flink 配置 cggroups 样例

答：Flink 配置 cggroups 样例需要在 flink-conf.yaml 文件中进行配置，具体步骤如下：1. 打开 flink-conf.yaml 文件；2. 添加 cggroups 样例配置；3. 保存文件并重启 Flink。

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flink MySql TwoPhaseCommitSink样例

以下是一个使用 Flink 的 `TwoPhaseCommitSinkFunction` 将数据写入 MySQL 数据库的样例代码：

public class MySqlTwoPhaseCommitSink extends TwoPhaseCommitSinkFunction<MyData, Connection, Void> {

    private final String username;
    private final String password;
    private final String drivername;
    private final String dburl;

    public MySqlTwoPhaseCommitSink(String username, String password, String drivername, String dburl) {
        this.username = username;
        this.password = password;
        this.drivername = drivername;
        this.dburl = dburl;
    }

    @Override
    public void invoke(TransactionContext transactionContext, MyData myData, Context context) throws Exception {
        // do nothing in invoke, wait for the prepareTransaction method
    }

    @Override
    public void prepareTransaction(TransactionContext transactionContext) throws Exception {
        // create a new connection
        Connection connection = DriverManager.getConnection(dburl, username, password);
        // set the connection to the transaction context
        transactionContext.setTransactionConnection(connection);
    }

    @Override
    public void commitTransaction(TransactionContext transactionContext) throws Exception {
        // get the connection from the transaction context
        Connection connection = transactionContext.getTransactionConnection();
        // commit the transaction
        connection.commit();
        // close the connection
        connection.close();
    }

    @Override
    public void abortTransaction(TransactionContext transactionContext) throws Exception {
        // get the connection from the transaction context
        Connection connection = transactionContext.getTransactionConnection();
        // rollback the transaction
        connection.rollback();
        // close the connection
        connection.close();
    }

    @Override
    public TypeInformation<Void> getProducedType() {
        // return void as the produced type
        return TypeInformation.of(Void.class);
    }

    @Override
    protected void finalize() throws Throwable {
        // close the connection if it hasn't been closed yet
        DriverManager.getConnection(dburl, username, password).close();
    }
}

在上面的代码中，`MyData` 是需要写入 MySQL 数据库的数据类型。在 `invoke` 方法中，我们不做任何操作，只是等待 `prepareTransaction` 方法。在 `prepareTransaction` 方法中，我们创建一个新的 MySQL 数据库连接并将其设置到事务上下文中。在 `commitTransaction` 和 `abortTransaction` 方法中，我们获取事务上下文中的连接并提交或回滚事务，然后关闭连接。在 `getProducedType` 方法中，我们返回 `void` 类型作为生产类型。在 `finalize` 方法中，我们确保关闭连接。

使用上面的代码，我们可以使用以下方式将数据写入 MySQL 数据库：

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

DataStream<MyData> dataStream = ... // get the data stream

MySqlTwoPhaseCommitSink sink = new MySqlTwoPhaseCommitSink(username, password, drivername, dburl);

TwoPhaseCommitSinkFunctionWrapper<MyData, Void> sinkWrapper =
    TwoPhaseCommitSinkFunctionWrapper.newBuilder(sink).build();

dataStream.addSink(sinkWrapper);

env.execute();

在上面的代码中，我们创建了一个 `MySqlTwoPhaseCommitSink` 对象，并使用 `TwoPhaseCommitSinkFunctionWrapper` 将其包装在内。然后，我们将数据流添加到这个包装器中，并执行任务。

flink hive sink样例代码

下面是一个使用Flink将数据写入到Hive表中的示例代码：

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.descriptors.Schema;
import org.apache.flink.table.descriptors.ConnectorDescriptor;
import org.apache.flink.table.descriptors.FormatDescriptor;
import org.apache.flink.table.descriptors.FileSystem;
import org.apache.flink.table.descriptors.OldCsv;
import org.apache.flink.table.sinks.TableSink;
import org.apache.flink.table.sinks.hive.HiveTableSink;

public class FlinkHiveSinkDemo {
    public static void main(String[] args) throws Exception {
        // 创建Flink的StreamExecutionEnvironment对象
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        // 创建TableEnvironment对象
        EnvironmentSettings settings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, settings);

        // 创建Hive Catalog
        String catalogName = "myhive";
        String defaultDatabase = "default";
        String hiveConfDir = "/path/to/hive/conf";
        HiveCatalog hiveCatalog = new HiveCatalog(catalogName, defaultDatabase, hiveConfDir);
        tableEnv.registerCatalog(catalogName, hiveCatalog);

        // 创建Hive表
        String tableName = "mytable";
        String[] fieldNames = {"name", "age", "gender"};
        String[] fieldTypes = {"STRING", "INT", "STRING"};
        tableEnv.sqlUpdate(String.format("CREATE TABLE %s (%s) PARTITIONED BY (dt STRING)", tableName, getFields(fieldNames, fieldTypes)));

        // 将DataStream转换为Table
        DataStream<Person> stream = env.fromElements(new Person("Alice", 18, "F"), new Person("Bob", 20, "M"));
        Table table = tableEnv.fromDataStream(stream, "name, age, gender");

        // 将Table写入Hive表
        TableSink sink = new HiveTableSink(tableName, catalogName, getFields(fieldNames, fieldTypes), new String[]{"dt"});
        tableEnv.registerTableSink("hiveSink", sink);
        table.insertInto("hiveSink");

        // 执行任务
        env.execute("Flink Hive Sink Demo");
    }

    private static String getFields(String[] fieldNames, String[] fieldTypes) {
        StringBuilder sb = new StringBuilder();
        for (int i = 0; i < fieldNames.length; i++) {
            sb.append(fieldNames[i]).append(" ").append(fieldTypes[i]);
            if (i < fieldNames.length - 1) {
                sb.append(",");
            }
        }
        return sb.toString();
    }

    public static class Person {
        public String name;
        public int age;
        public String gender;

        public Person() {}

        public Person(String name, int age, String gender) {
            this.name = name;
            this.age = age;
            this.gender = gender;
        }
    }
}

这个示例代码中，先创建了一个Hive Catalog，然后创建了一个Hive表。将一个DataStream转换为Table，并通过HiveTableSink将Table写入到Hive表中。在实际使用中，需要根据具体的业务场景进行调整。