apache flink vs spark

Apache Flink: Very reliable, one point not badApache Flink's backgroundWe summarize the data set types (types of datasets) that are primarily encountered in the current data processing aspect at a higher level of abstraction, and the processing models (execution models) that are available for processing data, which are often confusing, but are actually different conceptstype of data setThe data set types th

正运行到时候才知道是哪个子类，这样就不能提前做优化； 实际测，性能的差距在2.7倍左右 解决方法：Approach 1:make sure that only one memory segment implementation is ever loaded.We re-structured The code a bit to make sure this all places that produce long-lived and short-lived memory segments Insta Ntiate the same memorysegment subclass (Heap-or off-heap segment). Using factories rather than directly instantiating the memory segment classes, this is straightforward. 如果在代码里面只可能实例化其中的一个子类，另一个子类根本就没有被实例化过，那么JIT会意识到，并做优化；我们可以用factories来实例化对象，这样更方

timestamp, W window, TriggerContext ctx) throws IOException { count = ctx.getPartitionedState(stateDesc); longcount1; count.update(currentCount); if (currentCount >= maxCount) { count.update(0L); return TriggerResult.FIRE; } return TriggerResult.CONTINUE; }PurgingtriggerThe trigger is similar to a wrapper that transforms any given trigger into a purging trigger. Its implementation mechanism is that it receives a trigger instance

Each Flink program relies on a set of Flink libraries. The Flink itself consists of a set of classes and dependencies that are required to run. The combination of all classes and dependencies forms the core of the Flink runtime and must exist when a Flink program runs.

the linestring[] tokens = Value.tolowercase (). Split ("\\w+"); //Emit the pairs for(String token:tokens) {if(Token.length () > 0) {Out.collect (NewTuple2)); } } } } programming steps, and spark very similar obtain an execution environment,load/ This data,specify where to put the Results of your Computations,trigger the program executionint Counters The steps for summing and counting include defining, adding to con

. Triggers the execution of the program. Streamexecutionenvironment is the basis for all flink programs. Can be obtained by the following static methods:int port, String ... jarfiles)Usually only need to use the Getexecutionenvironment () method, because it will do the right thing according to the environment: if you execute your program on the IDE or as a normal Java program, it will create a local environment that will execute the

the distributed runtime of Apache FlinkTasks and Operator ChainsWhen distributed execution, Flink can link operator subtasks to tasks, each task is executed by one thread, which is an effective optimization, avoids the overhead of thread switching and buffering, improves the overall throughput under the premise of reducing delay, and the link behavior can be configuredJob managers,task Managers and clientsT

About Apache FlinkApache Flink is a scalable, open source batch processing and streaming platform. Its core module is a data flow engine that provides data distribution, communication, and fault tolerance on the basis of distributed stream data processing, with the following architectural diagram:The engine contains the following APIs:1. DataSet API for static data embedded in Java, Scala, and Python2. Data

the current task is executed in parallel (with multiple instances at the same time), a prefix is output before each record is output prefix . Prefix is the position of the current subtask in the global context.Sink in common connectorsFlink itself provides some connector support for third-party mainstream open source systems, which are: Elasticsearch Flume Kafka (0.8/0.9 version) Nifi Rabbitmq Twitter The sink of these third-party systems (except Twitter) are i