Common mistakes that happen often are application failure, a slow job that gets stuck due to numerous factors, mistakes in the aggregation, actions or transformations, an exception in the main thread and, of course, Out Of Memory (OOM).
Most of the time, application failure happens because one or more stages fail eventually. As discussed earlier in this chapter, Spark jobs comprise several stages. Stages aren't executed independently: for instance, a processing stage can't take place before the relevant input-reading stage. So, suppose that stage 1 executes successfully but stage 2 fails to execute, the whole application fails eventually. This can be shown as follows:
Figure 19: Two stages in a typical Spark job
To show an example, suppose you have the following three RDD operations as stages. The same can be visualized as shown in Figure 20, Figure 21, and Figure 22:
val rdd1 = sc.textFile(“hdfs://data/data.csv”)
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