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示例 通过表orders创建一个视图test： CREATE VIEW test (oderkey comment 'orderId',orderstatus comment 'status',half comment 'half') ASSELECT orderkey, orderstatus, totalprice / 2 AS half FROM orders; 通过表orders的汇总结果创建视图orders_by_date： CREATE VIEW orders_by_date ASSELECT orderdate, sum(totalprice) AS priceFROM ordersGROUP BY orderdate; 创建一个新视图来替换已经存在的视图： CREATE OR REPLACE VIEW test ASSELECT orderkey, orderstatus, totalprice / 4 AS quarterFROM orders 创建一个视图的同时设置表属性： create or replace view view1 comment 'the first view' TBLPROPERTIES('format'='orc') as select * from fruit;

示例 describe formatted show_table1 a; Describe Formatted Column ------------------------------ col_name a data_type integer min max num_nulls distinct_count 0 avg_col_len max_col_len num_trues num_falses comment (1 row)

编码函数 url_encode(value) → varchar 描述：对value进行转义处理，以便可以安全地将其包含在URL查询参数名和值中： 字母字符不会被编码。 字符 ., -, * 和 _不会被编码。 ASCII空格字符会被编码为+ 。 所有其他字符都将转换为UTF-8，并且字节被编码为字符串％XX，其中XX是UTF-8字节的大写十六进制值。 select url_encode('http://www.example.com:80/stu/index.html?name=xxx&age=25#teacher');-- http%3A%2F%2Fwww.example.com%3A80%2Fstu%2Findex.html%3Fname%3Dxxx%26age%3D25%23teacher url_decode(value) → varchar 描述：对value编码后的URL进行解码操作。 select url_decode('http%3A%2F%2Fwww.example.com%3A80%2Fstu%2Findex.html%3Fname%3Dxxx%26age%3D25%23teacher');-- http://www.example.com:80/stu/index.html?name=xxx&age=25#teacher

参数说明 IN | FROM schema_name 指定schema名称，未指定时默认使用当前的schema。 LIKE 'identifier_with_wildcards' identifier_with_wildcards只支持包含“*”和“|”的规则匹配表达式。 其中“*”可以匹配单个或多个字符，“|”适用于匹配多种规则匹配表达式中的任意一种的情况，它用于分隔这些规则匹配表达式。 规则匹配表达式首尾的空格，不会参与匹配计算。 partition_spec 一个可选参数，使用键值对来指定分区列表，键值对之间通过逗号分隔。需要注意，指定分区时，表名不支持模糊匹配。

限制 仅Hive数据源的Catalog支持视图的列描述。 在HetuEngine中创建的视图，视图的定义以编码方式存储在数据源里。在数据源可以查询到该视图，但无法对该视图执行操作。 视图是只读的，不可对它执行LOAD、INSERT操作。 视图可以包含ORDER BY和LIMIT子句，如果关联了该视图的查询语句也包含了这些子句，那么查询语句中的ORDER BY和LIMIT子句将以视图的结果为基础进行运算。

使用场景 通过CompiledPlan提交的作业，算子的并行度、算子的TTL都以CompiledPlan中的值为准，而不是“flink-conf.yaml”中的值。FlinkSQL支持通过修改作业的CompiledPlan来设置算子并行度。 修改CompiledPlan时不能破坏Json File文件结构，否则作业会提交失败。CompiledPlan的保存路径可以是HDFS路径也可以是OBS路径，本示例以HDFS路径为例。

示例 用指定列的查询结果创建新表orders_column_aliased： CREATE TABLE orders_column_aliased (order_date, total_price) ASSELECT orderdate, totalprice FROM orders; 用表orders的汇总结果新建一个表orders_by_data： CREATE TABLE orders_by_dateCOMMENT 'Summary of orders by date'WITH (format = 'ORC')ASSELECT orderdate, sum(totalprice) AS priceFROM ordersGROUP BY orderdate; 如果表orders_by_date不存在，则创建表orders_by_date： CREATE TABLE IF NOT EXISTS orders_by_date ASSELECT orderdate, sum(totalprice) AS priceFROM ordersGROUP BY orderdate; 用和表orders具有相同schema创建新表empty_orders table，但是没数据： CREATE TABLE empty_orders ASSELECT *FROM ordersWITH NO DATA; 使用VALUES创建表，参考 VALUES。 分区表示例: CREATE EXTERNAL TABLE hetu_copy(corderkey, corderstatus, ctotalprice, corderdate, cds) PARTITIONED BY(cds) SORT BY (corderkey, corderstatus) COMMENT 'test' STORED AS orc LOCATION '/user/hetuserver/tmp' TBLPROPERTIES (orc_bloom_filter_fpp = 0.3, orc_compress = 'SNAPPY', orc_compress_size = 6710422, orc_bloom_filter_columns = 'corderstatus,ctotalprice') as select * from hetu_test; CREATE TABLE hetu_copy1(corderkey, corderstatus, ctotalprice, corderdate, cds) WITH (partitioned_by = ARRAY['cds'], bucketed_by = ARRAY['corderkey', 'corderstatus'], sorted_by = ARRAY['corderkey', 'corderstatus'], bucket_count = 16, orc_compress = 'SNAPPY', orc_compress_size = 6710422, orc_bloom_filter_columns = ARRAY['corderstatus', 'ctotalprice'], external = true, format = 'orc', location = '/user/hetuserver/tmp ') as select * from hetu_test;

限制 EXCHANGE PARTITION： 被迁移的单个或多个分区，迁移前必须都是已存在的分区，并归属于来源表，且在目标表中不包含这些分区； 该操作涉及的表需要有相同的列定义，并且有相同的分区键； 如果表中包含索引，该操作会失败； 来源表和目标表中任意一个为事务表时，不允许Exchange partition操作； 对于目标表，在一次操作中，多个分区要么同时迁移成功，要么全部失败。对于来源表，操作成功后，所有迁移的分区都会被释放； Alter table change column不支持orc格式的表。 ALTER TABLE table_name ADD | DROP col_name命令仅对于ORC/PARQUET存储格式的非分区表可用。

示例 将表名从users修改为people： ALTER TABLE users RENAME TO people; 在表users中增加名为zip的列： ALTER TABLE users ADD COLUMN zip varchar; 从表users中删除名为zip的列： ALTER TABLE users DROP COLUMN zip; 将表users中列名id更改为user_id： ALTER TABLE users RENAME COLUMN id TO user_id; 修改分区操作： --创建两个分区表CREATE TABLE IF NOT EXISTS hetu_int_table5 (eid int, name String, salary String, destination String, dept String, yoj int) COMMENT 'Employee Names' partitioned by (dt timestamp,country String, year int, bonus decimal(10,3)) STORED AS TEXTFILE; CREATE TABLE IF NOT EXISTS hetu_int_table6 (eid int, name String, salary String, destination String, dept String, yoj int) COMMENT 'Employee Names' partitioned by (dt timestamp,country String, year int, bonus decimal(10,3)) STORED AS TEXTFILE; --添加分区ALTER TABLE hetu_int_table5 ADD IF NOT EXISTS PARTITION (dt='2008-08-08 10:20:30.0', country='IN', year=2001, bonus=500.23) PARTITION (dt='2008-08-09 10:20:30.0', country='IN', year=2001, bonus=100.50) ; --查看分区show partitions hetu_int_table5; dt | country | year | bonus -------------------------|---------|------|--------- 2008-08-09 10:20:30.000 | IN | 2001 | 100.500 2008-08-08 10:20:30.000 | IN | 2001 | 500.230 (2 rows) --删除分区ALTER TABLE hetu_int_table5 DROP IF EXISTS PARTITION (dt=timestamp '2008-08-08 10:20:30.0', country='IN', year=2001, bonus=500.23); --查看分区show partitions hetu_int_table5; dt | country | year | bonus -------------------------|---------|------|--------- 2008-08-09 10:20:30.000 | IN | 2001 | 100.500 (1 row) --迁移分区示例CREATE SCHEMA part_test;CREATE TABLE hetu_exchange_partition1 (a string, b string) PARTITIONED BY (ds string);CREATE TABLE part_test.hetu_exchange_partition2 (a string, b string) PARTITIONED BY (ds string);ALTER TABLE hetu_exchange_partition1 ADD PARTITION (ds='1'); --查看分区 show partitions hetu_exchange_partition1; ds ---- 1 (1 row) show partitions part_test.hetu_exchange_partition2; ds ----(0 rows) --迁移分区，从 T1 到 T2ALTER TABLE part_test.hetu_exchange_partition2 EXCHANGE PARTITION (ds='1') WITH TABLE hetu_exchange_partition1; --再次查看分区，可以看到分区迁移成功show partitions hetu_exchange_partition1; ds ---- (0 row) show partitions part_test.hetu_exchange_partition2; ds ----1(1 rows) --重命名分区CREATE TABLE IF NOT EXISTS hetu_rename_table ( eid int, name String, salary String, destination String, dept String, yoj int) COMMENT 'Employee details' partitioned by (year int) STORED AS TEXTFILE; ALTER TABLE hetu_rename_table ADD IF NOT EXISTS PARTITION (year=2001); SHOW PARTITIONS hetu_rename_table;year ------ 2001 (1 row) ALTER TABLE hetu_rename_table PARTITION (year=2001) rename to partition (year=2020); SHOW PARTITIONS hetu_rename_table;year ------ 2020 (1 row) --修改分区表create table altercolumn4(a integer, b string) partitioned by (c integer); --修改表的文件格式 alter table altercolumn4 SET FILEFORMAT textfile; insert into altercolumn4 values (100, 'Daya', 500); alter table altercolumn4 partition (c=500) change column b empname string comment 'changed column name to empname' first; --修改分区表的存储位置（需要先在hdfs上创建目录，执行语句后，无法查到之前插入的那条数据）alter table altercolumn4 partition (c=500) set Location '/user/hive/warehouse/c500'; --修改列 b 改名为name，同时类型从integer转为stringcreate table altercolumn1(a integer, b integer) stored as textfile; alter table altercolumn1 change column b name string; --修改altercolumn1的存储属性ALTER TABLE altercolumn1 CLUSTERED BY(a, name) SORTED BY(name) INTO 25 BUCKETS; --查看altercolumn1的属性describe formatted altercolumn1; Describe Formatted Table ---------------------------------------------------------------------------------------- # col_name data_type comment a integer name varchar # Detailed Table Information Database: default Owner: admintest LastAccessTime: 0 Location: hdfs://hacluster/user/hive/warehouse/altercolumn1 Table Type: MANAGED_TABLE # Table Parameters: STATS_GENERATED_VIA_STATS_TASK workaround for potential lack of HIVE-12730 numFiles 0 numRows 0 orc.compress.size 262144 orc.compression.codec GZIP orc.row.index.stride 10000 orc.stripe.size 67108864 presto_query_id 20210325_025238_00034_f63xj@default@HetuEngine presto_version rawDataSize 0 totalSize 0 transient_lastDdlTime 1616640758 # Storage Information SerDe Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe InputFormat: org.apache.hadoop.mapred.TextInputFormat OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat Compressed: No Num Buckets: 25 Bucket Columns: [a, name] Sort Columns: [SortingColumn{columnName=name, order=ASCENDING}] Storage Desc Params: serialization.format 1 (1 row) Query 20210325_090522_00091_f63xj@default@HetuEngine, FINISHED, 1 nodeSplits: 1 total, 1 done (100.00%)0:00 [0 rows, 0B] [0 rows/s, 0B/s]

语法 CREATE [EXTERNAL]① TABLE [IF NOT EXISTS] [catalog_name.][db_name.]table_name [ ( column_alias, ... ) ] [[PARTITIONED BY ①(col_name, ....)] [SORT BY① ([column [, column ...]])] ]① [COMMENT 'table_comment'] [ WITH ( property_name = expression [, ...] ) ]② [[STORED AS file_format]① [LOCATION 'hdfs_path']① [TBLPROPERTIES (orc_table_property = value [, ...] ) ] ]① AS query [ WITH [ NO ] DATA ]②

WITH WITH子句定义查询子句的命名关系，可以展平嵌套查询或简化子查询语句。 例如下面的查询语句是等价的： SELECT name, maxprice FROM (SELECT name, MAX(price) AS maxprice FROM fruit GROUP BY name) AS x;WITH x AS (SELECT name, MAX(price) AS maxprice FROM fruit GROUP BY name) SELECT name, maxprice FROM x; 多个子查询 with t1 as(select name,max(price) as maxprice from fruit group by name),t2 as(select name,avg(price) as avgprice from fruit group by name)select t1.*,t2.* from t1 join t2 on t1.name = t2.name; WITH的链式形式 WITHx AS (SELECT a FROM t),y AS (SELECT a AS b FROM x),z AS (SELECT b AS c FROM y)SELECT c FROM z; 父主题： HetuEngine DQL SQL语法说明

描述 DATABASE和SCHEMA在概念上是等价可互换的。 该语法用于删除数据库databasename，如果目标数据库不存在，将抛出错误提示，但如果使用了IF EXISTS子句则不会抛出错误提示。 可选参数RESTRICT|CASCADE用于指定删除的模式，默认是RESTRICT模式，在这种模式下，数据库必须为空，不包含任何表才能删除，如果是CASCADE模式，表示级联删除，会先删除数据库下面的表 ，再删除数据库。

示例 START TRANSACTION;START TRANSACTION ISOLATION LEVEL REPEATABLE READ;START TRANSACTION READ WRITE;START TRANSACTION ISOLATION LEVEL READ COMMITTED, READ ONLY;START TRANSACTION READ WRITE, ISOLATION LEVEL SERIALIZABLE; 不支持嵌套事务，也就是开启事务后，在commit之前不能再开启其他事务。

示例 -- 创建事务表create table upd_tb(col1 int,col2 string) with (format='orc',transactional=true);--插入数据insert into upd_tb values (3,'A'),(4,'B');--修改col1 = 4的数据update upd_tb set col1=5 where col1=4;--查询表，col1=4的记录已被修改select * from upd_tb; -- col1 | col2 ------|------ 5 | B 3 | A

示例 显示能够创建orders表的SQL语句： CREATE TABLE orders ( orderkey bigint, orderstatus varchar, totalprice double, orderdate date ) WITH (format = 'ORC', location='/user',orc_compress='ZLIB',external=true, "auto.purge"=false);show create table orders; Create Table ------------------------------------------------- CREATE TABLE hive.default.orders ( orderkey bigint, orderstatus varchar, totalprice double, orderdate date ) WITH ( external_location = 'hdfs://hacluster/user', format = 'ORC', orc_compress = 'ZLIB', orc_compress_size = 262144, orc_row_index_stride = 10000, orc_stripe_size = 67108864 ) (1 row)

示例 删除schema web： DROP SCHEMA web; 如果schema sales存在，删除该schema： DROP SCHEMA IF EXISTS sales; 级联删除schema test_drop，schema test_drop中存在表tb_web，会先删除tb_web，再删除test_drop： CREATE SCHEMA test_drop; USE test_drop; CREATE TABLE tb_web(col1 int); DROP DATABASE test_drop CASCADE;

TRY 评估一个表达式，如果出错，则返回Null。类似于编程语言中的try catch。try函数一般结合COALESCE使用，COALESCE可以将异常的空值转为0或者空，以下情况会被try捕获： 分母为0 错误的cast操作或者函数入参 数字超过了定义长度 不推荐使用，应该明确以上异常，做数据预处理 示例： 假设有以下表，字段origin_zip中包含了一些无效数据： -- 创建表create table shipping (origin_state varchar,origin_zip varchar,packages int ,total_cost int);-- 插入数据insert into shipping values ('California','94131',25,100),('California','P332a',5,72),('California','94025',0,155),('New Jersey','08544',225,490);-- 查询数据SELECT * FROM shipping; origin_state | origin_zip | packages | total_cost --------------+------------+----------+------------ California | 94131 | 25 | 100 California | P332a | 5 | 72 California | 94025 | 0 | 155 New Jersey | 08544 | 225 | 490 (4 rows) 不使用Try查询失败： SELECT CAST(origin_zip AS BIGINT) FROM shipping;Query failed: Cannot cast 'P332a' to BIGINT 使用Try返回NULL： SELECT TRY(CAST(origin_zip AS BIGINT)) FROM shipping; origin_zip ------------ 94131 NULL 94025 08544 (4 rows) 不使用try查询失败： SELECT total_cost/packages AS per_package FROM shipping;Query failed: Division by zero 使用TRY和COALESCE返回默认值： SELECT COALESCE(TRY(total_cost/packages),0) AS per_package FROM shipping; per_package ------------- 4 14 0 19 (4 rows)

CASE 标准的SQL CASE表达式有两种模式。 “简单模式”从左向右查找表达式的每个value，直到找出相等的expression： CASE expression WHEN value THEN result [ WHEN ... ] [ ELSE result ] END 返回匹配value的result。如果没有匹配到任何值，则返回ELSE子句的result；如果没有ELSE子句，则返回空。示例： select a,case a when 1 then 'one' when 2 then 'two' else 'many' end from (values (1),(2),(3),(4)) as t(a); a | _col1 ---|------- 1 | one 2 | two 3 | many 4 | many (4 rows) “查找模式”从左向右判断每个condition的布尔值，直到判断为真，返回匹配result： CASE WHEN condition THEN result [ WHEN ... ] [ ELSE result ] END 如果判断条件都不成立，则返回ELSE子句的result；如果没有ELSE子句，则返回空。示例： select a,b,casewhen a=1 then 'one'when b=2 then 'tow'else 'many' end from (values (1,2),(3,4),(1,3),(4,2)) as t(a,b); a | b | _col2 ---|---|------- 1 | 2 | one 3 | 4 | many 1 | 3 | one 4 | 2 | tow (4 rows)

SEMI JOIN、ANTI JOIN 当一张表在另一张表找到匹配的记录之后，半连接（semi-join）返回第一张表中的记录。与条件连接相反，即使在右节点中找到几条匹配的记录，左节点的表也只会返回一条记录。另外，右节点的表一条记录也不会返回。半连接通常使用IN或EXISTS作为连接条件。 而anti-join则与semi-join相反，即当在第二张表没有发现匹配记录时，才会返回第一张表里的记录；当使用not exists/not in的时候会用到。 其他支持的条件包括如下内容： where子句中的多个条件 别名关系 下标表达式 解引用表达式 强制转换表达式 特定函数调用 目前，只在如下情况下支持多个semi/anti join表达式：第一个表中的列在其直接后续的join表达式中被查询，且不与其他join表达式有关系。 示例如下：

IF IF函数是语言结构，它与下面的CASE表达式功能相同： CASE WHEN condition THEN true_value [ ELSE false_value ] END if(condition, true_value) 如果condition为真，返回true_value；否则返回NULL，true_value不进行计算。 select if(a=1,8) from (values (1),(1),(2)) as t(a); -- 8 8 NULLselect if(a=1,'value') from (values (1),(1),(2)) as t(a); -- value value NULL if(condition, true_value, false_value) 如果condition为真，返回true_value；否则计算并返回false_value 。 select if(a=1,'on','off') from (values (1),(1),(2)) as t(a); _col0 ------- on on off (3 rows)

NULLIF nullif(value1, value2) 如果value1与value2相等，返回NULL；否则返回value1 。 select nullif(a,b) from (values (1,1),(1,2)) as t(a,b); -- _col0 ------- NULL 1 (2 rows) ZEROIFNULL(value) 如果value为null，返回0，否则返回原值。目前支持数值类型还有varchar类型。 select zeroifnull(a),zeroifnull(b),zeroifnull(c) from (values (null,13.11,bigint '157'),(88,null,bigint '188'),(55,14.11,null)) as t(a,b,c); _col0 | _col1 | _col2 -------|-------|------- 0 | 13.11 | 157 88 | 0.00 | 188 55 | 14.11 | 0 (3 rows) NVL(value1,value2) 如果value1为NULL，返回value2，否则，返回value1。 select nvl(NULL,3); -- 3select nvl(2,3); --2 ISNULL(value) 如果value1为NULL，返回true，否则返回false。 Create table nulltest(col1 int,col2 int);insert into nulltest values(null,3);select isnull(col1),isnull(col2) from nulltest; _col0 | _col1 -------|------- true | false (1 row) ISNOTNULL(value) 如果value1为NULL，返回false，否则返回true。 select isnotnull(col1),isnotnull(col2) from nulltest; _col0 | _col1 -------|------- false | true (1 row)

Qualifying Column Names 当JOIN的两个relation有相同的列名时，列引用必须使用relation别名（如果relation有别名）或relation名称进行限定： SELECT nation.name, region.name FROM nation CROSS JOIN region;SELECT n.name, r.name FROM nation AS n CROSS JOIN region AS r;SELECT n.name, r.name FROM nation n CROSS JOIN region r;

JSON转其他类型 SELECT CAST(JSON 'null' AS VARCHAR);-- NULL SELECT CAST(JSON '1' AS INTEGER);-- 1 SELECT CAST(JSON '9223372036854775807' AS BIGINT);-- 9223372036854775807 SELECT CAST(JSON '"abc"' AS VARCHAR);-- abc SELECT CAST(JSON 'true' AS BOOLEAN);-- true SELECT CAST(JSON '1.234' AS DOUBLE);-- 1.234 SELECT CAST(JSON '[1,23,456]' AS ARRAY(INTEGER));-- [1, 23, 456] SELECT CAST(JSON '[1,null,456]' AS ARRAY(INTEGER));-- [1, NULL, 456] SELECT CAST(JSON '[[1,23],[456]]' AS ARRAY(ARRAY(INTEGER)));-- [[1, 23], [456]] SELECT CAST(JSON '{"k1":1, "k2":23, "k3":456}' AS MAP(VARCHAR, INTEGER));-- {k1=1, k2=23, k3=456} SELECT CAST(JSON '{"v1":123, "v2":"abc","v3":true}' AS ROW(v1 BIGINT, v2 VARCHAR, v3 BOOLEAN));-- {v1=123, v2=abc, v3=true} SELECT CAST(JSON '[123, "abc",true]' AS ROW(v1 BIGINT, v2 VARCHAR, v3 BOOLEAN));-- {value1=123, value2=abc, value3=true} SELECT CAST(JSON'[[1, 23], 456]'AS ARRAY(JSON));-- [JSON '[1,23]', JSON '456'] SELECT CAST(JSON'{"k1": [1, 23], "k2": 456}'AS MAP(VARCHAR,JSON));-- {k1 = JSON '[1,23]', k2 = JSON '456'} SELECT CAST(JSON'[null]'AS ARRAY(JSON));-- [JSON 'null']

JSON函数 NULL到JSON的转换并不能简单地实现。从独立的NULL进行转换将产生一个SQLNULL，而不是JSON 'null'。不过，在从包含NULL的数组或Map进行转换时，生成的JSON将包含NULL。 在从ROW转换为JSON时，结果是一个JSON数组，而不是一个JSON对象。这是因为对于SQL中的行，位置比名称更重要。 支持从BOOLEAN、TINYINT、SMALLINT、INTEGER、BIGINT、REAL、DOUBLE或VARCHAR进行转换。当数组的元素类型为支持的类型之一、Map的键类型是VARCHAR且Map的值类型是支持的类型之一或行的每个字段类型是支持的类型之一时支持从ARRAY、MAP或ROW进行转换。下面通过示例展示了转换的行为： SELECT CAST(NULL AS JSON);-- NULLSELECT CAST(1 AS JSON);-- JSON '1'SELECT CAST(9223372036854775807 AS JSON);-- JSON '9223372036854775807'SELECT CAST('abc' AS JSON);-- JSON '"abc"'SELECT CAST(true AS JSON);-- JSON 'true'SELECT CAST(1.234 AS JSON);-- JSON '1.234'SELECT CAST(ARRAY[1, 23, 456] AS JSON);-- JSON '[1,23,456]'SELECT CAST(ARRAY[1, NULL, 456] AS JSON);-- JSON '[1,null,456]'SELECT CAST(ARRAY[ARRAY[1, 23], ARRAY[456]] AS JSON);-- JSON '[[1,23],[456]]'SELECT CAST(MAP(ARRAY['k1', 'k2', 'k3'], ARRAY[1, 23, 456]) AS JSON);-- JSON '{＂k1＂:1,＂k2＂:23,＂k3＂:456}'SELECT CAST(CAST(ROW(123, 'abc', true) AS ROW(v1 BIGINT, v2 VARCHAR, v3 BOOLEAN)) AS JSON);-- JSON '[123,"abc",true]'

HetuEngine预留关键字 表1罗列了系统预留的关键字，以及它们在其他SQL标准中是否为预留关键字。如果需要使用这些关键字作为标识符，请加注双引号。 表1 关键字 Keyword SQL：2016 SQL-92 ALTER reserved reserved AND reserved reserved AS reserved reserved BETWEEN reserved reserved BY reserved reserved CASE reserved reserved CAST reserved reserved CONSTRAINT reserved reserved CREATE reserved reserved CROSS reserved reserved CUBE reserved reserved CURRENT_DATE reserved reserved CURRENT_PATH reserved reserved CURRENT_ROLE reserved reserved CURRENT_TIME reserved reserved CURRENT_TIMESTAMP reserved reserved CURRENT_USER reserved reserved DEALLOCATE reserved reserved DELETE reserved reserved DESCRIBE reserved reserved DISTINCT reserved reserved DROP reserved reserved ELSE reserved reserved END reserved reserved ESCAPE reserved reserved EXCEPT reserved reserved EXECUTE reserved reserved EXISTS reserved reserved EXTRACT reserved reserved FALSE reserved reserved FOR reserved reserved FROM reserved reserved FULL reserved reserved GROUP reserved reserved GROUPING reserved reserved HAVING reserved reserved IN reserved reserved INNER reserved reserved INSERT reserved reserved INTERSECT reserved reserved INTO reserved reserved IS reserved reserved JOIN reserved reserved LEFT reserved reserved LIKE reserved reserved LOCALTIME reserved reserved LOCALTIMESTAMP reserved reserved NATURAL reserved reserved NORMALIZE reserved reserved NOT reserved reserved NULL reserved reserved ON reserved reserved OR reserved reserved ORDER reserved reserved OUTER reserved reserved PREPARE reserved reserved RECURSIVE reserved reserved RIGHT reserved reserved ROLLUP reserved reserved SELECT reserved reserved TABLE reserved reserved THEN reserved reserved TRUE reserved reserved UESCAPE reserved reserved UNION reserved reserved UNNEST reserved reserved USING reserved reserved VALUES reserved reserved WHEN reserved reserved WHERE reserved reserved WITH reserved reserved 父主题： HetuEngine常见SQL语法说明

语法 CREATE TABLE [ IF NOT EXISTS] table_name ( { coulumn_name data_type [ COMMENT comment] [ WITH (property_name = expression [,…] ) ] | LIKE existing_table_name [ {INCLUDING| EXCLUDING} PROPERTIES] } ) [,…] [ COMMENT table_comment] [WITH (property_name = expression [,… ] ) ]

示例 创建基础表order01和order02 CREATE TABLE order01(id int,name string,tel string) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' LINES TERMINATED BY '\n'STORED AS TEXTFILE;CREATE TABLE order02(sku int, sku_name string, sku_describe string); 创建表orders_like01，它将包含表order01定义的列及表属性 CREATE TABLE orders_like01 like order01 INCLUDING PROPERTIES; 创建表orders_like02，它将包含表order02定义的列，并将表的存储格式设置为‘TEXTFILE’ CREATE TABLE orders_like02 like order02 STORED AS TEXTFILE; 创建表orders_like03，它将包含表order01定义的列及表属性，order02定义的列，以及额外的列c1和c2 CREATE TABLE orders_like03 (c1 int,c2 float,LIKE order01 INCLUDING PROPERTIES,LIKE order02); 创建表orders_like04和orders_like05，它们都会包含同一个表order_partition的定义，但orders_like04不会包含分区键信息，而orders_like05会包含分区键的信息 CREATE TABLE order_partition(id int,name string,tel string) PARTITIONED BY (sku int);CREATE TABLE orders_like04 (like order_partition);CREATE TABLE orders_like05 like order_partition;DESC orders_like04; Column | Type | Extra | Comment --------|---------|-------|--------- id | integer | | name | varchar | | tel | varchar | | sku | integer | | (4 rows)DESC orders_like05; Column | Type | Extra | Comment --------|---------|---------------|--------- id | integer | | name | varchar | | tel | varchar | | sku | integer | partition key | (4 rows)

描述 使用LIKE子句可以在一个新表中包含一个已存在的表所有的列定义。可以使用多个LIKE来复制多个表的列。 如果使用了INCLUDING PROPERTIES，表的所有属性也会被复制到新表，该选项最多只能对一个表生效。 对于从表中复制过来的属性，可以使用WITH子句指定属性名进行修改。 默认使用EXCLUDING PROPERTIES属性。 对于带分区的表，如果用括号包裹like子句，复制的列定义不会包含分区键的信息。

Bitwise函数 bit_count(x, bits) → bigint 计算2的补码表示法中x中设置的位数（视为有符号位的整数）。 SELECT bit_count(9, 64); -- 2SELECT bit_count(9, 8); -- 2SELECT bit_count(-7, 64); -- 62SELECT bit_count(-7, 8); -- 6 bitwise_and(x, y) → bigint 以二进制补码形式返回x和y按位与的结果。 select bitwise_and(8, 7); -- 0 bitwise_not(x) → bigint 以二进制补码形式返回x按位非的结果。 select bitwise_not(8);-- -9 bitwise_or(x, y) → bigint 以二进制补码形式返回x和y按位或的结果。 select bitwise_or(8,7);-- 15 bitwise_xor(x, y) → bigint 以二进制补码形式返回x和y按位异或的结果。 SELECT bitwise_xor(19,25); -- 10 bitwise_left_shift(value, shift) → [same as value] 描述：返回value左移shift位后的值。 SELECT bitwise_left_shift(1, 2); -- 4SELECT bitwise_left_shift(5, 2); -- 20SELECT bitwise_left_shift(0, 1); -- 0SELECT bitwise_left_shift(20, 0); -- 20 bitwise_right_shift(value, shift) → [same as value] 描述：返回value右移shift位后的值。 SELECT bitwise_right_shift(8, 3); -- 1SELECT bitwise_right_shift(9, 1); -- 4SELECT bitwise_right_shift(20, 0); -- 20SELECT bitwise_right_shift(0, 1); -- 0-- 右移超过64位，返回0SELECT bitwise_right_shift( 12, 64); -- 0 bitwise_right_shift_arithmetic(value, shift) → [same as value] 描述：返回value的算术右移值，当shift小于64位时，返回结果与bitwise_right_shift一样，当移动位数达到或者超过64位时，value是正数时返回0，负数时返回-1： SELECT bitwise_right_shift_arithmetic( 12, 64); -- 0 SELECT bitwise_right_shift_arithmetic(-45, 64); -- -1 父主题： HetuEngine SQL函数和操作符说明

示例 -- 删除原生/管控表Create table simple(id int, name string); Insert into simple values(1,'abc'),(2,'def'); select * from simple; id | name----|------ 1 | abc 2 | def(2 rows) Truncate table simple; select * from simple; id | name----|------(0 rows) --删除表分区Create table tb_truncate_part (id int, name string) partitioned by (age int, state string); Insert into tb_truncate_part values (1,'abc',10,'ap'),(2,'abc',10,'up'),(3,'abc',20,'ap'),(4,'abc',20,'up'); select * from tb_truncate_part; id | name | age | state----|------|-----|------- 2 | abc | 10 | up 3 | abc | 20 | ap 1 | abc | 10 | ap 4 | abc | 20 | up(4 rows) Truncate table tb_truncate_part partition (state = 'ap', age = 10); select * from tb_truncate_part;id | name | age | state----|------|-----|------- 4 | abc | 20 | up 2 | abc | 10 | up 3 | abc | 20 | ap(3 rows)