Skip to main content

Data Accelerators

Data sourced by Data Connectors can be locally materialized and accelerated using a Data Accelerator.

Acceleration is enabled on a dataset by setting the acceleration configuration. E.g.

datasets:
  - name: accelerated_dataset
    acceleration:
      enabled: true

For the complete reference specification see datasets.

By default, datasets will be locally materialized using in-memory Arrow records.

Data Accelerators using DuckDB, SQLite, or PostgreSQL engines can be used to materialize data in files or attached databases.

Currently supported Data Accelerators include:

Engine NameDescriptionStatusEngine Modes
arrowIn-Memory Arrow RecordsAlphamemory
duckdbEmbedded DuckDBAlphamemory, file
sqliteEmbedded SQLiteAlphamemory, file
postgresAttached PostgreSQLAlpha

Data types​

Data accelerators may not support all possible Apache Arrow data types. For complete compatibility, see specifications.

Filtered Refresh​

Often only a subset of the data in a federated table is used in applications or dashboards. Use the following options to filter the data Spice will accelerate to a working subset and reduce the amount of data that needs to be transferred and stored locally.

  • Refresh SQL - Specify the filter as arbitrary SQL to be pushed down to the remote source.
  • Refresh Data Window - Filters out data from the federated source outside the specified window.

Refresh SQL​

Specify filters for the data accelerated from the federated source via arbitrary SQL. Only supported for datasets configured with a full refresh mode (the default).

Filters will be pushed down to the remote source, and only the requested data will be transferred over the network.

Example:

datasets:
  - from: databricks:my_dataset
    name: accelerated_dataset
    acceleration:
      enabled: true
      refresh_mode: full
      refresh_check_interval: 10m
      refresh_sql: |
        SELECT * FROM accelerated_dataset WHERE city = 'Seattle'

For the complete reference, view the refresh_sql section of datasets.

Limitations
  • The refresh SQL only supports filtering data from the current dataset - joining across other datasets is not supported.
  • Selecting a subset of columns isn't supported - the refresh SQL needs to start with SELECT * FROM {name}.
  • Queries for data that have been filtered out will not fall back to querying against the federated table.

Refresh Data Window​

Filters data from the federated source outside than the specified window. The only supported window is a lookback starting from now() - refresh_data_window to now(). This flag is only supported for datasets configured with a full refresh mode (the default).

Used in combination with the time_column to identify the column that contains the timestamps to filter on. The time_format column (optional) can be used to instruct the Spice runtime how to interpret the timestamps in the time_column.

Can also be combined with refresh_sql to further filter the data based on the temporal dimension.

Example:

datasets:
  - from: databricks:my_dataset
    name: accelerated_dataset
    time_column: created_at
    acceleration:
      enabled: true
      refresh_mode: full
      refresh_check_interval: 10m
      refresh_sql: |
        SELECT * FROM accelerated_dataset WHERE city = 'Seattle'
      refresh_data_window: 1d

This configuration will only accelerate data from the federated source that matches the filter city = 'Seattle' and is less than 1 day old.

Behavior on Zero Results​

By default, accelerated datasets will only return results that have been accelerated locally. If the locally accelerated data is a subset of the full dataset in the federated source, i.e. through setting refresh_sql, refresh_data_window or configuring retention policies, queries against the accelerated dataset may return zero results, where the federated table would return results.

Control this behavior by setting on_zero_results in the acceleration configuration.

on_zero_results:

  • return_empty (Default) - Return an empty result set when no data is found in the accelerated dataset.
  • use_source - Fall back to querying the federated table when no data is found in the accelerated dataset.

Example:

datasets:
  - from: databricks:my_dataset
    name: accelerated_dataset
    acceleration:
      enabled: true
      refresh_sql: SELECT * FROM accelerated_dataset where city = 'Seattle'
      on_zero_results: use_source

In this example a query against accelerated_dataset within Spice like SELECT * FROM accelerated_dataset WHERE city = 'Portland' would initially query against the accelerated data, see that it returns zero results and then fallback to querying against the federated table in Databricks.

warning

It is possible that even though the accelerated table returns some results, it may not contain all the data that would be returned by the federated table. on_zero_results only controls the behavior in the simple case where no data is returned by the acceleration for a given query.

Data Accelerator Docs​