Flink Catalog

1.Flink侧创建

  按照SQL的解析处理流程在Parse解析SQL以后，进入执行流程——executeInternal。

  其中有个分支专门处理创建Catalog的SQL命令

} else if (operation instanceof CreateCatalogOperation) { return createCatalog((CreateCatalogOperation) operation);
createCatalog方法里完成两件事：1、创建Catalog对象；2、向catalogManager注册
Catalog catalog =
        FactoryUtil.createCatalog(
                catalogName, properties, tableConfig, userClassLoader);
catalogManager.registerCatalog(catalogName, catalog);

  创建Catalog会去全包查找对应的CatalogFactory的子类，然后使用配置的子类构建

final CatalogFactory legacyFactory =
        TableFactoryService.find(CatalogFactory.class, options, classLoader);
return legacyFactory.createCatalog(catalogName, options);

  这里注意，上面的步骤只查询classpath下的类，像HiveCatalog这种外置增加的，在这个步骤里找不到，会抛出NoMatchingTableFactoryException异常之后继续其他步骤处理来获取

} catch (NoMatchingTableFactoryException e) { // No matching legacy factory found, try using the new stack
    final DefaultCatalogContext discoveryContext =
            new DefaultCatalogContext(catalogName, options, configuration, classLoader);
    try { final CatalogFactory factory = getCatalogFactory(discoveryContext);

  最终在FactoryUtil.discoverFactory的方法中进行过滤查找，这里用到了type配置做过滤，基于Factory的

factoryIdentifier获取工厂的字段与配置做对比
final List matchingFactories =
        foundFactories.stream()
                .filter(f -> f.factoryIdentifier().equals(factoryIdentifier))
                .collect(Collectors.toList());

2.HiveCatalog

  获取到对应的Factory以后，会调用其createCatalog方法创建对应的Catalog

return new HiveCatalog(
        context.getName(),
        helper.getOptions().get(DEFAULT_DATABASE),
        helper.getOptions().get(HIVE_CONF_DIR),
        helper.getOptions().get(HADOOP_CONF_DIR),
        helper.getOptions().get(HIVE_VERSION));

  HiveCatalog的整个创建过程主要是发现Hive配置的过程，其他接口就是对库表的操作接口

  获取配置主要是基于上面hive-conf-dir、hadoop-conf-dir来的，首先是根据这两个配置去获取hive配置，如果都获取不到，会从classpath下面去获取hive的配置文件

URL hiveSite =
  Thread.currentThread().getContextClassLoader().getResource(HIVE_SITE_FILE);

3.IcebergCatalog

  Iceberg走的应该是前面TableFactoryService.find能找到的接口，因为它实现的是properties参数的接口，clusterHadoopConf()就是调用的Flink里的方法获取Hadoop的配置

@Override
public Catalog createCatalog(String name, Map properties) { return createCatalog(name, properties, clusterHadoopConf());
}

3.1.CatalogLoader

  第一步是创建CatalogLoader，这是Iceberg Catalog的类加载器

  这里可以配置自定义类加载器，相关配置：catalog-impl，如果没有配置则走默认

  默认流程根据catalog-type配置选择实例化Hive的还是Hadoop的，默认是Hive的

String catalogType = properties.getOrDefault(ICEBERG_CATALOG_TYPE, ICEBERG_CATALOG_TYPE_HIVE);
switch (catalogType.toLowerCase(Locale.ENGLISH)) { case ICEBERG_CATALOG_TYPE_HIVE:
    // The values of properties 'uri', 'warehouse', 'hive-conf-dir' are allowed to be null, in
    // that case it will
    // fallback to parse those values from hadoop configuration which is loaded from classpath.
    String hiveConfDir = properties.get(HIVE_CONF_DIR);
    String hadoopConfDir = properties.get(HADOOP_CONF_DIR);
    Configuration newHadoopConf = mergeHiveConf(hadoopConf, hiveConfDir, hadoopConfDir);
    return CatalogLoader.hive(name, newHadoopConf, properties);
  case ICEBERG_CATALOG_TYPE_HADOOP:
    return CatalogLoader.hadoop(name, hadoopConf, properties);
}

  创建CatalogLoader主要就是进行一些基本参数的设置

private HiveCatalogLoader(
    String catalogName, Configuration conf, Map properties) { this.catalogName = catalogName;
  this.hadoopConf = new SerializableConfiguration(conf);
  this.uri = properties.get(CatalogProperties.URI);
  this.warehouse = properties.get(CatalogProperties.WAREHOUSE_LOCATION);
  this.clientPoolSize =
      properties.containsKey(CatalogProperties.CLIENT_POOL_SIZE)
          ? Integer.parseInt(properties.get(CatalogProperties.CLIENT_POOL_SIZE))
          : CatalogProperties.CLIENT_POOL_SIZE_DEFAULT;
  this.properties = Maps.newHashMap(properties);
}

3.2.FlinkCatalog

  接下来就是进行一些配置然后创建FlinkCatalog

  配置里注意Hadoop有一个特殊的配置：base-namespace，这是配置namespa的，会自动带上前缀，应该就是在warehouse加上前缀

  这里还有缓存配置：cache-enabled、cache.expiration-interval-ms，控制Catalog是否缓存表入口

3.3.loadCatalog

  FlinkCatalog会使用CatalogLoader加载Catalog，最终会到CatalogUtil.loadCatalog()

  这里最终会用Class.forName来加载类，基于Constructor来构建实例

 ctor = DynConstructors.builder(Catalog.class).impl(impl).buildChecked();
  catalog = ctor.newInstance();

3.4.HiveCatalog

  Hive类型最终创建的是org.apache.iceberg.hive.HiveCatalog

  initialize初始化也基本上是进行配置，有两个注意的对象：FileIO、CachedClientPool

  io-impl可以配置文件读取，默认用Iceberg的HadoopFileIO

this.fileIO =
    fileIOImpl == null
        ? new HadoopFileIO(conf)
        : CatalogUtil.loadFileIO(fileIOImpl, properties, conf);

  CachedClientPool是一个Hive连接缓存，缓存的是HiveMetaStoreClient

return GET_CLIENT.invoke(
    hiveConf, (HiveMetaHookLoader) tbl -> null, HiveMetaStoreClient.class.getName());