kryo

How to display (or operate on) objects encoded by Kryo in Spark Dataset?

由 旧城冷巷雨未停 提交于 2021-02-16 13:55:08
问题 Say you have this: // assume we handle custom type class MyObj(val i: Int, val j: String) implicit val myObjEncoder = org.apache.spark.sql.Encoders.kryo[MyObj] val ds = spark.createDataset(Seq(new MyObj(1, "a"),new MyObj(2, "b"),new MyObj(3, "c"))) When do a ds.show , I got: +--------------------+ | value| +--------------------+ |[01 00 24 6C 69 6...| |[01 00 24 6C 69 6...| |[01 00 24 6C 69 6...| +--------------------+ I understand that it's because the contents are encoded into internal

How to display (or operate on) objects encoded by Kryo in Spark Dataset?

由 北战南征 提交于 2021-02-16 13:54:10
问题 Say you have this: // assume we handle custom type class MyObj(val i: Int, val j: String) implicit val myObjEncoder = org.apache.spark.sql.Encoders.kryo[MyObj] val ds = spark.createDataset(Seq(new MyObj(1, "a"),new MyObj(2, "b"),new MyObj(3, "c"))) When do a ds.show , I got: +--------------------+ | value| +--------------------+ |[01 00 24 6C 69 6...| |[01 00 24 6C 69 6...| |[01 00 24 6C 69 6...| +--------------------+ I understand that it's because the contents are encoded into internal

spark调优

由 随声附和 提交于 2021-02-14 07:58:21
摘要： 鉴于 Spark 基于内存计算这一天性，以下集群资源可能会造成 Spark 程序的瓶颈：CPU，带宽和内存。通常情况下，如果内存足够的情况下，瓶颈就是网络带宽，但有时，你也需要做一些优化，例如以序列化的格式存储RDD，来减少内存使用。本指南将涵盖两个主要主题：数据序列化(这对于良好的网络性能至关重要，并且还可以减少内存使用)、内存调优。同时也会讨论一些较小的主题。 官网地址：https://spark.apache.org/docs/2.1.0/tuning.html 1.数据序列化 2.内存调优 　　2.1 内存管理概述 　　2.2 内存消耗确定 　　2.3 数据结构调优 　　2.4 序列化RDD存储 　　2.5 垃圾回收调优 　　　　2.5.1 测量GC的影响 　　　　2.5.2 GC调优 3.其他 　　3.1 并行级别 　　3.2 Reduce任务的内存使用 　　3.3 广播大变量 　　3.4 数据本地性 4.总结 一、数据序列化 序列化在分布式系统中扮演着重要的角色，那些会让对象序列化过程缓慢，或是会消耗大量字节存储的序列化格式会大大降低计算速率。通常这是用户在优化Spark应用程序中应该调整的第一件事。Spark旨在在便利性（允许您使用操作中的任何Java类型）和性能之间取得平衡。它提供了下面两种序列化库： Java serialization 默认情况下

Kryo vs Encoder vs Java Serialization in Spark?

由 廉价感情. 提交于 2021-02-08 10:40:35
问题 Which serialization is used for which case, From spark documentation it says : It provides two serialization libraries: 1. Java(default) and 2. Kryo Now where did Encoders come from and why is it not given in the doc. And also from databricks it says Encoders performs faster for Datasets,what about RDD, and how do all these maps together. In which case which serializer should we use? 回答1: Encoders are used in Dataset only. Kryo is used internally in spark. Kryo and Java serialization is

MapWithStateRDDRecord with kryo

由 旧城冷巷雨未停 提交于 2021-02-08 07:54:49
问题 How can I register MapWithStateRDDRecord in kryo? When I'm trying to do. `sparkConfiguration.registerKryoClasses(Array(classOf[org.apache.spark.streaming.rdd.MapWithStateRDD))` I get an error class MapWithStateRDDRecord in package rdd cannot be accessed in package org.apache.spark.streaming.rdd [error] classOf[org.apache.spark.streaming.rdd.MapWithStateRDDRecord] I'd like to make sure that all serialization is done via kryo thus I set SparkConf().set("spark.kryo.registrationRequired", "true")

MapWithStateRDDRecord with kryo

由 [亡魂溺海] 提交于 2021-02-08 07:54:05
问题 How can I register MapWithStateRDDRecord in kryo? When I'm trying to do. `sparkConfiguration.registerKryoClasses(Array(classOf[org.apache.spark.streaming.rdd.MapWithStateRDD))` I get an error class MapWithStateRDDRecord in package rdd cannot be accessed in package org.apache.spark.streaming.rdd [error] classOf[org.apache.spark.streaming.rdd.MapWithStateRDDRecord] I'd like to make sure that all serialization is done via kryo thus I set SparkConf().set("spark.kryo.registrationRequired", "true")

NullPointerException in ProtoBuf when Kryo serialization is used with Spark

由 别来无恙 提交于 2021-02-04 21:06:44
问题 I am getting the following error in my spark application when it is trying to serialize a protobuf field which is a map of key String and value float. Kryo serialization is being used in the spark app. Caused by: java.lang.NullPointerException at com.google.protobuf.UnmodifiableLazyStringList.size(UnmodifiableLazyStringList.java:68) at java.util.AbstractList.add(AbstractList.java:108) at com.esotericsoftware.kryo.serializers.CollectionSerializer.read(CollectionSerializer.java:134) at com

RocksDB Java操作

由 大憨熊 提交于 2021-01-30 10:00:40
RocksDB其实是一种嵌入式的K:V数据库，系统无需安装，之前本人的安装 RocksDB安装 ，其实多此一举。由于RocksDB是C++开发的，它的Java API大多其实只是对C++ API的一种调用。 RocksDB的底层数据结构是一种LSM树，可以参考 LSM树（Log-Structured Merge Tree）存储引擎浅析 首先添加依赖 <dependency> <groupId> org.rocksdb </groupId> <artifactId> rocksdbjni </artifactId> <version> 6.6.4 </version> </dependency> public class Test { static { RocksDB. loadLibrary () ; } private static RocksDB rocksDB ; private static String path = "/Users/admin/Downloads/rowdb" ; public static void main (String[] args) throws RocksDBException { Options options = new Options() ; options.setCreateIfMissing( true ) ; rocksDB =

How to store nested custom objects in Spark Dataset?

由 a 夏天 提交于 2021-01-29 07:48:20
问题 The question is a follow-up of How to store custom objects in Dataset? Spark version: 3.0.1 Non-nested custom type is achievable: import spark.implicits._ import org.apache.spark.sql.{Encoder, Encoders} class AnObj(val a: Int, val b: String) implicit val myEncoder: Encoder[AnObj] = Encoders.kryo[AnObj] val d = spark.createDataset(Seq(new AnObj(1, "a"))) d.printSchema root |-- value: binary (nullable = true) However, if the custom type is nested inside a product type (i.e. case class ), it

Hazelcast with global serializer (Kryo) - There is no suitable de-serializer for type

由 拈花ヽ惹草 提交于 2021-01-28 06:12:10
问题 I’m using Hazelcast 3.9 to cluster user sessions. To serialize the session objects, I created a global serializer implemented with Kryo (or more precisely KryoReflectionFactorySupport that allow to serialize objects without default constructor). public class GlobalKryoSerializer implements StreamSerializer<Object> { //use ThreadLocal because Kryo is not thread safe private static final InheritableThreadLocal <Kryo> kryoThreadLocal = new InheritableThreadLocal <Kryo>() { @Override protected
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