partition in db

Question

HI ,

what hppend in databricks backend when partion is applied to table level

Answer 1

Hi Vineet SVineet S,

Thanks for reaching out to Microsoft Q&A.

Types of partitioning:

• DataFrame/RDD Level Partitioning: This type of partitioning focuses on distributing data across cluster nodes for parallel processing.
• Table Level Partitioning: Here, the goal is to organize data within storage systems to optimize query performance.
• When you create a table in Databricks, you can define the set of partitioning columns using the PARTITIONEDBY clause.
• As you insert or manipulate rows in the table, Databricks automatically dispatches rows into the appropriate partitions based on the specified partitioning columns https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-partitionhttps://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-partition

Recommendations for partitioning:

• Most tables with less than 1 TB of data do not require partitions due to built-in features and optimizations.
• Databricks recommends that each partition contains at least 1 GB of data. Tables with fewer, larger partitions tend to perform better than those with many smaller partitions.
• Databricks automatically clusters data in unpartitioned tables by ingestion time (available in Databricks Runtime 11.2 and above). This provides query benefits similar to datetime-based partitioning without manual tuning.
• While Delta Lake uses Parquet as its primary format, partitioning strategies differ. Hive-style partitioning used by Apache Spark when saving data in Parquet format is not directly applicable to Delta tables. Always interact with Delta Lake data using officially supported clients and APIs.

When to partition tables on Databricks:

https://docs.databricks.com/en/tables/partitions.html

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Answer 2

Hi Vineet S,

Thank you for posting query in Microsoft Q&A Platform.

When a partition is applied to a table in Databricks, it means that the data in the table is physically divided into multiple parts based on the partition key. Each partition contains a subset of the data in the table that corresponds to a specific value of the partition key.

When a query is executed on a partitioned table, Databricks uses the partition key to determine which partitions need to be read to satisfy the query. This allows Databricks to read only the data that is needed for the query, rather than reading the entire table.

In the backend, Databricks uses a number of techniques to optimize the performance of partitioned tables. For example, Databricks can use partition pruning to eliminate partitions that are not needed for a query, and it can use predicate pushdown to push filters down to the storage layer to reduce the amount of data that needs to be read.

Partitioning can significantly improve the performance of queries on large tables, especially when the queries only need to access a subset of the data in the table. However, partitioning can also increase the complexity of managing the table, as it requires careful consideration of the partition key and the partitioning strategy.

Hope this helps. Please let me know if any further queries.

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