Overview
更新时间：2020-12-13
支持的 Connectors
| 服务类型 | SPARK | FLINK | ||
|---|---|---|---|---|
| Source | Sink | Source | Sink | |
| BKAFKA | Y | Y | Y | Y |
| BOS | Y | Y | Y | Y |
| MQTT | Y | Y | Y | |
| RDS | Y | Y | Y | Y |
| ES | Y | Y | ||
| PALO | Y | Y | ||
| TSDB | Y | Y | ||
如何使用 Connector
CREATE TABLE source_kafka_table (
`field01` STRING,
`field02` BIGINT,
`field03` FLOAT,
`field04` BINARY,
`field05` INT,
`field06` TINYINT,
`field07` BOOLEAN,
`field08` DATA,
`field09` DOUBLE,
`field10` SMALLINT
) WITH (
'connector.type' = 'BKAFKA',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-source',
'format.encode' = 'JSON',
'connector.properties.bootstrap.servers' = 'kafka.gz.baidubce.com:9092',
'connector.properties.ssl.filename' = 'kafka-key_gz.zip',
'connector.properties.group.id' = 'bsc123',
'connector.read.startup.mode' = 'earliest'
);字段类型
| TYPE | SPARK | FLINK | MAPPING |
|---|---|---|---|
| TINYINT | Y | Y | BTYE / TINYINT |
| SMALLINT | Y | Y | SHORT / SMALLINT |
| INT | Y | Y | INT / INTEGER |
| BIGINT | Y | Y | LONG / BIGINT |
| FLOAT | Y | Y | FLOAT |
| DOUBLE | Y | Y | DOUBLE |
| STRING | Y | Y | STRING / CHAR / VARCHAR |
| BINARY | Y | Y | BINARY / VARBINARY / BYTES |
| BOOLEAN | Y | Y | BOOLEAN / BOOL |
| TIMESTAMP | Y | Y | TIMESTAMP / SQL_TIMESTAMP |
| DECIMAL | Y | Y | DECIMAL |
| DATE | Y | Y | DATE / LOCAL_DATE |
| TIME | Y | TIME / LOCAL_TIME | |
| ARRAY | Y | Y | ARRAY |
| MAP | Y | Y | MAP |
| ROW | Y | ROW | |
| STRUCT | Y | STRUCT |
时间属性
注意：SPARK 仅仅支持指定 source table 中某一时间类型的列作为 watermark 处理窗口的时间
属性如何支持 EVENTTIME 和 PROCTIME
| 属性名称 | 说明 | EVENTTIME 设置举例 | PROCTIME 设置举例 |
|---|---|---|---|
| watermark.field | 使用事件时间的字段作为时间提取 | 'field' | '' |
| watermark.threshold | 时间窗口的允许最大延迟设置 | '2 seconds'，支持单位有：milliseconds,seconds,minutes,hours |
'' |
| watermark.field.alias | SQL正文中使用的时间别名 | 'alias' | 'proctime' |
| watermark.field.pattern | 设置日期模式进行转换时间戳 | 'yyyy-MM-dd HH:mm:ss' | '' |
| watermark.field.timezone | 设置日期模式进行转换时区 | 'Asia/Shanghai' | '' |
EVENTTIME支持的时间字段类型及其对应参数设置
| 字段类型 | watermark.field.pattern | watermark.field.timezone | 说明 |
|---|---|---|---|
| BIGINT | 's'、'ms'、'second'、'millisecond' | '' | 使用的字段为LONG，转化为毫秒 |
| STRING | 'yyyy-MM-dd HH:mm:ss' | 'Asia/Shanghai' | 使用的字段能够通过指定的模式转换为日期 |
| TIMESTAMP | '' | '' | 使用的字段，必须符合TZ格式：2018-05-20T00:08:00Z |
日期格式对照标配表
| pattern | timezone |
|---|---|
| yyyy-MM-dd'T'HH:mm:ss.SSS | Asia/Shanghai |
| yyyy-MM-dd'T'HH:mm:ss.SSS'Z' | UTC |
| yyyy-MM-dd'T'HH:mm:ss | Asia/Shanghai |
| yyyy-MM-dd'T'HH:mm:ss'Z' | UTC |
| yyyy-MM-dd HH:mm:ss.SSS | Asia/Shanghai |
| yyyy-MM-dd HH:mm:ss.SSS'Z' | UTC |
| yyyy-MM-dd HH:mm:ss | Asia/Shanghai |
| yyyy-MM-dd HH:mm:ss'Z' | UTC |
带有EVENTTIME的 Connector
使用 EVENTTIME 时，需要指定 source 表中某一列作为时间戳，并配置其 watermark 等时间属性参数。
CREATE TABLE source_kafka_table (
`field01` STRING,
`field02` BIGINT, -- 时间戳为 BIGINT， 如 1523525179，单位为秒
`field03` FLOAT,
`field04` BINARY,
`field05` INT,
`field06` TINYINT,
`field07` BOOLEAN,
`field08` DATA,
`field09` DOUBLE,
`field10` SMALLINT
) WITH (
'connector.type' = 'BKAFKA',
'format.encode' = 'JSON',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-source',
'connector.properties.bootstrap.servers' = 'kafka.bj.baidubce.com:9091',
'connector.properties.ssl.filename' = 'kafka-key-bj.zip',
'connector.properties.group.id' = 'bsc123',
'connector.read.startup.mode' = 'earliest',
'watermark.field' = 'field02',
'watermark.threshold' = '1 minutes',
'watermark.field.pattern' = 's' -- 时间戳字段的单位默认为 ms
);
CREATE TABLE sink_kafka_table (
`timestamp` BIGINT,
`field01` STRING,
`count` BIGINT
) WITH (
'connector.type' = 'BKAFKA',
'format.encode' = 'JSON',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-sink',
'connector.properties.bootstrap.servers' = 'kafka.bj.baidubce.com:9091',
'connector.properties.ssl.filename' = 'kafka-key-bj.zip'
);
INSERT INTO
sink_kafka_table
SELECT
TO_BIGINT(TUMBLE_START(`field02`, INTERVAL '1' MINUTE)) AS `timestamp`,
`field01`,
COUNT(`field05`)
FROM
source_kafka_table
GROUP BY
TUMBLE(`field02`, INTERVAL '1' MINUTE),
`field01`CREATE TABLE source_kafka_table (
`field01` STRING,
`field02` STRING, -- 时间戳为 STRING， 如 2018-05-20 00:11:00
`field03` FLOAT,
`field04` BINARY,
`field05` INT,
`field06` TINYINT,
`field07` BOOLEAN,
`field08` DATA,
`field09` DOUBLE,
`field10` SMALLINT
) WITH (
'connector.type' = 'BKAFKA',
'format.encode' = 'JSON',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-source',
'connector.properties.bootstrap.servers' = 'kafka.bj.baidubce.com:9091',
'connector.properties.ssl.filename' = 'kafka-key-bj.zip',
'connector.properties.group.id' = 'bsc123',
'connector.read.startup.mode' = 'earliest',
'watermark.field' = 'field02',
'watermark.threshold' = '1 minutes'，
'watermark.field.pattern' = 'yyyy-MM-dd HH:mm:ss', -- 时间戳字段的数据格式
'watermark.field.timezone' = 'Asia/Shanghai'
);
CREATE TABLE sink_kafka_table (
`timestamp` TIMESTAMP,
`field01` STRING,
`count` BIGINT
) WITH (
'connector.type' = 'BKAFKA',
'format.encode' = 'JSON',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-sink',
'connector.properties.bootstrap.servers' = 'kafka.bj.baidubce.com:9091',
'connector.properties.ssl.filename' = 'kafka-key-bj.zip'
);
INSERT INTO
sink_kafka_table
SELECT
TUMBLE_START(`field02`, INTERVAL '1' MINUTE) AS `timestamp`,
`field01`,
COUNT(`field05`) AS `count`
FROM
source_kafka_table
GROUP BY
TUMBLE(`field02`, INTERVAL '1' MINUTE),
`field01`CREATE TABLE source_kafka_table (
`field01` STRING,
`field02` TIMESTAMP, -- 时间戳为 TIMESTAMP， 即 2018-05-20T00:11:00Z 这种 TZ 格式
`field03` FLOAT,
`field04` BINARY,
`field05` INT,
`field06` TINYINT,
`field07` BOOLEAN,
`field08` DATA,
`field09` DOUBLE,
`field10` SMALLINT
) WITH (
'connector.type' = 'BKAFKA',
'format.encode' = 'JSON',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-source',
'connector.properties.bootstrap.servers' = 'kafka.bj.baidubce.com:9091',
'connector.properties.ssl.filename' = 'kafka-key-bj.zip',
'connector.properties.group.id' = 'bsc123',
'connector.read.startup.mode' = 'earliest',
'watermark.field' = 'field02',
'watermark.threshold' = '1 minutes'
);
CREATE TABLE sink_kafka_table (
`timestamp` BIGINT,
`field01` STRING,
`count` BIGINT
) WITH (
'connector.type' = 'BKAFKA',
'format.encode' = 'JSON',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-sink',
'connector.properties.bootstrap.servers' = 'kafka.bj.baidubce.com:9091',
'connector.properties.ssl.filename' = 'kafka-key-bj.zip'
);
INSERT INTO
sink_kafka_table
SELECT
TO_BIGINT(TUMBLE_START(`field02`, INTERVAL '1' MINUTE)) AS `timestamp`,
`field01`,
COUNT(`field05`)
FROM
source_kafka_table
GROUP BY
TUMBLE(`field02`, INTERVAL '1' MINUTE),
`field01`CREATE TABLE source_kafka_table (
`field01` STRING,
`field02` TIMESTAMP, -- 时间戳为 TIMESTAMP， 即 2018-05-20T00:11:00Z 这种 TZ 格式
`field03` FLOAT,
`field04` BINARY,
`field05` INT,
`field06` TINYINT,
`field07` BOOLEAN,
`field08` DATA,
`field09` DOUBLE,
`field10` SMALLINT
) WITH (
'connector.type' = 'BKAFKA',
'format.encode' = 'JSON',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-source',
'connector.properties.bootstrap.servers' = 'kafka.bj.baidubce.com:9091',
'connector.properties.ssl.filename' = 'kafka-key-bj.zip',
'connector.properties.group.id' = 'bsc123',
'connector.read.startup.mode' = 'earliest',
'watermark.field' = 'field02',
'watermark.threshold' = '1 minutes',
'watermark.field.alias' = 'rowtime'
);
CREATE TABLE sink_kafka_table (
`timestamp` BIGINT,
`field01` STRING,
`count` BIGINT
) WITH (
'connector.type' = 'BKAFKA',
'format.encode' = 'JSON',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-sink',
'connector.properties.bootstrap.servers' = 'kafka.bj.baidubce.com:9091',
'connector.properties.ssl.filename' = 'kafka-key-bj.zip'
);
INSERT INTO
sink_kafka_table
SELECT
TO_BIGINT(TUMBLE_START(`rowtime`, INTERVAL '1' MINUTE)) AS `timestamp`,
`field01`,
COUNT(`field05`)
FROM
source_kafka_table
GROUP BY
TUMBLE(`rowtime`, INTERVAL '1' MINUTE),
`field01`CREATE TABLE source_kafka_table (
`field01` STRING,
`field02` TIMESTAMP, -- SPARK 支持窗口数据类型为 TIMESTAMP
`field03` FLOAT,
`field04` BINARY,
`field05` INT,
`field06` TINYINT,
`field07` BOOLEAN,
`field08` DATA,
`field09` DOUBLE,
`field10` SMALLINT
) WITH (
'connector.type' = 'BKAFKA',
'format.encode' = 'JSON',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-source',
'connector.properties.bootstrap.servers' = 'kafka.bj.baidubce.com:9091',
'connector.properties.ssl.filename' = 'kafka-key-bj.zip',
'watermark.field' = 'field02',
'watermark.threshold' = '10 seconds'
);
CREATE TABLE sink_kafka_table (
`timestamp` TIMESTAMP,
`field01` STRING,
`count` BIGINT
) WITH (
'connector.type' = 'BKAFKA',
'format.encode' = 'JSON',
'connector.topic' = 'xxxxxxxxxxxx__bsc-test-sink',
'connector.properties.bootstrap.servers' = 'kafka.bj.baidubce.com:9091',
'connector.properties.ssl.filename' = 'kafka-key-bj.zip'
);
INSERT INTO
sink_kafka_table
SELECT
window.start AS `timestamp`,
`field01`,
COUNT(`field05`) AS `count`
FROM
source_kafka_table
GROUP BY
window(`field02`, "1 MINUTE"),
`field01`带有 PROCTIME 的 Connector
使用进程的处理时间作为时间戳，FLINK不需要指定 source 表中某一列，只需要加入 SET job.streamTimeType = 'PROCESSTIME' 语句即可。
SET job.stream.timeType = 'PROCESSTIME'; -- 通过 SET 语句指定 Flink 使用 PROCTIME
CREATE TABLE source_mqtt_table (
`field01` STRING,
`field02` BIGINT,
`field03` FLOAT,
`field04` BINARY,
`field05` INT,
`field06` TINYINT,
`field07` BOOLEAN,
`field08` DATA,
`field09` DOUBLE,
`field10` SMALLINT
) WITH (
'connector.type' = 'MQTT',
'format.encode' = 'JSON',
'connector.url' = 'tcp://xxxxxx.mqtt.iot.gz.baidubce.com:1883',
'connector.topic' = 'xxxx',
'connector.username' = 'xxxxxxxxx/bsc_test',
'connector.password' = 'xxxxxxxx',
'connector.semantic' = 'at_least_once'
);