Flink 系列文章
1、Flink 部署、概念介绍、source、transformation、sink使用示例、四大基石介绍和示例等系列综合文章链接
13、Flink 的table api与sql的基本概念、通用api介绍及入门示例
14、Flink 的table api与sql之数据类型: 内置数据类型以及它们的属性
15、Flink 的table api与sql之流式概念-详解的介绍了动态表、时间属性配置（如何处理更新结果）、时态表、流上的join、流上的确定性以及查询配置
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及FileSystem示例（1）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Elasticsearch示例（2）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Kafka示例（3）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及JDBC示例（4）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Hive示例（6）
20、Flink SQL之SQL Client: 不用编写代码就可以尝试 Flink SQL，可以直接提交 SQL 任务到集群上
22、Flink 的table api与sql之创建表的DDL
24、Flink 的table api与sql之Catalogs
26、Flink 的SQL之概览与入门示例
30、Flink SQL之SQL 客户端（通过kafka和filesystem的例子介绍了配置文件使用-表、视图等）
41、Flink之Hive 方言介绍及详细示例
42、Flink 的table api与sql之Hive Catalog
43、Flink之Hive 读写及详细验证示例
44、Flink之module模块介绍及使用示例和Flink SQL使用hive内置函数及自定义函数详细示例–网上有些说法好像是错误的
文章目录
本文简单的介绍了SQL和SQL的入门，并以三个简单的示例进行介绍，由于示例涉及到其他的内容，需要了解更深入的内容请参考相关的文章。。
本文依赖flink和kafka、hadoop集群能正常使用。
本文分为2个部分，即介绍了Flink SQL和入门，并提供了完整的可验证通过的示例。
一、SQL
本文描述了 Flink 所支持的 SQL 语言，包括数据定义语言（Data Definition Language，DDL）、数据操纵语言（Data Manipulation Language，DML）以及查询语言。Flink 对 SQL 的支持基于实现了 SQL 标准的 Apache Calcite。
本文列出了目前（截至版本1.17） Flink SQL 所支持的所有语句：
-
SELECT (Queries)，具体内容参考文章：27、Flink 的SQL之SELECT (Queries)
-
CREATE TABLE, CATALOG, DATABASE, VIEW, FUNCTION
具体内容参考文章：22、Flink 的table api与sql之创建表的DDL -
DROP TABLE, DATABASE, VIEW, FUNCTION
-
ALTER TABLE, DATABASE, FUNCTION
-
INSERT
-
ANALYZE TABLE
具体内容参考文章：28、Flink 的SQL之DROP 语句、ALTER 语句、INSERT 语句、ANALYZE 语句 -
UPDATE
-
DELETE
-
SQL HINTS
-
DESCRIBE
-
EXPLAIN
-
USE
-
SHOW
-
LOAD
-
UNLOAD
具体内容参考文章： 29、Flink SQL之DESCRIBE、EXPLAIN、USE、SHOW、LOAD、UNLOAD、SET、RESET、JAR、JOB Statements、UPDATE、DELETE
1、数据类型
通用类型与（嵌套的）复合类型 （如：POJO、tuples、rows、Scala case 类) 都可以作为行的字段。
复合类型的字段任意的嵌套可被 值访问函数（内置函数） 访问。
通用类型将会被视为一个黑箱，且可以被 用户自定义函数 传递或引用。
对于 DDL 语句而言，我们支持所有在 数据类型 页面中定义的数据类型。
SQL查询不支持部分数据类型（cast 表达式或字符常量值）。
如：STRING, BYTES, RAW, TIME§ WITHOUT TIME ZONE, TIME§ WITH LOCAL TIME ZONE, TIMESTAMP§ WITHOUT TIME ZONE, TIMESTAMP§ WITH LOCAL TIME ZONE, ARRAY, MULTISET, ROW.
更多内容，请参考文章：14、Flink 的table api与sql之数据类型: 内置数据类型以及它们的属性
2、保留关键字
虽然 SQL 的特性并未完全实现，但是一些字符串的组合却已经被预留为关键字以备未来使用。如果你希望使用以下字符串作为你的字段名，请在使用时使用反引号将该字段名包起来（如 value, count ）。
A, ABS, ABSOLUTE, ACTION, ADA, ADD, ADMIN, AFTER, ALL, ALLOCATE, ALLOW, ALTER, ALWAYS, AND, ANALYZE, ANY, ARE, ARRAY, AS, ASC, ASENSITIVE, ASSERTION, ASSIGNMENT, ASYMMETRIC, AT, ATOMIC, ATTRIBUTE, ATTRIBUTES, AUTHORIZATION, AVG, BEFORE, BEGIN, BERNOULLI, BETWEEN, BIGINT, BINARY, BIT, BLOB, BOOLEAN, BOTH, BREADTH, BY, BYTES, C, CALL, CALLED, CARDINALITY, CASCADE, CASCADED, CASE, CAST, CATALOG, CATALOG_NAME, CEIL, CEILING, CENTURY, CHAIN, CHAR, CHARACTER, CHARACTERISTICS, CHARACTERS, CHARACTER_LENGTH, CHARACTER_SET_CATALOG, CHARACTER_SET_NAME, CHARACTER_SET_SCHEMA, CHAR_LENGTH, CHECK, CLASS_ORIGIN, CLOB, CLOSE, COALESCE, COBOL, COLLATE, COLLATION, COLLATION_CATALOG, COLLATION_NAME, COLLATION_SCHEMA, COLLECT, COLUMN, COLUMNS, COLUMN_NAME, COMMAND_FUNCTION, COMMAND_FUNCTION_CODE, COMMIT, COMMITTED, CONDITION, CONDITION_NUMBER, CONNECT, CONNECTION, CONNECTION_NAME, CONSTRAINT, CONSTRAINTS, CONSTRAINT_CATALOG, CONSTRAINT_NAME, CONSTRAINT_SCHEMA, CONSTRUCTOR, CONTAINS, CONTINUE, CONVERT, CORR, CORRESPONDING, COUNT, COVAR_POP, COVAR_SAMP, CREATE, CROSS, CUBE, CUME_DIST, CURRENT, CURRENT_CATALOG, CURRENT_DATE, CURRENT_DEFAULT_TRANSFORM_GROUP, CURRENT_PATH, CURRENT_ROLE, CURRENT_SCHEMA, CURRENT_TIME, CURRENT_TIMESTAMP, CURRENT_TRANSFORM_GROUP_FOR_TYPE, CURRENT_USER, CURSOR, CURSOR_NAME, CYCLE, DATA, DATABASE, DATE, DATETIME_INTERVAL_CODE, DATETIME_INTERVAL_PRECISION, DAY, DEALLOCATE, DEC, DECADE, DECIMAL, DECLARE, DEFAULT, DEFAULTS, DEFERRABLE, DEFERRED, DEFINED, DEFINER, DEGREE, DELETE, DENSE_RANK, DEPTH, DEREF, DERIVED, DESC, DESCRIBE, DESCRIPTION, DESCRIPTOR, DETERMINISTIC, DIAGNOSTICS, DISALLOW, DISCONNECT, DISPATCH, DISTINCT, DOMAIN, DOUBLE, DOW, DOY, DROP, DYNAMIC, DYNAMIC_FUNCTION, DYNAMIC_FUNCTION_CODE, EACH, ELEMENT, ELSE, END, END-EXEC, EPOCH, EQUALS, ESCAPE, EVERY, EXCEPT, EXCEPTION, EXCLUDE, EXCLUDING, EXEC, EXECUTE, EXISTS, EXP, EXPLAIN, EXTEND, EXTERNAL, EXTRACT, FALSE, FETCH, FILTER, FINAL, FIRST, FIRST_VALUE, FLOAT, FLOOR, FOLLOWING, FOR, FOREIGN, FORTRAN, FOUND, FRAC_SECOND, FREE, FROM, FULL, FUNCTION, FUSION, G, GENERAL, GENERATED, GET, GLOBAL, GO, GOTO, GRANT, GRANTED, GROUP, GROUPING, HAVING, HIERARCHY, HOLD, HOUR, IDENTITY, IMMEDIATE, IMPLEMENTATION, IMPORT, IN, INCLUDING, INCREMENT, INDICATOR, INITIALLY, INNER, INOUT, INPUT, INSENSITIVE, INSERT, INSTANCE, INSTANTIABLE, INT, INTEGER, INTERSECT, INTERSECTION, INTERVAL, INTO, INVOKER, IS, ISOLATION, JAVA, JOIN, K, KEY, KEY_MEMBER, KEY_TYPE, LABEL, LANGUAGE, LARGE, LAST, LAST_VALUE, LATERAL, LEADING, LEFT, LENGTH, LEVEL, LIBRARY, LIKE, LIMIT, LN, LOCAL, LOCALTIME, LOCALTIMESTAMP, LOCATOR, LOWER, M, MAP, MATCH, MATCHED, MAX, MAXVALUE, MEMBER, MERGE, MESSAGE_LENGTH, MESSAGE_OCTET_LENGTH, MESSAGE_TEXT, METHOD, MICROSECOND, MILLENNIUM, MIN, MINUTE, MINVALUE, MOD, MODIFIES, MODULE, MODULES, MONTH, MORE, MULTISET, MUMPS, NAME, NAMES, NATIONAL, NATURAL, NCHAR, NCLOB, NESTING, NEW, NEXT, NO, NONE, NORMALIZE, NORMALIZED, NOT, NULL, NULLABLE, NULLIF, NULLS, NUMBER, NUMERIC, OBJECT, OCTETS, OCTET_LENGTH, OF, OFFSET, OLD, ON, ONLY, OPEN, OPTION, OPTIONS, OR, ORDER, ORDERING, ORDINALITY, OTHERS, OUT, OUTER, OUTPUT, OVER, OVERLAPS, OVERLAY, OVERRIDING, PAD, PARAMETER, PARAMETER_MODE, PARAMETER_NAME, PARAMETER_ORDINAL_POSITION, PARAMETER_SPECIFIC_CATALOG, PARAMETER_SPECIFIC_NAME, PARAMETER_SPECIFIC_SCHEMA, PARTIAL, PARTITION, PASCAL, PASSTHROUGH, PATH, PERCENTILE_CONT, PERCENTILE_DISC, PERCENT_RANK, PLACING, PLAN, PLI, POSITION, POWER, PRECEDING, PRECISION, PREPARE, PRESERVE, PRIMARY, PRIOR, PRIVILEGES, PROCEDURE, PUBLIC, QUARTER, RANGE, RANK, RAW, READ, READS, REAL, RECURSIVE, REF, REFERENCES, REFERENCING, REGR_AVGX, REGR_AVGY, REGR_COUNT, REGR_INTERCEPT, REGR_R2, REGR_SLOPE, REGR_SXX, REGR_SXY, REGR_SYY, RELATIVE, RELEASE, REPEATABLE, RESET, RESTART, RESTRICT, RESULT, RETURN, RETURNED_CARDINALITY, RETURNED_LENGTH, RETURNED_OCTET_LENGTH, RETURNED_SQLSTATE, RETURNS, REVOKE, RIGHT, ROLE, ROLLBACK, ROLLUP, ROUTINE, ROUTINE_CATALOG, ROUTINE_NAME, ROUTINE_SCHEMA, ROW, ROWS, ROW_COUNT, ROW_NUMBER, SAVEPOINT, SCALE, SCHEMA, SCHEMA_NAME, SCOPE, SCOPE_CATALOGS, SCOPE_NAME, SCOPE_SCHEMA, SCROLL, SEARCH, SECOND, SECTION, SECURITY, SELECT, SELF, SENSITIVE, SEQUENCE, SERIALIZABLE, SERVER, SERVER_NAME, SESSION, SESSION_USER, SET, SETS, SIMILAR, SIMPLE, SIZE, SMALLINT, SOME, SOURCE, SPACE, SPECIFIC, SPECIFICTYPE, SPECIFIC_NAME, SQL, SQLEXCEPTION, SQLSTATE, SQLWARNING, SQL_TSI_DAY, SQL_TSI_FRAC_SECOND, SQL_TSI_HOUR, SQL_TSI_MICROSECOND, SQL_TSI_MINUTE, SQL_TSI_MONTH, SQL_TSI_QUARTER, SQL_TSI_SECOND, SQL_TSI_WEEK, SQL_TSI_YEAR, SQRT, START, STATE, STATEMENT, STATIC, STDDEV_POP, STDDEV_SAMP, STREAM, STRING, STRUCTURE, STYLE, SUBCLASS_ORIGIN, SUBMULTISET, SUBSTITUTE, SUBSTRING, SUM, SYMMETRIC, SYSTEM, SYSTEM_USER, TABLE, TABLESAMPLE, TABLE_NAME, TEMPORARY, THEN, TIES, TIME, TIMESTAMP, TIMESTAMPADD, TIMESTAMPDIFF, TIMEZONE_HOUR, TIMEZONE_MINUTE, TINYINT, TO, TOP_LEVEL_COUNT, TRAILING, TRANSACTION, TRANSACTIONS_ACTIVE, TRANSACTIONS_COMMITTED, TRANSACTIONS_ROLLED_BACK, TRANSFORM, TRANSFORMS, TRANSLATE, TRANSLATION, TREAT, TRIGGER, TRIGGER_CATALOG, TRIGGER_NAME, TRIGGER_SCHEMA, TRIM, TRUE, TYPE, UESCAPE, UNBOUNDED, UNCOMMITTED, UNDER, UNION, UNIQUE, UNKNOWN, UNNAMED, UNNEST, UPDATE, UPPER, UPSERT, USAGE, USER, USER_DEFINED_TYPE_CATALOG, USER_DEFINED_TYPE_CODE, USER_DEFINED_TYPE_NAME, USER_DEFINED_TYPE_SCHEMA, USING, VALUE, VALUES, VARBINARY, VARCHAR, VARYING, VAR_POP, VAR_SAMP, VERSION, VIEW, WEEK, WHEN, WHENEVER, WHERE, WIDTH_BUCKET, WINDOW, WITH, WITHIN, WITHOUT, WORK, WRAPPER, WRITE, XML, YEAR, ZONE
二、SQL入门
Flink SQL 使得使用标准 SQL 开发流应用程序变的简单。如果你曾经在工作中使用过兼容 ANSI-SQL 2011 的数据库或类似的 SQL 系统，那么就很容易学习 Flink。
1、Flink SQL环境准备
1）、安装Flink及提交任务方式
参考文章：
1、Flink1.12.7或1.13.5详细介绍及本地安装部署、验证
2、Flink1.13.5二种部署方式(Standalone、Standalone HA )、四种提交任务方式（前两种及session和per-job）验证详细步骤
2）、SQL客户端使用介绍
20、Flink SQL之SQL Client: 不用编写代码就可以尝试 Flink SQL，可以直接提交 SQL 任务到集群上
3）、简单示例
Flink SQL>SET execution.result-mode=tableau;
Flink SQL> show databases;
+------------------+
| database name |
+------------------+
| default_database |
+------------------+
1 row in set
Flink SQL> use default_database;
[INFO] Execute statement succeed.
Flink SQL> show tables;
Empty set
Flink SQL> SELECT 'Hello World';
+----+--------------------------------+
| op | _o__c0 |
+----+--------------------------------+
| +I | Hello World |
+----+--------------------------------+
Received a total of 1 row
Flink SQL> show functions;
Hive Session ID = 5d34cbf8-5984-4ec0-8527-e06a948ad7ca
+--------------------------------+
| function name |
+--------------------------------+
| ! |
| != |
| $sum0 |
| % |
| & |
| * |
| + |
| - |
| / |
| < |
| <= |
| <=> |
| <> |
| = |
| == |
| > |
| >= |
| IFNULL |
| SOURCE_WATERMARK |
| TYPEOF |
| ^ |
| _legacy_grouping__id |
| abs |
| acos |
| add_months |
| aes_decrypt |
| aes_encrypt |
| and |
| array |
| array_contains |
| as |
| asc |
| ascii |
| asin |
| assert_true |
| assert_true_oom |
| at |
| atan |
| atan2 |
| avg |
| base64 |
| between |
| bigint |
| bin |
| binary |
| bloom_filter |
| boolean |
| bround |
| cardinality |
| cardinality_violation |
| case |
| cast |
| cbrt |
| ceil |
| ceiling |
| char |
| charLength |
| char_length |
| character_length |
| chr |
| coalesce |
| collect |
| collect_list |
| collect_set |
| compute_stats |
| concat |
| concat_ws |
| context_ngrams |
| conv |
| corr |
| cos |
| cosh |
| cot |
| count |
| covar_pop |
| covar_samp |
| crc32 |
| create_union |
| currentDate |
| currentRange |
| currentRow |
| currentRowTimestamp |
| currentTime |
| currentTimestamp |
| current_authorizer |
| current_database |
| current_groups |
| current_user |
| date |
| dateFormat |
| date_add |
| date_format |
| date_sub |
| datediff |
| day |
| dayofmonth |
| dayofweek |
| decimal |
| decode |
| degrees |
| desc |
| distinct |
| div |
| divide |
| double |
| e |
| element |
| elt |
| encode |
| encryptphonenumber |
| end |
| enforce_constraint |
| equals |
| exp |
| explode |
| extract |
| extract_union |
| factorial |
| field |
| find_in_set |
| flatten |
| float |
| floor |
| floor_day |
| floor_hour |
| floor_minute |
| floor_month |
| floor_quarter |
| floor_second |
| floor_week |
| floor_year |
| format_number |
| fromBase64 |
| from_unixtime |
| from_utc_timestamp |
| get |
| get_json_object |
| get_splits |
| greaterThan |
| greaterThanOrEqual |
| greatest |
| grouping |
| hash |
| hex |
| histogram_numeric |
| hour |
| if |
| ifThenElse |
| in |
| in_bloom_filter |
| in_file |
| index |
| initCap |
| initcap |
| inline |
| instr |
| int |
| internal_interval |
| interval_day_time |
| interval_year_month |
| isFalse |
| isNotFalse |
| isNotNull |
| isNotTrue |
| isNull |
| isTrue |
| isfalse |
| isnotfalse |
| isnotnull |
| isnottrue |
| isnull |
| istrue |
| java_method |
| json_tuple |
| last_day |
| lcase |
| least |
| length |
| lessThan |
| lessThanOrEqual |
| levenshtein |
| like |
| likeall |
| likeany |
| ln |
| localTime |
| localTimestamp |
| locate |
| log |
| log10 |
| log2 |
| logged_in_user |
| lower |
| lowerCase |
| lpad |
| ltrim |
| map |
| map_keys |
| map_values |
| mask |
| mask_first_n |
| mask_hash |
| mask_last_n |
| mask_show_first_n |
| mask_show_last_n |
| matchpath |
| max |
| md5 |
| min |
| minus |
| minusPrefix |
| minute |
| mod |
| month |
| months_between |
| murmur_hash |
| named_struct |
| negative |
| next_day |
| ngrams |
| noop |
| noopstreaming |
| noopwithmap |
| noopwithmapstreaming |
| not |
| notBetween |
| notEquals |
| nullif |
| nvl |
| octet_length |
| or |
| over |
| overlay |
| parse_url |
| parse_url_tuple |
| percentile |
| percentile_approx |
| pi |
| plus |
| pmod |
| posexplode |
| position |
| positive |
| pow |
| power |
| printf |
| proctime |
| quarter |
| radians |
| rand |
| randInteger |
| rangeTo |
| reflect |
| reflect2 |
| regexp |
| regexpExtract |
| regexpReplace |
| regexp_extract |
| regexp_replace |
| regr_avgx |
| regr_avgy |
| regr_count |
| regr_intercept |
| regr_r2 |
| regr_slope |
| regr_sxx |
| regr_sxy |
| regr_syy |
| reinterpretCast |
| repeat |
| replace |
| replicate_rows |
| restrict_information_schema |
| reverse |
| rlike |
| round |
| row |
| rowtime |
| rpad |
| rtrim |
| second |
| sentences |
| sha |
| sha1 |
| sha2 |
| sha224 |
| sha256 |
.................
至此，我们的环境都准备好了。
2、Source 表介绍及示例
与所有 SQL 引擎一样，Flink 查询操作是在表上进行。与传统数据库不同，Flink 不在本地管理静态数据；相反，它的查询在外部表上连续运行。
Flink 数据处理流水线开始于 source 表。source 表产生在查询执行期间可以被操作的行；它们是查询时 FROM 子句中引用的表。这些表可能是 Kafka 的 topics，数据库，文件系统，或者任何其它 Flink 知道如何消费的系统。
可以通过 SQL 客户端或使用环境配置文件来定义表。SQL 客户端支持类似于传统 SQL 的 SQL DDL 命令。标准 SQL DDL 用于创建，修改，删除表。
Flink 支持不同的连接器和格式相结合以定义表。相关内容在本Flink专栏中均有介绍，请参考：alanchanchn的专栏-Flink专栏
下面是一个示例，定义一个以 CSV 文件作为存储格式的 source 表。由于Flink创建表涉及较多的内容，关于下面的示例请参考文章：16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及FileSystem示例（1）
Flink SQL> show catalogs;
Hive Session ID = 008f6263-1b8e-4eb7-b034-a2c8651809f1
+------------------+
| catalog name |
+------------------+
| alan_hivecatalog |
| default_catalog |
+------------------+
2 rows in set
Flink SQL> use catalog default_catalog;
Hive Session ID = 1b1a3fb2-e303-4c2a-bfc8-5f38c47aa0f6
[INFO] Execute statement succeed.
Flink SQL> show databases;
+------------------+
| database name |
+------------------+
| default_database |
+------------------+
1 row in set
Flink SQL> use default_database;
[INFO] Execute statement succeed.
Flink SQL> show tables;
Empty set
Flink SQL> CREATE TABLE alan_first_table (
> t_id BIGINT,
> t_name STRING,
> t_balance DOUBLE,
> t_age INT
> ) WITH (
> 'connector' = 'filesystem',
> 'path' = 'hdfs://HadoopHAcluster/flinktest/firstdemo/',
> 'format' = 'csv'
> );
[INFO] Execute statement succeed.
Flink SQL> show tables;
+------------------+
| table name |
+------------------+
| alan_first_table |
+------------------+
1 row in set
---能查出来数据是有前提的，那就是在创建表之前，我已经在hdfs://HadoopHAcluster/flinktest/firstdemo目录下上传了5个文件，每个文件一条数据
[alanchan@server4 testdata]$ hadoop fs -ls hdfs://HadoopHAcluster/flinktest/firstdemo
Found 5 items
-rw-r--r-- 3 alanchan supergroup 15 2023-09-07 10:24 hdfs://HadoopHAcluster/flinktest/firstdemo/dim_user1.txt
-rw-r--r-- 3 alanchan supergroup 19 2023-09-07 10:24 hdfs://HadoopHAcluster/flinktest/firstdemo/dim_user2.txt
-rw-r--r-- 3 alanchan supergroup 22 2023-09-07 10:24 hdfs://HadoopHAcluster/flinktest/firstdemo/dim_user3.txt
-rw-r--r-- 3 alanchan supergroup 20 2023-09-07 10:24 hdfs://HadoopHAcluster/flinktest/firstdemo/dim_user4.txt
-rw-r--r-- 3 alanchan supergroup 24 2023-09-07 10:24 hdfs://HadoopHAcluster/flinktest/firstdemo/dim_user5.txt
Flink SQL> select * from alan_first_table;
+----+----------------------+--------------------------------+--------------------------------+-------------+
| op | t_id | t_name | t_balance | t_age |
+----+----------------------+--------------------------------+--------------------------------+-------------+
| +I | 5 | alan_chan_chn | 52.23 | 38 |
| +I | 3 | alanchanchn | 32.23 | 28 |
| +I | 1 | alan | 12.23 | 18 |
| +I | 4 | alan_chan | 12.43 | 29 |
| +I | 2 | alanchan | 22.23 | 10 |
+----+----------------------+--------------------------------+--------------------------------+-------------+
Received a total of 5 rows
---带条件查询
Flink SQL> select * from alan_first_table where t_balance >=20;
+----+----------------------+--------------------------------+--------------------------------+-------------+
| op | t_id | t_name | t_balance | t_age |
+----+----------------------+--------------------------------+--------------------------------+-------------+
| +I | 3 | alanchanchn | 32.23 | 28 |
| +I | 2 | alanchan | 22.23 | 10 |
| +I | 5 | alan_chan_chn | 52.23 | 38 |
+----+----------------------+--------------------------------+--------------------------------+-------------+
Received a total of 3 rows
可以从该表中定义一个连续查询，当新行可用时读取并立即输出它们的结果。
3、连续查询介绍及示例
虽然最初设计时没有考虑流语义，但 SQL 是用于构建连续数据流水线的强大工具。Flink SQL 与传统数据库查询的不同之处在于，Flink SQL 持续消费到达的行并对其结果进行更新。
一个连续查询永远不会终止，并会产生一个动态表作为结果。动态表是 Flink 中 Table API 和 SQL 对流数据支持的核心概念。
连续流上的聚合需要在查询执行期间不断地存储聚合的结果。例如，假设你需要从传入的数据流中计算每个部门的员工人数。查询需要维护每个部门最新的计算总数，以便在处理新行时及时输出结果。
关于连续查询更多的内容，参考文章：15、Flink 的table api与sql之流式概念-详解的介绍了动态表、时间属性配置（如何处理更新结果）、时态表、流上的join、流上的确定性以及查询配置
下面的示例说明：
1、在flink创建一张表，提交连续查询的任务（其实就是一个查询session，动态显示表内的数据）
2、为方便模拟，使用kafka作为消息源，即表的连接类型为kafka，也即需要有kafka的运行环境
3、sql客户端的环境与本文上述示例一致
4、关于该示例更多的信息参考：16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Kafka示例（3）
Flink SQL> CREATE TABLE alanchan_kafka_table (
> `id` INT,
> name STRING,
> age INT,
> balance DOUBLE
> ) WITH (
> 'connector' = 'kafka',
> 'topic' = 't_kafka_source',
> 'scan.startup.mode' = 'earliest-offset',
> 'properties.bootstrap.servers' = '192.168.10.41:9092,192.168.10.42:9092,192.168.10.43:9092',
> 'format' = 'csv'
> );
[INFO] Execute statement succeed.
Flink SQL> show tables;
+----------------------+
| table name |
+----------------------+
| alan_first_table |
| alanchan_kafka_table |
+----------------------+
2 rows in set
-----kafka一条一条写入数据，下文中的查询结果会根据kafka中发送的消息逐条展示出来------
[alanchan@server2 bin]$ kafka-console-producer.sh --broker-list server1:9092 --topic t_kafka_source
>1,alan,15,100
>2,alanchan,20,200
>3,alanchanchn,25,300
>4,alan_chan,30,400
>5,alan_chan_chn,50,45
>
Flink SQL> select * from alanchan_kafka_table;
+----+-------------+--------------------------------+-------------+--------------------------------+
| op | id | name | age | balance |
+----+-------------+--------------------------------+-------------+--------------------------------+
| +I | 1 | alan | 15 | 100.0 |
| +I | 2 | alanchan | 20 | 200.0 |
| +I | 3 | alanchanchn | 25 | 300.0 |
| +I | 4 | alan_chan | 30 | 400.0 |
| +I | 5 | alan_chan_chn | 50 | 45.0 |
4、Sink 表介绍及示例
当运行此查询时，SQL 客户端实时但是以只读方式提供输出。存储结果，作为报表或仪表板的数据来源，需要写到另一个表。这可以使用 INSERT INTO 语句来实现。本节中引用的表称为 sink 表。INSERT INTO 语句将作为一个独立查询被提交到 Flink 集群中。
------创建数据源表，该表不能查询
Flink SQL> CREATE TABLE source_table (
> userId INT,
> age INT,
> balance DOUBLE,
> userName STRING
> ) WITH (
> 'connector' = 'datagen',
> 'rows-per-second'='100',
> 'fields.userId.kind'='sequence',
> 'fields.userId.start'='1',
> 'fields.userId.end'='1000',
>
> 'fields.balance.kind'='random',
> 'fields.balance.min'='1',
> 'fields.balance.max'='100',
>
> 'fields.age.min'='1',
> 'fields.age.max'='1000',
>
> 'fields.userName.length'='10'
> );
[INFO] Execute statement succeed.
----创建sink表，hdfs文件夹不需要手动创建，flink会自己创建
Flink SQL> CREATE TABLE alan_sink_table (
> t_id BIGINT,
> t_name STRING,
> t_balance DOUBLE,
> t_age INT
> ) WITH (
> 'connector' = 'filesystem',
> 'path' = 'hdfs://HadoopHAcluster/flinktest/firstsinkdemo/',
> 'format' = 'csv'
> );
[INFO] Execute statement succeed.
------批量插入sink表，也可以是动态的，但需要设置数据刷新频率，否则查不到结果，该事情在本Flink专栏中有说明
------此处也是提交一个flink任务，此处用的是yarn-session模式
Flink SQL> INSERT INTO alan_sink_table
> SELECT userId ,userName,balance,age FROM source_table;
Job ID: c2e1985745c5c938c56e26f8efe5a8db
------查询结果如下
Flink SQL> select * from alan_sink_table;
+----+----------------------+--------------------------------+--------------------------------+-------------+
| op | t_id | t_name | t_balance | t_age |
+----+----------------------+--------------------------------+--------------------------------+-------------+
| +I | 1 | d0c7d38b94 | 31.52935530019297 | 802 |
| +I | 2 | b880adc262 | 45.43292342494475 | 556 |
| +I | 3 | e1ce373b2e | 39.595138772111014 | 459 |
| +I | 4 | 3bd1242679 | 78.58761035208113 | 585 |
| +I | 5 | 88ba47bb2b | 4.870598793833649 | 508 |
| +I | 6 | 72bdba9132 | 48.33565877511729 | 115 |
| +I | 7 | 0fa82976d1 | 52.6978279057911 | 353 |
| +I | 8 | 8d546bab93 | 20.403401648898576 | 391 |
| +I | 9 | 9eb957d512 | 82.16967630094122 | 323 |
| +I | 10 | 5423755f01 | 49.12646233699912 | 769 |
| +I | 11 | da6c7936ea | 16.877530563314846 | 687 |
| +I | 12 | 3ef87eb75a | 68.65154273578702 | 434 |
| +I | 13 | e08320e927 | 8.403066874855323 | 292 |
| +I | 14 | 03e1ccfc69 | 98.61326426348097 | 653 |
......
+----+----------------------+--------------------------------+--------------------------------+-------------+
Received a total of 1000 rows
提交后，它将运行并将结果直接存储到 sink 表中，而不是将结果加载到系统内存中。
以上，简单的介绍了SQL和SQL的入门，并以三个简单的示例进行介绍，由于示例涉及到其他的内容，需要了解更深入的内容请参考相关的文章。