如何配置Flink DataStream作业来处理725MB表的不可变ListState？

import org.apache.flink.api.common.state.ListState;
import org.apache.flink.api.common.state.ListStateDescriptor;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

public class FlinkJob {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 配置ListState
        ListStateDescriptor<String> listStateDescriptor = new ListStateDescriptor<>("tableState", String.class);
        ListState<String> tableState = env.getOperatorStateStore().getListState(listStateDescriptor);

        // 加载表数据
        env.addSource(new TableSource()).setParallelism(1)
                .flatMap((String value, Collector<String> out) -> {
                    // 处理数据
                    // TODO: 进行数据处理逻辑
                });

        // 提交作业
        env.execute("Flink Job");
    }

    public static class TableSource implements SourceFunction<String> {
        @Override
        public void run(SourceContext<String> ctx) throws Exception {
            // 从外部数据源加载表数据
            // TODO: 加载表数据的逻辑
        }

        @Override
        public void cancel() {
            // 取消作业
        }
    }
}