databricks-upgrade-migration

Upgrade Databricks runtime versions and migrate between features. Use when upgrading DBR versions, migrating to Unity Catalog, or updating deprecated APIs and features. Trigger with phrases like "databricks upgrade", "DBR upgrade", "databricks migration", "unity catalog migration", "hive to unity".

الانتقال إلى التثبيت

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name	databricks-upgrade-migration
description	Upgrade Databricks runtime versions and migrate between features. Use when upgrading DBR versions, migrating to Unity Catalog, or updating deprecated APIs and features. Trigger with phrases like "databricks upgrade", "DBR upgrade", "databricks migration", "unity catalog migration", "hive to unity".
allowed-tools	Read, Write, Edit, Bash(databricks:*), Grep
version	1.0.0
license	MIT
author	Jeremy Longshore <jeremy@intentsolutions.io>
compatible-with	claude-code, codex, openclaw
tags	["saas","databricks","migration"]

Databricks Upgrade & Migration

Overview

Upgrade Databricks Runtime versions and migrate from Hive Metastore to Unity Catalog. Covers version compatibility, deprecated config removal, table migration via SYNC/CTAS, API endpoint updates, and Delta protocol upgrades.

Prerequisites

Admin access to workspace
Test environment (dev/staging) for validation before prod
Inventory of current workloads and dependencies

Instructions

Step 1: Runtime Version Upgrade

Version Compatibility Matrix

Current DBR	Target DBR	Key Changes	Effort
12.x LTS	13.3 LTS	Spark 3.4, Python 3.10 default	Low
13.3 LTS	14.3 LTS	Spark 3.5, improved AQE, Liquid Clustering GA	Medium
14.x	15.x LTS	Unity Catalog mandatory, legacy DBFS deprecated	High

Automated Upgrade Script

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

def plan_cluster_upgrade(
    cluster_id: str,
    target_version: str = "14.3.x-scala2.12",
    dry_run: bool = True,
) -> dict:
    """Plan and optionally execute a DBR version upgrade."""
    cluster = w.clusters.get(cluster_id)
    plan = {
        "cluster_id": cluster_id,
        "cluster_name": cluster.cluster_name,
        "current_version": cluster.spark_version,
        "target_version": target_version,
        "removals": [],
        "warnings": [],
    }

    # Check for deprecated Spark configs
    deprecated = {
        "spark.databricks.delta.preview.enabled": "GA in 13.x+",
        "spark.sql.legacy.createHiveTableByDefault": ,
        : ,
        : ,
    }

     key, reason  deprecated.items():
         cluster.spark_conf  key  cluster.spark_conf:
            plan[].append({: key, : reason})

    
       target_version    target_version:
        plan[].append()

      dry_run:
        clean_conf = {
            k: v  k, v  (cluster.spark_conf  {}).items()
             k   deprecated
        }
        w.clusters.edit(
            cluster_id=cluster_id,
            spark_version=target_version,
            cluster_name=cluster.cluster_name,
            spark_conf=clean_conf,
            node_type_id=cluster.node_type_id,
            num_workers=cluster.num_workers,
        )
        plan[] = 
    :
        plan[] = 

     plan


 cluster  w.clusters.():
    plan = plan_cluster_upgrade(cluster.cluster_id, dry_run=)
     plan[]  plan[]:
        ()
         r  plan[]:
            ()
         w_  plan[]:
            ()

-- List all Hive Metastore tables to migrate
SHOW DATABASES IN hive_metastore;
SHOW TABLES IN hive_metastore.my_database;

-- Get table sizes for migration planning
SELECT table_name, table_type,
       data_length / 1024 / 1024 AS size_mb
FROM hive_metastore.information_schema.tables
WHERE table_schema = 'my_database'
ORDER BY data_length DESC;

-- Create Unity Catalog destination
CREATE CATALOG IF NOT EXISTS analytics;
CREATE SCHEMA IF NOT EXISTS analytics.migrated;

-- Option A: SYNC (in-place — keeps data where it is, adds UC metadata)
-- Best for external tables already on cloud storage
SYNC SCHEMA analytics.migrated FROM hive_metastore.my_database;

-- Option B: CTAS (copies data — creates managed Delta tables)
-- Best for small-medium tables or format conversion
CREATE TABLE analytics.migrated.customers AS
SELECT * FROM hive_metastore.my_database.customers;

-- Option C: DEEP CLONE (best for Delta-to-Delta, preserves history)
CREATE TABLE analytics.migrated.orders
DEEP CLONE hive_metastore.my_database.orders;

-- Migrate views
CREATE VIEW analytics.migrated.customer_summary AS
SELECT * FROM analytics.migrated.customers
WHERE active = true;

-- Verify migration
SELECT 'source' AS system, COUNT(*) AS rows
FROM hive_metastore.my_database.customers
UNION ALL
SELECT 'target', COUNT(*)
FROM analytics.migrated.customers;

-- Grant access
GRANT USAGE ON CATALOG analytics TO `data-team`;
GRANT SELECT ON SCHEMA analytics.migrated TO `data-team`;

# Jobs API 2.0 → 2.1 changes
# Old: POST /api/2.0/jobs/create with flat task definition
# New: POST /api/2.1/jobs/create with tasks[] array (multi-task)

# Old (single task):
old_config = {
    "name": "my-job",
    "existing_cluster_id": "abc-123",
    "notebook_task": {"notebook_path": "/path"}
}

# New (multi-task):
new_config = {
    "name": "my-job",
    "tasks": [{
        "task_key": "main",
        "existing_cluster_id": "abc-123",
        "notebook_task": {"notebook_path": "/path"}
    }]
}

# The Python SDK uses the latest API version automatically
from databricks.sdk.service.jobs import Task, NotebookTask
job = w.jobs.create(
    name="my-job",
    tasks=[Task(
        task_key="main",
        existing_cluster_id="abc-123",
        notebook_task=NotebookTask(notebook_path="/path"),
    )],
)

-- Check current protocol version
DESCRIBE DETAIL analytics.silver.orders;
-- Look at: minReaderVersion, minWriterVersion

-- Upgrade to support Deletion Vectors (reader v3, writer v7)
ALTER TABLE analytics.silver.orders
SET TBLPROPERTIES (
    'delta.minReaderVersion' = '3',
    'delta.minWriterVersion' = '7',
    'delta.enableDeletionVectors' = 'true'
);

-- Enable Liquid Clustering (replaces partitioning + Z-order)
ALTER TABLE analytics.silver.orders CLUSTER BY (order_date, region);

-- WARNING: Protocol upgrades are irreversible.
-- If you need to downgrade, DEEP CLONE to a new table instead.

Issue	Cause	Solution
Library incompatible with new DBR	Python/Java version change	Pin library versions in `requirements.txt`, test in staging
`PERMISSION_DENIED` after migration	Missing Unity Catalog grants	Run `GRANT USAGE ON CATALOG`, `GRANT SELECT ON SCHEMA`
`SYNC` fails	Storage location inaccessible	Check cloud storage permissions and network config
Protocol downgrade error	Cannot lower protocol version	`DEEP CLONE` to a new table with lower protocol
`Table not found` after migration	Notebooks still reference `hive_metastore`	Update all references to `catalog.schema.table` format

# Current state
echo "CLI: $(databricks --version)"
echo "SDK: $(pip show databricks-sdk | grep Version)"
echo "Cluster DBR: $(databricks clusters get --cluster-id $CID | jq -r .spark_version)"

# Upgrade SDK
pip install --upgrade databricks-sdk

# Migrate all tables in a Hive Metastore database
source_db = "hive_metastore.legacy_data"
target_schema = "analytics.migrated"

tables = spark.sql(f"SHOW TABLES IN {source_db}").collect()
for t in tables:
    table_name = t.tableName
    print(f"Migrating {table_name}...")
    spark.sql(f"""
        CREATE TABLE IF NOT EXISTS {target_schema}.{table_name}
        AS SELECT * FROM {source_db}.{table_name}
    """)
    # Verify
    src_count = spark.table(f"{source_db}.{table_name}").count()
    tgt_count = spark.table(f"{target_schema}.{table_name}").count()
    status = "OK" if src_count == tgt_count else "MISMATCH"
    print(f"  {table_name}: {src_count} -> {tgt_count} [{status}]")