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1、实时查询维表

实时查询维表是指用户在 Flink 算子中直接访问外部数据库，比如用 MySQL 来进行关联，这种方式是同步方式，数据保证是最新的。但是，当我们的流计算数据过大，会对外 部系统带来巨大的访问压力，一旦出现比如连接失败、线程池满等情况，由于我们是同步调用，所以一般会导致线程阻塞、Task 等待数据返回，影响整体任务的吞吐量。而且这种方案对外部系统的 QPS 要求较高，在大数据实时计算场景下，QPS 远远高于普通的后台系统，峰值高达十万到几十万，整体作业瓶颈转移到外部系统

public class DimSync  extends RichMapFunction<fplOverview, String> {private static final Logger LOGGER = LoggerFactory.getLogger(DimSync.class);private Connection conn = null;public void open(Configuration parameters) throws Exception {super.open(parameters);conn = DriverManager.getConnection("jdbc:test:3306/mysqldb?characterEncoding=UTF-8", "root", "qyllt1314#");}@Overridepublic String map(fplOverview fplOverview) throws Exception {JSONObject jsonObject = JSONObject.parseObject(fplOverview.toJson());String dp_id = jsonObject.getString("dp_id");//根据 dp_id 查询  上周的 fpl_amount,ywPreparedStatement pst = conn.prepareStatement("select  max(fpl_amount) as fpl_amount,max(yearweek(datatime)) as yw \n" +"from fpl_overview \n" +"where datatime >= date_sub(curdate(), interval weekday(curdate()) + 7 Day)  # 上周第一天\n" +"and datatime < date_sub(curdate(), interval weekday(curdate()) + 0 Day)  # 上周最后一天+1 \n" +"and dp_id = ? \n" +"group by dp_id");pst.setString(1,dp_id);ResultSet resultSet = pst.executeQuery();String fpl_amount = null;String yw = null ;while (resultSet.next()){fpl_amount = resultSet.getString(1);yw = resultSet.getString(2);}pst.close();jsonObject.put("lastweek_fpl_amount",fpl_amount);jsonObject.put("lastweek_yw",yw)return jsonObject.toString();}public void close() throws Exception {super.close();conn.close();}

2、LRU 缓存 (flink 异步Id)

利用 Flink 的 RichAsyncFunction 读取 mysql 的数据到缓存中，我们在关联维度表时先去查询缓存，如果缓存中不存在这条数据，就利用客户端去查询 mysql，然后插入到缓存中。

public class JDBCAsyncFunction  extends RichAsyncFunction<fplOverview, JsonObject> {private SQLClient client;@Overridepublic void open(Configuration parameters) throws Exception {Vertx vertx = Vertx.vertx(new VertxOptions().setWorkerPoolSize(10).setEventLoopPoolSize(10));JsonObject config = new JsonObject().put("url", "jdbc:mysql://rm-bp161be65d56kbt4nzo.mysql.rds.aliyuncs.com:3306/mysqldb?characterEncoding=UTF-8;useSSL=false").put("driver_class", "com.mysql.cj.jdbc.Driver").put("max_pool_size", 10).put("user", "root").put("password", "");client = JDBCClient.createShared(vertx, config);}@Overridepublic void close() throws Exception {client.close();}@Overridepublic void asyncInvoke(fplOverview fplOverview, ResultFuture<JsonObject> resultFuture) throws Exception {client.getConnection(conn -> {if (conn.failed()) {return;}final SQLConnection connection = conn.result();// 执行sqlconnection.query("select  max(fpl_amount) as fpl_amount,max(yearweek(datatime)) as yw \n" +"from fpl_overview \n" +"where datatime >= date_sub(curdate(), interval weekday(curdate()) + 7 Day)  # 上周第一天\n" +"and datatime < date_sub(curdate(), interval weekday(curdate()) + 0 Day)  # 上周最后一天+1 \n" +"and dp_id = '" + fplOverview.getDp_id() + " ' " +"group by dp_id ", res2 -> {ResultSet rs = new ResultSet();if (res2.succeeded()) {rs = res2.result();}else{System.out.println("查询数据库出错");}List<JsonObject> stores = new ArrayList<>();for (JsonObject json : rs.getRows()) {stores.add(json);}connection.close();resultFuture.complete(stores);});});}}

3、预加载全量mysql数据

预加载全量mysql数据 使用 ScheduledExecutorService 每隔 5 分钟拉取一次维表数据，这种方式适用于那些实时场景不是很高，维表数据较小的场景

public class WholeLoad extends RichMapFunction<fplOverview,String> {private static final Logger LOGGER = LoggerFactory.getLogger(WholeLoad.class);// 定义map的结果，key为关联字段private static Map<String,String> cache ;@Overridepublic void open(Configuration parameters) throws Exception {super.open(parameters);cache = new HashMap<>();ScheduledExecutorService executor = Executors.newScheduledThreadPool(2);executor.scheduleAtFixedRate(new Runnable() {@Overridepublic void run() {try {load();} catch (Exception e) {e.printStackTrace();}}},0,5, TimeUnit.MINUTES); //从现在开始每隔5分钟查询数据}@Overridepublic String map(fplOverview fplOverview) throws Exception {JSONObject jsonObject = JSONObject.parseObject(fplOverview.toJson());String dp_id = jsonObject.getString("dp_id");// 获取对应id的结果String rs = cache.get(dp_id);JSONObject rsObject = JSONObject.parseObject(rs);jsonObject.putAll(rsObject);return jsonObject.toString();}public   void  load() throws Exception {Class.forName("com.mysql.jdbc.Driver");Connection con = DriverManager.getConnection("jdbc:mysql://test:3306/mysqldb?characterEncoding=UTF-8", "root", "qyllt1314#");// 执行查询的SQLPreparedStatement statement = con.prepareStatement("select  dp_id,max(fpl_amount) as fpl_amount,max(yearweek(datatime)) as yw \n" +"from fpl_overview \n" +"where datatime >= date_sub(curdate(), interval weekday(curdate()) + 7 Day)  # 上周第一天\n" +"and datatime < date_sub(curdate(), interval weekday(curdate()) + 0 Day)  # 上周最后一天+1 \n" +"group by dp_id");ResultSet rs = statement.executeQuery();while (rs.next()) {// 查询结果放入缓存String dp_id = rs.getString("dp_id");String fpl_amount = rs.getString("fpl_amount");String yw = rs.getString("yw");JSONObject jsonObject = JSONObject.parseObject("{}");jsonObject.put("lastweek_fpl_amount",fpl_amount);jsonObject.put("lastweek_yw",yw);cache.put(dp_id,jsonObject.toString());}System.out.println("数据输出测试:"+cache.toString());con.close();}
}