SnappyContext

Instance Constructors

new SnappyContext(sc: SparkContext)

Attributes
protected[org.apache.spark]
new SnappyContext(snappySession: SnappySession)

Attributes
protected[org.apache.spark]

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def appendToTempTableCache(df: DataFrame, table: String, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK): Unit

Append dataframe to cache table in Spark.
Append dataframe to cache table in Spark.
storageLevel
default storage level is MEMORY_AND_DISK
returns
@todo -> return type?

Annotations
@DeveloperApi()
final def asInstanceOf[T0]: T0

Definition Classes
Any
def baseRelationToDataFrame(baseRelation: BaseRelation): DataFrame

Definition Classes
SQLContext
def cacheTable(tableName: String): Unit

Definition Classes
SQLContext
def clear(): Unit
def clearCache(): Unit

Definition Classes
SQLContext
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def createApproxTSTopK(topKName: String, baseTable: String, keyColumnName: String, topkOptions: Map[String, String], allowExisting: Boolean): DataFrame

Create approximate structure to query top-K with time series support.
Create approximate structure to query top-K with time series support. Java friendly api.
topKName
the qualified name of the top-K structure
baseTable
the base table of the top-K structure, if any, or null
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception

To do
provide lot more details and examples to explain creating and using TopK with time series
def createApproxTSTopK(topKName: String, baseTable: Option[String], keyColumnName: String, topkOptions: Map[String, String], allowExisting: Boolean): DataFrame

Create approximate structure to query top-K with time series support.
Create approximate structure to query top-K with time series support.
topKName
the qualified name of the top-K structure
baseTable
the base table of the top-K structure, if any
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception

To do
provide lot more details and examples to explain creating and using TopK with time series
def createApproxTSTopK(topKName: String, baseTable: String, keyColumnName: String, inputDataSchema: StructType, topkOptions: Map[String, String], allowExisting: Boolean): DataFrame

Create approximate structure to query top-K with time series support.
Create approximate structure to query top-K with time series support. Java friendly api.
topKName
the qualified name of the top-K structure
baseTable
the base table of the top-K structure, if any, or null
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception

To do
provide lot more details and examples to explain creating and using TopK with time series
def createApproxTSTopK(topKName: String, baseTable: Option[String], keyColumnName: String, inputDataSchema: StructType, topkOptions: Map[String, String], allowExisting: Boolean = false): DataFrame

Create approximate structure to query top-K with time series support.
Create approximate structure to query top-K with time series support.
topKName
the qualified name of the top-K structure
baseTable
the base table of the top-K structure, if any
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception

To do
provide lot more details and examples to explain creating and using TopK with time series
def createDataFrame(data: List[_], beanClass: Class[_]): DataFrame

Definition Classes
SQLContext
def createDataFrame(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame

Definition Classes
SQLContext
def createDataFrame(rdd: RDD[_], beanClass: Class[_]): DataFrame

Definition Classes
SQLContext
def createDataFrame(rows: List[Row], schema: StructType): DataFrame

Definition Classes
SQLContext
Annotations
@DeveloperApi()
def createDataFrame(rowRDD: JavaRDD[Row], schema: StructType): DataFrame

Definition Classes
SQLContext
Annotations
@DeveloperApi()
def createDataFrame(rowRDD: RDD[Row], schema: StructType): DataFrame

Definition Classes
SQLContext
Annotations
@DeveloperApi()
def createDataFrame[A <: Product](data: Seq[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame

Definition Classes
SQLContext
Annotations
@Experimental()
def createDataFrame[A <: Product](rdd: RDD[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame

Definition Classes
SQLContext
Annotations
@Experimental()
def createDataset[T](data: List[T])(implicit arg0: Encoder[T]): Dataset[T]

Definition Classes
SQLContext
Annotations
@Experimental()
def createDataset[T](data: RDD[T])(implicit arg0: Encoder[T]): Dataset[T]

Definition Classes
SQLContext
Annotations
@Experimental()
def createDataset[T](data: Seq[T])(implicit arg0: Encoder[T]): Dataset[T]

Definition Classes
SQLContext
Annotations
@Experimental()
def createExternalTable(tableName: String, source: String, schema: StructType, options: Map[String, String]): DataFrame

Definition Classes
SQLContext
def createExternalTable(tableName: String, source: String, schema: StructType, options: Map[String, String]): DataFrame

Definition Classes
SQLContext
def createExternalTable(tableName: String, source: String, options: Map[String, String]): DataFrame

Definition Classes
SQLContext
def createExternalTable(tableName: String, source: String, options: Map[String, String]): DataFrame

Definition Classes
SQLContext
def createExternalTable(tableName: String, path: String, source: String): DataFrame

Definition Classes
SQLContext
def createExternalTable(tableName: String, path: String): DataFrame

Definition Classes
SQLContext
def createIndex(indexName: String, baseTable: String, indexColumns: Map[String, Option[SortDirection]], options: Map[String, String]): Unit

Create an index on a table.
Create an index on a table.
indexName
Index name which goes in the catalog
baseTable
Fully qualified name of table on which the index is created.
indexColumns
Columns on which the index has to be created with the direction of sorting. Direction can be specified as None.
options
Options for indexes. For e.g. column table index - ("COLOCATE_WITH"->"CUSTOMER"). row table index - ("INDEX_TYPE"->"GLOBAL HASH") or ("INDEX_TYPE"->"UNIQUE")
def createIndex(indexName: String, baseTable: String, indexColumns: Map[String, Boolean], options: Map[String, String]): Unit

Create an index on a table.
Create an index on a table.
indexName
Index name which goes in the catalog
baseTable
Fully qualified name of table on which the index is created.
indexColumns
Columns on which the index has to be created along with the sorting direction.The direction of index will be ascending if value is true and descending when value is false. Direction can be specified as null
options
Options for indexes. For e.g. column table index - ("COLOCATE_WITH"->"CUSTOMER"). row table index - ("INDEX_TYPE"->"GLOBAL HASH") or ("INDEX_TYPE"->"UNIQUE")
def createSampleTable(tableName: String, baseTable: String, schema: StructType, samplingOptions: Map[String, String], allowExisting: Boolean): DataFrame

Create a stratified sample table.
Create a stratified sample table. Java friendly version.
tableName
the qualified name of the table
baseTable
the base table of the sample table, if any, or null
schema
schema of the table
samplingOptions
sampling options like QCS, reservoir size etc.
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception

To do
provide lot more details and examples to explain creating and using sample tables with time series and otherwise
def createSampleTable(tableName: String, baseTable: Option[String], schema: StructType, samplingOptions: Map[String, String], allowExisting: Boolean = false): DataFrame

Create a stratified sample table.
Create a stratified sample table.
tableName
the qualified name of the table
baseTable
the base table of the sample table, if any
schema
schema of the table
samplingOptions
sampling options like QCS, reservoir size etc.
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception

To do
provide lot more details and examples to explain creating and using sample tables with time series and otherwise
def createSampleTable(tableName: String, baseTable: String, samplingOptions: Map[String, String], allowExisting: Boolean): DataFrame

Create a stratified sample table.
Create a stratified sample table. Java friendly version.
tableName
the qualified name of the table
baseTable
the base table of the sample table, if any, or null
samplingOptions
sampling options like QCS, reservoir size etc.
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception

To do
provide lot more details and examples to explain creating and using sample tables with time series and otherwise
def createSampleTable(tableName: String, baseTable: Option[String], samplingOptions: Map[String, String], allowExisting: Boolean): DataFrame

Create a stratified sample table.
Create a stratified sample table.
tableName
the qualified name of the table
baseTable
the base table of the sample table, if any
samplingOptions
sampling options like QCS, reservoir size etc.
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception

To do
provide lot more details and examples to explain creating and using sample tables with time series and otherwise
def createTable(tableName: String, provider: String, schemaDDL: String, options: Map[String, String], allowExisting: Boolean): DataFrame

Creates a SnappyData managed JDBC table which takes a free format ddl string.
Creates a SnappyData managed JDBC table which takes a free format ddl string. The ddl string should adhere to syntax of underlying JDBC store. SnappyData ships with inbuilt JDBC store, which can be accessed by Row format data store. The option parameter can take connection details.
```
   val props = Map(
     "url" -> s"jdbc:derby:$path",
     "driver" -> "org.apache.derby.jdbc.EmbeddedDriver",
     "poolImpl" -> "tomcat",
     "user" -> "app",
     "password" -> "app"
   )

val schemaDDL = "(OrderId INT NOT NULL PRIMARY KEY,ItemId INT, ITEMREF INT)"
snappyContext.createTable("jdbcTable", "jdbc", schemaDDL, props)
```
Any DataFrame of the same schema can be inserted into the JDBC table using DataFrameWriter API.
e.g.
```
case class Data(col1: Int, col2: Int, col3: Int)

val data = Seq(Seq(1, 2, 3), Seq(7, 8, 9), Seq(9, 2, 3), Seq(4, 2, 3), Seq(5, 6, 7))
val rdd = sc.parallelize(data, data.length).map(s => new Data(s(0), s(1), s(2)))
val dataDF = snc.createDataFrame(rdd)
dataDF.write.insertInto("jdbcTable")
```
tableName
Name of the table
provider
Provider name 'ROW' or 'JDBC'.
schemaDDL
Table schema as a string interpreted by provider
options
Properties for table creation. See options list for different tables. https://github.com/SnappyDataInc/snappydata/blob/master/docs/rowAndColumnTables.md
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception
returns
DataFrame for the table

Annotations
@Experimental()
def createTable(tableName: String, provider: String, schemaDDL: String, options: Map[String, String], allowExisting: Boolean): DataFrame

Creates a SnappyData managed JDBC table which takes a free format ddl string.
Creates a SnappyData managed JDBC table which takes a free format ddl string. The ddl string should adhere to syntax of underlying JDBC store. SnappyData ships with inbuilt JDBC store, which can be accessed by Row format data store. The option parameter can take connection details.
```
   val props = Map(
     "url" -> s"jdbc:derby:$path",
     "driver" -> "org.apache.derby.jdbc.EmbeddedDriver",
     "poolImpl" -> "tomcat",
     "user" -> "app",
     "password" -> "app"
   )

val schemaDDL = "(OrderId INT NOT NULL PRIMARY KEY,ItemId INT, ITEMREF INT)"
snappyContext.createTable("jdbcTable", "jdbc", schemaDDL, props)
```
Any DataFrame of the same schema can be inserted into the JDBC table using DataFrameWriter API.
e.g.
```
case class Data(col1: Int, col2: Int, col3: Int)

val data = Seq(Seq(1, 2, 3), Seq(7, 8, 9), Seq(9, 2, 3), Seq(4, 2, 3), Seq(5, 6, 7))
val rdd = sc.parallelize(data, data.length).map(s => new Data(s(0), s(1), s(2)))
val dataDF = snc.createDataFrame(rdd)
dataDF.write.insertInto("jdbcTable")
```
tableName
Name of the table
provider
Provider name 'ROW' or 'JDBC'.
schemaDDL
Table schema as a string interpreted by provider
options
Properties for table creation. See options list for different tables. https://github.com/SnappyDataInc/snappydata/blob/master/docs/rowAndColumnTables.md
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception
returns
DataFrame for the table
def createTable(tableName: String, provider: String, schema: StructType, options: Map[String, String], allowExisting: Boolean): DataFrame

Creates a SnappyData managed table.
Creates a SnappyData managed table. Any relation providers (e.g. row, column etc) supported by SnappyData can be created here.
```
case class Data(col1: Int, col2: Int, col3: Int)
val props = Map.empty[String, String]
val data = Seq(Seq(1, 2, 3), Seq(7, 8, 9), Seq(9, 2, 3), Seq(4, 2, 3), Seq(5, 6, 7))
val rdd = sc.parallelize(data, data.length).map(s => new Data(s(0), s(1), s(2)))
val dataDF = snc.createDataFrame(rdd)
snappyContext.createTable(tableName, "column", dataDF.schema, props)
```
For other external relation providers, use createExternalTable.
tableName
Name of the table
provider
Provider name such as 'COLUMN', 'ROW', 'JDBC' etc.
schema
Table schema
options
Properties for table creation. See options list for different tables. https://github.com/SnappyDataInc/snappydata/blob/master/docs/rowAndColumnTables.md
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception
returns
DataFrame for the table

Annotations
@Experimental()
def createTable(tableName: String, provider: String, schema: StructType, options: Map[String, String], allowExisting: Boolean = false): DataFrame

Creates a SnappyData managed table.
Creates a SnappyData managed table. Any relation providers (e.g. row, column etc) supported by SnappyData can be created here.
```
case class Data(col1: Int, col2: Int, col3: Int)
val props = Map.empty[String, String]
val data = Seq(Seq(1, 2, 3), Seq(7, 8, 9), Seq(9, 2, 3), Seq(4, 2, 3), Seq(5, 6, 7))
val rdd = sc.parallelize(data, data.length).map(s => new Data(s(0), s(1), s(2)))
val dataDF = snc.createDataFrame(rdd)
snappyContext.createTable(tableName, "column", dataDF.schema, props)
```
For other external relation providers, use createExternalTable.
tableName
Name of the table
provider
Provider name such as 'COLUMN', 'ROW', 'JDBC' etc.
schema
Table schema
options
Properties for table creation. See options list for different tables. https://github.com/SnappyDataInc/snappydata/blob/master/docs/rowAndColumnTables.md
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception
returns
DataFrame for the table
def createTable(tableName: String, provider: String, options: Map[String, String], allowExisting: Boolean): DataFrame

Creates a SnappyData managed table.
Creates a SnappyData managed table. Any relation providers (e.g. row, column etc) supported by SnappyData can be created here.
```
val airlineDF = snappyContext.createTable(stagingAirline,
  "column", Map("buckets" -> "29"))
```
For other external relation providers, use createExternalTable.
tableName
Name of the table
provider
Provider name such as 'COLUMN', 'ROW', 'JDBC', 'PARQUET' etc.
options
Properties for table creation
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception
returns
DataFrame for the table

Annotations
@Experimental()
def createTable(tableName: String, provider: String, options: Map[String, String], allowExisting: Boolean): DataFrame

Creates a SnappyData managed table.
Creates a SnappyData managed table. Any relation providers (e.g. row, column etc) supported by SnappyData can be created here.
```
val airlineDF = snappyContext.createTable(stagingAirline,
  "column", Map("buckets" -> "29"))
```
For other external relation providers, use createExternalTable.
tableName
Name of the table
provider
Provider name such as 'COLUMN', 'ROW', 'JDBC', 'PARQUET' etc.
options
Properties for table creation
allowExisting
When set to true it will ignore if a table with the same name is present, else it will throw table exist exception
returns
DataFrame for the table
def delete(tableName: String, filterExpr: String): Int

Delete all rows in table that match passed filter expression
Delete all rows in table that match passed filter expression
tableName
table name
filterExpr
SQL WHERE criteria to select rows that will be updated
returns
number of rows deleted

Annotations
@DeveloperApi()
def dropIndex(indexName: String, ifExists: Boolean): Unit

Drops an index on a table
Drops an index on a table
indexName
Index name which goes in catalog
ifExists
Drop if exists, else exit gracefully
def dropTable(tableName: String, ifExists: Boolean = false): Unit

Drop a SnappyData table created by a call to SnappyContext.createTable, createExternalTable or registerTempTable.
Drop a SnappyData table created by a call to SnappyContext.createTable, createExternalTable or registerTempTable.
tableName
table to be dropped
ifExists
attempt drop only if the table exists
def dropTempTable(tableName: String): Unit

Definition Classes
SQLContext
def emptyDataFrame: DataFrame

Definition Classes
SQLContext
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def experimental: ExperimentalMethods

Definition Classes
SQLContext
Annotations
@Experimental() @transient()
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def getAllConfs: Map[String, String]

Definition Classes
SQLContext
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getConf(key: String, defaultValue: String): String

Definition Classes
SQLContext
def getConf(key: String): String

Definition Classes
SQLContext
def hashCode(): Int

Definition Classes
AnyRef → Any
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
def insert(tableName: String, rows: ArrayList[ArrayList[_]]): Int

Insert one or more org.apache.spark.sql.Row into an existing table A user can insert a DataFrame using foreachPartition...
Insert one or more org.apache.spark.sql.Row into an existing table A user can insert a DataFrame using foreachPartition...
```
someDataFrame.foreachPartition (x => snappyContext.insert
   ("MyTable", x.toSeq)
)
```
returns
number of rows inserted

Annotations
@Experimental()
def insert(tableName: String, rows: Row*): Int

Insert one or more org.apache.spark.sql.Row into an existing table A user can insert a DataFrame using foreachPartition...
Insert one or more org.apache.spark.sql.Row into an existing table A user can insert a DataFrame using foreachPartition...
```
someDataFrame.foreachPartition (x => snappyContext.insert
   ("MyTable", x.toSeq)
)
```
returns
number of rows inserted

Annotations
@DeveloperApi()
def isCached(tableName: String): Boolean

Definition Classes
SQLContext
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
def listenerManager: ExecutionListenerManager

Definition Classes
SQLContext
Annotations
@Experimental()
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def newSession(): SnappyContext

Definition Classes
SnappyContext → SQLContext
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def put(tableName: String, rows: ArrayList[ArrayList[_]]): Int

Upsert one or more org.apache.spark.sql.Row into an existing table upsert a DataFrame using foreachPartition...
Upsert one or more org.apache.spark.sql.Row into an existing table upsert a DataFrame using foreachPartition...
```
someDataFrame.foreachPartition (x => snappyContext.put
   ("MyTable", x.toSeq)
)
```
Annotations
@Experimental()
def put(tableName: String, rows: Row*): Int

Upsert one or more org.apache.spark.sql.Row into an existing table upsert a DataFrame using foreachPartition...
Upsert one or more org.apache.spark.sql.Row into an existing table upsert a DataFrame using foreachPartition...
```
someDataFrame.foreachPartition (x => snappyContext.put
   ("MyTable", x.toSeq)
)
```
Annotations
@DeveloperApi()
def queryApproxTSTopK(topK: String, startTime: Long, endTime: Long, k: Int): DataFrame
def queryApproxTSTopK(topKName: String, startTime: Long, endTime: Long): DataFrame

To do
why do we need this method? K is optional in the above method
def queryApproxTSTopK(topKName: String, startTime: String = null, endTime: String = null, k: Int = 1): DataFrame

Fetch the topK entries in the Approx TopK synopsis for the specified time interval.
Fetch the topK entries in the Approx TopK synopsis for the specified time interval. See _createTopK_ for how to create this data structure and associate this to a base table (i.e. the full data set). The time interval specified here should not be less than the minimum time interval used when creating the TopK synopsis.
topKName
- The topK structure that is to be queried.
startTime
start time as string of the format "yyyy-mm-dd hh:mm:ss". If passed as null, oldest interval is considered as the start interval.
endTime
end time as string of the format "yyyy-mm-dd hh:mm:ss". If passed as null, newest interval is considered as the last interval.
k
Optional. Number of elements to be queried. This is to be passed only for stream summary
returns
returns the top K elements with their respective frequencies between two time

To do
provide an example and explain the returned DataFrame. Key is the attribute stored but the value is a struct containing count_estimate, and lower, upper bounds? How many elements are returned if K is not specified?
def range(start: Long, end: Long, step: Long, numPartitions: Int): DataFrame

Definition Classes
SQLContext
Annotations
@Experimental()
def range(start: Long, end: Long, step: Long): DataFrame

Definition Classes
SQLContext
Annotations
@Experimental()
def range(start: Long, end: Long): DataFrame

Definition Classes
SQLContext
Annotations
@Experimental()
def range(end: Long): DataFrame

Definition Classes
SQLContext
Annotations
@Experimental()
def read: DataFrameReader

Definition Classes
SQLContext
def readStream: DataStreamReader

Definition Classes
SQLContext
Annotations
@Experimental()
def saveStream[T](stream: DStream[T], aqpTables: Seq[String], transformer: Option[(RDD[T]) ⇒ RDD[Row]])(implicit v: scala.reflect.api.JavaUniverse.TypeTag[T]): Unit

:: DeveloperApi ::
:: DeveloperApi ::

Annotations
@DeveloperApi()
To do
do we need this anymore? If useful functionality, make this private to sql package ... SchemaDStream should use the data source API? Tagging as developer API, for now
def sessionState: SnappySessionState

Definition Classes
SnappyContext → SQLContext
def setConf(key: String, value: String): Unit

Definition Classes
SQLContext
def setConf(props: Properties): Unit

Definition Classes
SQLContext
def setSchema(schemaName: String): Unit

Set current database/schema.
Set current database/schema.
schemaName
schema name which goes in the catalog
val snappySession: SnappySession
def sparkContext: SparkContext

Definition Classes
SQLContext
val sparkSession: SparkSession

Definition Classes
SQLContext
def sql(sqlText: String): DataFrame

Definition Classes
SQLContext
def streams: StreamingQueryManager

Definition Classes
SQLContext
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def table(tableName: String): DataFrame

Definition Classes
SQLContext
def tableNames(databaseName: String): Array[String]

Definition Classes
SQLContext
def tableNames(): Array[String]

Definition Classes
SQLContext
def tables(databaseName: String): DataFrame

Definition Classes
SQLContext
def tables(): DataFrame

Definition Classes
SQLContext
def toString(): String

Definition Classes
AnyRef → Any
def truncateTable(tableName: String): Unit

Empties the contents of the table without deleting the catalog entry.
Empties the contents of the table without deleting the catalog entry.
tableName
full table name to be truncated
def udf: UDFRegistration

Definition Classes
SQLContext
def uncacheTable(tableName: String): Unit

Definition Classes
SQLContext
def update(tableName: String, filterExpr: String, newColumnValues: ArrayList[_], updateColumns: ArrayList[String]): Int

Update all rows in table that match passed filter expression
Update all rows in table that match passed filter expression
```
snappyContext.update("jdbcTable", "ITEMREF = 3" , Row(99) , "ITEMREF" )
```
tableName
table name which needs to be updated
filterExpr
SQL WHERE criteria to select rows that will be updated
newColumnValues
A list containing all the updated column values. They MUST match the updateColumn list passed
updateColumns
List of all column names being updated

Annotations
@Experimental()
def update(tableName: String, filterExpr: String, newColumnValues: Row, updateColumns: String*): Int

Update all rows in table that match passed filter expression
Update all rows in table that match passed filter expression
```
snappyContext.update("jdbcTable", "ITEMREF = 3" , Row(99) , "ITEMREF" )
```
tableName
table name which needs to be updated
filterExpr
SQL WHERE criteria to select rows that will be updated
newColumnValues
A single Row containing all updated column values. They MUST match the updateColumn list passed
updateColumns
List of all column names being updated

Annotations
@DeveloperApi()
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Deprecated Value Members

def applySchema(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.3.0) Use createDataFrame instead.
def applySchema(rdd: RDD[_], beanClass: Class[_]): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.3.0) Use createDataFrame instead.
def applySchema(rowRDD: JavaRDD[Row], schema: StructType): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.3.0) Use createDataFrame instead.
def applySchema(rowRDD: RDD[Row], schema: StructType): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.3.0) Use createDataFrame instead.
def jdbc(url: String, table: String, theParts: Array[String]): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.jdbc() instead.
def jdbc(url: String, table: String, columnName: String, lowerBound: Long, upperBound: Long, numPartitions: Int): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.jdbc() instead.
def jdbc(url: String, table: String): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.jdbc() instead.
def jsonFile(path: String, samplingRatio: Double): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.json() instead.
def jsonFile(path: String, schema: StructType): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.json() instead.
def jsonFile(path: String): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.json() instead.
def jsonRDD(json: JavaRDD[String], samplingRatio: Double): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.json() instead.
def jsonRDD(json: RDD[String], samplingRatio: Double): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.json() instead.
def jsonRDD(json: JavaRDD[String], schema: StructType): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.json() instead.
def jsonRDD(json: RDD[String], schema: StructType): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.json() instead.
def jsonRDD(json: JavaRDD[String]): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.json() instead.
def jsonRDD(json: RDD[String]): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.json() instead.
def load(source: String, schema: StructType, options: Map[String, String]): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.format(source).schema(schema).options(options).load() instead.
def load(source: String, schema: StructType, options: Map[String, String]): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.format(source).schema(schema).options(options).load() instead.
def load(source: String, options: Map[String, String]): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.format(source).options(options).load() instead.
def load(source: String, options: Map[String, String]): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.format(source).options(options).load() instead.
def load(path: String, source: String): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.format(source).load(path) instead.
def load(path: String): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated
Deprecated
(Since version 1.4.0) Use read.load(path) instead.
def parquetFile(paths: String*): DataFrame

Definition Classes
SQLContext
Annotations
@deprecated @varargs()
Deprecated
(Since version 1.4.0) Use read.parquet() instead.

Related Docs: object SnappyContext | package sql

class SnappyContext extends SQLContext with Serializable with internal.Logging

Instance Constructors

new SnappyContext(sc: SparkContext)

new SnappyContext(snappySession: SnappySession)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def appendToTempTableCache(df: DataFrame, table: String, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK): Unit

final def asInstanceOf[T0]: T0

def baseRelationToDataFrame(baseRelation: BaseRelation): DataFrame

def cacheTable(tableName: String): Unit

def clear(): Unit

def clearCache(): Unit

def clone(): AnyRef

def createApproxTSTopK(topKName: String, baseTable: String, keyColumnName: String, topkOptions: Map[String, String], allowExisting: Boolean): DataFrame

def createApproxTSTopK(topKName: String, baseTable: Option[String], keyColumnName: String, topkOptions: Map[String, String], allowExisting: Boolean): DataFrame

def createApproxTSTopK(topKName: String, baseTable: String, keyColumnName: String, inputDataSchema: StructType, topkOptions: Map[String, String], allowExisting: Boolean): DataFrame

def createApproxTSTopK(topKName: String, baseTable: Option[String], keyColumnName: String, inputDataSchema: StructType, topkOptions: Map[String, String], allowExisting: Boolean = false): DataFrame

def createDataFrame(data: List[_], beanClass: Class[_]): DataFrame

def createDataFrame(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame

def createDataFrame(rdd: RDD[_], beanClass: Class[_]): DataFrame

def createDataFrame(rows: List[Row], schema: StructType): DataFrame

def createDataFrame(rowRDD: JavaRDD[Row], schema: StructType): DataFrame

def createDataFrame(rowRDD: RDD[Row], schema: StructType): DataFrame

def createDataFrame[A <: Product](data: Seq[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame

def createDataFrame[A <: Product](rdd: RDD[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame

def createDataset[T](data: List[T])(implicit arg0: Encoder[T]): Dataset[T]

def createDataset[T](data: RDD[T])(implicit arg0: Encoder[T]): Dataset[T]

def createDataset[T](data: Seq[T])(implicit arg0: Encoder[T]): Dataset[T]

def createExternalTable(tableName: String, source: String, schema: StructType, options: Map[String, String]): DataFrame

def createExternalTable(tableName: String, source: String, schema: StructType, options: Map[String, String]): DataFrame

def createExternalTable(tableName: String, source: String, options: Map[String, String]): DataFrame

def createExternalTable(tableName: String, source: String, options: Map[String, String]): DataFrame

def createExternalTable(tableName: String, path: String, source: String): DataFrame

def createExternalTable(tableName: String, path: String): DataFrame

def createIndex(indexName: String, baseTable: String, indexColumns: Map[String, Option[SortDirection]], options: Map[String, String]): Unit

def createIndex(indexName: String, baseTable: String, indexColumns: Map[String, Boolean], options: Map[String, String]): Unit

def createSampleTable(tableName: String, baseTable: String, schema: StructType, samplingOptions: Map[String, String], allowExisting: Boolean): DataFrame

def createSampleTable(tableName: String, baseTable: Option[String], schema: StructType, samplingOptions: Map[String, String], allowExisting: Boolean = false): DataFrame

def createSampleTable(tableName: String, baseTable: String, samplingOptions: Map[String, String], allowExisting: Boolean): DataFrame

def createSampleTable(tableName: String, baseTable: Option[String], samplingOptions: Map[String, String], allowExisting: Boolean): DataFrame

def createTable(tableName: String, provider: String, schemaDDL: String, options: Map[String, String], allowExisting: Boolean): DataFrame

def createTable(tableName: String, provider: String, schemaDDL: String, options: Map[String, String], allowExisting: Boolean): DataFrame

def createTable(tableName: String, provider: String, schema: StructType, options: Map[String, String], allowExisting: Boolean): DataFrame

def createTable(tableName: String, provider: String, schema: StructType, options: Map[String, String], allowExisting: Boolean = false): DataFrame

def createTable(tableName: String, provider: String, options: Map[String, String], allowExisting: Boolean): DataFrame

def createTable(tableName: String, provider: String, options: Map[String, String], allowExisting: Boolean): DataFrame

def delete(tableName: String, filterExpr: String): Int

def dropIndex(indexName: String, ifExists: Boolean): Unit

def dropTable(tableName: String, ifExists: Boolean = false): Unit

def dropTempTable(tableName: String): Unit

def emptyDataFrame: DataFrame

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def experimental: ExperimentalMethods

def finalize(): Unit

def getAllConfs: Map[String, String]

final def getClass(): Class[_]

def getConf(key: String, defaultValue: String): String

def getConf(key: String): String

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

def insert(tableName: String, rows: ArrayList[ArrayList[_]]): Int

def insert(tableName: String, rows: Row*): Int

def isCached(tableName: String): Boolean

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def listenerManager: ExecutionListenerManager

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit