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Spice v0.11.1-alpha (April 22, 2024)

· 3 min read
Luke Kim
Luke Kim
Founder and CEO of Spice AI

The v0.11.1-alpha release introduces retention policies for accelerated datasets, native Windows installation support, and integration of catalog and schema settings for the Databricks Spark connector. Several bugs have also been fixed for improved stability.

Highlights​

  • Retention Policies for Accelerated Datasets: Automatic eviction of data from accelerated time-series datasets when a specified temporal column exceeds the retention period, optimizing resource utilization.

  • Windows Installation Support: Native Windows installation support, including upgrades.

  • Databricks Spark Connect Catalog and Schema Settings: Improved translation between DataFusion and Spark, providing better Spark Catalog support.

Contributors​

  • @phillipleblanc
  • @Jeadie
  • @ewgenius
  • @sgrebnov
  • @y-f-u
  • @lukekim
  • @digadeesh
  • @Sevenannn
  • @gloomweaver

New in this release​

What's Changed​

Full Changelog: https://github.com/spiceai/spiceai/compare/v0.11.0-alpha...v0.11.1-alpha

Resources​

Community​

Spice.ai started with the vision to make AI easy for developers. We are building Spice.ai in the open and with the community. Reach out on Discord or by email to get involved.

Spice v0.11-alpha (April 15, 2024)

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

The Spice v0.11-alpha release significantly improves the Databricks data connector with Databricks Connect (Spark Connect) support, adds the DuckDB data connector, and adds the AWS Secrets Manager secret store. In addition, enhanced control over accelerated dataset refreshes, improved SSL security for MySQL and PostgreSQL connections, and overall stability improvements have been added.

Highlights in v0.11-alpha​

DuckDB data connector: Use DuckDB databases or connections as a data source.

AWS Secrets Manager Secret Store: Use AWS Secrets Managers as a secret store.

Custom Refresh SQL: Specify a custom SQL query for dataset refresh using refresh_sql.

Dataset Refresh API: Trigger a dataset refresh using the new CLI command spice refresh or via API.

Expanded SSL support for Postgres: SSL mode now supports disable, require, prefer, verify-ca, verify-full options with the default mode changed to require. Added pg_sslrootcert parameter for setting a custom root certificate and the pg_insecure parameter is no longer supported.

Databricks Connect: Choose between using Spark Connect or Delta Lake when using the Databricks data connector for improved performance.

Improved SSL support for Postgres: ssl mode now supports disable, require, prefer, verify-ca, verify-full options with default mode changed to require. Added pg_sslrootcert parameter to allow setting custom root cert for postgres connector, pg_insecure parameter is no longer supported as redundant.

Internal architecture refactor: The internal architecture of spiced was refactored to simplify the creation data components and to improve alignment with DataFusion concepts.

New Contributors​

@edmondop's first contribution github.com/spiceai/spiceai/pull/1110!

Contributors​

  • @phillipleblanc
  • @Jeadie
  • @ewgenius
  • @sgrebnov
  • @y-f-u
  • @lukekim
  • @digadeesh
  • @Sevenannn
  • @gloomweaver
  • @ahirner

New in this release​

Full Changelog: https://github.com/spiceai/spiceai/compare/v0.10.2-alpha...v0.11.0-alpha

Resources​

Community​

Spice.ai started with the vision to make AI easy for developers. We are building Spice.ai in the open and with the community. Reach out on Discord or by email to get involved.

Spice.ai v0.10.2-alpha

· 2 min read
Jack Eadie
Jack Eadie
Token Plumber at Spice AI

Announcing the release of Spice v0.10.2-alpha (Apr 9, 2024)! 🔥

The v0.10.2-alpha release adds the MySQL data connector and makes external data connections more robust on initialization.

Highlights in v0.10.2-alpha​

  • MySQL data connector: Connect to any MySQL server, including SSL support.

  • Data connections verified at initialization: Verify endpoints and authorization for external data connections (e.g. databricks, spice.ai) at initialization.

New Contributors​

Contributors​

  • @phillipleblanc
  • @y-f-u
  • @ewgenius
  • @sgrebnov
  • @lukekim
  • @digadeesh
  • @jeadie

New in this release​

Full Changelog: https://github.com/spiceai/spiceai/compare/v0.10.1-alpha...v0.10.2-alpha

Resources​

Community​

Spice.ai started with the vision to make AI easy for developers. We are building Spice.ai in the open and with the community. Reach out on Discord or by email to get involved.

Spice.ai v0.10.1-alpha

· 2 min read
Luke Kim
Luke Kim
Founder and CEO of Spice AI

Announcing the release of Spice v0.10.1-alpha! 🔥

The v0.10.1-alpha release focuses on stability, bug fixes, and usability by improving error messages when using SQLite data accelerators, improving the PostgreSQL support, and adding a basic Helm chart.

Highlights in v0.10.1-alpha​

Improved PostgreSQL support for Data Connectors TLS is now supported with PostgreSQL Data Connectors and there is improved VARCHAR and BPCHAR conversions through Spice.

Improved Error messages Simplified error messages from Spice when propagating errors from Data Connectors and Accelerator Engines.

Spice Pods Command The spice pods command can give you quick statistics about models, dependencies, and datasets that are loaded by the Spice runtime.

Kubernetes Helm Deployment​

Spice.ai can be deployed to Kubernetes using Helm. Here's a quick guide to get started:

Step 1. (Optional) Start a local kind cluster:

go install sigs.k8s.io/[email protected]
kind create cluster

Step 2. Install Spice in your Kubernetes cluster using Helm:

helm repo add spiceai https://helm.spiceai.org
helm install spiceai spiceai/spiceai

Step 3. Verify that the Spice pods are running:

kubectl get pods
kubectl logs deploy/spiceai

Step 4. Run the Spice SQL REPL inside the running pod:

kubectl exec -it deploy/spiceai -- spiced --repl

Learn more about deploying Spice.ai to Kubernetes

Contributors​

  • @phillipleblanc
  • @mitchdevenport
  • @ewgenius
  • @sgrebnov
  • @lukekim
  • @digadeesh

New in this release​

Resources​

Community​

Spice.ai started with the vision to make AI easy for developers. We are building Spice.ai in the open and with the community. Reach out on Discord or by email to get involved.

Adding Spice - The Next Generation of Spice.ai OSS

· 4 min read
Phillip LeBlanc
Phillip LeBlanc
Co-Founder and CTO of Spice AI

TL;DR: We've rebuilt Spice.ai OSS from the ground up in Rust, as a unified SQL query interface and portable runtime to locally materialize, accelerate, and query datasets sourced from any database, data warehouse or data lake. Learn more at github.com/spiceai/spiceai.

In September, 2021, we introduced Spice.ai OSS as a runtime for building AI-driven applications using time-series data.

We quickly ran into a big problems in making these applications work... data, the fuel for intelligent software, was painfully difficult to access, operationalize, and use, not only in machine learning, but also in web frontends, backend applications, dashboards, data pipelines, and notebooks. And we had to make hard tradeoffs between cost and query performance.

We felt this pain every day building 100TB+ scale data and AI systems for the Spice.ai Cloud Platform. So we took our learnings and infused them back into Spice.ai OSS with the capabilities we wished we had.

We rebuilt Spice.ai OSS from the ground up in Rust, as a unified SQL query interface and portable runtime to locally materialize, accelerate, and query data tables sourced from any database, data warehouse or data lake.

Figure 1. Spice.ai OSS

Spice is a fast, lightweight (< 150Mb), single-binary, designed to be deployed alongside your application, dashboard, and within your data or machine learning pipelines. Spice federates SQL query across databases (MySQL, PostgreSQL, etc.), data warehouses (Snowflake, BigQuery, etc.) and data lakes (S3, MinIO, Databricks, etc.) so you can easily use and combine data wherever it lives. Datasets, declaratively defined, can be materialized and accelerated using your engine of choice, including DuckDB, SQLite, PostgreSQL, and in-memory Apache Arrow records, for ultra-fast, low-latency query. Accelerated engines run in your infrastructure giving you flexibility and control over price and performance.

Before Spice​

Figure 2. Before Spice, applications submit many queries to external data sources. Figure 2. Before Spice, applications submit many queries to external data sources.

With Spice​

Figure 3. With Spice, data is materialized and accelerated locally for fast, low-latency query.

Use-Cases​

The next-generation of Spice.ai OSS enables:

Better applications. Accelerate and co-locate data with frontend and backend applications, for high concurrent queries, serving more users with faster page loads and data updates. Try the CQRS sample app.

Snappy dashboards, analytics, and BI. Faster, more responsive dashboards without massive compute costs. Spice supports Arrow Flight SQL (JDBC/ODBC/ADBC) for connectivity with Tableau, Looker, PowerBI, and more. Watch the Apache Superset with Spice demo.

Faster data pipelines, machine learning training and inference. Co-locate datasets with pipelines where the data is needed to minimize data-movement and improve query performance. Predict hard drive failure with the SMART data demo.

Easily query many data sources. Federated SQL query across databases, data warehouses, and data lakes using Data Connectors.

Community Built​

Spice is open-source, Apache 2.0 licensed, and is built using industry-leading technologies including Apache DataFusion, Arrow, and Arrow Flight SQL. We're launching with several built-in Data Connectors and Accelerators and Spice is extensible so more will be added in each release. If you're interested in contributing, we'd love to welcome you to the community!

Getting Started​

You can download and run Spice in less than 30 seconds by following the quickstart at github.com/spiceai/spiceai.

Conclusion​

Spice, rebuilt in Rust, introduces a unified SQL query interface, making it simpler and faster to build data-driven applications. The lightweight Spice runtime is easy to deploy and makes it possible to materialize and query data from any source quickly and cost-effectively. Applications can serve more users, dashboards and analytics can be snappier, and data and ML pipelines finish faster, without the heavy lifting of managing data.

For developers this translates to less time wrangling data and more time creating innovative applications and business value.

Check out and star the project on GitHub!

Thank you,

Phillip