connector

Value Members

1. package config

   

2. package domain

   

   Provides classes to describe query data model.

   Provides classes to describe query data model.

   Overview

   Here are some essential enums and classes.

   com.ryft.spark.connector.domain.LogicalOperator - Logical operators (and, or, xor) used by com.ryft.spark.connector.query.RyftQuery.

   com.ryft.spark.connector.domain.Action - Represents action on RyftOne and corresponding REST endpoint. Currently supoprted Search and Count.

   com.ryft.spark.connector.domain.RyftQueryOptions - And important class representing REST Search query parameters, like fuzziness, surrounding, list of fields and number of nodes to use for search.

3. package exception

   

   Provides classes to represent connector specific exceptions.

4. package partitioner

   

5. package preferred

   

6. package query

   

   Provides classes to represent different RyftOne queries.

   Provides classes to represent different RyftOne queries.

   Overview

   The main class to use is com.ryft.spark.connector.query.RecordQuery, as so

   import com.ryft.spark.connector._
   import com.ryft.spark.connector.domain.{recordField, contains}
   import com.ryft.spark.connector.query.RecordQuery
   
   val query = RecordQuery(recordField("Date"), contains, "04/14/2015")
                      .and(recordField("Arrest"), contains, "false")

   For raw text searches com.ryft.spark.connector.query.SimpleQuery class should be used, as so

   import com.ryft.spark.connector._
   import com.ryft.spark.connector.query.SimpleQuery
   
   val query = SimpleQuery("James Bond")

7. package rdd

   

   Provides classes for RyftOne connector specific RDD operations.

8. package rest

   

9. package sql

   

   Provides classes for DataFrames and SparkSQL support.

   Provides classes for DataFrames and SparkSQL support.

   Overview

   These classes are not intended to be used directly but should be accessed via com.ryft.spark.connector.RyftDataFrameReader

   For example:

   import org.apache.spark.sql.SQLContext
   import org.apache.spark.sql.types._
   import org.apache.spark.{SparkConf, SparkContext}
   import com.ryft.spark.connector._
   
   // Definition for the DataFrame setup
   val schema = StructType(Seq(
   StructField("Arrest", BooleanType), StructField("Beat", IntegerType),
   StructField("Block", StringType), StructField("CaseNumber", StringType),
   StructField("_index", StructType(Seq(
     StructField("file", StringType), StructField("offset", StringType),
     StructField("length", IntegerType), StructField("fuzziness", ByteType)))
    )
   ))
   
   sqlContext.read.ryft(schema, "*.crimestat", "crime_table")

10. implicit def toSparkContextFunctions(sc: SparkContext): SparkContextFunctions

    

11. package util