我正在做一些流处理框架之间的基准比较，
我在这方面选择了wordcount这样的“hello world”任务（有些曲折），并测试了flink和hazelcast jet到目前为止，结果是flink需要80+s才能完成，而jet只需要30+s
我知道Flink很受欢迎，我做错什么了？真的很好奇
我的示例代码在这里
https://github.com/chinw/stream-processing-compare

以下是详细信息（规格、管道、日志）

测试的wordcount管道

Source (read from file, 5MB)
 -> Process: Split line into words (Here here is a bomb, every word emit 1000 times)
 -> Group/Count
 -> Sink (do nothing)

我的本地结果
macbook pro（13英寸，2020，四个thunderbolt 3端口）
2 ghz四核intel core i5（8个逻辑处理器）
16 gb 3733 mhz lpddr4x
jdk 11号
喷气机4.4
管道：

digraph DAG {
    "items" [localParallelism=1];
    "fused(flat-map, filter)" [localParallelism=8];
    "group-and-aggregate-prepare" [localParallelism=8];
    "group-and-aggregate" [localParallelism=8];
    "do-nothing-sink" [localParallelism=1];
    "items" -> "fused(flat-map, filter)" [queueSize=1024];
    "fused(flat-map, filter)" -> "group-and-aggregate-prepare" [label="partitioned", queueSize=1024];
    subgraph cluster_0 {
        "group-and-aggregate-prepare" -> "group-and-aggregate" [label="distributed-partitioned", queueSize=1024];
    }
    "group-and-aggregate" -> "do-nothing-sink" [queueSize=1024];
}

日志：

Start time: 2021-04-18T13:52:52.106
Duration: 00:00:36.459
Jet: finish in 36.45935081 seconds.

Start time: 2021-04-19T16:51:53.806
Duration: 00:00:30.143
Jet: finish in 30.625740453 seconds.

Start time: 2021-04-19T16:52:48.906
Duration: 00:00:37.207
Jet: finish in 37.862554137 seconds.

scala 2.11的flink 1.12.2 flink-config.yaml 配置：

jobmanager.rpc.address: localhost
jobmanager.rpc.port: 6123
jobmanager.memory.process.size: 2096m
taskmanager.memory.process.size: 12288m
taskmanager.numberOfTaskSlots: 8
parallelism.default: 8

管道：

{
  "nodes" : [ {
    "id" : 1,
    "type" : "Source: Custom Source",
    "pact" : "Data Source",
    "contents" : "Source: Custom Source",
    "parallelism" : 1
  }, {
    "id" : 2,
    "type" : "Flat Map",
    "pact" : "Operator",
    "contents" : "Flat Map",
    "parallelism" : 8,
    "predecessors" : [ {
      "id" : 1,
      "ship_strategy" : "REBALANCE",
      "side" : "second"
    } ]
  }, {
    "id" : 4,
    "type" : "Keyed Aggregation",
    "pact" : "Operator",
    "contents" : "Keyed Aggregation",
    "parallelism" : 8,
    "predecessors" : [ {
      "id" : 2,
      "ship_strategy" : "HASH",
      "side" : "second"
    } ]
  }, {
    "id" : 5,
    "type" : "Sink: Unnamed",
    "pact" : "Data Sink",
    "contents" : "Sink: Unnamed",
    "parallelism" : 8,
    "predecessors" : [ {
      "id" : 4,
      "ship_strategy" : "FORWARD",
      "side" : "second"
    } ]
  } ]
}

日志：

❯ flink run -c chiw.spc.flink.FlinkWordCountKt stream-processing-compare-1.0-SNAPSHOT.jar
Job has been submitted with JobID 163ce849a663e45f3c3028a98f260e7c
Program execution finished
Job with JobID 163ce849a663e45f3c3028a98f260e7c has finished.
Job Runtime: 88614 ms

❯ flink run -c chiw.spc.flink.FlinkWordCountKt stream-processing-compare-1.0-SNAPSHOT.jar
Job has been submitted with JobID fcf12488204969299e4e5d7f23f4ea6e
Program execution finished
Job with JobID fcf12488204969299e4e5d7f23f4ea6e has finished.
Job Runtime: 90165 ms

❯ flink run -c chiw.spc.flink.FlinkWordCountKt stream-processing-compare-1.0-SNAPSHOT.jar
Job has been submitted with JobID 37e349e4fad90cd7405546d30239afa4
Program execution finished
Job with JobID 37e349e4fad90cd7405546d30239afa4 has finished.
Job Runtime: 78908 ms

非常感谢你的帮助！