Starrocks Schema Evolution

StarRocks — Schema Evolution

When to Use

Load this skill when the user needs to:

• Add new metric or dimension columns to an existing StarRocks table
• Widen a column type (INT → BIGINT, VARCHAR(64) → VARCHAR(256), FLOAT → DOUBLE)
• Rename or reorder columns without breaking downstream queries
• Drop obsolete columns safely
• Monitor an in-progress schema change job and cancel it if needed
• Execute zero-downtime schema migrations in production
• Handle schema drift in dbt models backed by StarRocks tables
• Backfill a new column with a default value across partitioned tables

Schema Change Types in StarRocks

StarRocks 3.x classifies every ALTER TABLE COLUMN job into one of three internal change types. The type determines cost (latency, I/O, resource usage) and rollback safety.

Direct Schema Change (Instant / Metadata Only)

Metadata-level operation — no tablet data is rewritten.

Impact: Commits in seconds. No progress in SHOW ALTER TABLE COLUMN beyond FINISHED. Safe to run during peak hours.

Linked Schema Change (Background Backfill)

A background job rewrites affected column data incrementally, tablet by tablet.

Impact: Background job runs while the table stays fully readable and writable. Ingestion continues. Duration scales with data volume. Monitor with SHOW ALTER TABLE COLUMN.

Sorted Schema Change (Full Data Rewrite)

Requires full tablet rewrite because the physical sort order changes.

Impact: High I/O, proportional to total table size. Table remains available but with degraded write performance. Plan a maintenance window for large tables.

ADD COLUMN

Basic Syntax

-- Add a single nullable column (direct change — instant)
ALTER TABLE orders
ADD COLUMN is_fraud BOOLEAN NULL COMMENT 'Fraud flag from ML model';

-- Add a column with a default value at a specific position
ALTER TABLE orders
ADD COLUMN discount_amount DECIMAL(12, 2) DEFAULT 0.00
AFTER order_amount
COMMENT 'Promotional discount applied at order time';

-- Add a column as the first non-key column
ALTER TABLE events
ADD COLUMN ingest_version TINYINT DEFAULT 1 FIRST;

Adding Multiple Columns in One ALTER

Batching columns into a single ALTER TABLE statement is strongly preferred: StarRocks executes them as one schema-change job, minimising the number of tablet rewrites.

ALTER TABLE fact_sales
ADD COLUMN region_code     VARCHAR(8)     DEFAULT ''   AFTER country_code,
ADD COLUMN channel         VARCHAR(32)    DEFAULT 'unknown' AFTER region_code,
ADD COLUMN gross_margin    DECIMAL(18, 4) NULL         AFTER revenue,
ADD COLUMN is_promo        BOOLEAN        DEFAULT FALSE AFTER gross_margin;

Aggregate Key Table — Specifying Aggregate Function

For Aggregate Key tables, every non-key column must carry an aggregate function. When adding a column you must include it in the ALTER TABLE statement.

-- Table definition (reference):
-- CREATE TABLE agg_daily_metrics (
--     dt DATE, user_id BIGINT,                  -- key columns
--     session_cnt BIGINT SUM, ...               -- value columns
-- ) AGGREGATE KEY(dt, user_id)

ALTER TABLE agg_daily_metrics
ADD COLUMN ad_clicks BIGINT SUM DEFAULT 0
AFTER session_cnt
COMMENT 'Ad click count, summed during compaction';

-- HLL and BITMAP aggregate columns
ALTER TABLE agg_daily_metrics
ADD COLUMN uv_hll HLL HLL_UNION DEFAULT HLL_EMPTY() AFTER ad_clicks;

ALTER TABLE agg_daily_metrics
ADD COLUMN active_users_bitmap BITMAP BITMAP_UNION DEFAULT BITMAP_EMPTY() AFTER uv_hll;

Omitting the aggregate function on a new value column raises ERROR 1064: Aggregate function is required for value column.

Unique Key Table

Unique Key tables store the latest row per key (REPLACE semantics). Adding a value column follows standard syntax — no aggregate function required, but NULL or DEFAULT is strongly recommended so existing rows have a defined value.

ALTER TABLE dim_customer
ADD COLUMN loyalty_tier   VARCHAR(16) DEFAULT 'bronze' AFTER signup_date,
ADD COLUMN gdpr_deleted   BOOLEAN     DEFAULT FALSE    AFTER loyalty_tier;

Primary Key Table

Primary Key tables support full DML. Any new column must be either NULL or carry a DEFAULT value; otherwise existing rows cannot be read consistently.

ALTER TABLE orders_pk
ADD COLUMN fulfillment_sla_hours SMALLINT  DEFAULT 48   AFTER shipping_method,
ADD COLUMN last_modified_at      DATETIME  NULL         AFTER fulfillment_sla_hours;

For Primary Key tables, StarRocks 3.1+ treats constant-default ADD COLUMN as a direct (instant) change — no background rewrite occurs.

DROP COLUMN

Syntax

-- Drop a single non-key column
ALTER TABLE fact_sales
DROP COLUMN legacy_promo_code;

-- Drop multiple columns in one job
ALTER TABLE fact_sales
DROP COLUMN legacy_promo_code,
DROP COLUMN deprecated_flag,
DROP COLUMN tmp_etl_batch;

Constraints

Immediate vs Deferred Removal

Logically, the column disappears from DESCRIBE and query planning immediately after the job reaches FINISHED state. Physically, column data is removed from tablet files during the next background compaction. Disk space is reclaimed lazily — do not rely on DROP COLUMN for immediate storage reclamation on large tables.

Synchronous MV Impact

-- Identify MVs that reference a column before dropping it
SHOW CREATE MATERIALIZED VIEW mv_order_summary\G

-- If the MV references the column you plan to drop:
DROP MATERIALIZED VIEW IF EXISTS mv_order_summary;

-- Then proceed with the column drop
ALTER TABLE fact_sales DROP COLUMN old_status_code;

-- Recreate the MV if needed, excluding the dropped column
CREATE MATERIALIZED VIEW mv_order_summary
AS SELECT order_date, user_id, SUM(amount) AS total_amount
   FROM fact_sales
   GROUP BY order_date, user_id;

MODIFY COLUMN (Type Widening)

Allowed Widenings

Forbidden Modifications

Syntax

-- Widen INT to BIGINT
ALTER TABLE fact_orders
MODIFY COLUMN order_id BIGINT NOT NULL COMMENT 'Widened from INT';

-- Widen VARCHAR
ALTER TABLE dim_product
MODIFY COLUMN description VARCHAR(1024) NULL;

-- FLOAT → DOUBLE
ALTER TABLE sensor_readings
MODIFY COLUMN temperature DOUBLE NULL;

-- DATE → DATETIME (adds time component, always 00:00:00 for existing rows)
ALTER TABLE events
MODIFY COLUMN event_date DATETIME NOT NULL;

When widening a NOT NULL column you must repeat NOT NULL in the new definition; StarRocks does not inherit nullability automatically.

MODIFY COLUMN on Aggregate Key

You may widen the type of a value column, but the aggregate function must be restated and must not change:

ALTER TABLE agg_daily_metrics
MODIFY COLUMN session_cnt BIGINT SUM DEFAULT 0;
-- SUM must match the original aggregate function

REORDER COLUMNS

ORDER BY controls the prefix index (sort key) StarRocks builds per tablet. Reordering changes physical row layout — this is always a sorted schema change (full rewrite).

-- Original ORDER BY: (event_date, event_ts, user_id)
-- New intent: query by (user_id, event_date) most frequently

ALTER TABLE page_events
ORDER BY (user_id, event_date, event_ts);

For Primary Key tables the sort key is always identical to the primary key — reordering is not supported unless you are changing the entire primary key definition (which requires CREATE TABLE … AS SELECT).

For Duplicate Key and Unique Key tables with an explicit ORDER BY clause (StarRocks 3.0+), you can change the sort key independently from the key columns:

-- Duplicate Key table: key columns unchanged, sort order changed
ALTER TABLE ods.page_events
ORDER BY (user_id, event_date, event_ts, event_id);
-- event_id is still in DUPLICATE KEY() but moved to the end of sort

Monitoring Schema Changes

SHOW ALTER TABLE COLUMN

-- Show all schema change jobs for the current database
SHOW ALTER TABLE COLUMN;

-- Filter to a specific table
SHOW ALTER TABLE COLUMN
WHERE TableName = 'fact_orders';

-- Include finished/cancelled jobs (default shows recent N jobs)
SHOW ALTER TABLE COLUMN
WHERE TableName = 'fact_orders'
ORDER BY CreateTime DESC
LIMIT 20;

Key columns in the output:

Job State Machine

PENDING → WAITING_TXN → RUNNING → FINISHED
                   ↓              ↓
               CANCELLED      CANCELLED

Polling a Job in Shell

# Poll until the job finishes or is cancelled
watch -n 5 "mysql -h sr-fe -P 9030 -u root -e \
  \"SHOW ALTER TABLE COLUMN WHERE TableName = 'fact_orders'\" 2>/dev/null \
  | awk '{print \$1, \$7, \$8, \$9}'"

Cancel a Running Job

-- Cancel by table name (cancels the active job on that table)
CANCEL ALTER TABLE COLUMN ON fact_orders;

-- Verify cancellation
SHOW ALTER TABLE COLUMN
WHERE TableName = 'fact_orders'
ORDER BY CreateTime DESC
LIMIT 1;
-- State should be CANCELLED; original schema is restored

Cancellation is safe: StarRocks keeps the original tablet data intact during the job. On cancellation the new tablet copies are discarded.

Zero-Downtime Patterns

Pattern 1 — Adding Nullable Columns (Zero Downtime, Instant)

No downtime. The column becomes available immediately after the DDL commits.

-- Add nullable column — direct schema change
ALTER TABLE fact_orders
ADD COLUMN predicted_churn_score FLOAT NULL COMMENT 'ML model output, backfilled nightly';

Downstream queries that don't reference the column are unaffected. New writes can immediately populate the column. Old rows return NULL until a backfill job sets values.

Pattern 2 — Adding Columns with DEFAULT (Near-Zero Downtime)

Reads return the DEFAULT value instantly; background job fills tablets at rest.

ALTER TABLE fact_orders
ADD COLUMN order_channel VARCHAR(32) DEFAULT 'web' AFTER user_id;

Table remains fully readable and writable throughout. The backfill is tablet-granular and low-priority. In StarRocks 3.1+ on Primary Key tables, this is a direct (instant) change — no background job at all.

Pattern 3 — Type Widening (Near-Zero Downtime)

Background rewrite, but the table stays online.

-- Step 1: Widen the column
ALTER TABLE fact_orders
MODIFY COLUMN order_amount DECIMAL(18, 4) NULL;

-- Step 2: Monitor until FINISHED
SHOW ALTER TABLE COLUMN WHERE TableName = 'fact_orders';

-- Step 3: Update downstream DDL/models after FINISHED

Queries continue to work during the rewrite because StarRocks dual-reads old tablets (old type) and new tablets (new type) transparently until all tablets finish.

Pattern 4 — Sort Key Change (Requires Maintenance Window)

Full rewrite. Plan for:

• Duration: estimate ~30 min per 100 GB of tablet data on typical hardware
• Increased I/O on BE nodes during the job
• Temporarily degraded ingest performance

-- Pre-maintenance: notify dependent services, pause high-volume loaders if possible

-- Execute sort key change
ALTER TABLE ods.clickstream
ORDER BY (user_id, event_date, event_ts);

-- Monitor
SHOW ALTER TABLE COLUMN WHERE TableName = 'clickstream';

-- Post-maintenance: verify row counts match, spot-check queries
SELECT COUNT(*) FROM ods.clickstream;
SELECT user_id, COUNT(*) FROM ods.clickstream
WHERE event_date = CURRENT_DATE()
GROUP BY user_id LIMIT 10;

Pattern 5 — Expand-Contract for Breaking Changes

Use when the column type change is not directly allowed (e.g., changing DECIMAL scale, renaming a column in older StarRocks versions).

-- Phase 1: Expand — add the new column alongside the old one
ALTER TABLE dim_product
ADD COLUMN price_usd DECIMAL(18, 4) NULL AFTER price;

-- Phase 2: Backfill — populate the new column
UPDATE dim_product
SET price_usd = CAST(price AS DECIMAL(18, 4))
WHERE price_usd IS NULL;

-- (or use an INSERT OVERWRITE / Flink/Spark backfill job for large tables)

-- Phase 3: Migrate writers — update all ETL jobs to write price_usd
-- Phase 4: Migrate readers — update all dashboards/BI to read price_usd
-- Phase 5: Contract — drop the old column once readers/writers are fully migrated
ALTER TABLE dim_product DROP COLUMN price;

-- Phase 6: (Optional) Rename if desired — in StarRocks 3.2+
ALTER TABLE dim_product MODIFY COLUMN price_usd RENAME price;

dbt Model Evolution

StarRocks + dbt Materializations

dbt interacts with StarRocks schema evolution through two mechanisms:

1. on_schema_change — what dbt does when the target table schema differs from the model SQL
2. Manual ALTER TABLE — executed outside dbt to pre-stage structural changes

on_schema_change Config

# dbt_project.yml or model config block
models:
  my_project:
    marts:
      +materialized: incremental
      +on_schema_change: append_new_columns   # recommended for StarRocks

# In-model config (overrides project-level)
{{ config(
    materialized='incremental',
    unique_key='order_id',
   
) }}

Recommended default for StarRocks: append_new_columns. StarRocks handles the ALTER TABLE ADD COLUMN as a direct or linked change, keeping the table online. sync_all_columns is dangerous because it will DROP COLUMN if you remove a field from your SELECT — downstream consumers may break.

Adding a Column to a dbt-Managed Table

-- Option A: Let dbt handle it (append_new_columns)
-- 1. Add the column to the model SELECT list with a default/null expression
-- 2. Run: dbt run --select my_model
-- dbt issues: ALTER TABLE my_model ADD COLUMN new_col TYPE ...

-- Option B: Pre-stage the column manually (recommended for large tables)
-- 1. Run ALTER TABLE manually to add column with DEFAULT
ALTER TABLE marts.fact_orders
ADD COLUMN fulfillment_days SMALLINT DEFAULT NULL;

-- 2. Update the dbt model SQL to SELECT the column
-- 3. Run: dbt run --select fact_orders
-- dbt detects column already exists — no ALTER issued, just populates data

Full-Refresh Triggers

A dbt run --full-refresh will DROP and CREATE the table from scratch. Use this when:

• A sort key change is needed (not expressible via incremental ALTER)
• Aggregate function on a value column must change
• A column type needs to narrow (not allowed by ALTER)

# Full refresh — drops the table and rebuilds
dbt run --select fact_orders --full-refresh

# Confirm the table was recreated
dbt show --select fact_orders --limit 5

On large tables, a full refresh can be expensive. Pre-coordinate with downstream consumers.

dbt Snapshot Schema Evolution

When evolving a snapshot table (SCD2 target), dbt does NOT support ALTER TABLE on snapshot tables — it always does a full rebuild. Run with --full-refresh and accept the rebuild cost, or manually add columns to the snapshot table and let dbt populate them on the next run.

dbt snapshot --select scd_dim_customer --full-refresh

Backward-Compatible Checklist

Run through this checklist before executing any ALTER TABLE in production:

1. Identify all readers — query information_schema.tables and check BI tools, Flink jobs, Spark jobs, and application queries for references to the target table and column.

2. Identify synchronous MVs — run SHOW CREATE MATERIALIZED VIEW for each MV on the table; verify the column being dropped/modified is not referenced.

3. Check active load transactions — SHOW PROC '/transactions/<db>'; the schema change job will wait in WAITING_TXN until in-flight loads commit. High-frequency loaders can delay the job start.

4. Estimate job duration — for LINKED and SORT changes, estimate duration from table size:

   SELECT SUM(data_size) / 1024 / 1024 / 1024 AS size_gb
   FROM information_schema.partitions
   WHERE table_name = 'fact_orders' AND table_schema = 'marts';
   

5. Test on a dev/staging clone — execute the ALTER on a representative sample table first; verify SHOW ALTER TABLE COLUMN completes with FINISHED.

6. Backup partition manifest — for partitioned tables, snapshot SHOW PARTITIONS FROM <table> output before the change.

7. Confirm type widening is allowed — cross-check the FROM→TO pair against the allowed widening table above. If it is not listed, use the expand-contract pattern instead.

8. Verify DEFAULT values are semantically correct — a DEFAULT of 0 on a revenue column may corrupt aggregations; NULL is safer when the value is genuinely unknown.

9. Coordinate writer schema — if loaders use a schema-aware format (Avro, Protobuf), update producer schemas before or simultaneously with the ALTER.

10. Have a rollback plan — for LINKED changes, CANCEL ALTER TABLE COLUMN ON <table> is safe and leaves the original schema intact. For SORT changes, prepare a CREATE TABLE … AS SELECT restore script.

Anti-Patterns

Dropping Columns with Active Downstream Queries

-- ANTI-PATTERN: Drop immediately without coordination
ALTER TABLE fact_orders DROP COLUMN legacy_status;
-- Dashboards and Flink jobs reading legacy_status will fail with
-- "Unknown column 'legacy_status'" immediately after the job finishes

Fix: Use expand-contract. Keep the column, make new readers use the replacement column, then drop after a safe migration window.

Changing Aggregate Function on a Live Table

-- ANTI-PATTERN: Changing SUM → MAX on a column that already has aggregated data
ALTER TABLE agg_metrics MODIFY COLUMN revenue BIGINT MAX DEFAULT 0;
-- StarRocks will reject this: aggregate function cannot be changed on Aggregate Key column
-- Even if accepted in future versions, historical data was summed — using MAX now is meaningless

Fix: Add a new column with the new aggregate function; backfill from source data, not from the aggregated column.

Sort Key Change Without Maintenance Window

-- ANTI-PATTERN: Changing ORDER BY on a 2 TB table during business hours
ALTER TABLE ods.page_events ORDER BY (user_id, event_date, event_ts);
-- Full tablet rewrite for 2 TB → multi-hour I/O spike
-- BE nodes may OOM or throttle, impacting concurrent queries

Fix: Schedule during off-peak. Pre-scale BE nodes. Use resource groups to cap the ALTER job's CPU/IO if supported.

Multiple Sequential ALTER TABLE Jobs on the Same Table

-- ANTI-PATTERN: Running ALTERs serially
ALTER TABLE fact_orders ADD COLUMN col_a BIGINT DEFAULT 0;
-- (wait)
ALTER TABLE fact_orders ADD COLUMN col_b VARCHAR(32) DEFAULT '';
-- (wait)
ALTER TABLE fact_orders ADD COLUMN col_c BOOLEAN DEFAULT FALSE;
-- 3 separate background jobs = 3x the I/O

Fix: Batch all column additions into a single ALTER TABLE statement to execute as one job.

Using on_schema_change='sync_all_columns' in dbt Without Review

# ANTI-PATTERN
+on_schema_change: sync_all_columns

If a developer removes a column from a dbt model SELECT during refactoring, sync_all_columns will issue DROP COLUMN on the live table — breaking any consumer that was still reading that column.

Fix: Use append_new_columns as the default; issue explicit DROP COLUMN ALTER statements as a deliberate, reviewed step.

Widening a Key Column

-- ANTI-PATTERN: Trying to widen a key column directly
ALTER TABLE fact_orders MODIFY COLUMN order_id BIGINT NOT NULL;
-- order_id is in DUPLICATE KEY() → StarRocks rejects this
-- ERROR: Do not support change key column

Fix: Use CREATE TABLE new_table AS SELECT CAST(order_id AS BIGINT) AS order_id, ... FROM fact_orders, then swap via ALTER TABLE RENAME.

Output Expectations

When performing schema evolution tasks:

• Always state which schema change type (DIRECT / LINKED / SORT) the operation will trigger
• Include the full ALTER TABLE statement with explicit column positions (AFTER / FIRST) to make the change deterministic
• For Aggregate Key tables, always include the aggregate function in ADD/MODIFY statements
• Include the SHOW ALTER TABLE COLUMN monitoring query
• For SORT changes, include duration estimate guidance based on table size
• For dbt-managed tables, specify the on_schema_change value and whether a --full-refresh is needed

References to Consult When Needed

• StarRocks 3.x Documentation: ALTER TABLE — https://docs.starrocks.io/docs/sql-reference/sql-statements/data-definition/ALTER_TABLE/
• StarRocks 3.x Documentation: Schema Change Overview — https://docs.starrocks.io/docs/table_design/schema_change/
• StarRocks 3.x Documentation: Primary Key table DML — https://docs.starrocks.io/docs/table_design/table_types/primary_key_table/
• dbt StarRocks adapter: https://github.com/StarRocks/starrocks/tree/main/contrib/starrocks-connector-for-dbt