Delta Table, Delta Lake

Using Delta Tables in PySpark or SQL

If we directly query a existing delta table from ADLS using SQL, always use

 --back single quotation mark `
delta.`abfss://contain@account.dfs.windows.net/path_and_table`

# sql
-- register a table point it to existing Delta table location
delta_table_path = "dbfs:/mnt/delta/table_path"
# Register the Delta table in the metastore
spark.sql(f"""
CREATE TABLE table_name
USING DELTA
LOCATION '{delta_table_path}'
""")

-- Creating a Delta Table
%sql
CREATE TABLE my_delta_table (
id int,
name string
)
USING delta
LOCATION '/mnt/delta/my_delta_table';

# python
# Write a DataFrame to a Delta table
df.write.format("delta").save("/mnt/delta/my_delta_table")

# sql
-- Insert data
INSERT INTO my_delta_table VALUES (1, 'John Doe'), (2,
'Jane Doe');

Reading from a Delta table

#python
delta_df = spark.read.format("delta").load("/mnt/delta/my_delta_table")
delta_df.show()


#sql
-- Query Delta table
SELECT * FROM my_delta_table;

-- directly query delta table from adls.
-- use  ` back single quotation mark
SELECT * 
FROM 
delta.`abfss://adlsContainer@adlsAccount.dfs.windows.net/Path_and_TableName`
VERSION AS OF 4;

Managing Delta Tables

Optimizing Delta Tables

To improve performance, you can run an optimize operation to compact small files into larger ones.

# sql 
OPTIMIZE my_delta_table;

Z-order Clustering

Z-order clustering is used to improve query performance by colocating related data in the same set of files. it is a technique used in Delta Lake (and other databases) to optimize data layout for faster query performance.

# sql
OPTIMIZE my_delta_table ZORDER BY (date);

Upserts (Merge)

Delta Lake makes it easy to perform Upserts (MERGE operation), which allows you to insert or update data in your tables based on certain conditions.

using SQL scripts is the same as TSQL merge statement

% sql
MERGE INTO my_delta_table t
USING new_data n
ON t.id = n.id
WHEN MATCHED THEN UPDATE SET t.value = n.value
WHEN NOT MATCHED THEN INSERT (id, value) VALUES (n.id, n.value); 

In PySpark with Delta Lake:

The target table must be a Delta table and the source data is typically in a DataFrame.

Example Scenario

• Target Table: target_table; Contains existing records.
• Source DataFrame: source_df; Contains new or updated records.
• Goal: Update existing rows if a match is found or insert new rows if no match exists.

from delta.tables import DeltaTable
from pyspark.sql.functions import current_date, lit

# Define paths
target_table_path = "dbfs:/mnt/delta/target_table"

# Load the Delta table as a DeltaTable object
target_table = DeltaTable.forPath(spark, target_table_path)

# Source DataFrame (new data to upsert)
source_data = [
    (1, "Alice", "2023-01-01"),
    (2, "Bob", "2023-01-02"),
    (4, "Eve", "2023-01-04")  # New record
]
columns = ["id", "name", "date"]
source_df = spark.createDataFrame(source_data, columns)

# Perform the merge operation
target_table.alias("t").merge(
    source_df.alias("s"),
    "t.id = s.id"  # Join condition: match rows based on `id`
).whenMatchedUpdate(
    set={
        "name": "s.name",  # Update `name` column
        "date": "s.date"   # Update `date` column
    }
).whenNotMatchedInsert(
    values={
        "id": "s.id",      # Insert `id`
        "name": "s.name",  # Insert `name`
        "date": "s.date"   # Insert `date`
    }
).execute()

# Verify the result
result_df = spark.read.format("delta").load(target_table_path)
result_df.show()

Explanation of the Code

1. Target Table (target_table):
   • The Delta table is loaded using DeltaTable.forPath.
   • This table contains existing data where updates or inserts will be applied.
2. Source DataFrame (source_df):
   • This DataFrame contains new or updated records.
3. Join Condition ("t.id = s.id"):
   • Rows in the target table (t) are matched with rows in the source DataFrame (s) based on id.
4. whenMatchedUpdate:
   • If a matching row is found, update the name and date columns in the target table.
5. whenNotMatchedInsert:
   • If no matching row is found, insert the new record from the source DataFrame into the target table.
6. execute():
   • Executes the merge operation, applying updates and inserts.
7. Result Verification:
   • After the merge, the updated Delta table is read and displayed.

Conclusion

Delta Lake is a powerful solution for building reliable, high-performance data pipelines on top of data lakes. It enables advanced data management and analytics capabilities with features like ACID transactions, time travel, and schema enforcement, making it an ideal choice for large-scale, data-driven applications.

Delta tables are essential for maintaining high-quality, reliable, and performant data processing pipelines. They provide a way to bring transactional integrity and powerful performance optimizations to large-scale data lakes, enabling unified data processing for both batch and streaming use cases.

Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

(remove all space from the email account 😊)