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Spice v1.10.2 (Dec 22, 2025)

· 5 min read
Sergei Grebnov
Sergei Grebnov
Senior Software Engineer at Spice AI

Announcing the release of Spice v1.10.2! 🔥

v1.10.2 introduces Tiered Caching Acceleration with Localpod for multi-layer acceleration architectures, Periodic Acceleration Snapshots with configurable intervals, DynamoDB JSON Nesting for column consolidation, and Kafka/Debezium Batching for faster data ingestion. This release also includes fixes for SQLite accelerator decimal/date handling and real-time status reporting for the /v1/datasets and /v1/models API endpoints.

What's New in v1.10.2​

Tiered Caching with Localpod​

Multi-Layer Acceleration Architecture: The Localpod connector now supports caching refresh mode, enabling tiered acceleration where a persistent cache (e.g., file-mode DuckDB) feeds a fast in-memory cache (e.g., Arrow, memory-mode DuckDB).

Key Features:

  • Automatic Cache Propagation: New cache entries automatically propagate from parent to child accelerators
  • Warm Startup: Child accelerators initialize from existing parent data on startup, eliminating cold-start latency
  • Flexible Tiering: Combine any accelerator engines (DuckDB, SQLite, Cayenne) across tiers

Example spicepod.yaml configuration:

datasets:
  # Parent: persistent file-mode cache
  - from: https://api.example.com
    name: api_cache
    acceleration:
      enabled: true
      refresh_mode: caching
      engine: duckdb
      mode: file

  # Child: fast in-memory cache fed by parent
  - from: localpod:api_cache
    name: api_cache_memory
    acceleration:
      enabled: true
      refresh_mode: caching
      engine: arrow
      mode: memory

For more details, refer to the Localpod Data Connector Documentation.

Periodic Acceleration Snapshots​

Configurable Snapshot Intervals: A new snapshots_create_interval parameter enables periodic snapshot creation for accelerated datasets across all refresh modes. This provides better control over snapshot frequency and ensures consistent recovery points for accelerated data.

Example spicepod.yaml configuration:

datasets:
  - from: s3://my-bucket/data.parquet
    name: my_data
    acceleration:
      enabled: true
      engine: duckdb
      mode: file
      refresh_mode: caching
      snapshots: enabled
      params:
        snapshots_create_interval: 60s # Write a snapshot every 60 seconds

For more details, refer to the Data Acceleration Documentation.

DynamoDB JSON Nesting​

Consolidate Columns into JSON: The DynamoDB Data Connector now supports consolidating columns into a single JSON column using the json_object: "*" metadata option. This is useful when only a few columns are needed as discrete fields while the rest can be accessed as nested JSON.

Example spicepod.yaml configuration:

datasets:
  - from: dynamodb:my_table
    name: my_table
    columns:
      - name: PK
      - name: SK
      - name: data_json
        metadata:
          json_object: '*' # Captures all other columns as JSON

Example Output: Given a DynamoDB table with columns PK, SK, name, email, and status, the resulting table schema consolidates all non-specified columns into the data_json column:

PKSKdata_json
pk_1sort_1{"name": "Alice", "email": "[email protected]", "status": "active"}
pk_2sort_2{"name": "Bob", "email": "[email protected]", "status": "inactive"}

For more details, refer to the DynamoDB JSON Nesting Documentation.

Kafka/Debezium Batching​

Faster Data Ingestion: Configure message batching for Kafka and Debezium connectors to improve data ingestion throughput. Batching reduces processing overhead by grouping multiple messages together before insertion.

Key Features:

  • Configurable Batch Size: Control the maximum number of records per batch (default: 10,000)
  • Configurable Batch Duration: Set the maximum wait time before flushing a partial batch (default: 1s)

Example spicepod.yaml configuration:

datasets:
  - from: debezium:kafka-server.public.my_table
    name: my_table
    params:
      batch_max_size: 10000 # Max records per batch (default: 10000)
      batch_max_duration: 1s # Max wait time per batch (default: 1s)

For more details, refer to the Kafka Data Connector Documentation and Debezium Data Connector Documentation.

Additional Improvements & Bug Fixes​

  • Reliability: Fixed SQLite accelerator decimal and date type handling for improved data type accuracy.
  • Reliability: Fixed real-time status reporting for /v1/datasets and /v1/models API endpoints.
  • Reliability: Fixed Kafka warning when security.protocol is set to PLAINTEXT.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

New Cayenne Data Accelerator Recipe: New recipe demonstrating how to accelerate a local copy of the taxi trips dataset using Cayenne as the data accelerator engine. See Cayenne Data Accelerator Recipe for details.

New Dataset Partitioning Recipe: New recipe demonstrating how to partition accelerated datasets to improve query performance. See Dataset Partitioning for details.

The Spice Cookbook includes 84 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.10.2, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.10.2 image:

docker pull spiceai/spiceai:1.10.2

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

🎉 Spice is now available in the AWS Marketplace!

What's Changed​

Changelog​