class DeltaTable extends DeltaTableOperations
:: Evolving ::
Main class for programmatically interacting with Delta tables. You can create DeltaTable instances using the static methods.
DeltaTable.forPath(sparkSession, pathToTheDeltaTable)
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- @Evolving()
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0.3.0
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def
alias(alias: String): DeltaTable
:: Evolving ::
:: Evolving ::
Apply an alias to the DeltaTable. This is similar to
Dataset.as(alias)
or SQLtableName AS alias
.- Annotations
- @Evolving()
- Since
0.3.0
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def
as(alias: String): DeltaTable
:: Evolving ::
:: Evolving ::
Apply an alias to the DeltaTable. This is similar to
Dataset.as(alias)
or SQLtableName AS alias
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0.3.0
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def
asInstanceOf[T0]: T0
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def
delete(): Unit
:: Evolving ::
:: Evolving ::
Delete data from the table.
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- @Evolving()
- Since
0.3.0
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def
delete(condition: Column): Unit
:: Evolving ::
:: Evolving ::
Delete data from the table that match the given
condition
.- condition
Boolean SQL expression
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- @Evolving()
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0.3.0
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def
delete(condition: String): Unit
:: Evolving ::
:: Evolving ::
Delete data from the table that match the given
condition
.- condition
Boolean SQL expression
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- @Evolving()
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0.3.0
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
executeDelete(condition: Option[Expression]): Unit
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- DeltaTableOperations
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def
executeHistory(deltaLog: DeltaLog, limit: Option[Int]): DataFrame
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- DeltaTableOperations
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def
executeUpdate(set: Map[String, Column], condition: Option[Column]): Unit
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- DeltaTableOperations
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def
executeVacuum(deltaLog: DeltaLog, retentionHours: Option[Double]): DataFrame
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- DeltaTableOperations
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def
finalize(): Unit
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def
hashCode(): Int
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def
history(): DataFrame
:: Evolving ::
:: Evolving ::
Get the information available commits on this table as a Spark DataFrame. The information is in reverse chronological order.
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- @Evolving()
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0.3.0
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def
history(limit: Int): DataFrame
:: Evolving ::
:: Evolving ::
Get the information of the latest
limit
commits on this table as a Spark DataFrame. The information is in reverse chronological order.- limit
The number of previous commands to get history for
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- @Evolving()
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0.3.0
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final
def
isInstanceOf[T0]: Boolean
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def
makeUpdateTable(target: DeltaTable, onCondition: Option[Column], setColumns: Seq[(String, Column)]): UpdateTable
- Attributes
- protected
- Definition Classes
- DeltaTableOperations
-
def
merge(source: DataFrame, condition: Column): DeltaMergeBuilder
:: Evolving ::
:: Evolving ::
Merge data from the
source
DataFrame based on the given mergecondition
. 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 the DeltaMergeBuilder for a full description of this operation and what combinations of update, delete and insert operations are allowed.
Scala example to update a key-value Delta table with new key-values from a source DataFrame:
deltaTable .as("target") .merge( source.as("source"), "target.key = source.key") .whenMatched .updateExpr(Map( "value" -> "source.value")) .whenNotMatched .insertExpr(Map( "key" -> "source.key", "value" -> "source.value")) .execute()
Java example to update a key-value Delta table with new key-values from a source DataFrame:
deltaTable .as("target") .merge( source.as("source"), "target.key = source.key") .whenMatched .updateExpr( new HashMap<String, String>() {{ put("value" -> "source.value") }}) .whenNotMatched .insertExpr( new HashMap<String, String>() {{ put("key", "source.key"); put("value", "source.value"); }}) .execute()
- source
source Dataframe to be merged.
- condition
boolean expression as a Column object
- Annotations
- @Evolving()
- Since
0.3.0
-
def
merge(source: DataFrame, condition: String): DeltaMergeBuilder
:: Evolving ::
:: Evolving ::
Merge data from the
source
DataFrame based on the given mergecondition
. 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 the DeltaMergeBuilder for a full description of this operation and what combinations of update, delete and insert operations are allowed.
Scala example to update a key-value Delta table with new key-values from a source DataFrame:
deltaTable .as("target") .merge( source.as("source"), "target.key = source.key") .whenMatched .updateExpr(Map( "value" -> "source.value")) .whenNotMatched .insertExpr(Map( "key" -> "source.key", "value" -> "source.value")) .execute()
Java example to update a key-value Delta table with new key-values from a source DataFrame:
deltaTable .as("target") .merge( source.as("source"), "target.key = source.key") .whenMatched .updateExpr( new HashMap<String, String>() {{ put("value" -> "source.value"); }}) .whenNotMatched .insertExpr( new HashMap<String, String>() {{ put("key", "source.key"); put("value", "source.value"); }}) .execute();
- source
source Dataframe to be merged.
- condition
boolean expression as SQL formatted string
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- @Evolving()
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0.3.0
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notifyAll(): Unit
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def
sparkSession: SparkSession
- Attributes
- protected
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- DeltaTableOperations
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toDF: Dataset[Row]
:: Evolving ::
:: Evolving ::
Get a DataFrame (that is, Dataset[Row]) representation of this Delta table.
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- @Evolving()
- Since
0.3.0
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def
toStrColumnMap(map: Map[String, String]): Map[String, Column]
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- DeltaTableOperations
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def
toString(): String
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def
tryResolveReferences(sparkSession: SparkSession)(expr: Expression, planContainingExpr: LogicalPlan): Expression
- Attributes
- protected
- Definition Classes
- AnalysisHelper
-
def
update(condition: Column, set: Map[String, Column]): Unit
:: Evolving ::
:: Evolving ::
Update data from the table on the rows that match the given
condition
based on the rules defined byset
.Java example to increment the column
data
.import org.apache.spark.sql.Column; import org.apache.spark.sql.functions; deltaTable.update( functions.col("date").gt("2018-01-01"), new HashMap<String, Column>() {{ put("data", functions.col("data").plus(1)); }} );
- condition
boolean expression as Column object specifying which rows to update.
- set
rules to update a row as a Java map between target column names and corresponding update expressions as Column objects.
- Annotations
- @Evolving()
- Since
0.3.0
-
def
update(condition: Column, set: Map[String, Column]): Unit
:: Evolving ::
:: Evolving ::
Update data from the table on the rows that match the given
condition
based on the rules defined byset
.Scala example to increment the column
data
.import org.apache.spark.sql.functions._ deltaTable.update( col("date") > "2018-01-01", Map("data" -> col("data") + 1))
- condition
boolean expression as Column object specifying which rows to update.
- set
rules to update a row as a Scala map between target column names and corresponding update expressions as Column objects.
- Annotations
- @Evolving()
- Since
0.3.0
-
def
update(set: Map[String, Column]): Unit
:: Evolving ::
:: Evolving ::
Update rows in the table based on the rules defined by
set
.Java example to increment the column
data
.import org.apache.spark.sql.Column; import org.apache.spark.sql.functions; deltaTable.update( new HashMap<String, Column>() {{ put("data", functions.col("data").plus(1)); }} );
- set
rules to update a row as a Java map between target column names and corresponding update expressions as Column objects.
- Annotations
- @Evolving()
- Since
0.3.0
-
def
update(set: Map[String, Column]): Unit
:: Evolving ::
:: Evolving ::
Update rows in the table based on the rules defined by
set
.Scala example to increment the column
data
.import org.apache.spark.sql.functions._ deltaTable.update(Map("data" -> col("data") + 1))
- set
rules to update a row as a Scala map between target column names and corresponding update expressions as Column objects.
- Annotations
- @Evolving()
- Since
0.3.0
-
def
updateExpr(condition: String, set: Map[String, String]): Unit
:: Evolving ::
:: Evolving ::
Update data from the table on the rows that match the given
condition
, which performs the rules defined byset
.Java example to increment the column
data
.deltaTable.update( "date > '2018-01-01'", new HashMap<String, String>() {{ put("data", "data + 1"); }} );
- condition
boolean expression as SQL formatted string object specifying which rows to update.
- set
rules to update a row as a Java map between target column names and corresponding update expressions as SQL formatted strings.
- Annotations
- @Evolving()
- Since
0.3.0
-
def
updateExpr(condition: String, set: Map[String, String]): Unit
:: Evolving ::
:: Evolving ::
Update data from the table on the rows that match the given
condition
, which performs the rules defined byset
.Scala example to increment the column
data
.deltaTable.update( "date > '2018-01-01'", Map("data" -> "data + 1"))
- condition
boolean expression as SQL formatted string object specifying which rows to update.
- set
rules to update a row as a Scala map between target column names and corresponding update expressions as SQL formatted strings.
- Annotations
- @Evolving()
- Since
0.3.0
-
def
updateExpr(set: Map[String, String]): Unit
:: Evolving ::
:: Evolving ::
Update rows in the table based on the rules defined by
set
.Java example to increment the column
data
.deltaTable.updateExpr( new HashMap<String, String>() {{ put("data", "data + 1"); }} );
- set
rules to update a row as a Java map between target column names and corresponding update expressions as SQL formatted strings.
- Annotations
- @Evolving()
- Since
0.3.0
-
def
updateExpr(set: Map[String, String]): Unit
:: Evolving ::
:: Evolving ::
Update rows in the table based on the rules defined by
set
.Scala example to increment the column
data
.deltaTable.updateExpr(Map("data" -> "data + 1")))
- set
rules to update a row as a Scala map between target column names and corresponding update expressions as SQL formatted strings.
- Annotations
- @Evolving()
- Since
0.3.0
-
def
vacuum(): DataFrame
:: Evolving ::
:: Evolving ::
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.
note: This will use the default retention period of 7 days.
- Annotations
- @Evolving()
- Since
0.3.0
-
def
vacuum(retentionHours: Double): DataFrame
:: Evolving ::
:: Evolving ::
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.
- retentionHours
The retention threshold in hours. Files required by the table for reading versions earlier than this will be preserved and the rest of them will be deleted.
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- @Evolving()
- Since
0.3.0
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