4 posts tagged with "graphql"

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 v0.15.1-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.

The v0.14.1-alpha release is focused on quality, stability, and type support with improvements in PostgreSQL, DuckDB, and GraphQL data connectors.

Highlights​

• PostgreSQL acceleration and data connector: Support for Composite Types and UUID data types.
• DuckDB acceleration and data connector: Support for LargeUTF8 and DuckDB functions.
• GraphQL data connector: Improved error handling on invalid query syntax.
• Refresh SQL: Improved stability when overwriting STRUCT data types.

The v0.14-alpha release focuses on enhancing accelerated dataset performance and data integrity, with support for configuring primary keys and indexes. Additionally, the GraphQL data connector been introduced, along with improved dataset registration and loading error information.

Highlights​

• Accelerated Datasets: Ensure data integrity using primary key and unique index constraints. Configure conflict handling to either upsert new data or drop it. Create indexes on frequently filtered columns for faster queries on larger datasets.

• GraphQL Data Connector: Initial support for using GraphQL as a data source.

Example Spicepod showing how to use primary keys and indexes with accelerated datasets:

datasets:
  - from: eth.blocks
    name: blocks
    acceleration:
      engine: duckdb # Use DuckDB acceleration engine
      primary_key: '(hash, timestamp)'
      indexes:
        number: enabled # same as `CREATE INDEX ON blocks (number);`
        '(number, hash)': unique # same as `CREATE UNIQUE INDEX ON blocks (number, hash);`
      on_conflict:
        '(hash, number)': drop # possible values: drop (default), upsert
        '(hash, timestamp)': upsert

Primary Keys, constraints, and indexes are currently supported when using SQLite, DuckDB, and PostgreSQL acceleration engines.

Learn more with the indexing quickstart and the primary key sample.

Read the Local Acceleration documentation.