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Spice v2.1.2 (Jul 28, 2026)

ยท 3 min read
Sergei Grebnov
Senior Software Engineer at Spice AI

Spice v2.1.2 is now available! ๐Ÿ› ๏ธ

Spice v2.1.2 is a patch release focused on improving the DuckDB data accelerator. It upgrades the DuckDB engine to v1.5.5 and introduces the on_full_refresh parameter, giving file-mode DuckDB accelerations compact, predictable disk usage across repeated full refreshes.

What's New in v2.1.2โ€‹

Bounded DuckDB Acceleration File Growth with on_full_refreshโ€‹

File-mode DuckDB accelerations using refresh_mode: full now reclaim disk space on every refresh, keeping the database file compact and disk usage predictable for long-running deployments. Each full refresh bulk-loads a fresh copy of the data, and the new on_full_refresh modes ensure the space held by prior copies is returned rather than accumulating in the file.

The new on_full_refresh acceleration parameter controls how disk space is reclaimed after each full refresh:

acceleration:
engine: duckdb
mode: file
refresh_mode: full
params:
duckdb_file: /data/shared.duckdb
on_full_refresh: replace_file # default: reuse_file
  • reuse_file (default): Existing behavior โ€” refresh into the existing database file.
  • replace_file: Each full refresh streams data into a fresh staging database file, carries over every other object sharing the file (other datasets' tables, views, indexes, and Spice metadata), checkpoints it, and atomically replaces the configured file. Queries are never interrupted โ€” in-flight queries drain against the old file while new queries see the new file โ€” and space is fully reclaimed on every refresh.
  • checkpoint_file: After each refresh, run a CHECKPOINT in place, escalating to FORCE CHECKPOINT when concurrent transactions block the plain attempt (waiting for in-flight transactions; never aborting them).

DuckDB 1.5.5โ€‹

The DuckDB engine is upgraded from v1.5.3 to v1.5.5, bringing the latest upstream stability fixes.

Contributorsโ€‹

Breaking Changesโ€‹

No breaking changes.

Cookbook Updatesโ€‹

No new cookbook recipes.

The Spice Cookbook includes more than 100 recipes to help you get started with Spice quickly and easily.

Upgradingโ€‹

To upgrade to v2.1.2, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:2.1.2 image:

docker pull spiceai/spiceai:2.1.2

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 2.1.2

AWS Marketplace:

Spice is available in the AWS Marketplace.

What's Changedโ€‹

Changelogโ€‹

  • feat(duckdb): on_full_refresh: replace_file โ€” full refresh into a new database file, atomically replaced by @lukekim in #12135
  • feat(duckdb): 'on_full_refresh: checkpoint_file' to bound acceleration file growth by @sgrebnov in #12139

*Full Changelog: https://github.com/spiceai/spiceai/compare/v2.1.1...v2.1.2

Spice v2.0.1 (Jun 17, 2026)

ยท 4 min read
Phillip LeBlanc
Co-Founder and CTO of Spice AI

Spice v2.0.1 is now available! ๐Ÿ› ๏ธ

Spice v2.0.1 is a patch release focused on reliability and performance. It speeds up Apache Iceberg reads and fixes bugs across AWS S3 and object-store datasets, data acceleration, distributed query, and authenticated access.

What's New in v2.0.1โ€‹

Faster Iceberg Reads with Parallel File Scanningโ€‹

The Apache Iceberg reader now scans data files in parallel (#11331), improving read throughput and latency for Iceberg tables that span many files.

AWS S3 & Object-Store Reliabilityโ€‹

Three fixes improve S3 and object-store dataset behavior:

  • Refresh-skip restored (#11339): ETag/Version-based refresh-skip works reliably again, so unchanged S3 objects are no longer re-downloaded on every refresh.
  • Retry when source files are not yet available (#11342): an object-store dataset whose source files are not present at startup now retries and becomes ready once the data appears, instead of failing permanently.
  • Path-style addressing for dotted bucket names (#11347): on standard AWS, buckets whose names contain dots now default to path-style addressing, avoiding TLS wildcard certificate errors under virtual-hosted-style HTTPS.

Data Acceleration & Distributed Query Fixesโ€‹

Two fixes ensure accelerated datasets behave correctly in more configurations:

  • Acceleration endpoints (#11345): /v1/datasets/{name}/acceleration/refresh (and the related update-refresh-sql, partition-filters, and snapshots endpoints) now work for all accelerated datasets, fixing cases where some incorrectly reported Table is not accelerated.
  • Distributed clusters (#11226): the distributed query coordinator now serves accelerated data from executors for all accelerated datasets, instead of falling back to reading from the source for some.

Authenticated Query Fixesโ€‹

With authentication enabled, queries now consistently run as the requesting user (#11253), so per-user behavior such as results caching is correctly scoped to each user.

Contributorsโ€‹

Breaking Changesโ€‹

No breaking changes.

Cookbook Updatesโ€‹

No new cookbook recipes.

The Spice Cookbook includes more than 100 recipes to help you get started with Spice quickly and easily.

Upgradingโ€‹

To upgrade to v2.0.1, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:2.0.1 image:

docker pull spiceai/spiceai:2.0.1

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 2.0.1

AWS Marketplace:

Spice is available in the AWS Marketplace.

What's Changedโ€‹

Changelogโ€‹

Full Changelog: https://github.com/spiceai/spiceai/compare/v2.0.0...v2.0.1

Spice v1.11.4 (Mar 12, 2026)

ยท 5 min read
Sergei Grebnov
Senior Software Engineer at Spice AI

Announcing the release of Spice v1.11.4! โšก

Spice v1.11.4 is a patch release improving S3 metadata column query robustness and enabling on_zero_results: use_source for accelerated views.

What's New in v1.11.4โ€‹

Accelerated Views: on_zero_results: use_source Supportโ€‹

Accelerated views now support the on_zero_results: use_source configuration (#9699). Previously, accelerated views only supported on_zero_results: return_empty, which returned an empty result set when the accelerated data contained no matching rows. With this change, views can fall back to querying the source data when the accelerated query returns zero results, matching the behavior already available for accelerated datasets.

Example configuration:

views:
- name: sales_summary
sql: |
SELECT region, SUM(amount) as total
FROM sales
GROUP BY region
acceleration:
enabled: true
on_zero_results: use_source

How the Fallback Worksโ€‹

When an accelerated view is configured with on_zero_results: use_source, the following happens at query time:

  1. The accelerated store is queried first. The query runs against the view's accelerated data (e.g., Spice Cayenne, Arrow, DuckDB, or SQLite).

  2. If the accelerated query returns zero rows, the runtime falls back to re-executing the view's SQL query against the datasets it references.

  3. Referenced datasets are queried according to their own configuration. The view's SQL is re-executed against each referenced dataset as it is configured. This means:

    • If a referenced dataset is accelerated, the query hits that dataset's accelerated store โ€” not the raw data source.
    • If a referenced dataset is accelerated with on_zero_results: use_source and its accelerated store also returns zero rows, it will independently fall back to its own federated data source (e.g., Postgres, S3, etc.).
    • If a referenced dataset is federated (not accelerated), the query goes directly to the data source.

This means the fallback can chain through multiple layers: first the view's acceleration, then each referenced dataset's acceleration, and finally the original data source โ€” each layer independently applying its own on_zero_results behavior.

Example: Multi-layer fallback

datasets:
- from: postgres:orders
name: orders
acceleration:
enabled: true
refresh_sql: "SELECT * FROM orders WHERE created_at > now() - interval '7 days'"
on_zero_results: use_source # Falls back to Postgres if accelerated data has no matches

views:
- name: recent_orders_summary
sql: |
SELECT status, COUNT(*) as order_count
FROM orders
GROUP BY status
acceleration:
enabled: true
on_zero_results: use_source # Falls back to re-running the SQL against referenced datasets

In this example, a query like SELECT * FROM recent_orders_summary WHERE status = 'cancelled' follows this path:

  1. Queries recent_orders_summary in the view's accelerated store (DuckDB/SQLite).
  2. If zero rows are returned, re-executes SELECT status, COUNT(*) ... FROM orders GROUP BY status against the orders dataset.
  3. Since orders is accelerated, the query hits the orders accelerated store.
  4. If orders also returns zero rows (e.g., the refresh_sql excluded cancelled orders), it falls back to querying Postgres directly.

S3 Data Connector: More Robust Metadata Column Handlingโ€‹

Improved the robustness of metadata column (location, last_modified, size) handling for S3 datasets. Building on the v1.11.3 release, this update addresses an additional edge case where the query optimizer's projection swap could cause an index out of bounds panic when metadata columns are referenced in projections with filters or scalar functions.

Contributorsโ€‹

Breaking Changesโ€‹

No breaking changes.

Cookbook Updatesโ€‹

No new cookbook recipes.

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

Upgradingโ€‹

To upgrade to v1.11.4, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.11.4 image:

docker pull spiceai/spiceai:1.11.4

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 1.11.4

AWS Marketplace:

Spice is available in the AWS Marketplace.

What's Changedโ€‹

Changelogโ€‹

  • fix(s3): Make metadata column handling more robust by @sgrebnov in #9714
  • feat(views): Enable on_zero_results: use_source for accelerated views by @krinart in #9699

Full Changelog: https://github.com/spiceai/spiceai/compare/v1.11.3...v1.11.4

Spice v1.11.0 (Jan 28, 2026)

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

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

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 v1.11.0โ€‹

Spice Cayenne Accelerator Reaches Betaโ€‹

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 Upgradeโ€‹

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 Upgradeโ€‹

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 Enhancementsโ€‹

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

Distributed Query Improvementsโ€‹

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 Upgradeโ€‹

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 Snapshotsโ€‹

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 Improvementsโ€‹

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 Statementsโ€‹

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)โœ… Fullv8.0.0+SqlWithParams() with typed constructors (Int32Param, StringParam, TimestampParam, etc.)
spice-rs (Rust)โœ… Fullv3.0.0+query_with_params() with RecordBatch parameters
spice-dotnet (.NET)โœ… Fullv0.3.0+QueryWithParams() with typed parameter builders
spice-java (Java)โœ… Fullv0.5.0+queryWithParams() with typed Param constructors (Param.int64(), Param.string(), etc.)
spice.js (JavaScript)โœ… Fullv3.1.0+query() with parameterized query support
spicepy (Python)โœ… 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 v0.5.0โ€‹

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 โ†’ Float64, String โ†’ 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 Supportโ€‹

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 Tablesโ€‹

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)โ€‹

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

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

OpenTelemetry Improvementsโ€‹

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 Improvementsโ€‹

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)โ€‹

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 Connectorsโ€‹

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 Connectorโ€‹

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 Fixesโ€‹

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 Improvementsโ€‹

  • 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 Fixesโ€‹

  • 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

Contributorsโ€‹

Breaking Changesโ€‹

OTel Ingestion Port Changeโ€‹

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 mTLSโ€‹

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 Updatesโ€‹

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.

Upgradingโ€‹

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.

Dependenciesโ€‹

What's Changedโ€‹

Changelogโ€‹

Spice v1.11.0-rc.3 (Jan 23, 2026)

ยท 2 min read
Viktor Yershov
Senior Software Engineer at Spice AI

Announcing the release of Spice v1.11.0-rc.3! โญ

v1.11.0-rc.3 is a patch release that includes improvements to Hash Indexing for Arrow Acceleration and fixes for TLS connections with Flight SQL endpoints.

What's New in v1.11.0-rc.3โ€‹

Hash Indexing for Arrow Acceleration (experimental)โ€‹

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.

Flight SQL TLS Connection Fixesโ€‹

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.

Contributorsโ€‹

Breaking Changesโ€‹

No breaking changes.

Cookbook Updatesโ€‹

No major cookbook updates.

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

Upgradingโ€‹

To upgrade to v1.11.0-rc.3, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:v1.11.0-rc.3 image:

docker pull spiceai/spiceai:v1.11.0-rc.3

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 1.11.0-rc.3

AWS Marketplace:

Spice is available in the AWS Marketplace.

What's Changedโ€‹

Changelogโ€‹

  • Hash indexing for Arrow Acceleration by @lukekim in #8924
  • Improve validation and logging for hash indexes @lukekim in #9047
  • Fix TLS connection for grpc+tls:// Flight SQL endpoints and add custom CA certificate support @phillipleblanc in #9073

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

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

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

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.2โ€‹

Spice Cayenne Accelerator Reaches Betaโ€‹

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 Upgradeโ€‹

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 Upgradeโ€‹

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 Upgradeโ€‹

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 Snapshotsโ€‹

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 Connectorโ€‹

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 Improvementsโ€‹

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 Improvementsโ€‹

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 Enhancementsโ€‹

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

URL Tablesโ€‹

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)โ€‹

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 Improvementsโ€‹

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 Improvementsโ€‹

  • 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

Contributorsโ€‹

Breaking Changesโ€‹

No breaking changes.

Cookbook Updatesโ€‹

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.

Upgradingโ€‹

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.

Dependenciesโ€‹

Changelogโ€‹

Spice v1.11.0-rc.1 (Jan 6, 2026)

ยท 17 min read
Evgenii Khramkov
Senior Software Engineer at Spice AI

Announcing the release of Spice v1.11.0-rc.1! โญ

v1.11.0-rc.1 is the first release candidate for early testing of v1.11 features including Distributed Query with mTLS for enterprise-grade secure cluster communication, new SMB and NFS Data Connectors for direct network-attached storage access, Prepared Statements for improved query performance and security, Cayenne Accelerator Enhancements with Key-based deletion vectors and Amazon S3 Express One Zone support, Google LLM Support for expanded AI inference capabilities, and Spice Java SDK v0.5.0 with parameterized query support.

What's New in v1.11.0-rc.1โ€‹

Distributed Query with mTLSโ€‹

Enterprise-Grade Secure Cluster Communication: Distributed query cluster mode now enables mutual TLS (mTLS) by default for secure communication between schedulers and executors. Internal cluster communication includes highly privileged RPC calls like fetching Spicepod configuration and expanding secrets. mTLS ensures only authenticated nodes can join the cluster and access sensitive data.

Key Features:

  • Mutual TLS Authentication: All executor-to-scheduler and executor-to-executor gRPC connections on the internal cluster port (50052) are secured with mTLS, securing communication, and preventing unauthorized nodes from joining the cluster
  • Certificate Management CLI: New developer spice cluster tls init and spice cluster tls add commands for generating CA certificates and node certificates with proper SANs (Subject Alternative Names)
  • Simplified CLI Arguments: Renamed cluster arguments for clarity (--role, --scheduler-address, --node-mtls-*) with --scheduler-address implying --role executor
  • Port Separation: Public services (Flight queries, HTTP API, Prometheus metrics) remain on ports 50051, 8090, and 9090 respectively, while internal cluster services (SchedulerGrpcServer, ClusterService) are isolated on port 50052 with mTLS enforced
  • Development Mode: Use --allow-insecure-connections flag to disable mTLS requirement for local development and testing

Quick Start:

# Generate certificates for development
spice cluster tls init
spice cluster tls add scheduler1
spice cluster tls add executor1

# Start scheduler
spiced --role scheduler \
--node-mtls-ca-certificate-file ca.crt \
--node-mtls-certificate-file scheduler1.crt \
--node-mtls-key-file scheduler1.key

# Start executor
spiced --role executor \
--scheduler-address https://scheduler1:50052 \
--node-mtls-ca-certificate-file ca.crt \
--node-mtls-certificate-file executor1.crt \
--node-mtls-key-file executor1.key

For more details, refer to the Distributed Query Documentation.

SMB and NFS Data Connectorsโ€‹

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.

Prepared Statementsโ€‹

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)โœ… Fullv8.0.0+SqlWithParams() with typed constructors (Int32Param, StringParam, TimestampParam, etc.)
spice-rs (Rust)โœ… Fullv3.0.0+query_with_params() with RecordBatch parameters
spice-dotnet (.NET)โŒ Not yet-Coming soon
spice-java (Java)โœ… Fullv0.5.0+queryWithParams() with typed Param constructors (Param.int64(), Param.string(), etc.)
spice.js (JavaScript)โŒ Not yet-Coming soon
spicepy (Python)โŒ Not yet-Coming soon

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 Cayenne Accelerator Enhancementsโ€‹

The Spice Cayenne data accelerator has been improved with several key enhancements:

  • KeyBased Deletion Vectors: Improved deletion vector support using key-based lookups for more efficient data management and faster delete operations. KeyBased 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.

Example spicepod.yaml configuration:

datasets:
- from: s3://my-bucket/data.parquet
name: fast_data
acceleration:
enabled: true
engine: cayenne
mode: file
params:
# Use S3 Express One Zone for data files
cayenne_s3express_bucket: my-express-bucket--usw2-az1--x-s3

For more details, refer to the Cayenne Documentation.

Google LLM Supportโ€‹

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).

Spice Java SDK v0.5.0โ€‹

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 โ†’ Float64, String โ†’ 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.

OpenTelemetry Improvementsโ€‹

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.

Developer Experience Improvementsโ€‹

  • 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: Enhanced 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 Fixesโ€‹

  • 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

Contributorsโ€‹

Breaking Changesโ€‹

OTel Ingestion Port Changeโ€‹

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 mTLSโ€‹

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 Updatesโ€‹

No major cookbook updates.

The Spice Cookbook includes 84 recipes to help you get started with Spice quickly and easily.

Upgradingโ€‹

To try v1.11.0-rc.1, use one of the following methods:

CLI:

spice upgrade --version 1.11.0-rc.1

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.11.0-rc.1 image:

docker pull spiceai/spiceai:1.11.0-rc.1

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 1.11.0-rc.1

AWS Marketplace:

๐ŸŽ‰ Spice is available in the AWS Marketplace!

What's Changedโ€‹

Changelogโ€‹

Spice v1.10.1 (Dec 15, 2025)

ยท 5 min read
Jack Eadie
Token Plumber at Spice AI

Announcing the release of Spice v1.10.1! ๐Ÿš€

v1.10.1 is a patch release with Cayenne accelerator improvements including configurable compression strategies and improved partition ID handling, isolated refresh runtime for better query API responsiveness, and security hardening. In addition, the GO SDK, gospice v8 has been released.

What's New in v1.10.1โ€‹

Cayenne Accelerator Improvementsโ€‹

Several improvements and bug fixes for the Cayenne data accelerator:

  • Compression Strategies: The new cayenne_compression_strategy parameter enables choosing between zstd for compact storage or btrblocks for encoding-efficient compression.
  • Improved Vortex Defaults: Aligned Cayenne to Vortex footer configuration for better compatibility.
  • Partition ID Handling: Improved partition ID generation to avoid potential locking race conditions.

Example spicepod.yaml configuration:

datasets:
- from: s3://my-bucket/data.parquet
name: my_dataset
acceleration:
enabled: true
engine: cayenne
mode: file
params:
cayenne_compression_strategy: zstd # or btrblocks (default)

For more details, refer to the Cayenne Data Accelerator Documentation.

Isolated Refresh Runtimeโ€‹

Refresh tasks now run on a separate Tokio runtime isolated from the main query API. This prevents long-running or resource-intensive refresh operations from impacting query latency and ensures the /health endpoint remains responsive during heavy refresh workloads.

Security Hardeningโ€‹

Multiple security improvements have been implemented:

  • Recursion Depth Limits: Added limits to DynamoDB and S3 Vectors integrations to prevent stack overflow from deeply nested structures, mitigating potential DoS attacks.
  • Spicepod Summary API: The GET /v1/spicepods endpoint now returns summarized information instead of full spicepod.yaml representations, preventing potential sensitive information leakage.

Additional Improvements & Bug Fixesโ€‹

  • Performance: Fixed double hashing of user supplied cache keys, improving cache lookup efficiency.
  • Reliability: Fixed idle DynamoDB Stream handling for more stable CDC operations.
  • Reliability: Added warnings when multiple partitions are defined for the same table.
  • Performance: Eagerly drop cached records for results larger than max cache size.

Spice Go SDK v8โ€‹

The Spice Go SDK has been upgraded to v8 with a cleaner API, parameterized queries, and health check methods: gospice v8.0.0.

Key Features:

  • Cleaner API: New Sql() and SqlWithParams() methods with more intuitive naming.
  • Parameterized Queries: Safe, SQL-injection-resistant queries with automatic Go-to-Arrow type inference.
  • Typed Parameters: Explicit type control with constructors like Decimal128Param, TimestampParam, and more.
  • Health Check Methods: New IsSpiceHealthy() and IsSpiceReady() methods for instance monitoring.
  • Upgraded Dependencies: Apache Arrow v18 and ADBC Go driver v1.3.0.

Example usage with a local Spice runtime:

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

// Initialize client for local runtime
spice := gospice.NewSpiceClient()
defer spice.Close()

if err := spice.Init(
gospice.WithFlightAddress("grpc://localhost:50051"),
); err != nil {
panic(err)
}

// Parameterized query (safe from SQL injection)
reader, err := spice.SqlWithParams(
ctx,
"SELECT * FROM users WHERE id = $1 AND created_at > $2",
userId,
startTime,
)

Upgrade:

go get github.com/spiceai/gospice/v8@v8.0.0

For more details, refer to the Go SDK Documentation.

Contributorsโ€‹

Breaking Changesโ€‹

  • GET /v1/spicepods no longer returns the full spicepod.yaml JSON representation. A summary is returned instead. See #8404.

Cookbook Updatesโ€‹

No major cookbook updates.

The Spice Cookbook includes 82+ recipes to help you get started with Spice quickly and easily.

Upgradingโ€‹

To upgrade to v1.10.1, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.10.1 image:

docker pull spiceai/spiceai:1.10.1

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

๐ŸŽ‰ Spice is now available in the AWS Marketplace!

What's Changedโ€‹

Changelogโ€‹

  • Return summarized spicepods from /v1/spicepods by @phillipleblanc in #8404
  • DynamoDB tests and fixes by @lukekim in #8491
  • Use an isolated Tokio runtime for refresh tasks that is separate from the main query API by @phillipleblanc in #8504
  • fix: Avoid double hashing cache key by @peasee in #8511
  • fix: Remove unused Cayenne parameters by @peasee in #8500
  • feat: Support vortex zstd compressor by @peasee in #8515
  • Fix for idle DynamoDB Stream by @krinart in #8506
  • fix: Improve Cayenne errors, ID selection for table/partition creation by @peasee in #8523
  • Update dependencies by @phillipleblanc in #8513
  • Upgrade to gospice v8 by @lukekim in #8524
  • fix: Add recursion depth limits to prevent DoS via deeply nested data (DynamoDB + S3 Vectors) by @phillipleblanc in #8544
  • fix: Add warning when multiple partitions are defined for the same table by @peasee in #8540
  • fix: Eagerly drop cached records for results larger than max by @peasee in #8516
  • DDB Streams Integration Test + Memory Acceleration + Improved Warning by @krinart in #8520
  • fix(cluster): initialize secrets before object stores in executor by @sgrebnov in #8532
  • Show user-friendly error on empty DDB table by @krinart in #8586
  • Move 'test_projection_pushdown' to runtime-datafusion by @Jeadie in #8490
  • Fix stats for rewritten DistributeFileScanOptimizer plans by @mach-kernel in #8581

Spice v1.10.0 (Dec 9, 2025)

ยท 18 min read
William Croxson
Senior Software Engineer at Spice AI

Announcing the release of Spice v1.10.0! โšก

Spice v1.10.0 introduces a new Caching Acceleration Mode with stale-while-revalidate (SWR) semantics for disk-persisted, low-latency queries with background refresh. This release also adds the TinyLFU eviction policy for the SQL results cache, a preview of the DynamoDB Streams connector for real-time CDC, S3 location predicate pruning for faster partitioned queries, improved distributed query execution, and multiple security hardening improvements.

What's New in v1.10.0โ€‹

Caching Acceleration Modeโ€‹

Low-Latency Queries with Background Refresh: This release introduces a new caching acceleration mode that implements the stale-while-revalidate (SWR) pattern. Queries return cached results immediately while data refreshes asynchronously in the background, eliminating query latency spikes during refresh cycles. Cached data persists to disk using DuckDB, SQLite, or Cayenne file modes.

Key Features:

  • Stale-While-Revalidate (SWR): Returns cached data immediately while refreshing in the background, reducing query latency
  • Disk Persistence: Cached results persist across restarts using DuckDB, SQLite, or Cayenne file modes
  • Configurable Refresh: Control refresh intervals with refresh_check_interval to balance freshness and source load

Recommendation: Use retention configuration with caching acceleration to ensure stale data is cleaned up over time.

Example spicepod.yaml configuration:

datasets:
- from: http://localhost:7400
name: cached_data
time_column: fetched_at
acceleration:
enabled: true
engine: duckdb
mode: file # Persist cache to disk
refresh_mode: caching
refresh_check_interval: 10m
retention_check_enabled: true
retention_period: 24h
retention_check_interval: 1h

For more details, refer to the Data Acceleration Documentation.

TinyLFU Cache Eviction Policyโ€‹

Higher Cache Hit Rates for SQL Results Cache: A new TinyLFU cache eviction policy is now available for the SQL results cache. TinyLFU is a probabilistic cache admission policy that maintains higher hit rates than LRU while keeping memory usage predictable, making it ideal for workloads with varying query frequency patterns.

Example spicepod.yaml configuration:

runtime:
caching:
sql_results:
enabled: true
eviction_policy: tiny_lfu # default: lru

For more details, refer to the Caching Documentation and the Moka TinyLFU Documentation for details of the algorithm.

DynamoDB Streams Data Connector (Preview)โ€‹

Real-Time Change Data Capture for DynamoDB: The DynamoDB connector now integrates with DynamoDB Streams for real-time change data capture (CDC). This enables continuous synchronization of DynamoDB table changes into Spice for real-time query, search, and LLM-inference.

Key Features:

  • Real-Time CDC: Automatically captures inserts, updates, and deletes from DynamoDB tables as they occur
  • Table Bootstrapping: Performs an initial full table scan before streaming changes, ensuring complete data consistency
  • Acceleration Integration: Works with refresh_mode: changes to incrementally update accelerated datasets

Note: DynamoDB Streams must be enabled on your DynamoDB table. This feature is in preview.

Example spicepod.yaml configuration:

datasets:
- from: dynamodb:my_table
name: orders_stream
acceleration:
enabled: true
refresh_mode: changes # Enable Streams capture

For more details, refer to the DynamoDB Connector Documentation.

OpenTelemetry Metrics Exporterโ€‹

Spice can now push metrics to an OpenTelemetry collector, enabling integration with platforms such as Jaeger, New Relic, Honeycomb, and other OpenTelemetry-compatible backends.

Key Features:

  • Protocol Support: Supports the gRPC (default port 4317) protocol
  • Configurable Push Interval: Control how frequently metrics are pushed to the collector

Example spicepod.yaml configuration for gRPC:

runtime:
telemetry:
enabled: true
otel_exporter:
endpoint: 'localhost:4317'
push_interval: '30s'

For more details, refer to the Observability & Monitoring Documentation.

S3 Connector Improvementsโ€‹

S3 Location Predicate Pruning: The S3 data connector now supports location-based predicate pruning, dramatically reducing data scanned by pushing down location filter predicates to S3 listing operations. For partitioned datasets (e.g., year=2025/month=12/), Spice now skips listing irrelevant partitions entirely, significantly reducing query latency and S3 API costs.

AWS S3 Tables Write Support: Full read/write capability for AWS S3 Tables, enabling direct integration with AWS's managed table format for S3. Use standard SQL INSERT INTO to write data.

For more details, refer to the S3 Data Connector Documentation and Glue Data Connector Documentation.

Faster Distributed Query Executionโ€‹

Distributed query planning and execution have been significantly improved:

  • Fixed executor registration in cluster mode for more reliable distributed deployments
  • Improved hostname resolution for Flight server binding, enabling better executor discovery
  • Distributed accelerator registration: Data accelerators now properly register in distributed mode
  • Optimized query planning: DistributeFileScanOptimizer improvements for faster planning with large datasets

For more details, refer to the Distributed Query Documentation.

Search Improvementsโ€‹

Search capabilities have been improved with several performance and reliability enhancements:

  • Fixed FTS query blocking: Full-text search queries no longer block unnecessarily, improving query responsiveness
  • Optimized vector index operations: Eliminated unnecessary list_vectors calls for better performance
  • Improved limit pushdown: IndexerExec now properly handles limit pushdown for more efficient searches

For more details, refer to the Search Documentation.

Security Hardeningโ€‹

Multiple security improvements have been implemented:

  • SQL Identifier Quoting: Hardened SQL identifier quoting across all database connectors (PostgreSQL, MySQL, DuckDB, etc.) to prevent SQL injection attacks through table or column names
  • Token Redaction: Sensitive authentication tokens are now fully redacted in debug and error output, preventing accidental credential exposure in logs
  • Path Traversal Prevention: Fixed tar extraction operations to prevent directory traversal vulnerabilities when processing archived files
  • Input Sanitization: Added strict validation for top_n_sample order_by clause parsing to prevent injection attacks
  • Glue Credential Handling: Prevented automatic loading of AWS credentials from environment in Glue connector, ensuring explicit credential configuration

Developer Experience Improvementsโ€‹

  • Health probe metrics: Added health probe latency metrics for better observability
  • CLI improvements: Fixed .clear history command in the REPL to fully clear persisted history

Contributorsโ€‹

Breaking Changesโ€‹

No breaking changes.

Cookbook Updatesโ€‹

No major cookbook updates.

The Spice Cookbook includes 82 recipes to help you get started with Spice quickly and easily.

Upgradingโ€‹

To upgrade to v1.10.0, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.10.0 image:

docker pull spiceai/spiceai:1.10.0

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

๐ŸŽ‰ Spice is now available in the AWS Marketplace!

What's Changedโ€‹

Changelogโ€‹

Spice v1.10.0-rc.1 (Dec 2, 2025)

ยท 11 min read
David Stancu
Principal Software Engineer at Spice AI

Announcing the release of Spice v1.10.0-rc.1! โšก

v1.10.0-rc1 is a release candidate for early testing of v1.10 features including an all new caching acceleration mode, tiny_lfu caching policy, a new DynamoDB Streams connector (Preview), improvements to the DynamoDB connector, faster distributed query execution, S3 connector improvements, and security hardening for v1.10.0-stable.

What's New in v1.10.0-rc1โ€‹

Caching Acceleration Mode with SWR and TinyLFUโ€‹

This release introduces a new caching acceleration mode that implements the stale-while-revalidate (SWR) pattern using Data Accelerators such as DuckDB or Cayenne, enabling queries to return file-persisted cached results immediately while asynchronously refreshing data in the background. Combined with the new TinyLFU cache eviction policy, Spice can now maintain higher cache hit rates while keeping memory usage predictable.

Key Features:

  • Stale-While-Revalidate (SWR): Returns cached data immediately while refreshing in the background
  • Data Accelerator Support: Cached accelerators can persist data to disk using DuckDB, SQLite, or Cayenne file modes.
  • TinyLFU Cache Policy: Probabilistic cache admission policy that maintains high hit rates with minimal overhead
  • Predictable Memory Usage: Configurable memory limits with automatic eviction of less frequently used entries

Example Spicepod.yml configuration:

runtime:
caching:
sql_results:
enabled: true
eviction_policy: tiny_lfu # default lru

datasets:
- from: s3://my-bucket/data.parquet
name: cached_data
acceleration:
enabled: true
engine: duckdb
mode: file # Persist cache to disk
refresh_mode: caching
refresh_check_interval: 10m

For more details, refer to the Data Acceleration Documentation and Caching Documentation.

DynamoDB Streams Data Connector in Previewโ€‹

DynamoDB Connector now integrates with DynamoDB Streams which enables real-time streaming with support for both table bootstrapping and continuous change data capture (CDC). This connector automatically detects changes in DynamoDB tables and streams them into Spice for real-time query, search, and LLM-inference.

Key Features:

  • Real-Time CDC: Automatically captures inserts, updates, and deletes from DynamoDB tables
  • Table Bootstrapping: Initial full table load before streaming changes

Example Spicepod.yml configuration:

datasets:
- from: dynamodb:my_table
name: orders_stream
acceleration:
enabled: true
refresh_mode: changes

For more details, refer to the DynamoDB Connector Documentation.

Cayenne Accelerator Enhancementsโ€‹

The Cayenne data accelerator now supports:

  • Sort Columns Configuration: Optimize inserts by pre-sorting data on specified columns for improved query performance

Example Spicepod.yml configuration:

datasets:
- from: s3://my-bucket/data.parquet
name: sorted_data
acceleration:
enabled: true
engine: cayenne
mode: file_create
params:
sort_columns: timestamp,region

For more details, refer to the Cayenne Documentation.

S3 Connector Improvementsโ€‹

S3 Location Predicate Pruning: The S3 data connector now supports location-based predicate pruning, dramatically reducing data scanned by pushing down predicates to S3 listing operations. This optimization is especially effective for partitioned datasets stored in S3.

AWS S3 Tables Write Support: Full read/write capability for AWS S3 Tables, enabling fast integration with AWS's table format for S3.

For more details, refer to the S3 Tables Data Connector Documentation and Glue Data Connection Documentation.

Faster Distributed Query Executionโ€‹

Distributed query planning and execution have been significantly improved:

  • Fixed executor registration in cluster mode for more reliable distributed deployments
  • Improved hostname resolution for Flight server binding, enabling better executor discovery
  • Distributed accelerator registration: Data accelerators now properly register in distributed mode
  • Optimized query planning: DistributeFileScanOptimizer improvements for faster planning with large datasets

For more details, refer to the Distributed Query Documentation.

Search Improvementsโ€‹

Search capabilities have been improved with several performance and reliability enhancements:

  • Fixed FTS query blocking: Full-text search queries no longer block unnecessarily, improving query responsiveness
  • Optimized vector index operations: Eliminated unnecessary list_vectors calls for better performance
  • Improved limit pushdown: IndexerExec now properly handles limit pushdown for more efficient searches

For more details, refer to the Search Documentation.

Security Hardeningโ€‹

Multiple security improvements have been implemented:

  • SQL identifier quoting: Hardened SQL identifier quoting across all connectors to prevent injection attacks
  • Token redaction: Sensitive tokens are now fully redacted in debug output to prevent credential leakage
  • Path traversal prevention: Fixed tar extraction to prevent path traversal vulnerabilities
  • Input sanitization: Added validation for top_n_sample order_by parsing
  • Improved credential handling: Improved credential management in Glue connector

Developer Experience Improvementsโ€‹

  • Health probe metrics: Added health probe latency metrics for better observability
  • CLI improvements: Fixed .clear history command in the REPL to fully clear persisted history

Contributorsโ€‹

Breaking Changesโ€‹

No breaking changes.

Cookbook Updatesโ€‹

No major cookbook updates. The Spice Cookbook still offers 82+ recipes to help you prototype quickly.

Upgradingโ€‹

To try v1.10.0-rc1, use one of the following methods:

CLI:

spice upgrade --version 1.10.0-rc1

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.10.0-rc1 image:

docker pull spiceai/spiceai:1.10.0-rc1

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 1.10.0-rc1

AWS Marketplace:

๐ŸŽ‰ Spice is available in the AWS Marketplace.

What's Changedโ€‹

Changelogโ€‹