Ingestion time clustering -Initial load

[Shazam]

As per info available ingestion time clustering makes use of time of the time a file is written or ingested in databricks. In a use case where there is  new delta table and an etl which runs in timely fashion(say daily) inserting records, am able to understand how the timing of ingestion of file can be made use in clustering.

However in most of the cases it might be a migration of data from an existing platform to databricks and would have an initial load. How would ingestion time clustering be useful in such scenarios where large of amounts of records would be inserted into a table in a single go initially.

The second question: Lets say in daily batches records are inserted into a delta table which is making use of ingestion time clustering. The most used query against the table is based on the column date(yyyymmdd) available(select * from table where ='yyyymmdd'). How does ingestion time clustering help in reducing the query time

[Kaniz_Fatma]

Hi @Shazam, Great questions! Let’s break down each scenario:

1. Initial Data Migration: When migrating data from an existing platform to Databricks, you might have a large initial load of records. In this case, ingestion time clustering can still be useful. Here’s how:

   • Data Organization: Even during the initial load, you can organize the data into smaller files based on the ingestion time. For example, if you’re migrating historical data for the past year, you can create daily or weekly files. Each file would contain data ingested on that specific day or week.
   • Clustering Benefits:
     • Query Performance: By organizing data into smaller files based on ingestion time, you enable efficient pruning during query execution. When querying specific time ranges (e.g., a specific month or week), Databricks can skip reading irrelevant files. This reduces the amount of data scanned during queries.
     • Parallelism: Smaller files allow better parallelism during query execution. Databricks can read multiple small files concurrently, improving overall query performance.
     • Compaction: After the initial load, you can periodically compact the smaller files into larger ones (e.g., monthly or quarterly). This helps strike a balance between query performance and storage efficiency.

2. Daily Batches with Date-Based Queries: Let’s consider a scenario where records are inserted into a Delta table in daily batches, and the most common query filters data based on a date column (e.g., WHERE date_column = 'yyyymmdd').

   • Ingestion Time Clustering:

     • When using ingestion time clustering, each batch of records is written to a separate file based on its ingestion time (e.g., daily files).
     • The Delta table’s metadata maintains information about these files, including their ingestion timestamps.
     • Queries that filter on the date column can benefit from this organization.

   • Query Execution:

     • Suppose you run a query like SELECT * FROM table WHERE date_column = '20240506'.
     • With ingestion time clustering:
       • Databricks can quickly identify the relevant files (those ingested on May 6, 2024) based on their timestamps.
       • It only scans those specific files, skipping irrelevant data.
       • Parallelism is also improved because each file can be read concurrently.
     • Without ingestion time clustering:
       • Databricks would need to scan all files in the table, regardless of their ingestion time.
       • This could be inefficient, especially as the table grows over time.

   • Maintenance:

     • Regularly compact the smaller daily files into larger ones (e.g., weekly or monthly) to balance query performance and storage efficiency.

In summary, even during initial data migration and daily batch inserts, ingestion time clustering helps optimize query performance by organizing data into smaller files based on ingestion time. It allows efficient pruning, parallelism, and targeted scans for date-based queries. 😊🚀

Let me know if you’d like further clarification or have additional questions!

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