partition in databricks

Question

Hi Expert, How we can do partition in databricks ... can get some example Regards

Answer 1

Based on the documentation :

A partition is composed of a subset of rows in a table that share the same value for a predefined subset of columns called the partitioning columns. Using partitions can speed up queries against the table as well as data manipulation.To use partitions, you define the set of partitioning column when you create a table by including the PARTITIONED BY clause.When inserting or manipulating rows in a table Azure Databricks automatically dispatches rows into the appropriate partitions.

Partitioning in Databricks is a technique used to divide large datasets into smaller, more manageable parts based on column values. This is especially useful in big data environments as it optimizes query performance by reducing the amount of data scanned. When you work with Azure Databricks, you usually deal with Spark data frames or tables, and the partitioning is typically done on these. Here's a basic example of how to create a partitioned table in Databricks:

    # Example DataFrame
    data = [("John", 30, "New York"),
            ("Linda", 35, "Chicago"),
            ... # more data
           ]
    columns = ["Name", "Age", "City"]
    df = spark.createDataFrame(data, columns)

You can write this df to a storage location (like DBFS, Azure Blob Storage...) in a partitioned format. Here, you can choose a column to partition by. For example, if you want to partition by the 'City' column:

    df.write.partitionBy("City").format("parquet").save("/mnt/path/to/save/location")

When you read the partitioned data, Spark will automatically recognize the partitions and optimize the queries accordingly.

   partitioned_df = spark.read.format("parquet").load("/mnt/path/to/save/location")

When querying, you can use the partition column in your filters to speed up the query.

    partitioned_df.filter(partitioned_df.City == "New York").show()

Optionally, you can register the partitioned data as a table in Databricks for easier querying.

    partitioned_df.createOrReplaceTempView("partitioned_table")
    spark.sql("SELECT * FROM partitioned_table WHERE City = 'Chicago'").show()

Answer 2

Hi Shambhu Rai, Below is the example, which creates delta table named mytable and partitions the data by the year column. The data is loaded from source table named sorucetable. Let's say if you have 20 years of data there, then it will partition accordingly.

CREATE TABLE mytable
  USING DELTA
  PARTITIONED BY (year)
  AS SELECT * FROM sourcetable

Hope this helps. Please note, you can adjust the partitioning columns and source table to fit your specific needs.

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