Spice Data Accelerators

Accelerators materialize data locally from connected sources for faster queries and reduced load on source systems.

Basic Configuration

datasets:
  - from: postgres:my_table
    name: my_table
    acceleration:
      enabled: true
      engine: duckdb # arrow, duckdb, sqlite, cayenne, postgres, turso
      mode: memory # memory or file
      refresh_check_interval: 1h

Choosing an Accelerator

Use Case	Engine	Why
Small datasets (<1 GB), max speed	arrow	In-memory, lowest latency
Medium datasets (1-100 GB), complex SQL	duckdb	Mature SQL, memory management
Large datasets (100 GB-1+ TB), analytics	cayenne	Built on Vortex (Linux Foundation), 10-20x faster scans
Point lookups on large datasets	cayenne	100x faster random access vs Parquet
Simple queries, low resource usage	sqlite	Lightweight, minimal overhead
Async operations, concurrent workloads	turso	Native async, modern connection pooling
External database integration	postgres	Leverage existing PostgreSQL infra

Cayenne vs DuckDB

Choose Cayenne when datasets exceed ~1 TB, multi-file ingestion is needed, or point lookups are common. Choose DuckDB when datasets are under ~1 TB, complex SQL (window functions, CTEs) is needed, or DuckDB tooling is beneficial.

Supported Engines

Engine	Mode	Status
arrow	memory	Stable
duckdb	memory, file	Stable
sqlite	memory, file	Release Candidate
cayenne	file	Beta
postgres	N/A (attached)	Release Candidate
turso	memory, file	Beta

Refresh Modes

Mode	Description	Use Case
full	Complete dataset replacement on each refresh	Small, slowly-changing datasets
append (batch)	Adds new records based on a time_column	Append-only logs, time-series data
append (stream)	Continuous streaming without time column	Real-time event streams (Kafka, Debezium)
changes	CDC-based incremental updates via Debezium or DynamoDB Streams	Frequently updated transactional data
caching	Request-based row-level caching	API responses, HTTP endpoints

# Full refresh every 8 hours
acceleration:
  refresh_mode: full
  refresh_check_interval: 8h

# Append mode: check for new records from the last day every 10 minutes
acceleration:
  refresh_mode: append
  time_column: created_at
  refresh_check_interval: 10m
  refresh_data_window: 1d

# Continuous ingestion using Kafka
acceleration:
  refresh_mode: append

# CDC with Debezium or DynamoDB Streams
acceleration:
  refresh_mode: changes

Common Configurations

In-Memory with Interval Refresh

acceleration:
  enabled: true
  engine: arrow
  refresh_check_interval: 5m

File-Based with Append and Time Window

datasets:
  - from: postgres:events
    name: events
    time_column: created_at
    acceleration:
      enabled: true
      engine: duckdb
      mode: file
      refresh_mode: append
      refresh_check_interval: 1h
      refresh_data_window: 7d

With Retention Policy

Retention policies prevent unbounded growth of accelerated datasets. Spice supports time-based and custom SQL-based retention strategies:

datasets:
  - from: postgres:events
    name: events
    time_column: created_at
    acceleration:
      enabled: true
      engine: duckdb
      retention_check_enabled: true
      retention_period: 30d
      retention_check_interval: 1h

With SQL-Based Retention

acceleration:
  retention_check_enabled: true
  retention_check_interval: 1h
  retention_sql: "DELETE FROM logs WHERE status = 'archived'"

With Indexes (DuckDB, SQLite, Turso)

acceleration:
  enabled: true
  engine: sqlite
  indexes:
    user_id: enabled
    '(created_at, status)': unique
  primary_key: id

Engine-Specific Parameters

DuckDB

acceleration:
  engine: duckdb
  mode: file
  params:
    duckdb_file: ./data/cache.db

SQLite

acceleration:
  engine: sqlite
  mode: file
  params:
    sqlite_file: ./data/cache.sqlite

Constraints and Indexes

Accelerated datasets support primary key constraints and indexes:

acceleration:
  enabled: true
  engine: duckdb
  primary_key: order_id # Creates non-null unique index
  indexes:
    customer_id: enabled # Single column index
    '(created_at, status)': unique # Multi-column unique index

Snapshots (DuckDB, SQLite & Cayenne file mode)

Bootstrap file-based accelerations from S3 or filesystem snapshots on startup. This dramatically reduces cold-start latency in distributed deployments.

Snapshot triggers vary by refresh mode:

• refresh_complete: Creates snapshots after each refresh (full and batch-append modes)
• time_interval: Creates snapshots on a fixed schedule (all refresh modes)
• stream_batches: Creates snapshots after every N batches (streaming modes: Kafka, Debezium, DynamoDB Streams)

snapshots:
  enabled: true
  location: s3://my_bucket/snapshots/
  bootstrap_on_failure_behavior: warn # warn | retry | fallback
  params:
    s3_auth: iam_role

Per-dataset opt-in:

acceleration:
  enabled: true
  engine: duckdb
  mode: file
  snapshots:
    enabled: true

Memory Considerations

When using mode: memory (default), the dataset is loaded into RAM. Ensure sufficient memory including overhead for queries and the runtime. Mitigate with mode: file for duckdb, sqlite, turso, or cayenne accelerators.

Documentation

• Data Accelerators
• Datasets Reference
• Data Refresh
• Indexes
• Performance Tuning
• Memory Management