Data Refresh

Refresh Modes

Spice supports three modes to refresh/update local data from a connected data source. full is the default mode.

Mode	Description	Example
full	Replace/overwrite the entire dataset on each refresh	A table of users
append	Append/add data to the dataset on each refresh	Append-only, immutable datasets, such as time-series or log data
changes	Apply incremental changes	Customer order lifecycle table

Example:

datasets:
  - from: databricks:my_dataset
    name: accelerated_dataset
    acceleration:
      refresh_mode: full
      refresh_check_interval: 10m

Append

If the dataset definition includes a time_column and the refresh mode is append, data will be incrementally refreshed for data where the time_column value in the remote source is greater-than (gt) the max(time_column) value in the local acceleration.

E.g.

datasets:
  - from: databricks:my_dataset
    name: accelerated_dataset
    time_column: timestamp
    acceleration:
      refresh_mode: append # In conjuction with time_column, only fetch data greater than the latest local timestamp
      refresh_check_interval: 10m

When using mode: append, if late arriving data or clock-skew needs to be accounted for, an optional overlap can also be specified. See acceleration.refresh_append_overlap.

Changes (CDC)

Datasets configured with acceleration refresh_mode: changes require a Change Data Capture (CDC) supported data connector. Initial CDC support in Spice is supported by the Debezium data connector.

Filtered Refresh

Typically only a working subset of an entire dataset is used in an application or dashboard. Use these features to filter refresh data, creating a smaller subset for faster processing and to reduce the data transferred and stored locally.

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

Refresh SQL

Specify filters for data accelerated from the connected source using arbitrary SQL. Supported for full and append refresh modes.

Filters will be pushed down to the remote source when possible, so 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'

The refresh_sql parameter can be updated at runtime on-demand using PATCH /v1/datasets/:name/acceleration. This change is temporary and will revert to the spicepod.yml definition at the next runtime restart.

Example:

curl -i -X PATCH \
     -H "Content-Type: application/json" \
     -d '{
           "refresh_sql": "SELECT * FROM accelerated_dataset WHERE city = 'Bellevue'"
         }' \
     127.0.0.1:8090/v1/datasets/accelerated_dataset/acceleration

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

Limitations

• 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 fallback to querying the federated table.
• Refresh SQL modifications made via API are temporary and will revert after a runtime restart.

Refresh Data Window

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

This filter works with the time_column to identify the column containing timestamps for filtering. Optionally, the time_format can be specified to instruct the Spice runtime on how to interpret timestamps in the time_column.

It can also be used alongside refresh_sql to apply additional filtering based on time-related criteria.

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 only return locally materialized data. If this local data is a subset of the full dataset in the federated source—due to settings like refresh_sql, refresh_data_window, or retention policies—queries against the accelerated dataset may return zero results, even when the federated table would return results.

To address this, on_zero_results: use_source can be configured in the acceleration configuration. Queries returning zero results will fall back to the federated source, returning results from querying the underlying data.

The on_zero_results: use_source setting applies only to full and append refresh modes (not `changes).

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.

Refresh Interval

For accelerated datasets in full mode, the refresh_check_interval parameter controls how often the accelerated dataset is refreshed.

Example:

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth_recent_blocks
    acceleration:
      enabled: true
      refresh_mode: full
      refresh_check_interval: 10s

This configuration will refresh eth.recent_blocks data every 10 seconds.

Refresh On-Demand

Accelerated datasets can be refreshed on-demand via the refresh CLI command or POST /v1/datasets/:name/acceleration/refresh API endpoint.

On-demand refresh applies only to full and append refresh modes (not `changes).

CLI example:

spice refresh eth_recent_blocks

API example using cURL:

curl -i -XPOST 127.0.0.1:8090/v1/datasets/eth_recent_blocks/acceleration/refresh

with response:

HTTP/1.1 201 Created
content-type: application/json
content-length: 55
date: Thu, 11 Apr 2024 20:11:18 GMT

{"message":"Dataset refresh triggered for eth_recent_blocks."}

Note

On-demand refresh always initiates a new refresh, terminating any in-progress refresh for the dataset.

Refresh Retries

By default, data refreshes for accelerated datasets are retried on transient errors (connectivity issues, compute warehouse goes idle, etc.) using Fibonacci backoff strategy.

Retry behavior can be configured using the acceleration.refresh_retry_enabled and acceleration.refresh_retry_max_attempts parameters.

Data refresh retry applies to full and append refresh modes not changes which inherently supports data integrity and consistency through the CDC mechanism.

Example: Disable rertries

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth_recent_blocks
    acceleration:
      refresh_retry_enabled: false
      refresh_check_interval: 30s

Example: Limit retries to a maximum of 10 attempts

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth_recent_blocks
    acceleration:
      refresh_retry_max_attempts: 10
      refresh_check_interval: 30s

Retention Policy

Accelerated datasets can be set to automatically evict time-series data exceeding a retention period by setting a retention policy based on the configured time_column and acceleration.retention_period.

Retention policies apply to full and append refresh modes (not changes).

The policy is set using the acceleration.retention_check_enabled, acceleration.retention_period and acceleration.retention_check_interval parameters, along with the time_column and time_format dataset parameters.

Refresh Jitter

Accelerated datasets can include a random jitter in the refresh interval to prevent the Thundering herd problem, where multiple datasets refresh simultaneously. The jitter, ranging from 0 to refresh_jitter_max, is randomly added or subtracted from the refresh interval.

Refresh Jitter applies on the first dataset load, so on a restart of multiple similarily configured Spice instances at once, on restart they will load with jitter of 0 to refresh_jitter_max.

Example:

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth_recent_blocks
    acceleration:
      refresh_check_interval: 10s
      refresh_jitter_enabled: true
      refresh_jitter_max: 1s

In this example, the refresh interval will be between 9s and 11s.

Refresh jitter can be configured using the following parameters:

• refresh_jitter_enabled
• refresh_jitter_max