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The AUTO CDC APIs: Simplify change data capture with Lakeflow Declarative Pipelines

Lakeflow Declarative Pipelines simplifies change data capture (CDC) with the AUTO CDC and AUTO CDC FROM SNAPSHOT APIs.

note

The AUTO CDC APIs were previously called APPLY CHANGES, and had the same syntax.

The interface you use depends on the source of change data:

  • Use AUTO CDC to process changes from a change data feed (CDF).
  • Use AUTO CDC FROM SNAPSHOT (Public Preview, and only available for Python) 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 AUTO CDC API is supported in the Lakeflow Declarative Pipelines SQL and Python interfaces. The AUTO CDC FROM SNAPSHOT API is supported in the Lakeflow Declarative Pipelines Python interface.

Both AUTO CDC and AUTO CDC 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 AUTO CDC for Lakeflow Declarative Pipelines SQL, AUTO CDC for Lakeflow Declarative Pipelines Python, and AUTO CDC FROM SNAPSHOT for Lakeflow Declarative Pipelines Python.

note

This article describes how to update tables in your Lakeflow Declarative Pipelines 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 serverlessLakeflow Declarative Pipelines or the Lakeflow Declarative Pipelines Pro or Advanced editions.

How is CDC implemented with the AUTO CDC API?​

By automatically handling out-of-sequence records, the AUTO CDC API in Lakeflow Declarative Pipelines 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 Lakeflow Declarative Pipelines interprets as a monotonically increasing representation of the proper ordering of the source data. Lakeflow Declarative Pipelines automatically handles data that arrives out of order. For SCD type 2 changes, Lakeflow Declarative Pipelines 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 AUTO CDC, you first create a streaming table and then use the AUTO CDC ... INTO statement in SQL or the create_auto_cdc_flow() 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 Lakeflow Declarative Pipelines SQL reference or Python reference.

How is CDC implemented with the AUTO CDC FROM SNAPSHOT API?​

Preview

The AUTO CDC FROM SNAPSHOT API is in Public Preview.

AUTO CDC 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. AUTO CDC FROM SNAPSHOT is supported only by the Lakeflow Declarative Pipelines Python interface.

AUTO CDC FROM SNAPSHOT supports ingesting snapshots from multiple source types:

  • Use periodic snapshot ingestion to ingest snapshots from an existing table or view. AUTO CDC 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 AUTO CDC 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 AUTO CDC FROM SNAPSHOT, you first create a streaming table and then use the create_auto_cdc_from_snapshot_flow() 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 Lakeflow Declarative Pipelines detects an out-of-order snapshot, an error is thrown.

For syntax details, see the Lakeflow Declarative Pipelines Python reference.

Use multiple columns for sequencing​

You can sequence by multiple columns (for example, a timestamp and an ID to break ties), you can use a STRUCT to combine them: it orders by the first field of the STRUCT first, and in the event of a tie, considers the second field, and so on.

Example in SQL:

SQL
SEQUENCE BY STRUCT(timestamp_col, id_col)

Example in Python:

Python
sequence_by = struct("timestamp_col", "id_col")

Limitations​

The column used for sequencing must be a sortable data type.

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

The following sections provide examples of Lakeflow Declarative Pipelines 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 Lakeflow Declarative Pipelines. See Tutorial: Build an ETL pipeline using change data capture with Lakeflow Declarative Pipelines.

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:

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

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

dlt.create_streaming_table("target")

dlt.create_auto_cdc_flow(
  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
)

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:

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

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

dlt.create_streaming_table("target")

dlt.create_auto_cdc_flow(
  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"
)

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:

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

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

dlt.create_streaming_table("target")

dlt.create_auto_cdc_flow(
  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"]
)

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 Lakeflow Declarative Pipelines:

SQL
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 6 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

Python
import dlt

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

dlt.create_streaming_table("target")

dlt.create_auto_cdc_from_snapshot_flow(
 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:

Python
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.create_auto_cdc_from_snapshot_flow(
  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 AUTO CDC ... 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 Lakeflow Declarative Pipelines 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 AUTO CDC 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:

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

Read a change data feed from an AUTO CDC target table​

In Databricks Runtime 15.2 and above, you can read a change data feed from a streaming table that is the target of AUTO CDC or AUTO CDC FROM SNAPSHOT queries in the same way that you read a change data feed from other Delta tables. The following are required to read the change data feed from a target streaming table:

  • The target streaming table must be published to Unity Catalog. See Use Unity Catalog with your Lakeflow Declarative Pipelines.
  • To read the change data feed from the target streaming table, you must use Databricks Runtime 15.2 or above. To read the change data feed in a different pipeline, the pipeline must be configured to use Databricks Runtime 15.2 or above.

You read the change data feed from a target streaming table that was created in Lakeflow Declarative Pipelines the same way as reading a change data feed from other Delta tables. To learn more about using the Delta change data feed functionality, including examples in Python and SQL, see Use Delta Lake change data feed on Databricks.

note

The change data feed record includes metadata identifying the type of change event. When a record is updated in a table, the metadata for the associated change records typically includes _change_type values set to update_preimage and update_postimage events.

However, the _change_type values are different if updates are made to the target streaming table that include changing primary key values. When changes include updates to primary keys, the _change_type metadata fields are set to insert and delete events. Changes to primary keys can occur when manual updates are made to one of the key fields with an UPDATE or MERGE statement or, for SCD type 2 tables, when the __start_at field changes to reflect an earlier start sequence value.

The AUTO CDC query determines the primary key values, which differ for SCD type 1 and SCD type 2 processing:

  • For SCD type 1 processing and the Lakeflow Declarative Pipelines Python interface, the primary key is the value of the keys parameter in the create_auto_cdc_flow() function. For the Lakeflow Declarative Pipelines SQL interface the primary key is the columns defined by the KEYS clause in the AUTO CDC ... INTO statement.
  • For SCD type 2, the primary key is the keys parameter or KEYS clause plus the return value from the coalesce(__START_AT, __END_AT) operation, where __START_AT and __END_AT are the corresponding columns from the target streaming table.

Get data about records processed by a Lakeflow Declarative Pipelines CDC query​

note

The following metrics are captured only by AUTO CDC queries and not by AUTO CDC FROM SNAPSHOT queries.

The following metrics are captured by AUTO CDC 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, output for non-CDC flows, is not captured for AUTO CDC queries.

What data objects are used for Lakeflow Declarative Pipelines CDC processing?​

note
  • These data structures apply only to AUTO CDC processing, not AUTO CDC FROM SNAPSHOT processing.
  • These data structures apply only when the target table is published to the Hive metastore. If a pipeline publishes to Unity Catalog, the internal backing tables are inaccessible to users.

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 Lakeflow Declarative Pipelines 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 Lakeflow Declarative Pipelines 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.

Additional resources​

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