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diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md index 2e9966c0a2..bf4b968eb8 100644 --- a/docs/graphx-programming-guide.md +++ b/docs/graphx-programming-guide.md @@ -67,23 +67,6 @@ operators (e.g., [subgraph](#structural_operators), [joinVertices](#join_operato [aggregateMessages](#aggregateMessages)) as well as an optimized variant of the [Pregel](#pregel) API. In addition, GraphX includes a growing collection of graph [algorithms](#graph_algorithms) and [builders](#graph_builders) to simplify graph analytics tasks. - -## Migrating from Spark 1.1 - -GraphX in Spark 1.2 contains a few user facing API changes: - -1. To improve performance we have introduced a new version of -[`mapReduceTriplets`][Graph.mapReduceTriplets] called -[`aggregateMessages`][Graph.aggregateMessages] which takes the messages previously returned from -[`mapReduceTriplets`][Graph.mapReduceTriplets] through a callback ([`EdgeContext`][EdgeContext]) -rather than by return value. -We are deprecating [`mapReduceTriplets`][Graph.mapReduceTriplets] and encourage users to consult -the [transition guide](#mrTripletsTransition). - -2. In Spark 1.0 and 1.1, the type signature of [`EdgeRDD`][EdgeRDD] switched from -`EdgeRDD[ED]` to `EdgeRDD[ED, VD]` to enable some caching optimizations. We have since discovered -a more elegant solution and have restored the type signature to the more natural `EdgeRDD[ED]` type. - # Getting Started To get started you first need to import Spark and GraphX into your project, as follows: |