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Datasets

A Spicepod can contain one or more datasets referenced by relative path or defined inline.

Inline example:

spicepod.yaml

datasets:
  - from: spice.ai/spiceai/quickstart/datasets/taxi_trips
    name: taxi_trips
    acceleration:
      enabled: true
      mode: memory # / file
      engine: arrow # / duckdb / sqlite / postgres
      refresh_check_interval: 1h
      refresh_mode: full / append # update / incremental

spicepod.yaml

datasets:
  - from: databricks:spiceai.datasets.specific_table
    name: uniswap_eth_usd
    params:
      environment: prod
    acceleration:
      enabled: true
      mode: memory # / file
      engine: arrow # / duckdb
      refresh_check_interval: 1h
      refresh_mode: full / append # update / incremental

Relative path example:

spicepod.yaml

datasets:
  - ref: datasets/taxi_trips

datasets/taxi_trips/dataset.yaml

from: spice.ai/spiceai/quickstart/datasets/taxi_trips
name: taxi_trips
type: overwrite
acceleration:
  enabled: true
  refresh: 1h

from​

The from field is a string that represents the Uniform Resource Identifier (URI) for the dataset. This URI is composed of two parts: a prefix indicating the Data Connector to use to connect to the dataset, a delimiter, and the path to the dataset within the source.

The syntax for the from field is as follows:

from: <data_connector>:<path>
# OR
from: <data_connector>/<path>
# OR
from: <data_connector>://<path>

Where:

  • <data_connector>: The Data Connector to use to connect to the dataset

    Currently supported data connectors:

    If the Data Connector is not explicitly specified, it defaults to spiceai.

  • <delimiter>: The delimiter between the Data Connector and the path. Currently supported delimiters are :, /, and ://. Some connectors place additional restrictions on the allowed delimiters to better conform to the expected syntax of the underlying data source, i.e. s3:// is the only supported delimiter for the s3 connector.

  • <path>: The path to the dataset within the source.

ref​

An alternative to adding the dataset definition inline in the spicepod.yaml file. ref can be use to point to a directory with a dataset defined in a dataset.yaml file. For example, a dataset configured in a dataset.yaml in the "datasets/sample" directory can be referenced with the following:

dataset.yaml

from: spice.ai/spiceai/quickstart/datasets/taxi_trips
name: taxi_trips
type: overwrite
acceleration:
  enabled: true
  refresh: 1h

ref used in spicepod.yaml

version: v1
kind: Spicepod
name: duckdb
datasets:
  - ref: datasets/sample

name​

The name of the dataset. Used to reference the dataset in the pod manifest, as well as in external data sources.

description​

The description of the dataset. Used as part of the Semantic Data Model.

time_column​

Optional. The name of the column that represents the temporal (time) ordering of the dataset.

Required to enable a retention policy on the dataset.

time_format​

Optional. The format of the time_column. The following values are supported:

  • timestamp - Default. Timestamp without a timezone. E.g. 2016-06-22 19:10:25 with data type timestamp.
  • timestamptz - Timestamp with a timezone. E.g. 2016-06-22 19:10:25-07 with data type timestamptz.
  • unix_seconds - Unix timestamp in seconds. E.g. 1718756687.
  • unix_millis - Unix timestamp in milliseconds. E.g. 1718756687000.
  • ISO8601 - ISO 8601 format.
  • date - Date in YYYY-MM-DD format. E.g. 2024-01-01.

Spice emits a warning if the time_column from the data source is incompatible with the time_format config.

Limitations
  • String-based columns are assumed to be ISO8601 format.

time_partition_column​

(Optional) Specify the column that represents the physical partitioning of the dataset when using append-based acceleration. When the defined time_column is a fine-grained timestamp and the dataset is physically partitioned by a coarser granularity (for example, by date), setting time_partition_column to the partition column (e.g. date_col) improves partition pruning, excludes irrelevant partitions during refreshes, and optimizes scan efficiency.

time_partition_format​

(Optional) Define the format of the time_partition_column. For instance, if the physical partitions follow a date format (YYYY-MM-DD), set this value to date. The same format options as time_format are supported for time_partition_column.

unsupported_type_action​

Optional. Specifies the action to take when a data type that is not supported by the data connector is encountered.

The following values are supported:

  • error - Default. Return an error when an unsupported data type is encountered.
  • warn - Log a warning and ignore the column containing the unsupported data type.
  • ignore - Log nothing and ignore the column containing the unsupported data type.
  • string - Attempt to convert the unsupported data type to a string. Currently only supports converting the PostgreSQL JSONB type.
Limitations

Not all connectors support specifying an unsupported_type_action. When specified on a connector that does not support the option, the connector will fail to register. The following connectors support unsupported_type_action:

ready_state​

Supports one of two values:

  • on_registration: Mark the dataset as ready immediately, and queries on this table will fall back to the underlying source directly until the initial acceleration is complete
  • on_load: Mark the dataset as ready only after the initial acceleration. Queries against the dataset will return an error before the load has been completed.
datasets:
  - from: s3://my_bucket/my_dataset/
    name: my_dataset
    ready_state: on_registration # or on_load
    params: ...
    acceleration:
      enabled: true

acceleration​

Optional. Accelerate queries to the dataset by caching data locally.

acceleration.enabled​

Enable or disable acceleration, defaults to true.

acceleration.engine​

The acceleration engine to use, defaults to arrow. The following engines are supported:

  • arrow - Accelerated in-memory backed by Apache Arrow DataTables.
  • duckdb - Accelerated by an embedded DuckDB database.
  • postgres - Accelerated by a Postgres database.
  • sqlite - Accelerated by an embedded Sqlite database.

acceleration.mode​

Optional. The mode of acceleration. The following values are supported:

  • memory - Store acceleration data in-memory.
  • file - Store acceleration data in a file. Only supported for duckdb and sqlite acceleration engines.

mode is currently only supported for the duckdb engine.

acceleration.refresh_mode​

Optional. How to refresh the dataset. The following values are supported:

  • full - Refresh the entire dataset.
  • append - Append new data to the dataset. When time_column is specified, new records are fetched from the latest timestamp in the accelerated data at the acceleration.refresh_check_interval.

acceleration.refresh_check_interval​

Optional. How often data should be refreshed. For append datasets without a specific time_column, this config is not used. If not defined, the accelerator will not refresh after it initially loads data.

See Duration

acceleration.refresh_sql​

Optional. Filters the data fetched from the source to be stored in the accelerator engine. Only supported for full refresh_mode datasets.

Must be of the form SELECT * FROM {name} WHERE {refresh_filter}. {name} is the dataset name declared above, {refresh_filter} is any SQL expression that can be used to filter the data, i.e. WHERE city = 'Seattle' to reduce the working set of data that is accelerated within Spice from the data source.

Limitations
  • The refresh SQL only supports filtering data from the current dataset - joining across other datasets is not supported.
  • Queries for data that have been filtered out will not fall back to querying against the federated table.

acceleration.refresh_data_window​

Optional. A duration to filter dataset refresh source queries to recent data (duration into past from now). Requires time_column and time_format to also be configured. Only supported for full refresh mode datasets.

For example, refresh_data_window: 24h will include only records with a timestamp within the last 24 hours.

See Duration

acceleration.refresh_append_overlap​

Optional. A duration to specify how far back to include records based on the most recent timestamp found in the accelerated data. Requires time_column to also be configured. Only supported for append refresh mode datasets.

This setting can help mitigate missing data issues caused by late arriving data.

Example: If the latest timestamp in the accelerated data table is 2020-01-01T02:00:00Z, setting refresh_append_overlap: 1h will include records starting from 2020-01-01T01:00:00Z.

See Duration

acceleration.refresh_retry_enabled​

Optional. Specifies whether an accelerated dataset should retry data refresh in the event of transient errors. The default setting is true.

Retries utilize a Fibonacci backoff strategy. To disable refresh retries, set refresh_retry_enabled: false.

acceleration.refresh_retry_max_attempts​

Optional. Defines the maximum number of retry attempts when refresh retries are enabled. The default is undefined, allowing for unlimited attempts.

acceleration.refresh_on_startup​

Optional. Controls the refresh behavior of an accelerated dataset across restarts. Defaults to auto.

Supported Values​

  • auto (Default) – Maintains refresh state across restarts:
    • With refresh_check_interval: Schedules next refresh based on last successful refresh time, triggering immediately if interval has already elapsed
    • Without refresh_check_interval: No refresh (on-demand only)
  • always – Forces a dataset refresh on every startup, regardless of the existing acceleration state.

Setting refresh_on_startup: always ensures that accelerated data is always refreshed to match the source when the service restarts. This is useful in development environments or when data consistency is critical after deployment.

acceleration.params​

Optional. Parameters to pass to the acceleration engine. The parameters are specific to the acceleration engine used.

acceleration.engine_secret​

Optional. The secret store key to use the acceleration engine connection credential. For supported data connectors, use spice login to store the secret.

acceleration.retention_check_enabled​

Optional. Enable or disable retention policy check, defaults to false.

acceleration.retention_period​

Optional. The retention period for the dataset. Combine with time_column and time_format to determine if the data should be retained or not.

Required when acceleration.retention_check_enabled is true.

See Duration

acceleration.retention_check_interval​

Optional. How often the retention policy should be checked.

Required when acceleration.retention_check_enabled is true.

See Duration

acceleration.refresh_jitter_enabled​

Optional. Enable or disable refresh jitter, defaults to false. The refresh jitter adds/substracts a randomized time period from the refresh_check_interval.

acceleration.refresh_jitter_max​

Optional. The maximum amount of jitter to add to the refresh interval. The jitter is a random value between 0 and refresh_jitter_max. Defaults to 10% of refresh_check_interval.

metrics​

Optional. Enable component-specific metrics for the dataset. Each component can expose its own set of metrics that can be enabled selectively to monitor specific aspects of its operation.

Component metrics are disabled by default and can be enabled by adding a metrics section to the dataset configuration. Each metric can be enabled individually by specifying its name in the metrics list.

Example Configuration​

datasets:
  - from: mysql:my_table
    name: my_dataset
    metrics:
      - name: connection_count
        enabled: true
      - name: connections_in_pool
        enabled: true
      - name: active_wait_requests
        enabled: true
    params:
      mysql_host: localhost
      mysql_tcp_port: 3306
      mysql_user: root
      mysql_pass: ${secrets:MYSQL_PASS}

For detailed information about metrics available for specific components, see the component metrics documentation.

acceleration.indexes​

Optional. Specify which indexes should be applied to the locally accelerated table. Not supported for in-memory Arrow acceleration engine.

The indexes field is a map where the key is the column reference and the value is the index type.

A column reference can be a single column name or a multicolumn key. The column reference must be enclosed in parentheses if it is a multicolumn key.

See Indexes

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    acceleration:
      enabled: true
      engine: sqlite
      indexes:
        number: enabled # Index the `number` column
        '(hash, timestamp)': unique # Add a unique index with a multicolumn key comprised of the `hash` and `timestamp` columns

acceleration.primary_key​

Optional. Specify the primary key constraint on the locally accelerated table. Not supported for in-memory Arrow acceleration engine.

The primary_key field is a string that represents the column reference that should be used as the primary key. The column reference can be a single column name or a multicolumn key. The column reference must be enclosed in parentheses if it is a multicolumn key.

See Constraints

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    acceleration:
      enabled: true
      engine: sqlite
      primary_key: hash # Define a primary key on the `hash` column

acceleration.on_conflict​

Optional. Specify what should happen when a constraint is violated. Not supported for in-memory Arrow acceleration engine.

The on_conflict field is a map where the key is the column reference and the value is the conflict resolution strategy.

A column reference can be a single column name or a multicolumn key. The column reference must be enclosed in parentheses if it is a multicolumn key.

Only a single on_conflict target can be specified, unless all on_conflict targets are specified with drop.

The possible conflict resolution strategies are:

  • upsert - Upsert the incoming data when the primary key constraint is violated.
  • drop - Drop the data when the primary key constraint is violated.

See Constraints

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    acceleration:
      enabled: true
      engine: sqlite
      primary_key: hash
      indexes:
        '(number, timestamp)': unique
      on_conflict:
        # Upsert the incoming data when the primary key constraint on "hash" is violated,
        # alternatively "drop" can be used instead of "upsert" to drop the data update.
        hash: upsert

columns​

Optional. Define metadata and features for specific columns in the dataset.

datasets:
  - from: file:sales_data.parquet
    name: sales
    columns:
      - name: address_line1
        description: The first line of the address.
        embeddings:
          - from: hf_minilm
            row_id: order_number
            chunking:
              enabled: true
              target_chunk_size: 256
              overlap_size: 32

columns[*].name​

The name of the column in the table schema.

columns[*].description​

Optional. A description of the column's contents and purpose. Used as part of the Semantic Data Model.

columns[*].embeddings​

Optional. Create vector embeddings for this column.

columns[*].embeddings[*].from​

The embedding model to use, specify the component name.

columns[*].embeddings[*].row_id​

Optional. For datasets without a primary key, used to explicitly specify column(s) that uniquely identify a row.

Specifying a row_id enables unique identifier lookups for datasets from external systems that may not have a primary key.

columns[*].embeddings[*].chunking​

Optional. The configuration to enable and define the chunking strategy for the embedding column.

columns:
  - name: description
    embeddings:
      - from: hf_minilm
        chunking:
          enabled: true
          target_chunk_size: 512
          overlap_size: 128
          trim_whitespace: false

See embeddings[*].chunking for details.

embeddings​

Optional. Create vector embeddings for specific columns of the dataset.

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    embeddings:
      - column: extra_data
        use: hf_minilm

embeddings[*].column​

The column name to create an embedding for.

embeddings[*].use​

The embedding model to use, specific the component name embeddings[*].name.

embeddings[*].column_pk​

Optional. For datasets without a primary key, explicitly specify column(s) that uniquely identify a row.

embeddings[*].chunking​

Optional. The configuration to enable and define the chunking strategy for the embedding column.

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    embeddings:
      - column: extra_data
        use: hf_minilm
        chunking:
          enabled: true
          target_chunk_size: 512
          overlap_size: 128
          trim_whitespace: false

embeddings[*].chunking.enabled​

Optional. Enable or disable chunking for the embedding column. Defaults to false.

embeddings[*].chunking.target_chunk_size​

The desired size of each chunk, in tokens.

If the desired chunk size is larger than the maximum size of the embedding model, the maximum size will be used.

embeddings[*].chunking.overlap_size​

Optional. The number of tokens to overlap between chunks. Defaults to 0.

embeddings[*].chunking.trim_whitespace​

Optional. If enabled, the content of each chunk will be trimmed to remove leading and trailing whitespace. Defaults to true.

metadata​

Optional. Additional key-value metadata for the dataset. Used as part of the Semantic Data Model.

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    metadata:
      instructions: The last 128 blocks.