Hi @ibrar_aslam, A simplified explanation of your situation in a flowchart format. Correct me If I am wrong:-
Start
|
|--- List of streaming tables populated by Autoloader from S3
| |
| |--- Sources for live tables
| |
| |--- Autoloader Delta pipeline completes
| |
| |--- Trigger Delta Live Tables (DLT) pipeline
| |
| |--- Deduplication operation needed
| |
| |--- Current process: Compute rank of latest record
| | |
| | |--- Filter based on rank
| |
| |--- Issue: Need to recreate schema every time
| |
| |--- Even full refresh doesn't resolve problem
|
|--- Need suggestions for deduplication optimizations
| |
| |--- Address schema recreation issue for live table updates
|
End
The suggestions from my end :-
- Instead of computing the rank and filtering, use the
MERGE statement in DLT to efficiently de-duplicate the data. The MERGE statement allows you to insert, update, or delete data in a target table based on a condition that compares the target table with the source data.
# Example MERGE statement
merge_condition = "source.id = target.id AND source.timestamp > target.timestamp"
merged_df = (
spark.readStream
.format("delta")
.load("s3://your-bucket/your-path")
.alias("source")
.merge(
target_table.alias("target"),
merge_condition
)
.whenMatchedUpdateAll()
.whenNotMatchedInsertAll()
.execute()
)
2. When using the MERGE statement, partition the data by the columns that define the uniqueness of a record and order by the timestamp column to ensure that the latest record is kept.
3. Similar to deduplicating data, you can use the MERGE statement to update the schema of the live table. This approach ensures that schema changes are applied incrementally without the need to recreate the entire table.
# Example MERGE statement for schema updates
merge_condition = "source.name = target.name"
merged_schema = (
new_schema
.alias("source")
.merge(
target_table.alias("target"),
merge_condition
)
.whenMatchedUpdateColumnsByName(
columns=["type", "comment"]
)
.whenNotMatchedInsertAll()
.execute()
)
4. If you prefer not to use the MERGE statement for schema updates, you can use the CREATE TABLE IF NOT EXISTS statement in your DLT pipeline. This statement creates the table if it doesn't exist and updates the schema if there are any changes.
# Example CREATE TABLE IF NOT EXISTS statement
create_table_if_not_exists(
path="s3://your-bucket/your-path",
table_name="your_table",
schema=your_schema
)
By implementing these best practices and optimizations, you can improve the deduplication process within Delta Live Tables and efficiently manage schema updates without re-creating the entire table.
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For those of you who want to understand what an Autoloader and Streaming Tables are -
Explaining Autoloader in a way one can understand in one read ๐ฉโ๐ป:-
Autoloader in Databricks is like a hungry data monster that eagerly gobbles up new files as they land in your cloud storage buffet!
Imagine a big, friendly monster with a voracious appetite sitting at a cloud storage buffet table. Files in various formats like CSV, JSON, and Parquet are being served to it on platters labelled with their file types. The monster happily devours each file as soon as it arrives, processing them efficiently for your data needs!
Explaining Streaming Tables :-
Streaming tables in Databricks SQL are like magic portals that slurp up data as it flows, turning cloud storage chaos into organized datasets with the wave of a wand! ๐งโ
If you have any further questions or need additional guidance, feel free to ask! ๐๐.
