Summary
The Databricks Go SDK updated job run structures to include deployment and version ID fields. MLflow experiment objects now support trace locations, and disaster recovery stable URLs include a stable workspace ID field.
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More from databricks/databricks-sdk-go
Databricks CLI authentication no longer rejects small wrapper scripts, such as launcher scripts or version manager shims, as the legacy Python CLI. Small files in PATH are now rejected only if they are the pip-installed entry point for the legacy Python CLI, and a path set in DATABRICKS_CLI_PATH or databricks_cli_path skips this check.
Release v0.191.0 adds SDK methods for AgentKit memory and session store permissions, budget spend status queries, feature engineering materialization sets, and starting or stopping vector search endpoints. It also adds external spend inclusion for budgets, unhealthy-table notifications for data quality, relative error options for approximate count-distinct and percentile functions, and table trigger conditions and timing controls.
Adds SdpSource to ml.DataSource and CustomerVersionLabel, MaxDbu, and ReydenClusterSizeMultiplier to SQL warehouse requests and responses. Breaking: removes ReplicationConfig from sharing.CreateShare and sharing.ShareInfo.
The Go SDK introduces methods for ML experiment issue tracking and sandbox file operations, along with support for Realtime SQL warehouses and job duration targets. Breaking changes remove the EffectiveServerlessComputeId field from job tasks and replace ReadMask with View across Agentkit memory requests.
The SDK adds support for Slack and Microsoft Teams notification destinations, as well as ModelService securable types in Databricks Apps. Model provider configurations in Unity Catalog now include pricing fields and support for Bedrock Mantle.
The SDK adds account-level management for identity visibility filters and introduces statement timeout configuration for SQL warehouses. It also adds parent path support for pipelines, custom template formats for SQL alerts, and streaming shuffle partition settings for machine learning features.