Discretized Streams: Fault Tolerant Computing at Scale describes additions to the Spark system to handle streaming data. Compared to other streaming systems, Spark Streaming offers a more robust fault recovery and straggler handling strategies using the Resilient Distributed Dataset (RDD) memory abstraction. In addition to allowing parallel recovery, Spark Streaming is one of the first systems which can incorporate batch and interactive query models all within the same system.

What are RDDs?

RDDs are a memory abstraction model described in the original Spark paper. They are immutable and only allow a specified set of functional-like transformations to be operated on them. However, these seemingly constraining properties allow the computation done on RDDs to be completely deterministic, and RDDs can be computed in parallel without having to worry about synchronization. Another really interesting aspect of RDDs are their resilience to faults. By tracking the lineage of transformations done on RDDs, we can reconstruct any lost RDDs by simply retracing the lineage and recomputing from the source RDDs.

Stream Discretization

Spark Streaming is different from other streaming systems in that it discretizes streaming input using a sliding window. The discretized input are turned into RDDs and Spark Streaming can then perform small batch operations on them as it would have in general batched mode. By converting the input streaming data into batched RDDs, Spark Streaming can easily intermix between the streaming model and the general batched model because it operates over the same RDD abstraction.


Typically, streaming systems employ a constant operator model in which several constantly running workers wait for streaming data, operate on them, and output the data. Spark Streaming differs from this model in that it expresses all the operations done on the data through the RDDs. The worker nodes maintain no state of their own. If state is needed to operate on the data, Spark Streaming employs a special track operation, which provides access to a key-value store (structured as an RDD). The state for any data can be stored as a value in the key-value store and can be accessed again using the same key.

Parallel Recovery

When a fault occurs, the Spark Streaming model simply recalculates the RDDs that have been destroyed by retracing through the lineage of the graph. However, because the RDDs are immutable and deterministic, recomputation can be performed in parallel both in time and partition. This allows Spark Streaming model to have fault-recovery times on the order of seconds and tens of seconds, while existing systems take minutes.


Once again because RDDs are immutable and deterministic, Spark Streaming can perform speculative replication to handle any stragglers. It is not a problem if an RDD is computed twice because transformations on an RDD is deterministic, so no synchronization needs to take place when actually storing the RDD.


Overall, using RDDs to operate on streaming data provides a nice clean solution for fault-recovery and stragglers. Also, this idea of discretizing streaming data could lead to interesting future work in the area.