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启动flink Scala REPL
MacBook-Air:learn3 myhaspl$ ~/Documents/flink/libexec/bin/start-scala-shell.sh local
Starting Flink Shell:
log4j:WARN No appenders could be found for logger (org.apache.flink.configuration.GlobalConfiguration).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Starting local Flink cluster (host: localhost, port: 8081).
Connecting to Flink cluster (host: localhost, port: 8081).
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F L I N K - S C A L A - S H E L L
NOTE: Use the prebound Execution Environments to implement batch or streaming programs.
Batch - Use the 'benv' variable
* val dataSet = benv.readTextFile("/path/to/data")
* dataSet.writeAsText("/path/to/output")
* benv.execute("My batch program")
HINT: You can use print() on a DataSet to print the contents to the shell.
Streaming - Use the 'senv' variable
* val dataStream = senv.fromElements(1, 2, 3, 4)
* dataStream.countWindowAll(2).sum(0).print()
* senv.execute("My streaming program")
HINT: You can only print a DataStream to the shell in local mode.
scala>
scala> val text = benv.fromElements(
| "To be, or not to be,--that is the question:--",
| "Whether 'tis nobler in the mind to suffer",
| "The slings and arrows of outrageous fortune",
| "Or to take arms against a sea of troubles,")
scala> val counts = text.flatMap { _.toLowerCase.split("\\W+") }.map { (_, 1) }.groupBy(0).sum(1)
counts: org.apache.flink.api.scala.AggregateDataSet[(String, Int)] = org.apache.flink.api.scala.AggregateDataSet@254dfd34
scala> counts.print()
(a,1)
(against,1)
(and,1)
(arms,1)
(arrows,1)
(be,2)
(fortune,1)
(in,1)
(is,1)
(mind,1)
(nobler,1)
(not,1)
(of,2)
(or,2)
(outrageous,1)
(question,1)
(sea,1)
(slings,1)
(suffer,1)
(take,1)
(that,1)
(the,3)
(tis,1)
(to,4)
(troubles,1)
(whether,1)
print()命令将自动将指定的任务发送给JobManager执行，并在终端中显示计算结果。
可以将结果写入文件。然而，在这种情况下，您需要调用execute来运行您的程序:
benv.execute("MyProgram")
scala> val text = benv.fromElements("Each task slot represents a fixed subset of resources of the TaskManager. A TaskManager with three slots, for example, will dedicate 1/3 of its managed memory to each slot. Slotting the resources means that a subtask will not compete with subtasks from other jobs for managed memory, but instead has a certain amount of reserved managed memory. Note that no CPU isolation happens here; currently slots only separate the managed memory of tasks.")
text: org.apache.flink.api.scala.DataSet[String] = org.apache.flink.api.scala.DataSet@6c551798
scala> val counts = text.flatMap { _.toLowerCase.split("\\W+") }.map { (_, 1) }.groupBy(0).sum(1)
counts: org.apache.flink.api.scala.AggregateDataSet[(String, Int)] = org.apache.flink.api.scala.AggregateDataSet@2be71a78
scala> counts.print()
(1,1)
(3,1)
(a,4)
(amount,1)
(but,1)
(certain,1)
(compete,1)
(cpu,1)
(currently,1)
(dedicate,1)
(each,2)
(example,1)
(fixed,1)
(for,2)
(from,1)
(happens,1)
(has,1)
(here,1)
(instead,1)
(isolation,1)
(its,1)
(jobs,1)
(managed,4)
(means,1)
(memory,4)
(no,1)
(not,1)
(note,1)
(of,5)
(only,1)
(other,1)
(represents,1)
(reserved,1)
(resources,2)
(separate,1)
(slot,2)
(slots,2)
(slotting,1)
(subset,1)
(subtask,1)
(subtasks,1)
(task,1)
(taskmanager,2)
(tasks,1)
(that,2)
(the,3)
(three,1)
(to,1)
(will,2)
(with,2)
scala> :q
good bye ..
MacBook-Air:learn3 myhaspl$