Flink SQL 解析嵌套的 JSON 数据

下面将会演示如何在 DDL 里面定义 Map、Array、Row 类型的数据,以及在 SQL 里面如何获里面的值。

数据格式如下:

{
 "funcName": "test",
 "data": {
  "snapshots": [{
   "content_type": "application/x-gzip-compressed-jpeg",
   "url": "https://blog.csdn.net/xianpanjia4616"
  }],
  "audio": [{
   "content_type": "audio/wav",
   "url": " https://bss.csdn.net/m/topic/blog_star2020/detail?username=xianpanjia4616"
  }]
 },
 "resultMap": {
  "result": {
   "cover": "/data/test/log.txt"
  },
  "isSuccess": true
 },
 "meta": {
  "video_type": "normal"
 },
 "type": 2,
 "timestamp": 1610549997263,
 "arr": [{
  "address": "北京市海淀区",
  "city": "beijing"
 }, {
  "address": "北京市海淀区",
  "city": "beijing"
 }, {
  "address": "北京市海淀区",
  "city": "beijing"
 }],
 "map": {
  "flink": 456
 },
 "doublemap": {
  "inner_map": {
   "key": 123
  }
 }
}

上面的数据包含了 Map、Array、Row 等类型, 对于这样的数据格式,在建表 DDL 里面应该如何定义呢?

定义 DDL

CREATE TABLE kafka_source (
    funcName STRING,
    data ROW<snapshots ARRAY<ROW<content_type STRING,url STRING>>,audio ARRAY<ROW<content_type STRING,url STRING>>>,
    resultMap ROW<`result` MAP<STRING,STRING>,isSuccess BOOLEAN>,
    meta  MAP<STRING,STRING>,
    `type` INT,
    `timestamp` BIGINT,
    arr ARRAY<ROW<address STRING,city STRING>>,
    map MAP<STRING,INT>,
    doublemap MAP<STRING,MAP<STRING,INT>>,
    proctime as PROCTIME()
) WITH (
    'connector' = 'kafka', -- 使用 kafka connector
    'topic' = 'test',  -- kafka topic
    'properties.bootstrap.servers' = 'master:9092,storm1:9092,storm2:9092',  -- broker连接信息
    'properties.group.id' = 'jason_flink_test', -- 消费kafka的group_id
    'scan.startup.mode' = 'latest-offset',  -- 读取数据的位置
    'format' = 'json',  -- 数据源格式为 json
    'json.fail-on-missing-field' = 'true', -- 字段丢失任务不失败
    'json.ignore-parse-errors' = 'false'  -- 解析失败跳过
)

解析 SQL

select
funcName,
doublemap['inner_map']['key'],
count(data.snapshots[1].url),
`type`,
TUMBLE_START(proctime, INTERVAL '30' second) as t_start
from kafka_source
group by TUMBLE(proctime, INTERVAL '30' second),funcName,`type`,doublemap['inner_map']['key']

SQL 运行的结果

4> (true,test,123,6,2,2021-01-15T03:31)
4> (false,test,123,6,2,2021-01-15T03:31)
4> (true,test,123,8,2,2021-01-15T03:31)
4> (false,test,123,8,2,2021-01-15T03:31)
4> (true,test,123,10,2,2021-01-15T03:31)
4> (false,test,123,10,2,2021-01-15T03:31)
4> (true,test,123,13,2,2021-01-15T03:31)
4> (false,test,123,13,2,2021-01-15T03:31)
4> (true,test,123,15,2,2021-01-15T03:31)
4> (true,test,123,3,2,2021-01-15T03:31:30)

下面演示insert进map、row等相关格式数据

以如下数据作为样例：

{
    "id":1238123899121,
    "name":"asdlkjasjkdla998y1122",
    "date":"1990-10-14",
    "obj":{
        "time1":"12:12:43Z",
        "str":"sfasfafs",
        "lg":2324342345
    },
    "arr":[
        {
            "f1":"f1str11",
            "f2":134
        },
        {
            "f1":"f1str22",
            "f2":555
        }
    ],
    "time":"12:12:43Z",
    "timestamp":"1990-10-14T12:12:43Z",
    "map":{
        "flink":123
    },
    "mapinmap":{
        "inner_map":{
            "key":234
        }
    }
}

定义

CREATE TABLE json_source (
    id            BIGINT,
    name          STRING,
    `date`        DATE,
    obj           ROW<time1 TIME,str STRING,lg BIGINT>,
    arr           ARRAY<ROW<f1 STRING,f2 INT>>,
    `time`        TIME,
    `timestamp`   TIMESTAMP(3),
    `map`         MAP<STRING,BIGINT>,
    mapinmap      MAP<STRING,MAP<STRING,INT>>,
    proctime as PROCTIME()
 ) WITH (
    'connector.type' = 'kafka',  
    'connector.topic' = 'test',  
    'connector.properties.zookeeper.connect' = 'localhost:2181',
    'connector.properties.bootstrap.servers' = 'localhost:9092',   
    'connector.properties.group.id' = 'testGroup',    
    'connector.version'='universal',  
    'format.type' = 'json',     
    'connector.startup-mode'='latest-offset' 
);

获取

select
  id,
  name,
  `date`,
  obj.str,
  arr [1].f1,
  `map` ['flink'],
  mapinmap ['inner_map'] ['key']
from
  json_source;

构造

insert into
  json_source
select
  111 as id,
  'name' as name,
  Row(CURRENT_TIME, 'ss', 123) as obj,
  Array [Row('f',1),Row('s',2)] as arr,
  Map ['k1','v1','k2','v2'] as `map`,
  Map ['inner_map',Map['k','v']] as mapinmap;

Flink与JSON数据类型映射

Flink SQL typeJSON type

CHAR / VARCHAR / STRING

string

BOOLEAN

boolean

BINARY / VARBINARY

string with encoding: base64

DECIMAL

number

TINYINT

number

SMALLINT

number

INT

number

BIGINT

number

FLOAT

number

DOUBLE

number

DATE

string with format: date

TIME

string with format: time

TIMESTAMP

string with format: date-time

TIMESTAMP_WITH_LOCAL_TIME_ZONE

string with format: date-time (with UTC time zone)

INTERVAL

number

ARRAY

array

MAP / MULTISET

object

ROW

object

注意事项:

1. Json 中的每个 {} 都需要用 Row 类型来表示
2. map定义需要确定K、V类型，比如map<STRING,INT>，若{k1:v1,k2:'v2'....}中k/v有多有多种类型，需要用Row定义
3. Json 中的每个 [] 都需要用 Arrary 类型来表示
4. 数组的下标是从 1 开始的不是 0 如上面 SQL 中的 data.snapshots[1].url
5. 关键字在任何地方都需要加反引号 如上面 SQL 中的 type
6. select 语句中的字段类型和顺序一定要和结果表的字段类型和顺序保持一致
7. UDF 可以直接在建表语句中使用