Kafka Partitionings vs DB partitions

Hi @Janice Chi

If a large table is pre-partitioned in Bronze (e.g., 10 physical partitions), and this table's changes are captured via a single Kafka topic with 10 partitions — Does the number of Kafka partitions always align with the number of DB partitions or Delta table partitions? Can we rely on this alignment, or is this mapping independent and arbitrary from Kafka producer side?

The number of Kafka partitions does not inherently align with the number of database (e.g., IBM DB2) or Delta table partitions (e.g., in the Bronze layer). Kafka partitions are physical divisions managed by the producer’s partitioning strategy (e.g., key-based, round-robin, or custom), while Delta table partitions are logical, typically based on a column like claim_month. Similarly, DB2 partitions are defined by the database’s partitioning scheme, which may not match the Kafka producer’s logic. 

In your case, where IBM InfoSphere CDC publishes changes to Kafka (1 topic = 1 table), the assignment of messages to Kafka partitions depends on the CDC tool’s partitioning strategy. Unless explicitly configured to use a partitioning key that mirrors the Delta table’s or DB2’s partitioning scheme (e.g., claim_month), the mapping between Kafka partitions and DB/Delta partitions is arbitrary and independent. 

You cannot rely on an automatic alignment between Kafka partitions and DB/Delta table partitions. To achieve alignment, you must: 

1. Confirm or configure the Kafka producer (InfoSphere CDC) to partition messages using a key that matches the Delta table’s partitioning column (e.g., claim_month). 
2. Validate this by inspecting Kafka partition data to ensure messages consistently map to corresponding Delta partitions. 

Without such configuration, treat Kafka partitions as independent of DB/Delta partitions and design your pipeline to handle this mismatch, such as by extracting the partitioning column from message payloads during processing. 

How can we efficiently identify and target the relevant Delta table partitions (e.g., claim_month) during a MERGE INTO operation, given that Kafka partitions and offsets (e.g., Topic 1, Partition 1, Offsets 1–100) are physical and do not directly map to the logical partitioning used in Delta Lake? 

To correlate Kafka message offsets (e.g., Topic 1, Partition 1, Offset 1–100) with Delta table partitions (e.g., partitioned by claim_month) in the Silver layer for efficient MERGE INTO operations, you need to map Kafka messages to Delta partitions without scanning the entire table. Here’s a concise best practice approach 

Best Practices: Correlate Kafka Offsets to Delta Partitions

1. Include Partition Key in Kafka Messages Ensure Kafka messages contain the Delta table’s partition column (e.g., claim_month) in the payload or key. For example, a JSON message might include {"claim_id": "123", "claim_month": "2025-01", ...}. This allows mapping messages to Delta partitions like claim_month=2025-01.
2. Extract Partition key in spark Use Spark Structured Streaming to read Kafka messages and extract claim_month. 

      from pyspark.sql.functions import col, from_json
      # Read Kafka stream
      kafka_df = spark.readStream \
      .format("kafka") \
      .option("kafka.bootstrap.servers", "broker:port") \
      .option("subscribe", "Topic1") \
      .option("startingOffsets", """{"Topic1":{"1":1}}""") \
      .option("endingOffsets", """{"Topic1":{"1":100}}""") \
      .load()
      # Parse JSON and extract claim_month
      schema = "claim_id STRING, claim_month STRING, ..."
      parsed_df = kafka_df.selectExpr("CAST(value AS STRING) as json_value") \
      .select(from_json(col("json_value"), schema).alias("data")) \
      .select("data.*", col("offset").alias("kafka_offset"), col("partition").alias("kafka_partition"))
   

   3.Group by Partition key
   Identify distinct claim_month values to determine affected Delta partitions. 

      partition_values = parsed_df.select("claim_month").distinct().collect()
      partition_values = [row["claim_month"] for row in partition_values]
   

   4.Optimize MERGE INTO with Partition Filtering: Perform MERGE INTO for each claim_month to target specific Delta partitions, avoiding full table scans. 

python
from delta.tables import DeltaTable

delta_table = DeltaTable.forPath(spark, "/path/to/silver/tableX")
for partition_value in partition_values:
    partition_df = parsed_df.filter(col("claim_month") == partition_value)
    delta_table.alias("target") \
        .merge(
            partition_df.alias("source"),
            f"target.claim_month = '{partition_value}' AND target.claim_id = source.claim_id"
        ) \
        .whenMatchedUpdateAll() \
        .whenNotMatchedInsertAll() \
        .execute()

5.Track Kafka metadata

Store kafka_offset, kafka_partition, and kafka_topic in the Delta table for traceability. Example Schema: 

ColumnTypeDescriptionclaim_idStringUnique row identifierclaim_monthStringPartition key (e.g., 2025-01)......Other data fieldskafka_offsetLongKafka message offsetkafka_partitionintKafka partition numberkafka_topicStringKafka topic name 

6.Use Checkpointing
For streaming, use Spark checkpointing to track processed offsets. 

query = parsed_df.writeStream \
    .format("delta") \
    .option("checkpointLocation", "/path/to/checkpoint") \
    .trigger(availableNow=True) \
    .start("/path/to/silver/tableX")

To enhance query performance on the Delta table, apply Z-Order indexing on the claim_month and claim_id columns. This can be done using the command delta_table.optimize().executeZOrderBy("claim_month", "claim_id") in Spark. Z-Order indexing improves data skipping and query efficiency by clustering related data, making MERGE INTO operations faster, especially for large tables.  

Maintaining appropriate partition granularity, such as monthly partitions (e.g., claim_month=2025-01), is also critical to balance performance and metadata overhead. Overly granular partitions (e.g., daily) can lead to excessive metadata management, slowing down operations. Together, these strategies aligned partitioning, Z-Order indexing, and optimal partition granularity ensure efficient MERGE INTO operations by targeting specific Delta partitions, leveraging claim_month for pruning, and preserving Kafka metadata for traceability. 
This approach ensures efficient MERGE INTO operations by targeting specific Delta partitions, leveraging claim_month to prune irrelevant data, and maintaining traceability with Kafka metadata.

I hope this info helpful