The APPLY CHANGES APIs: Simplify change data capture with Delta Live Tables

Delta Live Tables simplifies change data capture (CDC) with the APPLY CHANGES and APPLY CHANGES FROM SNAPSHOT APIs. The interface you use depends on the source of change data:

  • Use APPLY CHANGES to process changes from a change data feed (CDF).

  • Use APPLY CHANGES FROM SNAPSHOT (Public Preview) to process changes in database snapshots.

Previously, the MERGE INTO statement was commonly used for processing CDC records on Databricks. However, MERGE INTO can produce incorrect results because of out-of-sequence records or requires complex logic to re-order records.

The APPLY CHANGES API is supported in the Delta Live Tables SQL and Python interfaces. The APPLY CHANGES FROM SNAPSHOT API is supported in the Delta Live Tables Python interface.

Both APPLY CHANGES and APPLY CHANGES FROM SNAPSHOT support updating tables using SCD type 1 and type 2:

  • Use SCD type 1 to update records directly. History is not retained for updated records.

  • Use SCD type 2 to retain a history of records, either on all updates or on updates to a specified set of columns.

For syntax and other references, see:

Note

This article describes how to update tables in your Delta Live Tables pipeline based on changes in source data. To learn how to record and query row-level change information for Delta tables, see Use Delta Lake change data feed on Databricks.

Requirements

To use the CDC APIs, your pipeline must be configured to use the Delta Live Tables Pro or Advanced editions.

How is CDC implemented with the APPLY CHANGES API?

By automatically handling out-of-sequence records, the APPLY CHANGES API in Delta Live Tables ensures correct processing of CDC records and removes the need to develop complex logic for handling out-of-sequence records. You must specify a column in the source data on which to sequence records, which Delta Live Tables interprets as a monotonically increasing representation of the proper ordering of the source data. Delta Live Tables automatically handles data that arrives out of order. For SCD type 2 changes, Delta Live Tables propagates the appropriate sequencing values to the target table’s __START_AT and __END_AT columns. There should be one distinct update per key at each sequencing value, and NULL sequencing values are unsupported.

To perform CDC processing with APPLY CHANGES, you first create a streaming table and then use the APPLY CHANGES INTO statement in SQL or the apply_changes() function in Python to specify the source, keys, and sequencing for the change feed. To create the target streaming table, use the CREATE OR REFRESH STREAMING TABLE statement in SQL or the create_streaming_table() function in Python. See the SCD type 1 and type 2 processing examples.

For syntax details, see the Delta Live Tables SQL reference or Python reference.

How is CDC implemented with the APPLY CHANGES FROM SNAPSHOT API?

Preview

The APPLY CHANGES FROM SNAPSHOT API is in Public Preview.

APPLY CHANGES FROM SNAPSHOT is a declarative API that efficiently determines changes in source data by comparing a series of in-order snapshots and then runs the processing required for CDC processing of the records in the snapshots. APPLY CHANGES FROM SNAPSHOT is supported only by the Delta Live Tables Python interface.

APPLY CHANGES FROM SNAPSHOT supports ingesting snapshots from multiple source types:

  • Use periodic snapshot ingestion to ingest snapshots from an existing table or view. APPLY CHANGES FROM SNAPSHOT has a simple, streamlined interface to support periodically ingesting snapshots from an existing database object. A new snapshot is ingested with each pipeline update, and the ingestion time is used as the snapshot version. When a pipeline is run in continuous mode, multiple snapshots are ingested with each pipeline update on a period determined by the trigger interval setting for the flow that contains the APPLY CHANGES FROM SNAPSHOT processing.

  • Use historical snapshot ingestion to process files containing database snapshots, such as snapshots generated from an Oracle or MySQL database or a data warehouse.

To perform CDC processing from any source type with APPLY CHANGES FROM SNAPSHOT, you first create a streaming table and then use the apply_changes_from_snapshot() function in Python to specify the snapshot, keys, and other arguments required to implement the processing. See the periodic snapshot ingestion and historical snapshot ingestion examples.

The snapshots passed to the API must be in ascending order by version. If Delta Live Tables detects an out-of-order snapshot, an error is thrown.

For syntax details, see the Delta Live Tables Python reference.

Limitations

The target of an APPLY CHANGES or APPLY CHANGES FROM SNAPSHOT query cannot be used as a source for a streaming table. A table that reads from the target of an APPLY CHANGES or APPLY CHANGES FROM SNAPSHOT query must be a materialized view.

Example: SCD type 1 and SCD type 2 processing with CDF source data

The following sections provide examples of Delta Live Tables SCD type 1 and type 2 queries that update target tables based on source events from a change data feed that:

  1. Creates new user records.

  2. Deletes a user record.

  3. Updates user records. In the SCD type 1 example, the last UPDATE operations arrive late and are dropped from the target table, demonstrating the handling of out-of-order events.

The following examples assume familiarity with configuring and updating Delta Live Tables pipelines. See Tutorial: Run your first Delta Live Tables pipeline.

To run these examples, you must begin by creating a sample dataset. See Generate test data.

The following are the input records for these examples:

userId

name

city

operation

sequenceNum

124

Raul

Oaxaca

INSERT

1

123

Isabel

Monterrey

INSERT

1

125

Mercedes

Tijuana

INSERT

2

126

Lily

Cancun

INSERT

2

123

null

null

DELETE

6

125

Mercedes

Guadalajara

UPDATE

6

125

Mercedes

Mexicali

UPDATE

5

123

Isabel

Chihuahua

UPDATE

5

If you uncomment the final row in the example data, it will insert the following record that specifies where records should be truncated:

userId

name

city

operation

sequenceNum

null

null

null

TRUNCATE

3

Note

All the following examples include options to specify both DELETE and TRUNCATE operations, but each is optional.

Process SCD type 1 updates

The following example demonstrates processing SCD type 1 updates:

import dlt
from pyspark.sql.functions import col, expr

@dlt.view
def users():
  return spark.readStream.format("delta").table("cdc_data.users")

dlt.create_streaming_table("target")

dlt.apply_changes(
  target = "target",
  source = "users",
  keys = ["userId"],
  sequence_by = col("sequenceNum"),
  apply_as_deletes = expr("operation = 'DELETE'"),
  apply_as_truncates = expr("operation = 'TRUNCATE'"),
  except_column_list = ["operation", "sequenceNum"],
  stored_as_scd_type = 1
)
-- Create and populate the target table.
CREATE OR REFRESH STREAMING TABLE target;

APPLY CHANGES INTO
  live.target
FROM
  stream(cdc_data.users)
KEYS
  (userId)
APPLY AS DELETE WHEN
  operation = "DELETE"
APPLY AS TRUNCATE WHEN
  operation = "TRUNCATE"
SEQUENCE BY
  sequenceNum
COLUMNS * EXCEPT
  (operation, sequenceNum)
STORED AS
  SCD TYPE 1;

After running the SCD type 1 example, the target table contains the following records:

userId

name

city

124

Raul

Oaxaca

125

Mercedes

Guadalajara

126

Lily

Cancun

After running the SCD type 1 example with the additional TRUNCATE record, records 124 and 126 are truncated because of the TRUNCATE operation at sequenceNum=3, and the target table contains the following record:

userId

name

city

125

Mercedes

Guadalajara

Process SCD type 2 updates

The following example demonstrates processing SCD type 2 updates:

import dlt
from pyspark.sql.functions import col, expr

@dlt.view
def users():
  return spark.readStream.format("delta").table("cdc_data.users")

dlt.create_streaming_table("target")

dlt.apply_changes(
  target = "target",
  source = "users",
  keys = ["userId"],
  sequence_by = col("sequenceNum"),
  apply_as_deletes = expr("operation = 'DELETE'"),
  except_column_list = ["operation", "sequenceNum"],
  stored_as_scd_type = "2"
)
-- Create and populate the target table.
CREATE OR REFRESH STREAMING TABLE target;

APPLY CHANGES INTO
  live.target
FROM
  stream(cdc_data.users)
KEYS
  (userId)
APPLY AS DELETE WHEN
  operation = "DELETE"
SEQUENCE BY
  sequenceNum
COLUMNS * EXCEPT
  (operation, sequenceNum)
STORED AS
  SCD TYPE 2;

After running the SCD type 2 example, the target table contains the following records:

userId

name

city

__START_AT

__END_AT

123

Isabel

Monterrey

1

5

123

Isabel

Chihuahua

5

6

124

Raul

Oaxaca

1

null

125

Mercedes

Tijuana

2

5

125

Mercedes

Mexicali

5

6

125

Mercedes

Guadalajara

6

null

126

Lily

Cancun

2

null

An SCD type 2 query can also specify a subset of output columns to be tracked for history in the target table. Changes to other columns are updated in place rather than generating new history records. The following example demonstrates excluding the city column from tracking:

The following example demonstrates using track history with SCD type 2:

import dlt
from pyspark.sql.functions import col, expr

@dlt.view
def users():
  return spark.readStream.format("delta").table("cdc_data.users")

dlt.create_streaming_table("target")

dlt.apply_changes(
  target = "target",
  source = "users",
  keys = ["userId"],
  sequence_by = col("sequenceNum"),
  apply_as_deletes = expr("operation = 'DELETE'"),
  except_column_list = ["operation", "sequenceNum"],
  stored_as_scd_type = "2",
  track_history_except_column_list = ["city"]
)
-- Create and populate the target table.
CREATE OR REFRESH STREAMING TABLE target;

APPLY CHANGES INTO
  live.target
FROM
  stream(cdc_data.users)
KEYS
  (userId)
APPLY AS DELETE WHEN
  operation = "DELETE"
SEQUENCE BY
  sequenceNum
COLUMNS * EXCEPT
  (operation, sequenceNum)
STORED AS
  SCD TYPE 2
TRACK HISTORY ON * EXCEPT
  (city)

After running this example without the additional TRUNCATE record, the target table contains the following records:

userId

name

city

__START_AT

__END_AT

123

Isabel

Chihuahua

1

6

124

Raul

Oaxaca

1

null

125

Mercedes

Guadalajara

2

null

126

Lily

Cancun

2

null

Generate test data

The code below is provided to generate an example dataset for use in the example queries present in this tutorial. Assuming that you have the proper credentials to create a new schema and create a new table, you can run these statements with either a notebook or Databricks SQL. The following code is not intended to be run as part of a Delta Live Tables pipeline:

CREATE SCHEMA IF NOT EXISTS cdc_data;

CREATE TABLE
  cdc_data.users
AS SELECT
  col1 AS userId,
  col2 AS name,
  col3 AS city,
  col4 AS operation,
  col5 AS sequenceNum
FROM (
  VALUES
  -- Initial load.
  (124, "Raul",     "Oaxaca",      "INSERT", 1),
  (123, "Isabel",   "Monterrey",   "INSERT", 1),
  -- New users.
  (125, "Mercedes", "Tijuana",     "INSERT", 2),
  (126, "Lily",     "Cancun",      "INSERT", 2),
  -- Isabel is removed from the system and Mercedes moved to Guadalajara.
  (123, null,       null,          "DELETE", 6),
  (125, "Mercedes", "Guadalajara", "UPDATE", 6),
  -- This batch of updates arrived out of order. The above batch at sequenceNum 5 will be the final state.
  (125, "Mercedes", "Mexicali",    "UPDATE", 5),
  (123, "Isabel",   "Chihuahua",   "UPDATE", 5)
  -- Uncomment to test TRUNCATE.
  -- ,(null, null,      null,          "TRUNCATE", 3)
);

Example: Periodic snapshot processing

The following example demonstrates SCD type 2 processing that ingests snapshots of a table stored at mycatalog.myschema.mytable. The results of processing are written to a table named target.

mycatalog.myschema.mytable records at the timestamp 2024-01-01 00:00:00

Key

Value

1

a1

2

a2

mycatalog.myschema.mytable records at the timestamp 2024-01-01 12:00:00

Key

Value

2

b2

3

a3

import dlt

@dlt.view(name="source")
def source():
 return spark.read.table("mycatalog.myschema.mytable")

dlt.create_streaming_table("target")

dlt.apply_changes_from_snapshot(
 target="target",
 source="source",
 keys=["key"],
 stored_as_scd_type=2
)

After processing the snapshots, the target table contains the following records:

Key

Value

__START_AT

__END_AT

1

a1

2024-01-01 00:00:00

2024-01-01 12:00:00

2

a2

2024-01-01 00:00:00

2024-01-01 12:00:00

2

b2

2024-01-01 12:00:00

null

3

a3

2024-01-01 12:00:00

null

Example: Historical snapshot processing

The following example demonstrates SCD type 2 processing that updates a target table based on source events from two snapshots stored in a cloud storage system:

Snapshot at timestamp, stored in /<PATH>/filename1.csv

Key

TrackingColumn

NonTrackingColumn

1

a1

b1

2

a2

b2

4

a4

b4

Snapshot at timestamp + 5, stored in /<PATH>/filename2.csv

Key

TrackingColumn

NonTrackingColumn

2

a2_new

b2

3

a3

b3

4

a4

b4_new

The following code example demonstrates processing SCD type 2 updates with these snapshots:

import dlt

def exist(file_name):
  # Storage system-dependent function that returns true if file_name exists, false otherwise

# This function returns a tuple, where the first value is a DataFrame containing the snapshot
# records to process, and the second value is the snapshot version representing the logical
# order of the snapshot.
# Returns None if no snapshot exists.
def next_snapshot_and_version(latest_snapshot_version):
  latest_snapshot_version = latest_snapshot_version or 0
  next_version = latest_snapshot_version + 1
  file_name = "dir_path/filename_" + next_version + ".csv"
  if (exist(file_name)):
    return (spark.read.load(file_name), next_version)
   else:
     # No snapshot available
     return None

dlt.create_streaming_live_table("target")

dlt.apply_changes_from_snapshot(
  target = "target",
  source = next_snapshot_and_version,
  keys = ["Key"],
  stored_as_scd_type = 2,
  track_history_column_list = ["TrackingCol"]
)

After processing the snapshots, the target table contains the following records:

Key

TrackingColumn

NonTrackingColumn

__START_AT

__END_AT

1

a1

b1

1

2

2

a2

b2

1

2

2

a2_new

b2

2

null

3

a3

b3

2

null

4

a4

b4_new

1

null

Add, change, or delete data in a target streaming table

If your pipeline publishes tables to Unity Catalog, you can use data manipulation language (DML) statements, including insert, update, delete, and merge statements, to modify the target streaming tables created by APPLY CHANGES INTO statements.

Note

  • DML statements that modify the table schema of a streaming table are not supported. Ensure that your DML statements do not attempt to evolve the table schema.

  • DML statements that update a streaming table can be run only in a shared Unity Catalog cluster or a SQL warehouse using Databricks Runtime 13.3 LTS and above.

  • Because streaming requires append-only data sources, if your processing requires streaming from a source streaming table with changes (for example, by DML statements), set the skipChangeCommits flag when reading the source streaming table. When skipChangeCommits is set, transactions that delete or modify records on the source table are ignored. If your processing does not require a streaming table, you can use a materialized view (which does not have the append-only restriction) as the target table.

Because Delta Live Tables uses a specified SEQUENCE BY column and propagates appropriate sequencing values to the __START_AT and __END_AT columns of the target table (for SCD type 2), you must ensure that DML statements use valid values for these columns to maintain the proper ordering of records. See How is CDC implemented with the APPLY CHANGES API?.

For more information about using DML statements with streaming tables, see Add, change, or delete data in a streaming table.

The following example inserts an active record with a start sequence of 5:

INSERT INTO my_streaming_table (id, name, __START_AT, __END_AT) VALUES (123, 'John Doe', 5, NULL);

Get data about records processed by a Delta Live Tables CDC query

Note

The following metrics are captured only by APPLY CHANGES queries, and not by APPLY CHANGES FROM SNAPSHOT queries.

The following metrics are captured by APPLY CHANGES queries:

  • num_upserted_rows: The number of output rows upserted into the dataset during an update.

  • num_deleted_rows: The number of existing output rows deleted from the dataset during an update.

The num_output_rows metric, which is output for non-CDC flows, is not captured for apply changes queries.

What data objects are used for Delta Live Tables CDC processing?

Note: The following data structures apply only to APPLY CHANGES processing, not APPLY CHANGES FROM SNAPSHOT processing.

When you declare the target table in the Hive metastore, two data structures are created:

  • A view using the name assigned to the target table.

  • An internal backing table used by Delta Live Tables to manage CDC processing. This table is named by prepending __apply_changes_storage_ to the target table name.

For example, if you declare a target table named dlt_cdc_target, you will see a view named dlt_cdc_target and a table named __apply_changes_storage_dlt_cdc_target in the metastore. Creating a view allows Delta Live Tables to filter out the extra information (for example, tombstones and versions) required to handle out-of-order data. To view the processed data, query the target view. Because the schema of the __apply_changes_storage_ table might change to support future features or enhancements, you should not query the table for production use. If you add data manually to the table, the records are assumed to come before other changes because the version columns are missing.

If a pipeline publishes to Unity Catalog, the internal backing tables are inaccessible to users.