Mutable Maps have a convenient getOrElseUpdate function, that allows you to look up a value by key, and compute/store the value if there isn't one already present: @ val m = collection.mutable.Map("one"-> 1, "two"-> 2, "three"-> 3) @ m.getOrElseUpdate("three",-1) // already present, returns existing value res87: Int = 3 @ m // `m` is unchanged res88: mutable.

Spark version : 2 Steps:. install conda on all nodes (python2.7) ( pip install conda ) create requirement1.txt with "numpy > requirement1.txt "Run kmeans.py application in yarn-client mode.

Runner.main(Runner.scala) Caused by: java.awt.HeadlessException at getOrElseUpdate(MapLike.scala:189) at scala.collection.mutable.AbstractMap. Feb 11, 2021 HashMap.getOrElseUpdate(HashMap.scala:86) ~[scala-library-2.12.10.jar:?] at org.neo4j.cypher.internal.compiler.helpers. scala> val results = sqlContext.sql("SELECT * FROM my_keyspace.my_table") getOrElseUpdate(BlockManager.scala:711) at org.apache.spark.rdd.RDD. Sep 10, 2020 getOrElseUpdate(BlockManager.scala:881) at org.apache.spark.rdd.RDD.

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AbstractMap.getOrElseUpdate(Map.scala:80) [error] at scala.reflect.macros. runtime.MacroRuntimes.standardMacroRuntime(MacroRuntimes. Runner.main(Runner.scala) Caused by: java.awt.HeadlessException at getOrElseUpdate(MapLike.scala:189) at scala.collection.mutable.AbstractMap. Feb 11, 2021 HashMap.getOrElseUpdate(HashMap.scala:86) ~[scala-library-2.12.10.jar:?] at org.neo4j.cypher.internal.compiler.helpers. scala> val results = sqlContext.sql("SELECT * FROM my_keyspace.my_table") getOrElseUpdate(BlockManager.scala:711) at org.apache.spark.rdd.RDD.

All  Feb 24, 2016 Scala allows the special keyword lazy in front of val in order to change the val to one that is lazily initialized. While lazy initialization seems  Our elegant styles are featured in over 800 boutiques around the world. SCALA designs come from the head and hearts of the tailors, beaders and designers.

No Spark shuffle block is larger than 2GB (Integer.MAX_VALUE bytes) therefore you need additional / smaller partitions.You should adjust spark.default.parallelism and spark.sql.shuffle.partitions (default 200) such that the number of partitions can accommodate your data without reaching the 2GB limit (you could try aiming for 256MB / partition so for 200GB you get 800 partitions).

UnsatisfiedLinkError: /mxnet/scala-package/init-native/linux-x86_64/target/libmxnet-init-scala getOrElseUpdate(MapLike.scala:194) [INFO] at  getOrElseUpdate(Map.scala:82) [INFO] at scala.reflect.macros.runtime.MacroRuntimes.standardMacroRuntime(MacroRuntimes.scala:50)  getOrElseUpdate$(MapLike.scala:203) [error] at scala.collection.mutable.AbstractMap.getOrElseUpdate(Map.scala:80) [error] at scala.reflect.macros.runtime. TypeAnalyzer$UDTAnalyzerInstance$UDTAnalyzerCache$$anonfun$getOrElseUpdate$2$$anonfun$apply$4.apply(TypeAnalyzer.scala:481)  getOrElseUpdate(Map.scala:80) [error] at scala.reflect.macros.runtime.MacroRuntimes.standardMacroRuntime(MacroRuntimes.scala:38)  getOrElseUpdate(Map.scala:80).

This is typically logic you would write in Java, and it looks great in some ways: it uses pattern matching, the tuple arrow ( -> ), etc. But it turns out that Scala collections already provide the getOrElseUpdate method on mutable maps. The 8 lines above translate simply into: 1 2. def getModelState(modelPrefixedId: String) = modelStates.

As I understand, TrieMap.getOrElseUpdate is still not truly atomic, and this fixes only returned result (it could return different instances for different callers before this fix), so the updater function still might be called several times, as documentation (for 2.11.7) says: Note: This method will invoke op at most once.

Benchmarks for groupBy / map getOrElseUpdate slowdown - GroupByBench.scala. Skip to content. All gists Back to GitHub Sign in Sign up Sign in Sign up {{ message }} No Spark shuffle block is larger than 2GB (Integer.MAX_VALUE bytes) therefore you need additional / smaller partitions.You should adjust spark.default.parallelism and spark.sql.shuffle.partitions (default 200) such that the number of partitions can accommodate your data without reaching the 2GB limit (you could try aiming for 256MB / partition so for 200GB you get 800 partitions). The following examples show how to use scala.collection.mutable.Set.These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. /** * Convert this promise to a Scala future. *

* This is equivalent to FutureConverters.toScala(this), however, it converts the wrapped completion stage to * a future rather than this, which means if the wrapped completion stage itself wraps a Scala future, it will * simply return that wrapped future.
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and lo and behold I found a problem: Testing my old code for Spark 2 Structured Streaming with Apache Kafka was suddenly broken with an The following examples show how to use redis.clients.jedis.Jedis.These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

and lo and behold I found a problem: Testing my old code for Spark 2 Structured Streaming with Apache Kafka was suddenly broken with an The following examples show how to use redis.clients.jedis.Jedis.These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Customer cannot submit Spark job in InsightEdge version 15.0 with specific Kubernetes versions While testing Spark Project Hive, there are RuntimeExceptions as follows, VersionsSuite: success sanity check *** FAILED *** java.lang.RuntimeException: download Once the project is set up, go to Scala > Run Setup Diagnostics… and make sure to check the field “Use Scala-compatible JDT content assist proposals” Done.
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at scala.collection.mutable.AbstractMap.getOrElseUpdate(Map.scala:91) Could you please advise how to overcome the error? Reply. Valentin Nikotin. February 21, 2017 4

If you use recursive getOrElseUpdate you can easily end up with a map that contains the same key twice with different values: val map = mutable. Map [ String, String ] () map.getOrElseUpdate ("key", { map.getOrElseUpdate ("key", "value1") "value2" }) map The second call could come from another place in the application this is just a sample. Scala’s Predef object offers an implicit conversion that lets you write key -> value as an alternate syntax ms getOrElseUpdate (k, d) If key k is defined in map Transform the builders into their results in a pass over the mutable Map before handing it to immutable.HashMapBuilder.addAll which already has an optimized path when the operation is a mutable.HashMap.