​

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

DataFusion v51 ClickBench Performance

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:

SELECT * FROM t
|> WHERE a > 10
|> ORDER BY b
|> LIMIT 5;

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​

Spice has been upgraded to Apache Arrow Rust 57.2.0, bringing major performance improvements and new capabilities.

Arrow 57 Parquet Metadata Parsing Performance

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

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 Feature Improvements in v1.11:

  • 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:

datasets:
  - from: s3://my-bucket/data.parquet
    name: my_dataset
    acceleration:
      enabled: true
      engine: cayenne
      mode: file
      snapshots: enabled
      snapshots_trigger: time_interval
      snapshots_trigger_threshold: 1h
      snapshots_creation_policy: on_changed

Snapshots API and CLI: New API endpoints and CLI commands for managing snapshots programmatically. List, create, and restore snapshots directly from the command line or via HTTP.

For more details, refer to the Acceleration Snapshots 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:

datasets:
  - from: scylladb:my_keyspace.my_table
    name: scylla_data
    acceleration:
      enabled: true
      engine: duckdb

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

Distributed Query Improvements​

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

Credential Propagation: Azure 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.

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.

DynamoDB Connector Enhancements​

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

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:

-- Partitions are automatically inferred from paths
SELECT * FROM 's3://bucket/data/' WHERE year = '2024' AND month = '01'

Enable via spicepod.yml:

runtime:
  params:
    url_tables: enabled

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
  • Arrow Flight async support: Non-blocking query execution via Arrow Flight protocol

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.

Additional Improvements​

  • 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

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

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

New SMB Data Connector Recipe: New recipe demonstrating how to use ScyllaDB 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-rc.2, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:v1.11.0-rc.2 image:

docker pull spiceai/spiceai:v1.11.0-rc.2

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

Spice is available in the AWS Marketplace.

Dependencies​

Changelog​

Spice v1.8.0 (Oct 6, 2025)

· 20 min read
Phillip LeBlanc
Phillip LeBlanc
Co-Founder and CTO of Spice AI

Announcing the release of Spice v1.8.0! 🧊

Spice v1.8.0 delivers major advances in data writes, scalable vector search, and now in preview—managed acceleration snapshots for fast cold starts. This release introduces write support for Iceberg tables using standard SQL INSERT INTO, partitioned S3 Vector indexes for petabyte-scale vector search, and preview of the AI SQL function for direct LLM integration in SQL. Additional improvements include improved reliability, and the v3.0.3 release of the Spice.js Node.js SDK.

What's New in v1.8.0​

Iceberg Table Write Support (Preview)​

Append Data to Iceberg Tables with SQL INSERT INTO: Spice now supports writing to Iceberg tables and catalogs using standard SQL INSERT INTO statements. This enables data ingestion, transformation, and pipeline use cases—no Spark or external writer required.

  • Append-only: Initial version targets appends; no overwrite or delete.
  • Schema validation: Inserted data must match the target table schema.
  • Secure by default: Writes are only enabled for datasets or catalogs explicitly marked with access: read_write.

Example Spicepod configuration:

catalogs:
  - from: iceberg:https://glue.ap-northeast-3.amazonaws.com/iceberg/v1/catalogs/111111/namespaces
    name: ice
    access: read_write

datasets:
  - from: iceberg:https://iceberg-catalog-host.com/v1/namespaces/my_namespace/tables/my_table
    name: iceberg_table
    access: read_write

Example SQL usage:

-- Insert from another table
INSERT INTO iceberg_table
SELECT * FROM existing_table;

-- Insert with values
INSERT INTO iceberg_table (id, name, amount)
VALUES (1, 'John', 100.0), (2, 'Jane', 200.0);

-- Insert into catalog table
INSERT INTO ice.sales.transactions
VALUES (1001, '2025-01-15', 299.99, 'completed');

Note: Only Iceberg datasets and catalogs with access: read_write support writes. Internal Spice tables and other connectors remain read-only.

Learn more in the Iceberg Data Connector documentation.

Acceleration Snapshots for Fast Cold Starts (Preview)​

Bootstrap Managed Accelerations from Object Storage: Spice now supports managed acceleration snapshots in preview, enabling datasets accelerated with file-based engines (DuckDB or SQLite) to bootstrap from a snapshot stored in object storage (such as S3) if the local acceleration file does not exist on startup. This dramatically reduces cold start times and enables ephemeral storage for accelerations with persistent recovery.

Key features:

  • Rapid readiness: Datasets can become ready in seconds by downloading a pre-built snapshot, skipping lengthy initial acceleration.
  • Hive-style partitioning: Snapshots are organized by month, day, and dataset for easy retention and management.
  • Flexible bootstrapping: Configurable fallback and retry behavior if a snapshot is missing or corrupted.

Example Spicepod configuration:

snapshots:
  enabled: true
  location: s3://some_bucket/some_folder/ # Folder for storing snapshots
  bootstrap_on_failure_behavior: warn # Options: warn, retry, fallback
  params:
    s3_auth: iam_role # All S3 dataset params accepted here

datasets:
  - from: s3://some_bucket/some_table/
    name: some_table
    params:
      file_format: parquet
      s3_auth: iam_role
    acceleration:
      enabled: true
      snapshots: enabled # Options: enabled, disabled, bootstrap_only, create_only
      engine: duckdb
      mode: file
      params:
        duckdb_file: /nvme/some_table.db

How it works:

  • On startup, if the acceleration file does not exist, Spice checks the snapshot location for the latest snapshot and downloads it.
  • Snapshots are stored as: s3://some_bucket/some_folder/month=2025-09/day=2025-09-30/dataset=some_table/some_table_<timestamp>.db
  • If no snapshot is found, a new acceleration file is created as usual.
  • Snapshots are written after each refresh (unless configured otherwise).

Supported snapshot modes:

  • enabled: Download and write snapshots.
  • bootstrap_only: Only download on startup, do not write new snapshots.
  • create_only: Only write snapshots, do not download on startup.
  • disabled: No snapshotting.

Note: This feature is only supported for file-based accelerations (DuckDB or SQLite) with dedicated files.

Why use acceleration snapshots?

  • Faster cold starts: Skip waiting for full acceleration on startup.
  • Ephemeral storage: Use fast local disks (e.g., NVMe) for acceleration, with persistent recovery from object storage.
  • Disaster recovery: Recover from federated source outages by bootstrapping from the latest snapshot.

Partitioned S3 Vector Indexes​

Efficient, Scalable Vector Search with Partitioning: Spice now supports partitioning Amazon S3 Vector indexes and scatter-gather queries using a partition_by expression in the dataset vector engine configuration. Partitioned indexes enable faster ingestion, lower query latency, and scale to billions of vectors.

Example Spicepod configuration:

datasets:
  - name: reviews
    vectors:
      enabled: true
      engine: s3_vectors
      params:
        s3_vectors_bucket: my-bucket
        s3_vectors_index: base-embeddings
      partition_by:
        - 'bucket(50, PULocationID)'
    columns:
      - name: body
        embeddings:
          from: bedrock_titan
      - name: title
        embeddings:
          from: bedrock_titan

See the Amazon S3 Vectors documentation for details.

AI SQL function for LLM Integration (Preview)​

LLMs Directly In SQL: A new asynchronous ai SQL function enables direct calls to LLMs from SQL queries for text generation, translation, classification, and more. This feature is released in preview and supports both default and model-specific invocation.

Example Spicepod model configuration:

models:
  - name: gpt-4o
    from: openai:gpt-4o
    params:
      openai_api_key: ${secrets:openai_key}

Example SQL usage:

-- basic usage with default model
SELECT ai('hi, this prompt is directly from SQL.');
-- basic usage with specified model
SELECT ai('hi, this prompt is directly from SQL.', 'gpt-4o');
-- Using row data as input to the prompt
SELECT ai(concat_ws(' ', 'Categorize the zone', Zone, 'in a single word. Only return the word.')) AS category
FROM taxi_zones
LIMIT 10;

Learn more in the SQL Reference AI documentation.

Remote Endpoint Support for Spice CLI​

Run CLI Commands Remotely: The Spice CLI now supports connecting to remote Spice instances, enabling you to run spice sql, spice search, and spice chat commands from your local machine against a remote spiced daemon or to Spice Cloud. Previously, these commands required running on the same machine as the runtime. Now, new flags allow remote execution:

  • --cloud: Connect to a Spice Cloud instance (requires --api-key).
  • --endpoint <endpoint>: Connect to a remote Spice instance via HTTP or Arrow Flight SQL (gRPC). Supports http://, https://, grpc://, or grpc+tls:// schemes.

Examples:

# Run SQL queries against a remote Spice instance
spice sql --endpoint http://remote-host:8090

# Use Spice Cloud for chat or search
spice chat --cloud --api-key <your-api-key>
spice search --cloud --api-key <your-api-key>

Supported CLI Commands:

  • spice sql --cloud / spice sql --endpoint <endpoint>
  • spice search --cloud / spice search --endpoint <endpoint>
  • spice chat --cloud / spice chat --endpoint <endpoint>

Additional Flags:

  • --headers: Pass custom HTTP headers to the remote endpoint.
  • --tls-root-certificate-file: Specify a root certificate for TLS verification.
  • --user-agent: Set a custom user agent for requests.

For more details, see the Spice CLI Command Reference.

Spice.js v3.0.3 SDK​

Spice.js v3.0.3 Released: The official Spice.ai Node.js/JavaScript SDK has been updated to v3.0.3, bringing cross-platform support, new APIs, and improved reliability for both Node.js and browser environments.

  • Modern Query Methods: Use sql(), sqlJson(), and nsql() for flexible querying, streaming, and natural language to SQL.
  • Browser Support: SDK now works in browsers and web applications, automatically selecting the optimal transport (gRPC or HTTP).
  • Health Checks & Dataset Refresh: Easily monitor Spice runtime health and trigger dataset refreshes on demand.
  • Automatic HTTP Fallback: If gRPC/Flight is unavailable, the SDK falls back to HTTP automatically.
  • Migration Guidance: v3 requires Node.js 20+, uses camelCase parameters, and introduces a new package structure.

Example usage:

import { SpiceClient } from '@spiceai/spice'

const client = new SpiceClient(apiKey)
const table = await client.sql('SELECT * FROM my_table LIMIT 10')
console.table(table.toArray())

See Spice.js SDK documentation for full details, migration tips, and advanced usage.

Additional Improvements​

  • Reliability: Improved logging, error handling, and network readiness checks across connectors (Iceberg, Databricks, etc.).
  • Vector search durability and scale: Refined logging, stricter default limits, safeguards against index-only scans and duplicate results, and always-accessible metadata for robust queryability at scale.
  • Cache behavior: Tightened cache logic for modification queries.
  • Full-Text Search: FTS metadata columns now usable in projections; max search results increased to 1000.
  • RRF Hybrid Search: Reciprocal Rank Fusion (RRF) UDTF enhancements for advanced hybrid search scenarios.

Contributors​

Breaking Changes​

This release introduces two breaking changes associated with the search observability and tooling.

Firstly, the document_similarity tool has been renamed to search. This has the equivalent change to tracing of these tool calls:

## Old: v1.7.1
>> spice trace tool_use::document_similarity
>> curl -XPOST http://localhost:8090/v1/tools/document_similarity \
  -d '{
    "datasets": ["my_tbl"],
    "text": "Welcome to another Spice release"
  }'

## New: v1.8.0
>> spice trace tool_use::search
>> curl -XPOST http://localhost:8090/v1/tools/search \
  -d '{
    "datasets": ["my_tbl"],
    "text": "Welcome to another Spice release"
  }'

Secondly, the vector_search task in runtime.task_history has been renamed to search.

Cookbook Updates​

The Spice Cookbook now includes 80 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.8.0, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.8.0 image:

docker pull spiceai/spiceai:1.8.0

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

🎉 Spice is now available in the AWS Marketplace!

What's Changed​

Dependencies​

  • iceberg-rust: Upgraded to v0.7.0-rc.1
  • mimalloc: Upgraded from 0.1.47 to 0.1.48
  • azure_core: Upgraded from 0.27.0 to 0.28.0
  • Jimver/cuda-toolkit: Upgraded from 0.2.27 to 0.2.28

Changelog​

Spice v1.5.2 (Aug 11, 2025)

· 7 min read
Kevin Zimmerman
Kevin Zimmerman
Principal Software Engineer at Spice AI

Announcing the release of Spice v1.5.2! 🛠️

Spice v1.5.2 introduces a new Amazon Bedrock Models Provider for converse API (Nova) compatible models, AWS Redshift support using the Postgres data connector, and Hadoop Catalog Support for Iceberg tables along with several bug fixes and improvements.

What's New in v1.5.2​

Amazon Bedrock Models Provider: Adds a new Amazon Bedrock LLM Provider. Models compatible with the Converse API (Nova) are supported.

Amazon Bedrock provides access to a range of foundation models for generative AI. Spice supports using Bedrock-hosted models by specifying the bedrock prefix in the from field and configuring the required parameters.

Supported Model IDs:

  • amazon.nova-lite-v1:0
  • amazon.nova-micro-v1:0
  • amazon.nova-premier-v1:0
  • amazon.nova-pro-v1:0

Refer to the Amazon Bedrock documentation for details on available models and cross-region inference profiles.

Example Spicepod.yaml:

models:
  - from: bedrock:us.amazon.nova-lite-v1:0
    name: novash
    params:
      aws_region: us-east-1
      aws_access_key_id: ${ secrets:AWS_ACCESS_KEY_ID }
      aws_secret_access_key: ${ secrets:AWS_SECRET_ACCESS_KEY }
      bedrock_guardrail_identifier: arn:aws:bedrock:abcdefg012927:0123456789876:guardrail/hello
      bedrock_guardrail_version: DRAFT
      bedrock_trace: enabled
      bedrock_temperature: 42

For more information, see the Amazon Bedrock Documentation.

AWS Redshift Support for Postgres Data Connector: Spice now supports connecting to Amazon Redshift using the PostgreSQL data connector. Redshift is a columnar OLAP database compatible with PostgreSQL, allowing you to use the same connector and configuration parameters.

To connect to Redshift, use the format postgres:schema.table in your Spicepod and set the connection parameters to match your Redshift cluster settings.

Example Spicepod.yaml:

# Example datasets for Redshift TPCH tables
datasets:
  - from: postgres:public.customer
    name: customer
    params:
      pg_host: ${secrets:PG_HOST}
      pg_port: 5439
      pg_sslmode: prefer
      pg_db: dev
      pg_user: ${secrets:PG_USER}
      pg_pass: ${secrets:PG_PASS}
  - from: postgres:public.lineitem
    name: lineitem
    params:
      pg_host: ${secrets:PG_HOST}
      pg_port: 5439
      pg_sslmode: prefer
      pg_db: dev
      pg_user: ${secrets:PG_USER}
      pg_pass: ${secrets:PG_PASS}

Redshift types are mapped to PostgreSQL types. See the PostgreSQL connector documentation for details on supported types and configuration.

Hadoop Catalog Support for Iceberg: The Iceberg Data and Catalog connectors now support connecting to Hadoop catalogs on filesystem (file://) or S3 object storage (s3://, s3a://). This enables connecting to Iceberg catalogs without a separate catalog provider service.

Example Spicepod.yaml:

catalogs:
  - from: iceberg:file:///tmp/hadoop_warehouse/
    name: local_hadoop
  - from: iceberg:s3://my-bucket/hadoop_warehouse/
    name: s3_hadoop

  # Example datasets
  - from: iceberg:file:///data/hadoop_warehouse/test/my_table_1
    name: local_hadoop
  - from: iceberg:s3://my-bucket/hadoop_warehouse/test/my_table_2
    name: s3_hadoop

For more details, see the Iceberg Data Connector documentation and the Iceberg Catalog Connector documentation.

Parquet Reader: Optional Parquet Page Index: Fixed an issue where the Parquet reader, using arrow-rs and DataFusion, errored on files missing page indexes, despite the Parquet spec allowing optional indexes. The Spice team contributed optional page index support to arrow-rs (PR #6) and configurable handling in DataFusion (PR #93). A new runtime parameter, parquet_page_index, makes Parquet Page Indexes configurable in Spice:

runtime:
  params:
    parquet_page_index: required # Options: required, skip, auto
  • required: (Default) Errors if page indexes are absent.
  • skip: Ignores page indexes, potentially reducing query performance.
  • auto: Uses page indexes if available; skips otherwise.

This improves compatibility and query flexibility for Parquet datasets.

Contributors​

Breaking Changes​

Amazon S3 Vectors Vector Engine: Amazon S3 Vectors is currently a preview AWS service. A recent update to the Amazon S3 Vectors service API introduced a breaking change that affects the integration when projecting (selecting) the embedding column. This results in the following error:

Json error: whilst decoding field 'data': expected [ got nullReceived only partial JSON payload from QueryVectors

The issue is expected to be resolved in the next release of Spice. A current workaround is to limit queries to non-embedding columns.

i.e. instead of:

SELECT url, title, scored, body_embedding
FROM vector_search(pulls, 'bugs in DuckDB', 4)
WHERE state = 'OPEN'
ORDER BY score DESC
LIMIT 4;

Remove the *_embedding column from the projection. E.g.

SELECT url, title, scored
FROM vector_search(pulls, 'bugs in DuckDB', 4)
WHERE state = 'OPEN'
ORDER BY score DESC
LIMIT 4;

This issue and workaround also applies to SELECT * FROM vector_search(..). E.g.

SELECT *
FROM vector_search(pulls, 'bugs in DuckDB', 4)
WHERE state = 'OPEN'
ORDER BY score DESC
LIMIT 4;

Cookbook Updates​

The Spice Cookbook includes 75 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.5.2, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.5.2 image:

docker pull spiceai/spiceai:1.5.2

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

🎉 Spice is also now available in the AWS Marketplace!

What's Changed​

Dependencies​

No major dependency updates.

Changelog​

  • fixes for databricks OpenAI compatibility (#6629) by @Jeadie in #6629
  • Update spicepod.schema.json (#6632) by @app/github-actions in #6632
  • Remove 'stream_options' from databricks LLMs (#6637) by @Jeadie in #6637
  • Move retry and rate limiting logic for Amazon bedrock out of embeddings. (#6626) by @Jeadie in #6626
  • Disable Metal precomplation in integration_llms.yml (#6649) by @Jeadie in #6649
  • fix: Hadoop integration test (#6660) by @peasee in #6660
  • feat: Add Hadoop Catalog Data Component (#6658) by @peasee in #6658
  • update datafusion-table-providers to latest spiceai tag (#6661) by @mach-kernel in #6661
  • feat: Add Hadoop Catalog connectors for Iceberg (#6659) by @peasee in #6659
  • Make FullTextSearchExec robust to RecordBatch column ordering. (#6675) by @Jeadie in #6675
  • Make 'runtime-object-store' crate (#6674) by @Jeadie in #6674
  • fix: Support include for Iceberg (#6663) by @peasee in #6663
  • feat: Add Hadoop TPCH benchmark (#6678) by @peasee in #6678
  • feat: Add Hadoop metadata_path parameter (#6680) by @peasee in #6680
  • fix: Automatically infer Hadoop warehouse scheme (#6681) by @peasee in #6681
  • Amazon Bedrock, specifically Nova models (#6673) by @Jeadie in [#6673](https://github.com/spiceai/spiceai/pull/6673
  • fix perplexity_auth_token parameters for web_search (#6685) by @Jeadie in #6685
  • Fix AWS Auth issue (#6699) by @Advayp in #6699
  • Limit Concurrent Requests for GitHub (#6672) by @Advayp in #6672
  • Add runtime parameter to enable more permissive parquet reading when page indexes are missing (#6716) by @phillipleblanc in #6716
  • Improve Flight REPL error messages (#6696) by @lukekim in #6696
  • Fixes from search tests (#6710) by @Jeadie in #6710

Spice v1.0.6 (Mar 17, 2025)

· 4 min read
Sergei Grebnov
Sergei Grebnov
Senior Software Engineer at Spice AI

Announcing the release of Spice v1.0.6 ⚡

Spice v1.0.6 improves stability for DuckDB acceleration, Iceberg Data/Catalog connector improvements when using AWS Glue, and fixes an issue with the ready_state: on_registration federation fallback when using DuckDB. In addition, redundant data refreshes on startup are avoided for accelerations with persistent data.

Highlights in v1.0.6​

  • Iceberg Data/Catalog Connector Improvements: Improves Iceberg data & catalog connector reliability, including bug fixes for AWS Glue API rate-limiting and compatibility, REST API pagination support, explicit AWS credential handling, and support for AWS STS role assumption.

  • Fixes On-Registration Fallback when using DuckDB: Previously, when using DuckDB as a data accelerator and the ready_state: on_registration configuration, queries made during the initial data refresh did not properly fallback to the federated source. This is now fixed.

  • DuckDB downgraded for Stability: DuckDB has been downgraded to v1.1.3 due to a regression in memory handling tracked by duckdb/duckdb issue #16640. Once resolved and validated, Spice will re-upgrade to v1.2.x.

  • Expanded Integration Tests: Additional integration tests covering federated accelerator behavior and graceful shutdown processes have been added.

  • Optimized Data Refresh for Persistent Accelerations: Changed behavior in v1.0.6. When using persistent (file-mode) acceleration without a defined refresh interval, Spice performs a full refresh at startup only if no previously accelerated data is available. This ensures efficient startup behavior by avoiding unnecessary refreshes. This logic applies only to full refreshes when no refresh interval is specified.

To maintain the previous behavior and always refresh on every startup, set:

acceleration:
  refresh_on_startup: always

Contributors​

  • @peasee
  • @phillipleblanc
  • @sgrebnov
  • @lukekim
  • @Sevenannn

Breaking Changes​

Starting from v1.0.6 when using persistent (file-mode) acceleration without a defined refresh interval, Spice performs a full refresh at startup only if no previously accelerated data is available. To maintain the previous behavior and always refresh on every startup, set:

acceleration:
  refresh_on_startup: always

Cookbook Updates​

No new recipes.

Upgrading​

To upgrade to v1.0.6, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.0.6 image:

docker pull spiceai/spiceai:1.0.6

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

What's Changed​

Dependencies​

Changelog​

  • Implement proper ready_state: on_registration for federation enabled accelerators by @phillipleblanc in #5019
  • Add indexes and primary keys mismatch detection for DuckDB Acceleration by @sgrebnov in #5045
  • Add comprehensive integration tests for the ready_state behavior by @phillipleblanc in #5042
  • Add test Spicepod for acceleration with constraints by @sgrebnov in #4891
  • Add test Spicepod for DuckDB append acceleration with constraints by @sgrebnov in #4898
  • Add DuckDB graceful shutdown test to E2E CI tests by @sgrebnov in #5047
  • Update duckdb_append_with_pk_and_indexes.yaml (work for duckdb 1.1.x) by @sgrebnov in #5067
  • fix: Downgrade to DuckDB 1.1.3 by @peasee in #5055
  • fix: Acceleration federation integration test by @peasee in #5070
  • Improvements to Iceberg Catalog/Data Connector by @phillipleblanc in #5071
  • Add Results-Cache-Status to indicate query result came from cache by @phillipleblanc in #4809
  • fix: Spice.ai schema inference by @peasee in #4674
  • Add refresh_on_startup Spicepod configuration param by @phillipleblanc and @sgrebnov in #5086
  • Test restart behavior of DuckDB file acceleration against glue iceberg table by @Sevenannn #5075
  • Run Iceberg Data Connector - DuckDB File mode integration test by @Sevenannn #5069
  • Integration test for glue iceberg catalog by @Sevenannn #5077

Full Changelog: https://github.com/spiceai/spiceai/compare/v1.0.5...v1.0.6

Spice v1.0.5 (Mar 11, 2025)

· 4 min read
Sergei Grebnov
Sergei Grebnov
Senior Software Engineer at Spice AI

Announcing the release of Spice v1.0.5 🧊

Spice v1.0.5 expands Iceberg support with the introduction of the Iceberg Data Connector, in addition to the existing Iceberg Catalog Connector. This new connector enables direct dataset creation and configuration for specific Iceberg objects, enabling federated and accelerated SQL queries on Apache Iceberg tables.

Performance improvements include object-store optimized Parquet pruning in append mode, where object-store metadata is now leveraged alongside Hive partitioning to optimize file pruning. This results in faster and more efficient queries.

DuckDB has been upgraded to v1.2.0, along with additional stability improvements, including improved graceful shutdown and the ability to configure the DuckDB memory limit.

Additional updates include support for the Arrow Map type.

Highlights in v1.0.5​

  • New Iceberg Data Connector: Enables direct dataset creation and querying of Iceberg tables.

    Example usage in spicepod.yaml:

    datasets:
      - from: iceberg:https://iceberg-catalog-host.com/v1/namespaces/my_namespace/tables/my_table
        name: my_table
        params:
          # Same as Iceberg Catalog Connector
        acceleration:
          enabled: true

    For detailed setup instructions, authentication options, and configuration parameters, refer to the Iceberg Data Connector documentation.

  • Improved Parquet pruning in append mode: Uses object-store metadata for more efficient file pruning.

  • DuckDB upgrade to v1.2.0 with improved graceful shutdown: Read the DuckDB v1.2.0 announcement for details, including breaking changes for map and list_reduce. Graceful shutdown of DuckDB has been improved for better stability across restarts.

  • Configurable DuckDB memory limit: Use the duckdb_memory_limit parameter to set the DuckDB acceleration memory limit:

    - from: spice.ai:path.to.my_dataset
       name: my_dataset
       acceleration:
         params:
           duckdb_memory_limit: '2GB'
         enabled: true
         engine: duckdb
         mode: file

Contributors​

  • @peasee
  • @phillipleblanc
  • @sgrebnov
  • @lukekim

Breaking Changes​

Upgrading​

To upgrade to v1.0.5, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.0.5 image:

docker pull spiceai/spiceai:1.0.5

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

What's Changed​

Dependencies​

Changelog​

  • fix: Update OpenAI model health check by @peasee in #4849
  • fix: Allow metrics endpoint setting in CLI by @peasee in #4939
  • DuckDB acceleration: fix Decimal with zero scale support by @sgrebnov in #4922
  • Introduce runtime shutdown state by @sgrebnov in #4917
  • Add support for Flight and HTTP endpoints configuration to Spice CLI (run and sql) by @sgrebnov and @lukekim in #4913
  • Fix Datafusion resources deallocation during shutdown by @sgrebnov in #4912
  • DuckDB: fix error handling during record batch insertion by @sgrebnov in #4894
  • DuckDB: add support for Map Arrow type for DuckDB acceleration by @sgrebnov in #4887
  • Upgrade to DuckDB v1.2.0 by @sgrebnov in #4842
  • Gracefully shutdown the runtime and deallocate static resources by @sgrebnov in #4879
  • Implement an Iceberg Data Connector by @phillipleblanc in #4941
  • Don't trace canceled dataset refresh during runtime termination by @sgrebnov in #4958
  • Use metadata column last_modified when specified as a time_column by @phillipleblanc in #4970
  • Add duckdb_memory_limit param support for DuckDB acceleration by @sgrebnov in #4971
  • Add Iceberg dataset integration test by @phillipleblanc in #4950

Full Changelog: https://github.com/spiceai/spiceai/compare/v1.0.4...v1.0.5

Spice v1.0.1 (Jan 27, 2025)

· 5 min read
Qianqian Liu
Qianqian Liu
Software Engineer at Spice AI

Spice v1.0.1 focuses on an improved developer experience, with automatic CUDA GPU detection for local models, in addition to bug fixes. Notably, the Iceberg Catalog Connector now supports AWS Glue including Sig v4 authentication.

Highlights in v1.0.1​

  • AWS Glue Support for Iceberg Catalog Connector: The Iceberg Catalog Connector now supports AWS Glue. Example spicepod.yaml configuration:
- from: iceberg:https://glue.ap-northeast-2.amazonaws.com/iceberg/v1/catalogs/123456789012/namespaces
  name: glue

  • spice upgrade CLI Command: The spice upgrade CLI command detects more edge cases for a smoother upgrade experience.

  • GPU Acceleration Detection: The Spice CLI now automatically detects and enables CUDA (NVIDIA GPUs) GPU acceleration when supported in addition to Metal (M-Series on macOS).

  • Python SDK: The Python SDK (spicepy) has updated to v3.0.0, aligning the SDK with the Runtime

Breaking changes​

No breaking changes.

Dependencies​

No major dependency changes.

Cookbook​

Upgrading​

To upgrade to v1.0.1, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.0.1 image:

docker pull spiceai/spiceai:1.0.1

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

Contributors​

  • @Jeadie
  • @phillipleblanc
  • @ewgenius
  • @peasee
  • @Sevenannn
  • @sgrebnov
  • @lukekim

What's Changed​

- Update acknowledgements by @github-actions in https://github.com/spiceai/spiceai/pull/4459
- docs: 1.0 release notes by @peasee in https://github.com/spiceai/spiceai/pull/4440
- Create a release-only workflow that uses a previous run's artifacts by @phillipleblanc in https://github.com/spiceai/spiceai/pull/4461
- Add publish-only CUDA workflow by @phillipleblanc in https://github.com/spiceai/spiceai/pull/4462
- Fix the CUDA release workflow by @phillipleblanc in https://github.com/spiceai/spiceai/pull/4463
- docs: Update SECURITY.md for stable by @peasee in https://github.com/spiceai/spiceai/pull/4465
- docs: Update endgame by @peasee in https://github.com/spiceai/spiceai/pull/4460
- docs: Promote HF and File model components by @peasee in https://github.com/spiceai/spiceai/pull/4457
- fix: E2E test release installation by @peasee in https://github.com/spiceai/spiceai/pull/4466
- Fix publish part of CUDA workflow by @phillipleblanc in https://github.com/spiceai/spiceai/pull/4467
- Fix broken docs links in README by @ewgenius in https://github.com/spiceai/spiceai/pull/4468
- Update benchmark snapshots by @github-actions in https://github.com/spiceai/spiceai/pull/4474
- Update openapi.json by @github-actions in https://github.com/spiceai/spiceai/pull/4477
- Add instruction to force-install CPU runtime to v1.0 release notes by @sgrebnov in https://github.com/spiceai/spiceai/pull/4469
- feat: Add WIP testoperator dispatch workflow by @peasee in https://github.com/spiceai/spiceai/pull/4478
- Fix Bug: invalid REPL cursor position on Windows by @sgrebnov in https://github.com/spiceai/spiceai/pull/4480
- feat: Download latest spiced commit for testoperators by @peasee in https://github.com/spiceai/spiceai/pull/4483
- Add compute engine image by @lukekim in https://github.com/spiceai/spiceai/pull/4486
- fix: Testoperator git fetch depth by @peasee in https://github.com/spiceai/spiceai/pull/4484
- feat: New spicepods, testoperator improvements, TPCDS Q1 fix by @peasee in https://github.com/spiceai/spiceai/pull/4475
- Add 87 CUDA compatiblity to build CI by @Jeadie in https://github.com/spiceai/spiceai/pull/4489
- Use OpenAI golang client in `spice chat` by @Jeadie in https://github.com/spiceai/spiceai/pull/4491
- Verify `search` and `chat` on Windows as part of AI installation tests by @sgrebnov in https://github.com/spiceai/spiceai/pull/4492
- feat: Add testoperator dispatch command by @peasee in https://github.com/spiceai/spiceai/pull/4479
- Run CUDA builds on non-GPU instances by @phillipleblanc in https://github.com/spiceai/spiceai/pull/4496
- Use upgraded spice cli when performing runtime upgrade in spice upgrade by @Sevenannn in https://github.com/spiceai/spiceai/pull/4490
- Revert "Use OpenAI golang client in `spice chat` (#4491)" by @Jeadie in https://github.com/spiceai/spiceai/pull/4532
- Make Anthropic rate limit error message friendlier by @sgrebnov in https://github.com/spiceai/spiceai/pull/4501
- Update supported CUDA targets: add 87(cli), remove 75 by @sgrebnov in https://github.com/spiceai/spiceai/pull/4509
- Support AWS Glue for Iceberg catalog connector by @phillipleblanc in https://github.com/spiceai/spiceai/pull/4517
- Package CUDA runtime libraries into artifact for Windows by @phillipleblanc in https://github.com/spiceai/spiceai/pull/4497

**Full Changelog**: https://github.com/spiceai/spiceai/compare/v1.0.0...v1.0.1

Resources​

Community​

Spice.ai started with the vision to make AI easy for developers. We are building Spice.ai in the open and with the community. Reach out on Slack or by email to get involved.