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Phillip LeBlanc

Phillip LeBlanc

Co-Founder and CTO of Spice AI
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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 v1.3.2 (June 2, 2025)

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

Announcing the release of Spice v1.3.2! ❄️

Spice v1.3.2 is a patch release with fixes to the DuckDB data accelerator and Snowflake data connector.

Changes:

  • DuckDB Data Accelerator: Supports ORDER BY rand() for randomized result ordering and ORDER BY NULL for SQL compatibility.

  • Snowflake Data Connector: Adds TIMESTAMP_NTZ(0) type for timestamps with seconds precision.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

No new cookbook recipes.

The Spice Cookbook now includes 67 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.3.2, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.3.2 image:

docker pull spiceai/spiceai:1.3.2

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

What's Changed​

Dependencies​

No major dependency changes.

Changelog​

  • Handle Snowflake Timestamp NTZ with seconds precision (#6084) by @kczimm in #6084
  • Fix DuckDB acceleration ORDER BY rand() and ORDER BY NULL (#6071) by @phillipleblanc in #6071

Full Changelog: https://github.com/spiceai/spiceai/compare/v1.3.1...v1.3.2

Spice v1.3.0 (May 19, 2025)

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

Announcing the release of Spice v1.3.0! 🏎️

Spice v1.3.0 accelerates data and AI applications with significantly improved query performance, reliability, and expanded Databricks integration. New support for the Databricks SQL Statement Execution API enables direct SQL queries on Databricks SQL Warehouses, complementing Mosaic AI model serving and embeddings (introduced in v1.2.2) and existing Databricks catalog and dataset integrations. This release upgrades to DataFusion v46, optimizes results caching performance, and strengthens security with least-privilege sandboxed improvements.

What's New in v1.3.0​

  • Databricks SQL Statement Execution API Support: Added support for the Databricks SQL Statement Execution API, enabling direct SQL queries against Databricks SQL Warehouses for optimized performance in analytics and reporting workflows.

    Example spicepod.yml configuration:

    datasets:
      - from: databricks:spiceai.datasets.my_awesome_table
        name: my_awesome_table
        params:
          mode: sql_warehouse
          databricks_endpoint: ${env:DATABRICKS_ENDPOINT}
          databricks_sql_warehouse_id: ${env:DATABRICKS_SQL_WAREHOUSE_ID}
          databricks_token: ${env:DATABRICKS_TOKEN}

    For details, see the Databricks Data Connector documentation.

  • Improved Results Cache Performance & Hashing Algorithm: Spice now supports an alternative results cache hashing algorithm, ahash, in addition to siphash, being the default. Configure it via:

    runtime:
      results_cache:
        hashing_algorithm: ahash # or siphash

    The hashing algorithm determines how cache keys are hashed before being stored, impacting both lookup speed and protection against potential DOS attacks.

    Using ahash improves performance for large queries or query plans. Combined with results cache optimizations, it reduces 99th percentile request latency and increases total requests/second for queries with large result sets (100k+ cached rows). The following charts show performance tested against the TPCH Query #17 on a scale factor 5 dataset (30+ million rows, 5GB):

    LatencyReq/sec
    Improvements for the 99th percentile query latency, compared against 1.2.2 with cache key type and hashing algorithm.Improvements for the requests/second, compared against 1.2.2 with cache key type and hashing algorithm.

    Note: ahash was not available in v1.2.2, so it is excluded from comparisons.

    To learn more, refer to the Results Cache Hashing Algorithm documentation.

  • SQL Query Performance: Optimized the critical SQL query path, reducing overhead and improving response times for simple queries by 10-20%.

  • DuckDB Acceleration: Fixed a bug in the DuckDB acceleration engine causing query failures under high concurrency when querying datasets accelerated into multiple DuckDB files.

  • Container Security: The container image now runs as a non-root user with enhanced sandboxing and includes only essential dependencies for a slimmer, more secure image.

DataFusion v46 Highlights​

Spice.ai is built on the DataFusion query engine. The v46 release brings:

  • Faster Performance 🚀: DataFusion 46 introduces significant performance enhancements, including a 2x faster median() function for large datasets without grouping, 10–100% speed improvements in FIRST_VALUE and LAST_VALUE window functions by avoiding sorting, and a 40x faster uuid() function. Additional optimizations, such as a 50% faster repeat() string function, accelerated chr() and to_hex() functions, improved grouping algorithms, and Parquet row group pruning with NOT LIKE filters, further boost overall query efficiency.

  • New range() Table Function: A new table-valued function range(start, stop, step) has been added to make it easy to generate integer sequences — similar to PostgreSQL’s generate_series() or Spark’s range(). Example: SELECT * FROM range(1, 10, 2);

  • UNION [ALL | DISTINCT] BY NAME Support: DataFusion now supports UNION BY NAME and UNION ALL BY NAME, which align columns by name instead of position. This matches functionality found in systems like Spark and DuckDB and simplifies combining heterogeneously ordered result sets.

    Example:

    SELECT col1, col2 FROM t1
    UNION ALL BY NAME
    SELECT col2, col1 FROM t2;

See the DataFusion 46.0.0 release notes for details.

Spice.ai adopts the latest minus one DataFusion release for quality assurance and stability. The upgrade to DataFusion v47 is planned for Spice v1.4.0 in June.

Contributors​

Breaking Changes​

The container image now always runs as a non-root user (UID/GID 65534) with minimal dependencies, resulting in a smaller, more secure image. Standard Linux tools, including bash, are no longer included.

Kubernetes Deployments:

  • Use of the v1.3.0+ Helm chart is required, which includes a securityContext ensuring the sandbox user has required file access.

  • For deployments using a lower version than the v1.3.0 Helm chart, add the following securityContext to the pod specification:

securityContext:
  runAsUser: 65534
  runAsGroup: 65534
  fsGroup: 65534

See the Docker Sandbox Guide for details on how to update custom Docker images to restore the previous behavior.

Cookbook Updates​

  • Added Accelerated Views: Pre-calculate and materialize data derived from one or more underlying datasets.

The Spice Cookbook now includes 67 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.3.0, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.3.0 image:

docker pull spiceai/spiceai:1.3.0

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

What's Changed​

Dependencies​

Changelog​

See the full list of changes at: v1.2.2...v1.3.0

Spice v1.1.2 (Apr 14, 2025)

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

Announcing the release of Spice v1.1.2! 🔗

Spice v1.1.2 improves Delta Lake Data Connector performance, introduces new Accept headers for the /v1/sql and /v1/nsql endpoints to include query metadata with results, and resolves an issue with the Snowflake Data Connector when handling wide tables (>600 columns).

The official Tableau Connector for Spice.ai v0.1 has been released, making it easy to connect to both self-hosted Spice.ai and Spice Cloud instances using Tableau.

What's New in v1.1.2​

Query Spice.ai using Tableau

  • Delta Lake Data Connector: Upgraded delta_kernel to v0.9, and optimized scan operations, reducing query execution time by up to 20% on large datasets.

  • Snowflake Data Connector: Fixed a bug that caused failures when loading tables with more than 600 columns.

  • Query Metadata (SQL and NSQL): Added support for the application/vnd.spiceai.sql.v1+json Accept header on the /v1/sql endpoint, and the application/vnd.spiceai.nsql.v1+json Accept header on the /v1/nsql endpoint, enabling responses to include metadata such as the executed SQL query and schema alongside results.

Example:

curl -XPOST "http://localhost:8090/v1/nsql" \
  -H "Content-Type: application/json" \
  -H "Accept: application/vnd.spiceai.nsql.v1+json" \
  -d '{
    "query": "What’s the highest tip any passenger gave?"
  }' | jq

Example response:

{
  "row_count": 1,
  "schema": {
    "fields": [
      {
        "name": "highest_tip",
        "data_type": "Float64",
        "nullable": true,
        "dict_id": 0,
        "dict_is_ordered": false,
        "metadata": {}
      }
    ],
    "metadata": {}
  },
  "data": [
    {
      "highest_tip": 428.0
    }
  ],
  "sql": "SELECT MAX(\"tip_amount\") AS \"highest_tip\"\nFROM \"spice\".\"public\".\"taxi_trips\""
}

For details, see the SQL Query API and NSQL API documentation.

Contributors​

Breaking Changes​

No breaking changes in this release.

Cookbook Updates​

No major cookbook additions.

The Spice Cookbook now includes 65 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.1.2, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.1.2 image:

docker pull spiceai/spiceai:1.1.2

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

What's Changed​

Dependencies​

Changelog​

  • Backport - Fix schema inference for Snowflake tables with large number of columns #5348 by @ewgenius in #5350
  • Upgrade delta_kernel to 0.9 (#5343) by @phillipleblanc in #5356
  • Add basic support for application/vnd.spiceai.sql.v1+json format (#5333) by @sgrebnov in #5333
  • Convert DataFusion filters to Delta Kernel predicates by @phillipleblanc in #5362
  • revert to previous pdf-extract; remove test for encrypted pdf support by @kczimm in #5355
  • Turn off delta_kernel::log_segment logging and refactor log filtering by @phillipleblanc in #5367
  • Extend application/vnd.spiceai.sql.v1+json with schema and row_count fields by @sgrebnov in #5365
  • Make separate vnd.spiceai.sql.v1+json and vnd.spiceai.nsql.v1+json MIME types by @sgrebnov in #5382

Full Changelog: v1.1.1...v1.1.2

Spice v1.1.1 (Apr 7, 2025)

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

Announcing the release of Spice v1.1.1! 📊

Spice v1.1.1 introduces several key updates, including a new Component Metrics System, improved Delta Data Connector performance, improved MCP tool descriptions, and expanded runtime results caching options. This release also adds detailed MySQL connection pool metrics for better observability. Component Metrics are Prometheus-compatible and accessible via the metrics endpoint.

Highlights v1.1.1​

  • Component Metrics System: A new system for monitoring components, starting with MySQL connection pool metrics. These metrics provide insights into MySQL connection performance and can be selectively enabled in the dataset configuration. Metrics are exposed in Prometheus format via the metrics endpoint.

For more details, see the Component Metrics documentation.

  • Results Caching Enhancements: Added a cache_key_type option for runtime results caching. Options include:
    • plan (Default): Uses the query's logical plan as the cache key. Matches semantically equivalent queries but requires query parsing.
    • sql: Uses the raw SQL string as the cache key. Provides faster lookups but requires exact string matches. Use sql for predictable queries without dynamic functions like NOW().

Example spicepod.yaml configuration:

runtime:
  results_cache:
    enabled: true
    cache_max_size: 128MiB
    cache_key_type: sql # Use SQL for the results cache key
    item_ttl: 1s

For more details, see the runtime configuration documentation.

  • Delta Data Connector: Improved scan performance for faster query performance.

  • MCP Tools: Improved descriptions for built-in MCP tools to improve usability.

  • MySQL Component Metrics: Added detailed metrics for monitoring MySQL connections, such as connection count and pool activity.

Example spicepod.yaml configuration:

datasets:
  - from: mysql:my_table
    name: my_dataset
    metrics:
      - name: connection_count
        enabled: true
      - name: connections_in_pool
        enabled: true
      - name: active_wait_requests
        enabled: true
    params:
      mysql_host: localhost
      mysql_tcp_port: 3306
      mysql_user: root
      mysql_pass: ${secrets:MYSQL_PASS}

For more details, see the MySQL Data Connector documentation.

  • spice.js SDK: The spice.js SDK has been updated to v2.0.1 and includes several important security updates.

New Contributors 🎉​

Contributors​

Breaking Changes​

No breaking changes in this release.

Cookbook Updates​

The Spice Cookbook now includes 65 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.1.1, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.1.1 image:

docker pull spiceai/spiceai:1.1.1

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

What's Changed​

Dependencies​

  • No major dependency changes.

Changelog​

- fix: Testoperator DuckDB, SQLite, Postgres, Spicecloud by [@peasee](https://github.com/peasee) in [#5190](https://github.com/spiceai/spiceai/pull/5190)
- Update Helm Chart and SECURITY.md to v1.1.0 by [@lukekim](https://github.com/lukekim) in [#5223](https://github.com/spiceai/spiceai/pull/5223)
- Update version.txt to v1.1.1-unstable by [@lukekim](https://github.com/lukekim) in [#5224](https://github.com/spiceai/spiceai/pull/5224)
- Update Cargo.lock to v1.1.1-unstable by [@lukekim](https://github.com/lukekim) in [#5225](https://github.com/spiceai/spiceai/pull/5225)
- Add tests for `verify_schema_source_path` in `ListingTableConnector` by [@phillipleblanc](https://github.com/phillipleblanc) in [#5221](https://github.com/spiceai/spiceai/pull/5221)
- Reduce noise from debug logging by [@phillipleblanc](https://github.com/phillipleblanc) in [#5227](https://github.com/spiceai/spiceai/pull/5227)
- Improve `openai_test_chat_messages` integration test reliability by [@Sevenannn](https://github.com/Sevenannn) in [#5222](https://github.com/spiceai/spiceai/pull/5222)
- Verify the checkpoints existence before shutting down runtime in integration tests directly querying checkpoint by [@Sevenannn](https://github.com/Sevenannn) in [#5232](https://github.com/spiceai/spiceai/pull/5232)
- Fix CORS support for json content-type api by [@sgrebnov](https://github.com/sgrebnov) in [#5241](https://github.com/spiceai/spiceai/pull/5241)
- Fix ModelGradedScorer error: The 'metadata' parameter is only allowed when 'store' is enabled. by [@sgrebnov](https://github.com/sgrebnov) in [#5231](https://github.com/spiceai/spiceai/pull/5231)
- fix: Use `pulls-with-spice-action` and switch to `spiceai-macos` runners by [@peasee](https://github.com/peasee) in [#5238](https://github.com/spiceai/spiceai/pull/5238)
- Use v1.0.3 pulls with spice action by [@lukekim](https://github.com/lukekim) in [#5244](https://github.com/spiceai/spiceai/pull/5244)
- feat: Build ODBC binaries, run testoperator on ODBC by [@peasee](https://github.com/peasee) in [#5237](https://github.com/spiceai/spiceai/pull/5237)
- Bump timeout for several integration test runtime load_components & readiness check by [@Sevenannn](https://github.com/Sevenannn) in [#5229](https://github.com/spiceai/spiceai/pull/5229)
- Validate port is available before binding port for docker container in integration tests by [@Sevenannn](https://github.com/Sevenannn) in [#5248](https://github.com/spiceai/spiceai/pull/5248)
- Update datafusion-table-providers to fix the schema for PostgreSQL materialized views by [@ewgenius](https://github.com/ewgenius) in [#5259](https://github.com/spiceai/spiceai/pull/5259)
- Verify flight server is ready for flight integration tests by [@Sevenannn](https://github.com/Sevenannn) in [#5240](https://github.com/spiceai/spiceai/pull/5240)
- fix: Publish to MinIO inside of matrix on build_and_release by [@peasee](https://github.com/peasee) in [#5258](https://github.com/spiceai/spiceai/pull/5258)
- fix: TPCDS on zero results benchmarks by [@peasee](https://github.com/peasee) in [#5263](https://github.com/spiceai/spiceai/pull/5263)
- Use model as a judge scorer for Financebench by [@sgrebnov](https://github.com/sgrebnov) in [#5264](https://github.com/spiceai/spiceai/pull/5264)
- Fix FinanceBench llm scorer secret name by [@sgrebnov](https://github.com/sgrebnov) in [#5276](https://github.com/spiceai/spiceai/pull/5276)
- Implements support for `runtime.results_cache.cache_key_type` by [@phillipleblanc](https://github.com/phillipleblanc) in [#5265](https://github.com/spiceai/spiceai/pull/5265)
- fix: Testoperator MS SQL, query overrides, dispatcher by [@peasee](https://github.com/peasee) in [#5279](https://github.com/spiceai/spiceai/pull/5279)
- refactor: Delete old benchmarks by [@peasee](https://github.com/peasee) in [#5283](https://github.com/spiceai/spiceai/pull/5283)
- Imporve embedding column parsing performance test by [@Sevenannn](https://github.com/Sevenannn) in [#5268](https://github.com/spiceai/spiceai/pull/5268)
- Add Support for AWS Session Token in S3 Data Connector by [@kczimm](https://github.com/kczimm) in [#5243](https://github.com/spiceai/spiceai/pull/5243)
- Implement Component Metrics system + MySQL connection pool metrics by [@phillipleblanc](https://github.com/phillipleblanc) in [#5290](https://github.com/spiceai/spiceai/pull/5290)
- Add default descriptions to built-in MCP tools by [@lukekim](https://github.com/lukekim) in [#5293](https://github.com/spiceai/spiceai/pull/5293)
- fix: Vector search with cased columns by [@peasee](https://github.com/peasee) in [#5295](https://github.com/spiceai/spiceai/pull/5295)
- Run delta kernel scan in a blocking Tokio thread. by [@phillipleblanc](https://github.com/phillipleblanc) in [#5296](https://github.com/spiceai/spiceai/pull/5296)
- Expose the `mysql_pool_min` and `mysql_pool_max` connection pool parameters by [@phillipleblanc](https://github.com/phillipleblanc) in [#5297](https://github.com/spiceai/spiceai/pull/5297)
- use patched pdf-extract by [@kczimm](https://github.com/kczimm) in [#5270](https://github.com/spiceai/spiceai/pull/5270)

Full Changelog: v1.1.0...v1.1.1