Data scientists often implement machine learning algorithms in imperative languages such as Java, Matlab and R. Yet such implementations fail to achieve the performance and scalability of specialised data-parallel processing frameworks. Our goal is to execute imperative Java programs in a data-parallel fashion with high
throughput and low latency. This raises two challenges: how to support the arbitrary mutable state of Java programs without compromising scalability, and how to re
cover that state after failure with low overhead. Our idea is to infer the dataflow and the types of state accesses from a Java program and use this information
to generate a stateful dataflow graph (SDG) . By explicitly separating data
from mutablestate, SDGs have specific features to enable this translation: to ensure scalability, distributed state can be partitioned across nodes if computation can occur entirely in parallel; if this is not possible, partial state gives nodes local instances for independent computation, which are reconciled according
to application semantics. For fault tolerance, large inmemory state is checkpointed asynchronously without global coordination. We show that the performance of
SDGs for several imperative online applications matches
that of existing data-parallel processing frameworks.