Hi,
正常情况是可以自动提交分区的，我看你commit policy指定了metastore+success-file，可以检查一下分区目录下success
file是否创建了。如果success file也没有的话说明没有触发分区提交。另外提交分区时会打印类似这样的日志，可以在log中查找一下
LOG.info("Partition {} of table {} is ready to be committed",
partSpec, tableIdentifier);
LOG.info("Committed partition {} to metastore", partitionSpec);
LOG.info("Committed partition {} with success file", context.partitionSpec());
On Sun, Nov 1, 2020 at 5:36 PM 陈帅 <[email protected]> wrote:
> 最后，在hive shell中执行 “msck repair table team;” 命令后就能查询到写的数据了，难道flink hive
> streaming不能自动注册hive分区吗？还是我使用的姿势不对？
>
> 陈帅 <[email protected]> 于2020年11月1日周日 下午5:24写道：
>
> > 改用 TEXTFILE 存储hive表数据以便下载hive文件观察内容
> > ") STORED AS TEXTFILE TBLPROPERTIES ("
> >
> > 这是生成的hive表建表语句
> >
> > hive> show create table team;
> > OK
> > CREATE TABLE `team`(
> > `team_id` int,
> > `team_name` string,
> > `create_time` string,
> > `update_time` string,
> > `op` string)
> > PARTITIONED BY (
> > `dt` string,
> > `hr` string,
> > `mi` string)
> > ROW FORMAT SERDE
> > 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
> > STORED AS INPUTFORMAT
> > 'org.apache.hadoop.mapred.TextInputFormat'
> > OUTPUTFORMAT
> > 'org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat'
> > LOCATION
> > 'hdfs://localhost:9000/user/hive/warehouse/ods.db/team'
> > TBLPROPERTIES (
> > 'is_generic'='false',
> > 'partition.time-extractor.timestamp-pattern'='$dt $hr:$mi:00',
> > 'sink.partition-commit.delay'='1 min',
> > 'sink.partition-commit.policy.kind'='metastore,success-file',
> > 'sink.partition-commit.trigger'='partition-time',
> > 'transient_lastDdlTime'='1604222266')
> > Time taken: 0.252 seconds, Fetched: 25 row(s)
> >
> > 另外，下载了hive文件内容如下
> > 1001<0x01>Sun<0x01>2020-10-31 11:25:38<0x01>2020-10-31
> 11:25:38<0x01>INSERT
> >
> > 还是查询不到结果
> > hive> select * from team;
> > OK
> > Time taken: 0.326 seconds
> >
> > 陈帅 <[email protected]> 于2020年11月1日周日 下午5:10写道：
> >
> >>
> >>
> 之前没加watermark和设置分区是能够写hive文件并查询出来的，只是设置分区后hive文件是生成出来了但查询不出来，所以我感觉跟watermark设置与否没太大关系。
> >> 生成的hive分区文件路径类似于
> /user/hive/warehouse/ods.db/team/dt=20201101/hr=16/mi=30/
> >> part-dc55d200-dd03-4f26-8a3e-60bfa1dd97f2-0-3
> >>
> >> 陈帅 <[email protected]> 于2020年11月1日周日 下午4:43写道：
> >>
> >>>
> 我查过hive文件是有生成的，按照我定义的partition。按照你的建议在ds2这个stream上加了watermark，运行后hive文件也生成了，但同样通过hive
> >>> shell查不到数据。
> >>>
> >>> import com.alibaba.fastjson.JSON;
> >>> import com.alibaba.fastjson.JSONObject;
> >>> import org.apache.flink.api.common.serialization.SimpleStringSchema;
> >>> import org.apache.flink.api.common.typeinfo.TypeInformation;
> >>> import org.apache.flink.api.common.typeinfo.Types;
> >>> import org.apache.flink.api.java.typeutils.RowTypeInfo;
> >>> import org.apache.flink.streaming.api.CheckpointingMode;
> >>> import org.apache.flink.streaming.api.TimeCharacteristic;
> >>> import org.apache.flink.streaming.api.datastream.DataStream;
> >>> import
> >>>
> org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions;
> >>> import
> >>> org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
> >>> import
> >>>
> org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
> >>> import org.apache.flink.streaming.api.windowing.time.Time;
> >>> import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
> >>> import
> >>> org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase;
> >>> import org.apache.flink.table.api.EnvironmentSettings;
> >>> import org.apache.flink.table.api.SqlDialect;
> >>> import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
> >>> import org.apache.flink.table.catalog.hive.HiveCatalog;
> >>> import org.apache.flink.types.Row;
> >>> import org.apache.flink.types.RowKind;
> >>>
> >>> import java.time.Duration;
> >>> import java.time.Instant;
> >>> import java.time.LocalDateTime;
> >>> import java.time.ZoneId;
> >>> import java.time.format.DateTimeFormatter;
> >>> import java.util.Properties;
> >>>
> >>> public class MysqlCDC2Hive {
> >>>
> >>> private static final DateTimeFormatter dtf =
> >>> DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss");
> >>>
> >>> public static void main(String[] args) throws Exception {
> >>> StreamExecutionEnvironment streamEnv =
> >>> StreamExecutionEnvironment.getExecutionEnvironment();
> >>>
> >>> streamEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
> >>> streamEnv.setParallelism(3);
> >>> streamEnv.enableCheckpointing(60000);
> >>>
> >>> EnvironmentSettings tableEnvSettings =
> >>> EnvironmentSettings.newInstance()
> >>> .useBlinkPlanner()
> >>> .inStreamingMode()
> >>> .build();
> >>> StreamTableEnvironment tableEnv =
> >>> StreamTableEnvironment.create(streamEnv, tableEnvSettings);
> >>>
> >>>
> tableEnv.getConfig().getConfiguration().set(ExecutionCheckpointingOptions.CHECKPOINTING_MODE,
> >>> CheckpointingMode.EXACTLY_ONCE);
> >>>
> >>>
> tableEnv.getConfig().getConfiguration().set(ExecutionCheckpointingOptions.CHECKPOINTING_INTERVAL,
> >>> Duration.ofMinutes(1));
> >>>
> >>> String catalogName = "hive_catalog";
> >>> HiveCatalog catalog = new HiveCatalog(
> >>> catalogName,
> >>> "default",
> >>> "/Users/chenshuai/dev/apache-hive-2.3.4-bin/conf",
> >>> "2.3.4"
> >>> );
> >>> tableEnv.registerCatalog(catalogName, catalog);
> >>> tableEnv.useCatalog(catalogName);
> >>>
> >>> MyDateFormat2 myDateFormat = new MyDateFormat2();
> >>> tableEnv.registerFunction("my_date_format", myDateFormat);
> >>>
> >>> tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS cdc");
> >>> tableEnv.executeSql("DROP TABLE IF EXISTS cdc.team");
> >>> tableEnv.executeSql("CREATE TABLE cdc.team(\n" +
> >>> " team_id INT,\n" +
> >>> " team_name STRING,\n" +
> >>> " create_time TIMESTAMP,\n" +
> >>> " update_time TIMESTAMP,\n" +
> >>> " proctime as proctime()\n" +
> >>> ") WITH (\n" +
> >>> " 'connector' = 'mysql-cdc',\n" +
> >>> " 'hostname' = 'localhost',\n" +
> >>> " 'port' = '3306',\n" +
> >>> " 'username' = 'root',\n" +
> >>> " 'password' = 'root',\n" +
> >>> " 'database-name' = 'test',\n" +
> >>> " 'table-name' = 'team'\n" +
> >>> ")");
> >>>
> >>> tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS kafka");
> >>> tableEnv.executeSql("DROP TABLE IF EXISTS kafka.team");
> >>> tableEnv.executeSql("CREATE TABLE kafka.team (\n" +
> >>> " team_id INT,\n" +
> >>> " team_name STRING,\n" +
> >>> " create_time TIMESTAMP,\n" +
> >>> " update_time TIMESTAMP\n" +
> >>> ") WITH (\n" +
> >>> " 'connector' = 'kafka',\n" +
> >>> " 'topic' = 'team',\n" +
> >>> " 'scan.startup.mode' = 'earliest-offset',\n" +
> >>> " 'properties.bootstrap.servers' =
> 'localhost:9092',\n"
> >>> +
> >>> " 'format' = 'changelog-json'\n" +
> >>> ")");
> >>>
> >>> tableEnv.executeSql("INSERT INTO kafka.team \n" +
> >>> "SELECT team_id, team_name, create_time, update_time
> \n"
> >>> +
> >>> "FROM cdc.team");
> >>>
> >>> // 定义带op字段的stream
> >>> Properties properties = new Properties();
> >>> properties.setProperty("bootstrap.servers", "localhost:9092");
> >>> properties.setProperty("group.id", "test1`");
> >>>
> >>> FlinkKafkaConsumerBase<String> consumer = new
> >>> FlinkKafkaConsumer<>(
> >>> "team",
> >>> new SimpleStringSchema(),
> >>> properties
> >>> ).setStartFromEarliest();
> >>>
> >>> DataStream<String> ds = streamEnv.addSource(consumer);
> >>>
> >>> String[] fieldNames = {"team_id", "team_name", "create_time",
> >>> "update_time", "op"};
> >>> TypeInformation[] types = {Types.INT, Types.STRING,
> >>> Types.STRING, Types.STRING, Types.STRING};
> >>> DataStream<Row> ds2 = ds.map(str -> {
> >>> JSONObject jsonObject = JSON.parseObject(str);
> >>> String op = jsonObject.getString("op");
> >>> JSONObject data = jsonObject.getJSONObject("data");
> >>> int arity = fieldNames.length;
> >>> Row row = new Row(arity);
> >>> row.setField(0, data.get("team_id"));
> >>> row.setField(1, data.get("team_name"));
> >>> row.setField(2, data.get("create_time"));
> >>> row.setField(3, data.get("update_time"));
> >>> String operation = getOperation(op);
> >>> row.setField(4, operation);
> >>>
> >>> return row;
> >>> }, new RowTypeInfo(types, fieldNames))
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> *.assignTimestampsAndWatermarks(new
> >>> BoundedOutOfOrdernessTimestampExtractor<Row>(Time.minutes(1)) {
> >>> @Override public long extractTimestamp(Row row) {
> >>> String dt = (String) row.getField(2); LocalDateTime ldt
> =
> >>> LocalDateTime.parse(dt, dtf); Instant instant =
> >>> ldt.atZone(ZoneId.systemDefault()).toInstant(); long
> >>> timeInMillis = instant.toEpochMilli(); return
> timeInMillis;
> >>> } });*
> >>>
> >>> tableEnv.registerDataStream("merged_team", ds2);
> >>>
> >>> tableEnv.getConfig().setSqlDialect(SqlDialect.HIVE);
> >>>
> >>> tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS ods");
> >>> tableEnv.executeSql("DROP TABLE IF EXISTS ods.team");
> >>>
> >>> tableEnv.executeSql("CREATE TABLE ods.team (\n" +
> >>> " team_id INT,\n" +
> >>> " team_name STRING,\n" +
> >>> " create_time STRING,\n" +
> >>> " update_time STRING,\n" +
> >>> " op STRING\n" +
> >>> ") PARTITIONED BY (\n" +
> >>> " dt STRING,\n" +
> >>> " hr STRING,\n" +
> >>> " mi STRING\n" +
> >>> ") STORED AS PARQUET TBLPROPERTIES (\n" +
> >>> " 'sink.partition-commit.trigger' =
> >>> 'partition-time',\n" +
> >>> " 'sink.partition-commit.delay' = '1 min',\n" +
> >>> " 'sink.partition-commit.policy.kind' =
> >>> 'metastore,success-file',\n" +
> >>> " 'partition.time-extractor.timestamp-pattern' = '$dt
> >>> $hr:$mi:00'\n" +
> >>> ")");
> >>>
> >>> tableEnv.getConfig().setSqlDialect(SqlDialect.DEFAULT);
> >>> tableEnv.executeSql("INSERT INTO ods.team \n" +
> >>> "SELECT team_id, team_name, create_time, update_time,
> >>> op, \n" +
> >>> " DATE_FORMAT(TO_TIMESTAMP(create_time, 'yyyy-MM-dd
> >>> HH:mm:ss'), 'yyyyMMdd') as dt, \n" +
> >>> " DATE_FORMAT(TO_TIMESTAMP(create_time, 'yyyy-MM-dd
> >>> HH:mm:ss'), 'HH') as hr, \n" +
> >>> " DATE_FORMAT(TO_TIMESTAMP(create_time, 'yyyy-MM-dd
> >>> HH:mm:ss'), 'mm') as mi \n" +
> >>> "FROM merged_team");
> >>> tableEnv.execute("MysqlCDC2Hive2");
> >>>
> >>> streamEnv.execute("");
> >>> }
> >>>
> >>> private static String getOperation(String op) {
> >>> String operation = "INSERT";
> >>> for (RowKind rk : RowKind.values()) {
> >>> if (rk.shortString().equals(op)) {
> >>> switch (rk) {
> >>> case UPDATE_BEFORE:
> >>> case UPDATE_AFTER:
> >>> operation = "UPDATE";
> >>> break;
> >>> case DELETE:
> >>> operation = "DELETE";
> >>> break;
> >>> case INSERT:
> >>> default:
> >>> operation = "INSERT";
> >>> break;
> >>> }
> >>> break;
> >>> }
> >>> }
> >>> return operation;
> >>> }
> >>> }
> >>>
> >>> Jark Wu <[email protected]> 于2020年11月1日周日 上午11:04写道：
> >>>
> >>>> 你检查一下 hive 文件是否正常生成了？
> >>>>
> >>>> 我看你上面的代码，kafka->hive 流程中是没有 watermark 的，而"partition-time" 的 trigger
> >>>> policy 是基于 watermark 驱动的，所以可能是这个原因导致 hive 中没有数据。
> >>>>
> >>>> Best,
> >>>> Jark
> >>>>
> >>>>
> >>>> [1]:
> >>>>
> https://ci.apache.org/projects/flink/flink-docs-master/dev/table/connectors/filesystem.html#sink-partition-commit-trigger
> >>>>
> >>>> On Sat, 31 Oct 2020 at 17:25, 陈帅 <[email protected]> wrote:
> >>>>
> >>>>>
> 谢谢Jark细致解答，我按照你给的思路试了下。遇到一个问题是，在不开hive分区的情况下写入和读取是没有问题的，但在开启hive表时间分区后，写入是成功了，然而通过hive
> >>>>> shell查不到数据，表结构是正确的。(代码我注释掉了) 能帮忙看下是哪里写得不对吗？
> >>>>>
> >>>>> cdc -> kafka示例消息如下
> >>>>> {"data":{"team_id":1001,"team_name":"Sun","create_time":"2020-10-31
> >>>>> 11:25:38","update_time":"2020-10-31 11:25:38"},"op":"+I"}
> >>>>>
> >>>>> import com.alibaba.fastjson.JSON;
> >>>>> import com.alibaba.fastjson.JSONObject;
> >>>>> import org.apache.flink.api.common.serialization.SimpleStringSchema;
> >>>>> import org.apache.flink.api.common.typeinfo.TypeInformation;
> >>>>> import org.apache.flink.api.common.typeinfo.Types;
> >>>>> import org.apache.flink.api.java.typeutils.RowTypeInfo;
> >>>>> import org.apache.flink.streaming.api.CheckpointingMode;
> >>>>> import org.apache.flink.streaming.api.TimeCharacteristic;
> >>>>> import org.apache.flink.streaming.api.datastream.DataStream;
> >>>>> import
> >>>>>
> org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions;
> >>>>> import
> >>>>>
> org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
> >>>>> import
> org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
> >>>>> import
> >>>>> org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase;
> >>>>> import org.apache.flink.table.api.EnvironmentSettings;
> >>>>> import org.apache.flink.table.api.SqlDialect;
> >>>>> import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
> >>>>> import org.apache.flink.table.catalog.hive.HiveCatalog;
> >>>>> import org.apache.flink.types.Row;
> >>>>> import org.apache.flink.types.RowKind;
> >>>>>
> >>>>> import java.time.Duration;
> >>>>> import java.util.Properties;
> >>>>>
> >>>>> public class MysqlCDC2Hive {
> >>>>> public static void main(String[] args) throws Exception {
> >>>>> StreamExecutionEnvironment streamEnv =
> >>>>> StreamExecutionEnvironment.getExecutionEnvironment();
> >>>>>
> >>>>> streamEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
> >>>>> streamEnv.setParallelism(3);
> >>>>> streamEnv.enableCheckpointing(60000);
> >>>>>
> >>>>> EnvironmentSettings tableEnvSettings =
> >>>>> EnvironmentSettings.newInstance()
> >>>>> .useBlinkPlanner()
> >>>>> .inStreamingMode()
> >>>>> .build();
> >>>>> StreamTableEnvironment tableEnv =
> >>>>> StreamTableEnvironment.create(streamEnv, tableEnvSettings);
> >>>>>
> >>>>>
> tableEnv.getConfig().getConfiguration().set(ExecutionCheckpointingOptions.CHECKPOINTING_MODE,
> >>>>> CheckpointingMode.EXACTLY_ONCE);
> >>>>>
> >>>>>
> tableEnv.getConfig().getConfiguration().set(ExecutionCheckpointingOptions.CHECKPOINTING_INTERVAL,
> >>>>> Duration.ofMinutes(1));
> >>>>>
> >>>>> String catalogName = "hive_catalog";
> >>>>> HiveCatalog catalog = new HiveCatalog(
> >>>>> catalogName,
> >>>>> "default",
> >>>>> "/Users/chenshuai/dev/apache-hive-2.3.4-bin/conf",
> >>>>> "2.3.4"
> >>>>> );
> >>>>> tableEnv.registerCatalog(catalogName, catalog);
> >>>>> tableEnv.useCatalog(catalogName);
> >>>>>
> >>>>> MyDateFormat2 myDateFormat = new MyDateFormat2();
> >>>>> tableEnv.registerFunction("my_date_format", myDateFormat);
> >>>>>
> >>>>> tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS cdc");
> >>>>> tableEnv.executeSql("DROP TABLE IF EXISTS cdc.team");
> >>>>> tableEnv.executeSql("CREATE TABLE cdc.team(\n" +
> >>>>> " team_id INT,\n" +
> >>>>> " team_name STRING,\n" +
> >>>>> " create_time TIMESTAMP,\n" +
> >>>>> " update_time TIMESTAMP,\n" +
> >>>>> " proctime as proctime()\n" +
> >>>>> ") WITH (\n" +
> >>>>> " 'connector' = 'mysql-cdc',\n" +
> >>>>> " 'hostname' = 'localhost',\n" +
> >>>>> " 'port' = '3306',\n" +
> >>>>> " 'username' = 'root',\n" +
> >>>>> " 'password' = 'root',\n" +
> >>>>> " 'database-name' = 'test',\n" +
> >>>>> " 'table-name' = 'team'\n" +
> >>>>> ")");
> >>>>>
> >>>>> tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS kafka");
> >>>>> tableEnv.executeSql("DROP TABLE IF EXISTS kafka.team");
> >>>>> tableEnv.executeSql("CREATE TABLE kafka.team (\n" +
> >>>>> " team_id INT,\n" +
> >>>>> " team_name STRING,\n" +
> >>>>> " create_time TIMESTAMP,\n" +
> >>>>> " update_time TIMESTAMP\n" +
> >>>>> ") WITH (\n" +
> >>>>> " 'connector' = 'kafka',\n" +
> >>>>> " 'topic' = 'team',\n" +
> >>>>> " 'scan.startup.mode' = 'earliest-offset',\n" +
> >>>>> " 'properties.bootstrap.servers' =
> >>>>> 'localhost:9092',\n" +
> >>>>> " 'format' = 'changelog-json'\n" +
> >>>>> ")");
> >>>>>
> >>>>> tableEnv.executeSql("INSERT INTO kafka.team \n" +
> >>>>> "SELECT team_id, team_name, create_time, update_time
> >>>>> \n" +
> >>>>> "FROM cdc.team");
> >>>>>
> >>>>> // 定义带op字段的stream
> >>>>> Properties properties = new Properties();
> >>>>> properties.setProperty("bootstrap.servers",
> "localhost:9092");
> >>>>> properties.setProperty("group.id", "test");
> >>>>>
> >>>>> FlinkKafkaConsumerBase<String> consumer = new
> >>>>> FlinkKafkaConsumer<>(
> >>>>> "team",
> >>>>> new SimpleStringSchema(),
> >>>>> properties
> >>>>> ).setStartFromEarliest();
> >>>>>
> >>>>> DataStream<String> ds = streamEnv.addSource(consumer);
> >>>>>
> >>>>> String[] fieldNames = {"team_id", "team_name", "create_time",
> >>>>> "update_time", "op"};
> >>>>> TypeInformation[] types = {Types.INT, Types.STRING,
> >>>>> Types.STRING, Types.STRING, Types.STRING};
> >>>>> DataStream<Row> ds2 = ds.map(str -> {
> >>>>> JSONObject jsonObject = JSON.parseObject(str);
> >>>>> String op = jsonObject.getString("op");
> >>>>> JSONObject data = jsonObject.getJSONObject("data");
> >>>>> int arity = fieldNames.length;
> >>>>> Row row = new Row(arity);
> >>>>> row.setField(0, data.get("team_id"));
> >>>>> row.setField(1, data.get("team_name"));
> >>>>> row.setField(2, data.get("create_time"));
> >>>>> row.setField(3, data.get("update_time"));
> >>>>> String operation = getOperation(op);
> >>>>> row.setField(4, operation);
> >>>>>
> >>>>> return row;
> >>>>> }, new RowTypeInfo(types, fieldNames));
> >>>>>
> >>>>> tableEnv.registerDataStream("merged_team", ds2);
> >>>>>
> >>>>> tableEnv.getConfig().setSqlDialect(SqlDialect.HIVE);
> >>>>>
> >>>>> tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS ods");
> >>>>> tableEnv.executeSql("DROP TABLE IF EXISTS ods.team");
> >>>>>
> >>>>> tableEnv.executeSql("CREATE TABLE ods.team (\n" +
> >>>>> " team_id INT,\n" +
> >>>>> " team_name STRING,\n" +
> >>>>> " create_time STRING,\n" +
> >>>>> " update_time STRING,\n" +
> >>>>> " op STRING\n" +
> >>>>> // ") PARTITIONED BY (\n" +
> >>>>> // " ts_date STRING,\n" +
> >>>>> // " ts_hour STRING,\n" +
> >>>>> // " ts_minute STRING\n" +
> >>>>> ") STORED AS PARQUET TBLPROPERTIES (\n" +
> >>>>> " 'sink.partition-commit.trigger' =
> >>>>> 'partition-time',\n" +
> >>>>> " 'sink.partition-commit.delay' = '1 min',\n" +
> >>>>> " 'sink.partition-commit.policy.kind' =
> >>>>> 'metastore,success-file',\n" +
> >>>>> " 'partition.time-extractor.timestamp-pattern' =
> >>>>> '$ts_date $ts_hour:$ts_minute:00'\n" +
> >>>>> ")");
> >>>>>
> >>>>> tableEnv.getConfig().setSqlDialect(SqlDialect.DEFAULT);
> >>>>> tableEnv.executeSql("INSERT INTO ods.team \n" +
> >>>>> "SELECT team_id, team_name, create_time, update_time,
> >>>>> op \n" +
> >>>>> // " DATE_FORMAT(TO_TIMESTAMP(create_time, 'yyyy-MM-dd
> >>>>> HH:mm:ss'), 'yyyyMMdd') as ts_date, \n" +
> >>>>> // " DATE_FORMAT(TO_TIMESTAMP(create_time, 'yyyy-MM-dd
> >>>>> HH:mm:ss'), 'HH') as ts_hour, \n" +
> >>>>> // " DATE_FORMAT(TO_TIMESTAMP(create_time, 'yyyy-MM-dd
> >>>>> HH:mm:ss'), 'mm') as ts_minute \n" +
> >>>>> "FROM merged_team");
> >>>>> tableEnv.execute("MysqlCDC2Hive2");
> >>>>>
> >>>>> streamEnv.execute("");
> >>>>> }
> >>>>>
> >>>>> private static String getOperation(String op) {
> >>>>> String operation = "INSERT";
> >>>>> for (RowKind rk : RowKind.values()) {
> >>>>> if (rk.shortString().equals(op)) {
> >>>>> switch (rk) {
> >>>>> case UPDATE_BEFORE:
> >>>>> case UPDATE_AFTER:
> >>>>> operation = "UPDATE";
> >>>>> break;
> >>>>> case DELETE:
> >>>>> operation = "DELETE";
> >>>>> break;
> >>>>> case INSERT:
> >>>>> default:
> >>>>> operation = "INSERT";
> >>>>> break;
> >>>>> }
> >>>>> break;
> >>>>> }
> >>>>> }
> >>>>> return operation;
> >>>>> }
> >>>>> }
> >>>>>
> >>>>> Jark Wu <[email protected]> 于2020年10月31日周六 下午1:45写道：
> >>>>>
> >>>>>> 1. 是的。目前 Hive不支持直接消费 changlog ，这个主要原因是 hive 对 cdc 的支持不是很好。即使是 hive
> >>>>>> ACID/transaction 功能，由于其与其他计算引擎集成的不好，也鲜有人用。
> >>>>>>
> >>>>>> 2. cdc -> kafka -> hive streaming 的方案是可行的，不过 kafka -> hive streaming
> >>>>>> 相当于原始数据同步，到 hive 中仍然是 cdc logs 内容，并没有实时合并，需要用户自己写 query 在 hive
> >>>>>> 中进行合并。merge过程可以参考这篇文章[1]。
> >>>>>>
> >>>>>> 3. 你可以 ts + INTERVAL '8' HOUR
> >>>>>>
> >>>>>> PS: 在1.12中，我们计划让 hive 也能直接写 changelog 数据，这样的话 cdc 可以直接 -> hive
> >>>>>> streaming，不需要中间的 kafka。 不过到了 hive 中后，仍然需要另外写 query 将数据做实时merge。
> >>>>>>
> >>>>>> Best,
> >>>>>> Jark
> >>>>>>
> >>>>>> On Sat, 31 Oct 2020 at 13:26, 罗显宴 <[email protected]> wrote:
> >>>>>>
> >>>>>>> hive3可以hive2不可以，换了kafka也没用吧，hive3之前一般都不支持数据仓库的更改。不知道回答的对不对，欢迎指正。
> >>>>>>>
> >>>>>>>
> >>>>>>> | |
> >>>>>>> 罗显宴
> >>>>>>> |
> >>>>>>> |
> >>>>>>> 邮箱：[email protected]
> >>>>>>> |
> >>>>>>>
> >>>>>>> 签名由 网易邮箱大师 定制
> >>>>>>>
> >>>>>>> 在2020年10月31日 12:06，陈帅 写道：
> >>>>>>> 我想使用flink sql的mysql-cdc connector直接将mysql表数据实时同步进hive，运行后抛
> >>>>>>>
> >>>>>>> Exception in thread "main"
> org.apache.flink.table.api.TableException:
> >>>>>>> AppendStreamTableSink doesn't support consuming update and delete
> >>>>>>> changes
> >>>>>>> which is produced by node TableSourceScan(table=[[hive_catalog,
> cdc,
> >>>>>>> team]], fields=[team_id, team_name, create_time, update_time])
> >>>>>>>
> >>>>>>> 我的问题：
> >>>>>>> 1. 是不是因为hive2不支持delete/update，如果换hive 3能否支持呢？
> >>>>>>> 2. 如果要支持这种场景是不是中间需要加一层kafka介质（通过 changelog-json 格式)，即cdc ->
> >>>>>>> kafka，然后kafka
> >>>>>>> -> hive streaming? 谢谢！
> >>>>>>> 3. DATE_FORMAT函数出来的时间是UTC的，怎么转成GMT+8，只能通过UDF么？
> >>>>>>>
> >>>>>>> sql语句如下
> >>>>>>>
> >>>>>>> CREATE DATABASE IF NOT EXISTS cdc
> >>>>>>>
> >>>>>>> DROP TABLE IF EXISTS cdc.team
> >>>>>>>
> >>>>>>> CREATE TABLE team(
> >>>>>>> team_id BIGINT,
> >>>>>>> team_name STRING,
> >>>>>>> create_time TIMESTAMP,
> >>>>>>> update_time TIMESTAMP,
> >>>>>>> proctime as proctime()
> >>>>>>> ) WITH (
> >>>>>>> 'connector' = 'mysql-cdc',
> >>>>>>> 'hostname' = 'localhost',
> >>>>>>> 'port' = '3306',
> >>>>>>> 'username' = 'root',
> >>>>>>> 'password' = 'root',
> >>>>>>> 'database-name' = 'test',
> >>>>>>> 'table-name' = 'team'
> >>>>>>> )
> >>>>>>>
> >>>>>>> CREATE DATABASE IF NOT EXISTS ods
> >>>>>>>
> >>>>>>> DROP TABLE IF EXISTS ods.team
> >>>>>>>
> >>>>>>> CREATE TABLE ods.team (
> >>>>>>> team_id BIGINT,
> >>>>>>> team_name STRING,
> >>>>>>> create_time TIMESTAMP,
> >>>>>>> update_time TIMESTAMP,
> >>>>>>> ) PARTITIONED BY (
> >>>>>>> ts_date STRING,
> >>>>>>> ts_hour STRING,
> >>>>>>> ts_minute STRING,
> >>>>>>> ) STORED AS PARQUET TBLPROPERTIES (
> >>>>>>> 'sink.partition-commit.trigger' = 'partition-time',
> >>>>>>> 'sink.partition-commit.delay' = '1 min',
> >>>>>>> 'sink.partition-commit.policy.kind' = 'metastore,success-file',
> >>>>>>> 'partition.time-extractor.timestamp-pattern' = '$ts_date
> >>>>>>> $ts_hour:$ts_minute:00'
> >>>>>>> )
> >>>>>>>
> >>>>>>> INSERT INTO ods.team
> >>>>>>> SELECT team_id, team_name, create_time, update_time,
> >>>>>>> my_date_format(create_time,'yyyy-MM-dd', 'Asia/Shanghai'),
> >>>>>>> my_date_format(create_time,'HH', 'Asia/Shanghai'),
> >>>>>>> my_date_format(create_time,'mm', 'Asia/Shanghai')
> >>>>>>> FROM cdc.team
> >>>>>>>
> >>>>>>
>
--
Best regards!
Rui Li
