5 posts tagged with "mysql"

🎉 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.

Announcing the release of Spice v0.18.1-beta. 🏎️

The v0.18.1-beta release continues to improve runtime performance and reliability. Performance for accelerated queries joining multiple datasets has been significantly improved with join push-down support. GraphQL, MySQL, and SharePoint data connectors have better reliability and error handling, and a new Microsoft SQL Server data connector has been introduced. Task History now has fine-grained configuration, including the ability to disable the feature entirely. A new spice search CLI command has been added, enabling development-time embeddings-based searches across datasets.

Highlights in v0.18.1-beta​

Join push-down for accelerations: Queries to the same accelerator will now push-down joins, significantly improving acceleration performance for queries joining multiple tables.

Microsoft SQL Server Data Connector: Use from: mssql: to access and accelerate Microsoft SQL Server datasets.

Example spicepod.yml:

datasets:
  - from: mssql:path.to.my_dataset
    name: my_dataset
    params:
      mssql_connection_string: ${secrets:mssql_connection_string}

See the Microsoft SQL Server Data Connector documentation.

Task History: Task History can be configured in the spicepod.yml, including the ability to include, or truncate outputs such as the results of a SQL query.

Example spicepod.yml:

runtime:
  task_history:
    enabled: true
    captured_output: truncated
    retention_period: 8h
    retention_check_interval: 15m

See the Task History Spicepod reference for more information on possible values and behaviors.

Search CLI Command Use the spice search CLI command to perform embeddings-based searches across search configure datasets. Note: Search requires the ai feature to be installed.

Refresh on File Changes: File Data Connector data refreshes can be configured to be triggered when the source file is modified through a file system watcher. Enable the watcher by adding file_watcher: enabled to the acceleration parameters.

Example spicepod.yml:

datasets:
  - from: file://path/to/my_file.csv
    name: my_file
    acceleration:
      enabled: true
      refresh_mode: full
      params:
        file_watcher: enabled

The v0.15.2-alpha minor release focuses on enhancing stability, performance, and introduces Catalog Providers for streamlined access to Data Catalog tables. Unity Catalog, Databricks Unity Catalog, and the Spice.ai Cloud Platform Catalog are supported in v0.15.2-alpha. The reliability of federated query push-down has also been improved for the MySQL, PostgreSQL, ODBC, S3, Databricks, and Spice.ai Cloud Platform data connectors.

Highlights in v0.15.2-alpha​

Catalog Providers: Catalog Providers streamline access to Data Catalog tables. Initial catalog providers supported are Databricks Unity Catalog, Unity Catalog and Spice.ai Cloud Platform Catalog.

For example, to configure Spice to connect to tpch tables in the Spice.ai Cloud Platform Catalog use the new catalogs: section in the spicepod.yml:

catalogs:
  - name: spiceai
    from: spiceai
    include:
      - tpch.*

sql> show tables
+---------------+--------------+---------------+------------+
| table_catalog | table_schema | table_name    | table_type |
+---------------+--------------+---------------+------------+
| spiceai       | tpch         | region        | BASE TABLE |
| spiceai       | tpch         | part          | BASE TABLE |
| spiceai       | tpch         | customer      | BASE TABLE |
| spiceai       | tpch         | lineitem      | BASE TABLE |
| spiceai       | tpch         | partsupp      | BASE TABLE |
| spiceai       | tpch         | supplier      | BASE TABLE |
| spiceai       | tpch         | nation        | BASE TABLE |
| spiceai       | tpch         | orders        | BASE TABLE |
| spice         | runtime      | query_history | BASE TABLE |
+---------------+--------------+---------------+------------+

Time: 0.001866958 seconds. 9 rows.

ODBC Data Connector Push-Down: The ODBC Data Connector now supports query push-down for joins, improving performance for joined datasets configured with the same odbc_connection_string.

Improved Spicepod Validation Improved spicepod.yml validation has been added, including warnings when loading resources with duplicate names (datasets, views, models, embeddings).

The Spice v0.11-alpha release significantly improves the Databricks data connector with Databricks Connect (Spark Connect) support, adds the DuckDB data connector, and adds the AWS Secrets Manager secret store. In addition, enhanced control over accelerated dataset refreshes, improved SSL security for MySQL and PostgreSQL connections, and overall stability improvements have been added.

Highlights in v0.11-alpha​

DuckDB data connector: Use DuckDB databases or connections as a data source.

AWS Secrets Manager Secret Store: Use AWS Secrets Managers as a secret store.

Custom Refresh SQL: Specify a custom SQL query for dataset refresh using refresh_sql.

Dataset Refresh API: Trigger a dataset refresh using the new CLI command spice refresh or via API.

Expanded SSL support for Postgres: SSL mode now supports disable, require, prefer, verify-ca, verify-full options with the default mode changed to require. Added pg_sslrootcert parameter for setting a custom root certificate and the pg_insecure parameter is no longer supported.

Databricks Connect: Choose between using Spark Connect or Delta Lake when using the Databricks data connector for improved performance.

Improved SSL support for Postgres: ssl mode now supports disable, require, prefer, verify-ca, verify-full options with default mode changed to require. Added pg_sslrootcert parameter to allow setting custom root cert for postgres connector, pg_insecure parameter is no longer supported as redundant.

Internal architecture refactor: The internal architecture of spiced was refactored to simplify the creation data components and to improve alignment with DataFusion concepts.

Announcing the release of Spice v0.10.2-alpha (Apr 9, 2024)! 🔥

The v0.10.2-alpha release adds the MySQL data connector and makes external data connections more robust on initialization.

Highlights in v0.10.2-alpha​

• MySQL data connector: Connect to any MySQL server, including SSL support.

• Data connections verified at initialization: Verify endpoints and authorization for external data connections (e.g. databricks, spice.ai) at initialization.