Graph data is prevalent in many domains, but it has usually required specialized engines to analyze. This design is onerous for users and precludes optimization across complete workflows. We present GraphFrames, an integrated system that lets users combine graph algorithms, pattern matching and relational queries, and optimizes work across them. GraphFrames generalize the ideas in previous graph-on-RDBMS systems, such as GraphX and Vertexica, by letting the system materialize multiple views of the graph (not just the specific triplet views in these systems) and executing both iterative algorithms and pattern matching using joins. To make applications easy to write, GraphFrames provide a concise, declarative API based on the “data frame” concept in R that can be used for both interactive queries and standalone programs. Under this API, GraphFrames use a graph-aware join optimization algorithm across the whole computation that can select from the available views. We implement GraphFrames over Spark SQL, enabling parallel execution on Spark and integration with custom code. We find that GraphFrames make it easy to express end-to-end workflows and match or exceed the performance of standalone tools, while enabling optimizations across workflow steps that cannot occur in current systems. In addition, we show that GraphFrames’ view abstraction makes it easy to further speed up interactive queries by registering the appropriate view, and that the combination of graph and relational data allows for other optimizations, such as attribute-aware partitioning.