Just transform the list in a DataStream. A datastream can be finite.
One solution, in the context of a Streaming environment is to use Kafka, or any other distributed broker, although Flink ships with a KafkaSource. 1)Create a Kafka Topic dedicated to your list of key/values. Inject your values into this topic, partitionned by the keys. So that you recover the keys in Flink. 2) Create a source for the stream of tuple your analysing -> output1 (Tuples). 3) Create a KafkaSource, and parse/recover your key value pairs from this source (e.g a first map operator) : map1 -> output 2 (K,V), then : a) If you need all key/Value pairs at each operator : broadcast all partitions from the output 1 to the analysis operator b) if you dont need all key/values pairs, just chain output1 to the analysis operator. Partitioning of K,V pairs will depend on Kafka partitioning strategy, and can be controlled in Flink anyway. 4) The analysis operator : will perform a RichCoFlatMapFunction, and can be Checkpointed. When receiving K,V pairs from output2, store them in a local state. When receiving tuple, should be able to to filter with the help of the local state, and propagate downstream or not. > Message du 30/05/16 13:41 > De : leon_mcl...@tutanota.com > A : "User" > Copie à : > Objet : Elegantly sharing state in a streaming environment > >Hello Flink team, How can i partition and share static state among instances of a streaming operator? I have a huge list of keys and values, which are used to filter tuples in a stream. The list does not change. Currently i am sharing the list with each operator instance via the constructor, although only a subset of the list is required per operator (the assignment of subset to operator instance is known). I cannot use DataSet based functions in a streaming execution environment to assign sub lists. I also cannot use DataStream based partitioning functions as the list is static, i.e. not a DataStream. The dilemma exists as i am mixing static (DataSet type) content with streaming content. Is there any other approach aside from using an additional tool (e.g. distributed cache)? Thanks in advance. Regards Leon
