Hi Dian ! Thx a lot for your reply, it's very helpful for us.
чт, 15 окт. 2020 г. в 04:30, Dian Fu <dian0511...@gmail.com>: > Hi Rinat, > > It's called in single thread fashion and so there is no need for the > synchronization. > > Besides, there is a pair of open/close methods in the ScalarFunction and > you could also override them and perform the initialization work in the > open method. > > Regards, > Dian > > 在 2020年10月15日，上午3:19，Sharipov, Rinat <r.shari...@cleverdata.ru> 写道： > > Hi mates ! > > I keep moving in my research of new features of PyFlink and I'm really > excited about that functionality. > My main goal is to understand how to integrate our ML registry, powered by > ML Flow and PyFlink jobs and what restrictions we have. > > I need to bootstrap the UDF function on it's startup when it's > instantiated in the Apache Beam process, but I don't know how it's called > by PyFlink in single thread fashion or shared among multiple threads. In > other words, I want to know, should I care about synchronization of my > bootstrap logic or not. > > Here is a code example of my UDF function: > > > > > > > > > > > > > > *class MyFunction(ScalarFunction): def __init__(self): > self.initialized = False def __bootstrap(self): return > "bootstrapped" def eval(self, urls): if self.initialized: > self.__bootstrap() return "my-result"my_function = udf(MyFunction(), > [DataTypes.ARRAY(DataTypes.STRING())], DataTypes.STRING())* > > > Thx a lot for your help ! > > >
