Spark并发写Hudi建议

涉及到并发场景，推荐采用分区间并发写的方式：即不同的写入任务写不同的分区
 分区并发参数控制：
- SQL方式：
```
set hoodie.support.partition.lock=true;
```
- DataSource Api方式：
```
df.write
.format("hudi")
.options(xxx)
.option("hoodie.support.partition.lock", "true")
.mode(xxx)
.save("/tmp/tablePath")
```
所有参与分区间并发写入的任务，都必须配置上述参数。

不建议同分区内并发写，这种并发写入需要开启Hudi OCC方式并发写入，必须严格遵守并发参数配置，否则会出现表数据损坏的问题。

并发OCC参数控制：

SQL方式：

// 开启OCC
set hoodie.write.concurrency.mode=optimistic_concurrency_control;
set hoodie.cleaner.policy.failed.writes=LAZY;

// 开启并发锁 zk锁
set hoodie.write.lock.provider=org.apache.hudi.client.transaction.lock.ZookeeperBasedLockProvider; // 设置使用zk锁
set hoodie.write.lock.zookeeper.url=<zookeeper_url>;  // 设置使用zk 地址
set hoodie.write.lock.zookeeper.port=<zookeeper_port>; // 设置使用zk端口
set hoodie.write.lock.zookeeper.lock_key=<table_name>;  // 设置锁名称
set hoodie.write.lock.zookeeper.base_path=<table_path>; // 设置zk锁路径

DataSource Api方式：

df.write
.format("hudi")
.options(xxx)
.option("hoodie.write.concurrency.mode", "optimistic_concurrency_control")
.option("hoodie.cleaner.policy.failed.writes", "LAZY")
.option("hoodie.write.lock.zookeeper.url", "zookeeper_url")
.option("hoodie.write.lock.zookeeper.port", "zookeeper_port")
.option("hoodie.write.lock.zookeeper.lock_key", "table_name")
.option("hoodie.write.lock.zookeeper.base_path", "table_path")
.mode(xxx)
.save("/tmp/tablePath")