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DeltaTable

class delta.tables.DeltaTable(spark: SparkSession, jdt: JavaObject)

Main class for programmatically interacting with Delta tables. You can create DeltaTable instances using the path of the Delta table.:

deltaTable = DeltaTable.forPath(spark, "/path/to/table")

In addition, you can convert an existing Parquet table in place into a Delta table.:

deltaTable = DeltaTable.convertToDelta(spark, "parquet.`/path/to/table`")

Added in version 0.4.

toDF() → DataFrame

Get a DataFrame representation of this Delta table.

Added in version 0.4.

alias(aliasName: str) → DeltaTable

Apply an alias to the Delta table.

Added in version 0.4.

generate(mode: str) → None

Generate manifest files for the given delta table.

Parameters:

mode –

mode for the type of manifest file to be generated The valid modes are as follows (not case sensitive):

• ”symlink_format_manifest”: This will generate manifests in symlink format

  for Presto and Athena read support.

See the online documentation for more information.

Added in version 0.5.

delete(condition: Column | str | None = None) → None

Delete data from the table that match the given condition.

Example:

deltaTable.delete("date < '2017-01-01'")        # predicate using SQL formatted string

deltaTable.delete(col("date") < "2017-01-01")   # predicate using Spark SQL functions

Parameters:

condition (str or pyspark.sql.Column) – condition of the update

Added in version 0.4.

update(condition: str | Column, set: Dict[str, str | Column]) → None

update(*, set: Dict[str, str | Column]) → None

Update data from the table on the rows that match the given condition, which performs the rules defined by set.

Example:

# condition using SQL formatted string
deltaTable.update(
    condition = "eventType = 'clck'",
    set = { "eventType": "'click'" } )

# condition using Spark SQL functions
deltaTable.update(
    condition = col("eventType") == "clck",
    set = { "eventType": lit("click") } )

Parameters:

• condition (str or pyspark.sql.Column) – Optional condition of the update

• set (dict with str as keys and str or pyspark.sql.Column as values) – Defines the rules of setting the values of columns that need to be updated. Note: This param is required. Default value None is present to allow positional args in same order across languages.

Added in version 0.4.

merge(source: DataFrame, condition: str | Column) → DeltaMergeBuilder

Merge data from the source DataFrame based on the given merge condition. This returns a DeltaMergeBuilder object that can be used to specify the update, delete, or insert actions to be performed on rows based on whether the rows matched the condition or not. See DeltaMergeBuilder for a full description of this operation and what combinations of update, delete and insert operations are allowed.

Example 1 with conditions and update expressions as SQL formatted string:

deltaTable.alias("events").merge(
    source = updatesDF.alias("updates"),
    condition =