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DeltaTable

class delta.tables.DeltaTable(spark, jdt)

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`")

New in version 0.4.

Note

Evolving

toDF()

Get a DataFrame representation of this Delta table.

Note

Evolving

alias(aliasName)

Apply an alias to the Delta table.

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Evolving

generate(mode)

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.

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Evolving

delete(condition=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

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Evolving

update(condition=None, set=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.

Note

Evolving

merge(source, condition)

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 = "events.eventId = updates.eventId"
  ).whenMatchedUpdate(set =
    {
      "data": "updates.data",
      "count": "events.count + 1"
    }
  ).whenNotMatchedInsert(values =
    {
      "date": "updates.date",
      "eventId": "updates.eventId",
      "data": "updates.data",
      "count": "1"
    }
  ).execute()

Example 2 with conditions and update expressions as Spark SQL functions:

from pyspark.sql.functions import *

deltaTable.alias("events").merge(
    source = updatesDF.alias("updates"),
    condition = expr("events.eventId = updates.eventId")
  ).whenMatchedUpdate(set =
    {
      "data" : col("updates.data"),
      "count": col("events.count") + 1
    }
  ).whenNotMatchedInsert(values =
    {
      "date": col("updates.date"),
      "eventId": col("updates.eventId"),
      "data": col("updates.data"),
      "count": lit("1")
    }
  ).execute()
Parameters:
  • source (pyspark.sql.DataFrame) – Source DataFrame
  • condition (str or pyspark.sql.Column) – Condition to match sources rows with the Delta table rows.
Returns:

builder object to specify whether to update, delete or insert rows based on whether the condition matched or not
Return type:

delta.tables.DeltaMergeBuilder

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Evolving

vacuum(retentionHours=None)

Recursively delete files and directories in the table that are not needed by the table for maintaining older versions up to the given retention threshold. This method will return an empty DataFrame on successful completion.

Example:

deltaTable.vacuum()     # vacuum files not required by versions more than 7 days old

deltaTable.vacuum(100)  # vacuum files not required by versions more than 100 hours old
Parameters:retentionHours – Optional number of hours retain history. If not specified, then the default retention period of 168 hours (7 days) will be used.

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Evolving

history(limit=None)

Get the information of the latest limit commits on this table as a Spark DataFrame. The information is in reverse chronological order.

Example:

fullHistoryDF = deltaTable.history()    # get the full history of the table

lastOperationDF = deltaTable.history(1) # get the last operation
Parameters:limit – Optional, number of latest commits to returns in the history.
Returns:Table’s commit history. See the online Delta Lake documentation for more details.
Return type:pyspark.sql.DataFrame

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Evolving

classmethod convertToDelta(sparkSession, identifier, partitionSchema=None)

Create a DeltaTable from the given parquet table. Takes an existing parquet table and constructs a delta transaction log in the base path of the table. Note: Any changes to the table during the conversion process may not result in a consistent state at the end of the conversion. Users should stop any changes to the table before the conversion is started.

Example:

# Convert unpartitioned parquet table at path 'path/to/table'
deltaTable = DeltaTable.convertToDelta(
    spark, "parquet.`path/to/table`")

# Convert partitioned parquet table at path 'path/to/table' and partitioned by
# integer column named 'part'
partitionedDeltaTable = DeltaTable.convertToDelta(
    spark, "parquet.`path/to/table`", "part int")
Parameters:
  • sparkSession (pyspark.sql.SparkSession) – SparkSession to use for the conversion
  • identifier (str) – Parquet table identifier formatted as “parquet.`path`”
  • partitionSchema –
  • partitionSchema – Hive DDL formatted string, or pyspark.sql.types.StructType
Returns:

DeltaTable representing the converted Delta table
Return type:

DeltaTable

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Evolving

classmethod forPath(sparkSession, path)

Create a DeltaTable for the data at the given path using the given SparkSession.

Parameters:sparkSession (pyspark.sql.SparkSession) – SparkSession to use for loading the table
Returns:loaded Delta table
Return type:DeltaTable

Example:

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

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Evolving

classmethod isDeltaTable(sparkSession, identifier)

Check if the provided identifier string, in this case a file path, is the root of a Delta table using the given SparkSession.

Parameters:
  • sparkSession – SparkSession to use to perform the check
  • path – location of the table
Returns:

If the table is a delta table or not
Return type:

bool

Example:

DeltaTable.isDeltaTable(spark, "/path/to/table")

Note

Evolving

class delta.tables.DeltaMergeBuilder(spark, jbuilder)

Builder to specify how to merge data from source DataFrame into the target Delta table. Use delta.tables.DeltaTable.merge() to create an object of this class. Using this builder, you can specify 1, 2 or 3 when clauses of which there can be at most 2 whenMatched clauses and at most 1 whenNotMatched clause. Here are the constraints on these clauses.

  • Constraints in the whenMatched clauses:

    • There can be at most one update action and one delete action in whenMatched clauses.

    • Each whenMatched clause can have an optional condition. However, if there are two whenMatched clauses, then the first one must have a condition.

    • When there are two whenMatched clauses and there are conditions (or the lack of) such that a row matches both clauses, then the first clause/action is executed. In other words, the order of the whenMatched clauses matter.

    • If none of the whenMatched clauses match a source-target row pair that satisfy the merge condition, then the target rows will not be updated or deleted.

    • If you want to update all the columns of the target Delta table with the corresponding column of the source DataFrame, then you can use the whenMatchedUpdateAll(). This is equivalent to:

      whenMatchedUpdate(set = {
  "col1": "source.col1",
  "col2": "source.col2",
  ...    # for all columns in the delta table
})

  • Constraints in the whenNotMatched clauses:

    • This clause can have only an insert action, which can have an optional condition.

    • If whenNotMatchedInsert is not present or if it is present but the non-matching source row does not satisfy the condition, then the source row is not inserted.

    • If you want to insert all the columns of the target Delta table with the corresponding column of the source DataFrame, then you can use whenNotMatchedInsertAll(). This is equivalent to:

      whenMatchedInsert(values = {
  "col1": "source.col1",
  "col2": "source.col2",
  ...    # for all columns in the delta table
})

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

deltaTable.alias("events").merge(
    source = updatesDF.alias("updates"),
    condition = "events.eventId = updates.eventId"
  ).whenMatchedUpdate(set =
    {
      "data": "updates.data",
      "count": "events.count + 1"
    }
  ).whenNotMatchedInsert(values =
    {
      "date": "updates.date",
      "eventId": "updates.eventId",
      "data": "updates.data",
      "count": "1"
    }
  ).execute()

Example 2 with conditions and update expressions as Spark SQL functions:

from pyspark.sql.functions import *

deltaTable.alias("events").merge(
    source = updatesDF.alias("updates"),
    condition = expr("events.eventId = updates.eventId")
  ).whenMatchedUpdate(set =
    {
      "data" : col("updates.data"),
      "count": col("events.count") + 1
    }
  ).whenNotMatchedInsert(values =
    {
      "date": col("updates.date"),
      "eventId": col("updates.eventId"),
      "data": col("updates.data"),
      "count": lit("1")
    }
  ).execute()

New in version 0.4.

Note

Evolving

whenMatchedUpdate(condition=None, set=None)

Update a matched table row based on the rules defined by set. If a condition is specified, then it must evaluate to true for the row to be updated.

See DeltaMergeBuilder for complete usage details.

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.
Returns:

this builder

Note

Evolving

whenMatchedUpdateAll(condition=None)

Update all the columns of the matched table row with the values of the corresponding columns in the source row. If a condition is specified, then it must be true for the new row to be updated.

See DeltaMergeBuilder for complete usage details.

Parameters:condition (str or pyspark.sql.Column) – Optional condition of the insert
Returns:this builder

Note

Evolving

whenMatchedDelete(condition=None)

Delete a matched row from the table only if the given condition (if specified) is true for the matched row.

See DeltaMergeBuilder for complete usage details.

Parameters:condition (str or pyspark.sql.Column) – Optional condition of the delete
Returns:this builder

Note

Evolving

whenNotMatchedInsert(condition=None, values=None)

Insert a new row to the target table based on the rules defined by values. If a condition is specified, then it must evaluate to true for the new row to be inserted.

See DeltaMergeBuilder for complete usage details.

Parameters:
  • condition (str or pyspark.sql.Column) – Optional condition of the insert
  • values (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.
Returns:

this builder

Note

Evolving

whenNotMatchedInsertAll(condition=None)

Insert a new target Delta table row by assigning the target columns to the values of the corresponding columns in the source row. If a condition is specified, then it must evaluate to true for the new row to be inserted.

See DeltaMergeBuilder for complete usage details.

Parameters:condition (str or pyspark.sql.Column) – Optional condition of the insert
Returns:this builder

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Evolving

execute()

Execute the merge operation based on the built matched and not matched actions.

See DeltaMergeBuilder for complete usage details.

Note

Evolving

Indices and tables