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
java
} 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
java
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
java
} 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
java
factoryIdentifier获取工厂的字段与配置做对比
final List<Factory> 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
java
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
java
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
java
@Override
public Catalog createCatalog(String name, Map<String, String> 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
java
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
java
private HiveCatalogLoader(
String catalogName, Configuration conf, Map<String, String> 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
java
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
java
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
java
return GET_CLIENT.invoke(
hiveConf, (HiveMetaHookLoader) tbl -> null, HiveMetaStoreClient.class.getName());