Welcome to Delta Lake’s Python documentation page¶

DeltaTable¶

class delta.tables.DeltaTable(spark: pyspark.sql.session.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`")

New in version 0.4.

toDF() → pyspark.sql.dataframe.DataFrame¶: Get a DataFrame representation of this Delta table.

New in version 0.4.

alias(aliasName: str) → delta.tables.DeltaTable¶: Apply an alias to the Delta table.

New 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.

New in version 0.5.

delete(condition: Union[str, pyspark.sql.column.Column, 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

New in version 0.4.

update(condition: Union[str, pyspark.sql.column.Column, None] = None, set: Optional[Dict[str, Union[str, pyspark.sql.column.Column]]] = None) → 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.

New in version 0.4.

merge(source: pyspark.sql.dataframe.DataFrame, condition: Union[str, pyspark.sql.column.Column]) → delta.tables.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 = "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

New in version 0.4.

vacuum(retentionHours: Optional[float] = None) → pyspark.sql.dataframe.DataFrame¶

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.

New in version 0.4.

history(limit: Optional[int] = None) → pyspark.sql.dataframe.DataFrame¶

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

New in version 0.4.

detail() → pyspark.sql.dataframe.DataFrame¶

Get the details of a Delta table such as the format, name, and size.

Example:

detailDF = deltaTable.detail() # get the full details of the table

:return Information of the table (format, name, size, etc.) :rtype: pyspark.sql.DataFrame

Note

Evolving

New in version 2.1.

classmethod convertToDelta(sparkSession: pyspark.sql.session.SparkSession, identifier: str, partitionSchema: Union[str, pyspark.sql.types.StructType, None] = None) → delta.tables.DeltaTable¶

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 – Hive DDL formatted string, or pyspark.sql.types.StructType

Returns

DeltaTable representing the converted Delta table

Return type

DeltaTable

New in version 0.4.

classmethod forPath(sparkSession: pyspark.sql.session.SparkSession, path: str) → delta.tables.DeltaTable¶

Instantiate a DeltaTable object representing the data at the given path, If the given path is invalid (i.e. either no table exists or an existing table is not a Delta table), it throws a not a Delta table error.

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")

New in version 0.4.

classmethod forName(sparkSession: pyspark.sql.session.SparkSession, tableOrViewName: str) → delta.tables.DeltaTable¶

Instantiate a DeltaTable object using the given table or view name. If the given tableOrViewName is invalid (i.e. either no table exists or an existing table is not a Delta table), it throws a not a Delta table error.

The given tableOrViewName can also be the absolute path of a delta datasource (i.e. delta.`path`), If so, instantiate a DeltaTable object representing the data at the given path (consistent with the forPath).

Parameters

sparkSession – SparkSession to use for loading the table
tableOrViewName – name of the table or view

Returns

loaded Delta table

Return type

DeltaTable

Example:

deltaTable = DeltaTable.forName(spark, "tblName")

New in version 0.7.

classmethod create(sparkSession: Optional[pyspark.sql.session.SparkSession] = None) → delta.tables.DeltaTableBuilder¶

Return DeltaTableBuilder object that can be used to specify the table name, location, columns, partitioning columns, table comment, and table properties to create a Delta table, error if the table exists (the same as SQL CREATE TABLE).

See DeltaTableBuilder for a full description and examples of this operation.

Parameters: sparkSession – SparkSession to use for creating the table
Returns: an instance of DeltaTableBuilder
Return type: DeltaTableBuilder

Note

Evolving

New in version 1.0.

classmethod createIfNotExists(sparkSession: Optional[pyspark.sql.session.SparkSession] = None) → delta.tables.DeltaTableBuilder¶

Return DeltaTableBuilder object that can be used to specify the table name, location, columns, partitioning columns, table comment, and table properties to create a Delta table, if it does not exists (the same as SQL CREATE TABLE IF NOT EXISTS).

See DeltaTableBuilder for a full description and examples of this operation.

Parameters: sparkSession – SparkSession to use for creating the table
Returns: an instance of DeltaTableBuilder
Return type: DeltaTableBuilder

Note

Evolving

New in version 1.0.

classmethod replace(sparkSession: Optional[pyspark.sql.session.SparkSession] = None) → delta.tables.DeltaTableBuilder¶

Return DeltaTableBuilder object that can be used to specify the table name, location, columns, partitioning columns, table comment, and table properties to replace a Delta table, error if the table doesn’t exist (the same as SQL REPLACE TABLE).

See DeltaTableBuilder for a full description and examples of this operation.

Parameters: sparkSession – SparkSession to use for creating the table
Returns: an instance of DeltaTableBuilder
Return type: DeltaTableBuilder

Note

Evolving

New in version 1.0.

classmethod createOrReplace(sparkSession: Optional[pyspark.sql.session.SparkSession] = None) → delta.tables.DeltaTableBuilder¶

Return DeltaTableBuilder object that can be used to specify the table name, location, columns, partitioning columns, table comment, and table properties replace a Delta table, error if the table doesn’t exist (the same as SQL REPLACE TABLE).

See DeltaTableBuilder for a full description and examples of this operation.

Parameters: sparkSession – SparkSession to use for creating the table
Returns: an instance of DeltaTableBuilder
Return type: DeltaTableBuilder

Note

Evolving

New in version 1.0.

classmethod isDeltaTable(sparkSession: pyspark.sql.session.SparkSession, identifier: str) → bool¶

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")

New in version 0.4.

upgradeTableProtocol(readerVersion: int, writerVersion: int) → None¶

Updates the protocol version of the table to leverage new features. Upgrading the reader version will prevent all clients that have an older version of Delta Lake from accessing this table. Upgrading the writer version will prevent older versions of Delta Lake to write to this table. The reader or writer version cannot be downgraded.

See online documentation and Delta’s protocol specification at PROTOCOL.md for more details.

New in version 0.8.

restoreToVersion(version: int) → pyspark.sql.dataframe.DataFrame¶

Restore the DeltaTable to an older version of the table specified by version number.

Example:

io.delta.tables.DeltaTable.restoreToVersion(1)

Parameters: version – target version of restored table
Returns: Dataframe with metrics of restore operation.
Return type: pyspark.sql.DataFrame

New in version 1.2.

restoreToTimestamp(timestamp: str) → pyspark.sql.dataframe.DataFrame¶

Restore the DeltaTable to an older version of the table specified by a timestamp. Timestamp can be of the format yyyy-MM-dd or yyyy-MM-dd HH:mm:ss

Example:

io.delta.tables.DeltaTable.restoreToTimestamp('2021-01-01')
io.delta.tables.DeltaTable.restoreToTimestamp('2021-01-01 01:01:01')

Parameters: timestamp – target timestamp of restored table
Returns: Dataframe with metrics of restore operation.
Return type: pyspark.sql.DataFrame

New in version 1.2.

optimize() → delta.tables.DeltaOptimizeBuilder¶

Optimize the data layout of the table. This returns a DeltaOptimizeBuilder object that can be used to specify the partition filter to limit the scope of optimize and also execute different optimization techniques such as file compaction or order data using Z-Order curves.

See the DeltaOptimizeBuilder for a full description of this operation.

Example:

deltaTable.optimize().where("date='2021-11-18'").executeCompaction()

Returns: an instance of DeltaOptimizeBuilder.
Return type: DeltaOptimizeBuilder

New in version 2.0.

class delta.tables.DeltaMergeBuilder(spark: pyspark.sql.session.SparkSession, jbuilder: JavaObject)¶

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 any number of whenMatched and whenNotMatched clauses. 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:
```
whenNotMatchedInsert(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.

whenMatchedUpdate(condition: Union[str, pyspark.sql.column.Column, None] = None, set: Optional[Dict[str, Union[str, pyspark.sql.column.Column]]] = None) → delta.tables.DeltaMergeBuilder¶

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

New in version 0.4.

whenMatchedUpdateAll(condition: Union[str, pyspark.sql.column.Column, None] = None) → delta.tables.DeltaMergeBuilder¶

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

New in version 0.4.

whenMatchedDelete(condition: Union[str, pyspark.sql.column.Column, None] = None) → delta.tables.DeltaMergeBuilder¶

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

New in version 0.4.

whenNotMatchedInsert(condition: Union[str, pyspark.sql.column.Column, None] = None, values: Optional[Dict[str, Union[str, pyspark.sql.column.Column]]] = None) → delta.tables.DeltaMergeBuilder¶

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

New in version 0.4.

whenNotMatchedInsertAll(condition: Union[str, pyspark.sql.column.Column, None] = None) → delta.tables.DeltaMergeBuilder¶

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

New in version 0.4.

execute() → None¶

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

See DeltaMergeBuilder for complete usage details.

New in version 0.4.

class delta.tables.DeltaTableBuilder(spark: pyspark.sql.session.SparkSession, jbuilder: JavaObject)¶

Builder to specify how to create / replace a Delta table. You must specify the table name or the path before executing the builder. You can specify the table columns, the partitioning columns, the location of the data, the table comment and the property, and how you want to create / replace the Delta table.

After executing the builder, a DeltaTable object is returned.

Use delta.tables.DeltaTable.create(), delta.tables.DeltaTable.createIfNotExists(), delta.tables.DeltaTable.replace(), delta.tables.DeltaTable.createOrReplace() to create an object of this class.

Example 1 to create a Delta table with separate columns, using the table name:

deltaTable = DeltaTable.create(sparkSession)
    .tableName("testTable")
    .addColumn("c1", dataType = "INT", nullable = False)
    .addColumn("c2", dataType = IntegerType(), generatedAlwaysAs = "c1 + 1")
    .partitionedBy("c1")
    .execute()

Example 2 to replace a Delta table with existing columns, using the location:

df = spark.createDataFrame([('a', 1), ('b', 2), ('c', 3)], ["key", "value"])

deltaTable = DeltaTable.replace(sparkSession)
    .tableName("testTable")
    .addColumns(df.schema)
    .execute()

New in version 1.0.

Note

Evolving

tableName(identifier: str) → delta.tables.DeltaTableBuilder¶

Specify the table name. Optionally qualified with a database name [database_name.] table_name.

Parameters: identifier (str) – the table name
Returns: this builder

Note

Evolving

New in version 1.0.

location(location: str) → delta.tables.DeltaTableBuilder¶

Specify the path to the directory where table data is stored, which could be a path on distributed storage.

Parameters: location (str) – the data stored location
Returns: this builder

Note

Evolving

New in version 1.0.

comment(comment: str) → delta.tables.DeltaTableBuilder¶

Comment to describe the table.

Parameters: comment (str) – the table comment
Returns: this builder

Note

Evolving

New in version 1.0.

addColumn(colName: str, dataType: Union[str, pyspark.sql.types.DataType], nullable: bool = True, generatedAlwaysAs: Optional[str] = None, comment: Optional[str] = None) → delta.tables.DeltaTableBuilder¶

Specify a column in the table

Parameters

colName (str) – the column name
dataType (str or pyspark.sql.types.DataType) – the column data type
nullable (bool) – whether column is nullable
generatedAlwaysAs (str) – a SQL expression if the column is always generated as a function of other columns. See online documentation for details on Generated Columns.
comment (str) – the column comment

Returns

this builder

Note

Evolving

New in version 1.0.

addColumns(cols: Union[pyspark.sql.types.StructType, List[pyspark.sql.types.StructField]]) → delta.tables.DeltaTableBuilder¶

Specify columns in the table using an existing schema

Parameters: cols (pyspark.sql.types.StructType or a list of pyspark.sql.types.StructType.) – the columns in the existing schema
Returns: this builder

Note

Evolving

New in version 1.0.

partitionedBy(*cols: Union[str, List[str], Tuple[str, ...]]) → delta.tables.DeltaTableBuilder¶

Specify columns for partitioning

Parameters: cols (str or list name of columns) – the partitioning cols
Returns: this builder

Note

Evolving

New in version 1.0.

property(key: str, value: str) → delta.tables.DeltaTableBuilder¶

Specify a table property

Parameters: key – the table property key
Returns: this builder

Note

Evolving

New in version 1.0.

execute() → delta.tables.DeltaTable¶

Execute Table Creation.

Return type: DeltaTable

Note

Evolving

New in version 1.0.

class delta.tables.DeltaOptimizeBuilder(spark: pyspark.sql.session.SparkSession, jbuilder: JavaObject)¶

Builder class for constructing OPTIMIZE command and executing.

Use delta.tables.DeltaTable.optimize() to create an instance of this class.

New in version 2.0.0.

where(partitionFilter: str) → delta.tables.DeltaOptimizeBuilder¶

Apply partition filter on this optimize command builder to limit the operation on selected partitions.

Parameters: partitionFilter (str) – The partition filter to apply
Returns: DeltaOptimizeBuilder with partition filter applied
Return type: DeltaOptimizeBuilder

New in version 2.0.

executeCompaction() → pyspark.sql.dataframe.DataFrame¶

Compact the small files in selected partitions.

Returns: DataFrame containing the OPTIMIZE execution metrics
Return type: pyspark.sql.DataFrame

New in version 2.0.

executeZOrderBy(*cols: Union[str, List[str], Tuple[str, ...]]) → pyspark.sql.dataframe.DataFrame¶

Z-Order the data in selected partitions using the given columns.

Parameters: cols (str or list name of columns) – the Z-Order cols
Returns: DataFrame containing the OPTIMIZE execution metrics
Return type: pyspark.sql.DataFrame

New in version 2.0.

Exceptions¶

exception delta.exceptions.DeltaConcurrentModificationException(desc, stackTrace, cause=None)¶: The basic class for all Delta commit conflict exceptions.

New in version 1.0.

Note

Evolving

exception delta.exceptions.ConcurrentWriteException(desc, stackTrace, cause=None)¶: Thrown when a concurrent transaction has written data after the current transaction read the table.

New in version 1.0.

Note

Evolving

exception delta.exceptions.MetadataChangedException(desc, stackTrace, cause=None)¶: Thrown when the metadata of the Delta table has changed between the time of read and the time of commit.

New in version 1.0.

Note

Evolving

exception delta.exceptions.ProtocolChangedException(desc, stackTrace, cause=None)¶: Thrown when the protocol version has changed between the time of read and the time of commit.

New in version 1.0.

Note

Evolving

exception delta.exceptions.ConcurrentAppendException(desc, stackTrace, cause=None)¶: Thrown when files are added that would have been read by the current transaction.

New in version 1.0.

Note

Evolving

exception delta.exceptions.ConcurrentDeleteReadException(desc, stackTrace, cause=None)¶: Thrown when the current transaction reads data that was deleted by a concurrent transaction.

New in version 1.0.

Note

Evolving

exception delta.exceptions.ConcurrentDeleteDeleteException(desc, stackTrace, cause=None)¶: Thrown when the current transaction deletes data that was deleted by a concurrent transaction.

New in version 1.0.

Note

Evolving

exception delta.exceptions.ConcurrentTransactionException(desc, stackTrace, cause=None)¶: Thrown when concurrent transaction both attempt to update the same idempotent transaction.

New in version 1.0.

Note

Evolving

Others¶

delta.pip_utils.configure_spark_with_delta_pip(spark_session_builder: pyspark.sql.session.SparkSession.Builder, extra_packages: Optional[List[str]] = None) → pyspark.sql.session.SparkSession.Builder¶

Utility function to configure a SparkSession builder such that the generated SparkSession will automatically download the required Delta Lake JARs from Maven. This function is required when you want to

Install Delta Lake locally using pip, and
Execute your Python code using Delta Lake + Pyspark directly, that is, not using spark-submit –packages io.delta:… or pyspark –packages io.delta:….

builder = SparkSession.builder .master(“local[*]”) .appName(“test”)

spark = configure_spark_with_delta_pip(builder).getOrCreate()
If you would like to add more packages, use the extra_packages parameter.

builder = SparkSession.builder .master(“local[*]”) .appName(“test”) my_packages = [“org.apache.spark:spark-sql-kafka-0-10_2.12:x.y.z”] spark = configure_spark_with_delta_pip(builder, extra_packages=my_packages).getOrCreate()

Parameters

spark_session_builder – SparkSession.Builder object being used to configure and create a SparkSession.
extra_packages – Set other packages to add to Spark session besides Delta Lake.

Returns

Updated SparkSession.Builder object

New in version 1.0.

Note

Evolving

Welcome to Delta Lake’s Python documentation page¶

DeltaTable¶

Exceptions¶

Others¶

Indices and tables¶

Table of Contents

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