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第六章：volatile关键字
学习目标 1 内存可见性问题 1.1 什么是内存可见性 1.2 内存可见性问题的原因 2 volatile的作用与使用场景 2.1 volatile关键字介绍 2.2 volatile的内存语义 2.3 volatile的适用场景 2.4 volatile不能解决的问题 3 volatile与synchronized的区别 3.1 可见性与... -
第四章：线程安全问题
学习目标 1. 什么是线程安全 1.1 线程安全的定义 1.2 线程安全的重要性 2. 共享资源访问的竞态条件 2.1 什么是竞态条件 2.2 竞态条件示例 2.3 竞态条件的类型 读-改-写竞态条件 检查再执行竞态条件 3. 线程安全问题的表现形式 3.1 原子性问题 3.2 可见性问题 3.3 有序性问题 4. 实战案例：商品... -
Writes
Spark Writes To use Iceberg in Spark, first configure Spark catalogs . Some plans are only available when using Iceberg SQL extensions in Spark 3. Iceberg uses Apache Spark’s D... -
11. Redis commands mapping
Redis command Sync / Async APIRedisson.create(config) Reactive APIredisson.reactive() RxJava3 APIredisson.rxJava() AUTH Config.setPassword() - - APPEND RBinarySt... -
Introduction
Documentation Apache Iceberg is an open table format for huge analytic datasets. Iceberg adds tables to compute engines including Spark, Trino, PrestoDB, Flink, Hive and Impala u... -
Java Quickstart
Java API Quickstart Create a table Tables are created using either a Catalog or an implementation of the Tables interface. Using a Hive catalog The Hive catalog connects to... -
第五章：synchronized关键字
学习目标 1. 对象锁与类锁的概念 1.1 对象锁（实例锁） 1.2 类锁（静态锁） 1.3 对象锁与类锁的对比 2. synchronized修饰方法与代码块的区别 2.1 修饰方法 vs 修饰代码块 2.2 不同锁对象的选择 3. synchronized的底层实现原理 3.1 Monitor监视器 3.2 字节码层面的实现 3.3 ... -
DDL
Spark DDL To use Iceberg in Spark, first configure Spark catalogs . Iceberg uses Apache Spark’s DataSourceV2 API for data source and catalog implementations. CREATE TABLE Spark... -
Java Custom Catalog
Custom Catalog It’s possible to read an iceberg table either from an hdfs path or from a hive table. It’s also possible to use a custom metastore in place of hive. The steps to do... -
Distributed counters
Id generator Redis or Valkey based Java Id generator RIdGenerator generates unique numbers but not monotonically increased. At first request, batch of id numbers is allocated and...
