Hi Billy, Flink's internal operators are implemented to not allocate heap space proportional to the size of the input data. Whenever Flink needs to hold data in memory (e.g., for sorting or building a hash table) the data is serialized into managed memory. If all memory is in use, Flink starts spilling to disk. This blog post discusses how Flink uses its managed memory [1] (still up to date, even though it's almost 2 years old). The runtime code should actually quite stable. Most of the code has been there for several years (even before Flink was donated to the ASF) and we haven't seen many bugs reported for the DataSet runtime. Of course this does not mean that the code doesn't contain bugs.
However, Flink does not take care of the user code. For example a GroupReduceFunction that collects a lot of data, e.g., in a List on the heap, can still kill a program. I would check if you have user functions that require lots of heap memory. Also reducing the size of the managed memory to have more heap space available might help. If that doesn't solve the problem, it would be good if you could share some details about your job (which operators, which local strategies, how many operators) that might help to identify the misbehaving operator. Thanks, Fabian [1] https://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.html 2017-04-19 16:09 GMT+02:00 Newport, Billy <billy.newp...@gs.com>: > How does Flink use memory? We’re seeing cases when running a job on larger > datasets where it throws OOM exceptions during the job. We’re using the > Dataset API. Shouldn’t flink be streaming from disk to disk? We workaround > by using fewer slots but it seems unintuitive that I need to change these > settings given Flink != Spark. Why isn’t Flinks memory usage constant? Why > couldn’t I run a job with a single task and a single slot for any size job > successfully other than it takes much longer to run. > > > > Thanks > > Billy > > > > >
