2 posts tagged with "delta-lake"

🎉 After 47 releases, Spice.ai OSS has reached production readiness with the 1.0-stable milestone!

The core runtime and features such as query federation, query acceleration, catalog integration, search and AI-inference have all graduated to stable status along with key component graduations across data connectors, data accelerators, catalog connectors, and AI model providers.

Highlights in v1.0-stable​

• Stable Data Connectors: The following data connectors have graduated to Stable:

  • Delta Lake
  • MySQL
  • Dremio
  • PostgreSQL
  • Databricks (mode: delta_lake)
  • DuckDB
  • S3

• Stable Data Accelerators: The following data accelerators have graduated to Stable:

  • DuckDB
  • Arrow

• Unity Catalog Connector: Graduated to Stable.

• Databricks (mode: spark_connect) Data Connector: Graduated to Beta.

• Beta Catalog Connectors: The Iceberg and Databricks catalog connectors graduated to Beta.

• OpenAI Model & Embeddings Provider: Graduated to Release Candidate (RC).

• Alpha Model Providers: The Anthropic and xAI (Grok) model providers graduated to Alpha.

Breaking Changes​

• Default Runtime Version: The CLI will install the GPU accelerated AI-capable Runtime by default (if supported), when running spice install or spice run. To force-install the non-GPU version, run spice install ai --cpu.

• Default OpenAI Model: The default OpenAI model has updated to gpt-4o-mini.

• Identifier Normalization: Unquoted identifiers such as table names are no longer normalized to lowercase. Identifiers will now retain their exact case as provided.

• Sandboxed Docker Image: The Runtime Docker Image now runs the spiced process as the nobody user in a minimal chroot sandbox.

• Insecure S3 and ABFS endpoints: The S3 and ABFS connectors now enforce insecure endpoint checks, preventing HTTP endpoints unless allow_http is explicitly enabled. Refer to the documentation for details.

The v0.15.1-alpha minor release focuses on enhancing stability, performance, and usability. Memory usage has been significantly improved for the postgres and duckdb acceleration engines which now use stream processing. A new Delta Lake Data Connector has been added, sharing a delta-kernel-rs based implementation with the Databricks Data Connector supporting deletion vectors.

Highlights​

Improved memory usage for PostgreSQL and DuckDB acceleration engines: Large dataset acceleration with PostgreSQL and DuckDB engines has reduced memory consumption by streaming data directly to the accelerated table as it is read from the source.

Delta Lake Data Connector: A new Delta Lake Data Connector has been added for using Delta Lake outside of Databricks.

ODBC Data Connector Streaming: The ODBC Data Connector now streams results, reducing memory usage, and improving performance.

GraphQL Object Unnesting: The GraphQL Data Connector can automatically unnest objects from GraphQL queries using the unnest_depth parameter.