Skip to main content
Iceberg supports in-place table evolution - you can evolve a table schema or change partition layout without costly data rewrites or table migrations. This is a fundamental advantage over traditional table formats.

​
Why Evolution Matters

Traditional table formats make schema and partition changes expensive:
Hive Example: Changing from daily to hourly partitions requires:
  1. Creating a new table with the new partition scheme
  2. Rewriting all historical data to the new table
  3. Updating all queries to use the new table name
  4. Managing the migration cutover period
Iceberg eliminates these costs through metadata-only evolution operations.

​
Schema Evolution

Iceberg supports comprehensive schema changes as metadata operations:

​
Supported Operations

Add

Add new columns to the table or nested structs

Drop

Remove existing columns from the table or nested structs

Rename

Rename existing columns or fields in nested structs

Update

Widen column types using safe type promotions

Reorder

Change the order of columns or struct fields

Default Values

Set initial and write defaults for fields (v3+)

​
Adding Columns

Add new columns anywhere in the schema: 
// Add a top-level column
table.updateSchema()
  .addColumn("email", Types.StringType.get())
  .commit();

// Add a nested column in a struct
table.updateSchema()
  .addColumn("customer.phone", Types.StringType.get())
  .commit();

// Add with default value (v3+)
table.updateSchema()
  .addColumn("status", Types.StringType.get())
  .setDefault("status", "pending")
  .commit();
When a column is added:
  • It gets a new, unique field ID
  • Existing data files don’t contain the column
  • Reads return null (or the default value) for old files
  • New writes include the column

​
Dropping Columns

Remove columns from the current schema: 
// Drop a column
table.updateSchema()
  .deleteColumn("deprecated_field")
  .commit();

// Drop a nested field
table.updateSchema()
  .deleteColumn("metadata.internal_id") 
  .commit();
When a column is dropped:
  • It’s removed from the current schema
  • The field ID is never reused
  • Old data files still contain the column (not rewritten)
  • Reads don’t return the column
  • The column can be added back with a new field ID

​
Renaming Columns

Change column names without affecting data: 
// Rename a column  
table.updateSchema()
  .renameColumn("customer_name", "full_name")
  .commit();

// Rename nested field
table.updateSchema()
  .renameColumn("address.zipcode", "address.postal_code")
  .commit();
When a column is renamed:
  • The field ID stays the same
  • Data files are unchanged
  • Both old and new queries work immediately

​
Type Promotion

Widen column types using safe promotions: 
// Promote int to long
table.updateSchema()
  .updateColumn("count", Types.LongType.get())
  .commit();

// Promote float to double  
table.updateSchema()
  .updateColumn("value", Types.DoubleType.get())
  .commit();

// Widen decimal precision
table.updateSchema()
  .updateColumn("price", Types.DecimalType.of(12, 2)) // was decimal(10,2)
  .commit();
Valid type promotions: | From | To | Notes | |------|----|----- -| | int | long | Safe - no data loss | | float | double | Safe - no precision loss | | decimal(P,S) | decimal(P’,S) | Only widen precision (P’ > P) | | date | timestamp, timestamp_ns | v3+ only |
Promotion from timestamp to timestamptz is not allowed as it changes semantic meaning.

​
Reordering Columns

Change the column order in query results: 
// Move column to first position
table.updateSchema()
  .moveFirst("id")
  .commit();

// Move column after another  
table.updateSchema()
  .moveAfter("email", "name")
  .commit();

// Move column before another
table.updateSchema()
  .moveBefore("created_at", "updated_at")
  .commit();
Column order changes:
  • Affect the order in SELECT * queries
  • Don’t require data file rewrites
  • Don’t affect column identification (still by field ID)

​
Using Spark SQL

-- Add column
ALTER TABLE db.table ADD COLUMN email string;

-- Add column with comment
ALTER TABLE db.table ADD COLUMN phone string COMMENT 'Contact phone';

-- Drop column  
ALTER TABLE db.table DROP COLUMN deprecated_field;

-- Rename column
ALTER TABLE db.table RENAME COLUMN old_name TO new_name;

-- Change column type (promotion only)
ALTER TABLE db.table ALTER COLUMN count TYPE bigint;

-- Reorder columns
ALTER TABLE db.table ALTER COLUMN email AFTER name;
ALTER TABLE db.table ALTER COLUMN id FIRST;

​
Correctness Guarantees

Iceberg guarantees that schema evolution changes are independent and free of side-effects:
Each column has a unique field ID. Adding column_b will never accidentally read data that was written as column_a.Why this matters: Formats that track columns by name can reuse a deleted column’s name, causing data corruption.
Removing column_a doesn’t change the values in column_b or any other column.Why this matters: Formats that track columns by position must shift all subsequent columns when one is deleted.
Promoting count from int to long doesn’t change values in any other column.Why this matters: Column updates are isolated and predictable.
Moving email before name doesn’t change which data belongs to which column.Why this matters: Field IDs, not positions, identify columns.
These guarantees are possible because Iceberg uses unique field IDs to track columns, not names or positions.

​
Partition Evolution

Iceberg allows changing partition layout without rewriting data: 
// Update partition spec
table.updateSpec()
  .addField(bucket("id", 8))      // Add bucketing on id
  .removeField("category")         // Remove category partition
  .commit();

​
How It Works

When you evolve a partition spec:
  1. Old data keeps its partition layout - Files written with the old spec are unchanged
  2. New data uses the new layout - New writes use the updated partition spec
  3. Metadata tracks both - Each partition spec has a unique ID
  4. Split planning - Queries plan old and new layouts separately
Before Evolution:                After Evolution:

Partition Spec v1:               Partition Spec v1:
  months(timestamp)                months(timestamp)
                                   ↓
  2008-01/ (unchanged)             2008-01/ (unchanged)
  2008-02/ (unchanged)             2008-02/ (unchanged)
  ...
  2008-12/ (unchanged)             2008-12/ (unchanged)
                                   
                                 Partition Spec v2:  
                                   days(timestamp)
                                   ↓
                                   2009-01-01/ (new)
                                   2009-01-02/ (new)
                                   ...

​
Partition Evolution Example

A logs table starts with monthly partitions, then switches to daily: 
-- Create table with monthly partitions  
CREATE TABLE logs (
  level string,
  message string,
  ts timestamp
) USING iceberg
PARTITIONED BY (months(ts));

-- Write data for 2023 (monthly partitions)
INSERT INTO logs VALUES ...;

-- Evolve to daily partitions for 2024 data
ALTER TABLE logs 
ADD PARTITION FIELD days(ts);

ALTER TABLE logs  
DROP PARTITION FIELD month_ts;

-- New data uses daily partitions
INSERT INTO logs VALUES ...;

-- Queries work across both layouts!
SELECT * FROM logs
WHERE ts BETWEEN '2023-12-15' AND '2024-01-15';
-- Prunes monthly partitions for 2023, daily partitions for 2024

​
Why Partition Evolution Works

Iceberg’s hidden partitioning makes evolution possible:
  • Queries filter on source columns (ts), not partition values
  • Iceberg derives appropriate partition filters for each spec
  • Users don’t need to know about partition layout changes
  • Old and new data coexist seamlessly

​
Using Spark SQL

-- Add a partition field
ALTER TABLE db.table 
ADD PARTITION FIELD bucket(16, user_id);

-- Add with custom name
ALTER TABLE db.table
ADD PARTITION FIELD bucket(8, id) AS id_bucket;

-- Remove a partition field (by name)
ALTER TABLE db.table  
DROP PARTITION FIELD category;

-- Replace partition spec (drops all existing fields)
ALTER TABLE db.table
REPLACE PARTITION FIELD days(ts) WITH hours(ts);

​
Using Java API

// Add partition fields
table.updateSpec()
  .addField(bucket("user_id", 16))
  .addField("user_bucket", bucket("user_id", 16)) // with custom name
  .commit();

// Remove partition field by name  
table.updateSpec()
  .removeField("category")
  .commit();

// Remove by transform
table.updateSpec()
  .removeField(bucket("id", 8))
  .commit();

​
Sort Order Evolution

Iceberg also supports evolving the sort order: 
// Replace sort order
table.replaceSortOrder()
  .asc("id", NullOrder.NULLS_LAST)
  .desc("category", NullOrder.NULLS_FIRST)  
  .commit();
When sort order changes:
  • Old data keeps its original sort order
  • New data is written with the new sort order
  • Engines can choose whether to sort (or write unsorted if expensive)
-- Using Spark SQL
ALTER TABLE db.table 
WRITE ORDERED BY id, category DESC NULLS FIRST;

​
Evolution Best Practices

Create a table branch or copy to test evolution operations before applying to production.
table.manageSnapshots()
  .createBranch("test-schema", currentSnapshotId)
  .commit();
While technically allowed (with new field ID), reusing names can confuse users and queries.Better to use a new name: customer_email_v2 instead of reusing customer_email.
Monitor partition file sizes:
  • Too large (> 1GB) → Consider finer granularity (daily → hourly)
  • Too small (< 100MB) → Consider coarser granularity (hourly → daily)
When adding required fields in v3, always set a default value:
table.updateSchema()
  .addRequiredColumn("version", Types.IntegerType.get())
  .setDefault("version", 1)
  .commit();
Use table properties to track significant schema evolution:
table.updateProperties()
  .set("schema.change.2024-03-01", "Added user_id field for tracking")
  .commit();

​
Limitations and Constraints

Type Promotion Restrictions:
  • Cannot promote if a partition field uses the source column with an incompatible transform
  • Example: Cannot promote date to timestamp if bucket(date) is a partition field (hash would change)
Struct Evolution Restrictions:
  • Cannot convert a primitive to a struct or vice versa
  • Cannot move fields in/out of nested structs
  • Cannot change struct field IDs
Map Key Evolution:
  • Cannot add or drop struct fields in map keys (would change equality semantics)

​
Migration Scenarios

​
Migrating from Hive

Evolve Hive partition columns into hidden partitions: 
-- Hive table with explicit partition column
-- event_date is both a partition and a column

-- After migration to Iceberg:  
ALTER TABLE events DROP COLUMN event_date;  -- Remove partition column
-- Partition values now derived from event_time
-- Queries on event_time automatically filter partitions

​
Changing Partition Granularity

Gracefully transition from coarse to fine granularity: 
-- Start: Monthly partitions (2023 data)
-- Evolve: Add daily partitions
ALTER TABLE metrics ADD PARTITION FIELD days(ts);
ALTER TABLE metrics DROP PARTITION FIELD month_ts;

-- 2023 data: Monthly partitions (large files, coarse pruning)
-- 2024+ data: Daily partitions (right-sized files, fine pruning)  
-- Queries work seamlessly across both!

​
Learn More

Schemas

Understand Iceberg’s schema structure and field IDs

Partitioning

Learn about partition transforms and hidden partitioning

Branching

Use branches to test schema changes safely

Build docs developers (and LLMs) love

Get started for freeTalk to us