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Posted to commits@flink.apache.org by go...@apache.org on 2022/08/25 09:05:51 UTC
[flink] branch master updated: [FLINK-28493][docs] Add document to describe "ANALYZE TABLE" syntax
This is an automated email from the ASF dual-hosted git repository.
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new 7166625ff78 [FLINK-28493][docs] Add document to describe "ANALYZE TABLE" syntax
7166625ff78 is described below
commit 7166625ff787181a59a10f4d75218b4ac87254ed
Author: zhengyunhong.zyh <33...@qq.com>
AuthorDate: Tue Aug 9 00:07:00 2022 +0800
[FLINK-28493][docs] Add document to describe "ANALYZE TABLE" syntax
This closes #20506
---
docs/content.zh/docs/dev/table/sql/analyze.md | 385 +++++++++++++++++++++++++
docs/content.zh/docs/dev/table/sql/overview.md | 3 +-
docs/content/docs/dev/table/sql/analyze.md | 382 ++++++++++++++++++++++++
docs/content/docs/dev/table/sql/overview.md | 3 +-
4 files changed, 771 insertions(+), 2 deletions(-)
diff --git a/docs/content.zh/docs/dev/table/sql/analyze.md b/docs/content.zh/docs/dev/table/sql/analyze.md
new file mode 100644
index 00000000000..a43573dd96b
--- /dev/null
+++ b/docs/content.zh/docs/dev/table/sql/analyze.md
@@ -0,0 +1,385 @@
+---
+title: "ANALYZE 语句"
+weight: 8
+type: docs
+aliases:
+ - /zh/dev/table/sql/analyze.html
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+<a name="analyze-statements"></a>
+
+# ANALYZE 语句
+
+`ANALYZE` 语句被用于为存在的表收集统计信息，并将统计信息写入该表的 catalog 中。当前版本中，`ANALYZE` 语句只支持 `ANALYZE TABLE`，
+且只能由用户手动触发。
+
+<span class="label label-danger">注意</span> 现在, `ANALYZE TABLE` 只支持批模式（Batch Mode），且只能用于已存在的表，
+如果表不存在或者是视图（View）则会报错。
+
+
+<a name="run-a-analyze-table-statement"></a>
+
+## 执行 ANALYZE TABLE 语句
+
+{{< tabs "analyze table" >}}
+{{< tab "Java" >}}
+可以使用 `TableEnvironment` 的 `executeSql()` 方法执行 `ANALYZE TABLE` 语句。
+
+以下示例展示了如何在 `TableEnvironment` 中执行一条 `ANALYZE TABLE` 语句。
+{{< /tab >}}
+{{< tab "Scala" >}}
+可以使用 `TableEnvironment` 的 `executeSql()` 方法执行 `ANALYZE TABLE` 语句。
+
+以下示例展示了如何在 `TableEnvironment` 中执行一条 `ANALYZE TABLE` 语句。
+{{< /tab >}}
+{{< tab "Python" >}}
+可以使用 `TableEnvironment` 的 `execute_sql()` 方法执行 `ANALYZE TABLE` 语句。
+
+以下示例展示了如何在 `TableEnvironment` 中执行一条 `ANALYZE TABLE` 语句。
+{{< /tab >}}
+{{< tab "SQL CLI" >}}
+
+`ANALYZE TABLE` 语句可以在 [SQL CLI]({{< ref "docs/dev/table/sqlClient" >}}) 中执行。
+
+以下示例展示了如何在 SQL CLI 中执行一条 `ANALYZE TABLE` 语句。
+
+{{< /tab >}}
+{{< /tabs >}}
+
+{{< tabs "a5de1760-e363-4b8d-9d6f-0bacb35b9dcf" >}}
+{{< tab "Java" >}}
+```java
+TableEnvironment tableEnv = TableEnvironment.create(...);
+
+// 注册名为 “Store” 的非分区表
+tableEnv.executeSql(
+ "CREATE TABLE Store (" +
+ " `id` BIGINT NOT NULl," +
+ " `location` VARCHAR(32)," +
+ " `owner` VARCHAR(32)" +
+ ") with (...)");
+
+// 注册名为 “Orders” 的分区表
+tableEnv.executeSql(
+ "CREATE TABLE Orders (" +
+ " `id` BIGINT NOT NULl," +
+ " `product` VARCHAR(32)," +
+ " `amount` INT," +
+ " `sold_year` BIGINT", +
+ " `sold_month` BIGINT", +
+ " `sold_day` BIGINT" +
+ ") PARTITIONED BY (`sold_year`, `sold_month`, `sold_day`) "
+ ") with (...)");
+
+// 非分区表，收集表级别的统计信息(表的统计信息主要为行数(row count))。
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS");
+
+// 非分区表，收集表级别的统计信息和所有列的列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// 非分区表，收集表级别的统计信息和指定列(列: location)的列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS FOR COLUMNS location");
+
+
+// 假设分区表 “Orders” 有 4 个分区，分区信息如下：
+// Partition1 : (sold_year='2022', sold_month='1', sold_day='10')
+// Partition2 : (sold_year='2022', sold_month='1', sold_day='11')
+// Partition3 : (sold_year='2022', sold_month='2', sold_day='10')
+// Partition4 : (sold_year='2022', sold_month='2', sold_day='11')
+
+
+// 分区表，收集分区 Partition1 的表级别统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS");
+
+// 分区表，收集分区 Partition1 和 Partition2 的表级别统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS");
+
+// 分区表，为所有分区收集表级别统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS");
+
+// 分区表，收集分区 Partition1 的表级别统计信息和所有列的统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// 分区表，收集分区 Partition1 和 Partition2 的表级别统计信息和所有列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// 分区表，为所有分区收集表级别统计信息和所有列的统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// 分区表，收集分区 Partition1 的表级别统计信息和分区中指定列(列: amount)的列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR COLUMNS amount");
+
+// 分区表，收集分区 Partition1 和 Partition2 的表级别统计信息和分区中指定列(列: amount，列: product)的列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+
+// 分区表，收集所有分区的表级别统计信息和指定列(列: amount，列: product)的列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+```
+{{< /tab >}}
+{{< tab "Scala" >}}
+```scala
+val tableEnv = TableEnvironment.create(...)
+
+// 注册名为 “Store” 的非分区表
+tableEnv.executeSql(
+ "CREATE TABLE Store (" +
+ " `id` BIGINT NOT NULl," +
+ " `location` VARCHAR(32)," +
+ " `owner` VARCHAR(32)" +
+ ") with (...)");
+
+// 注册名为 “Orders” 的分区表
+tableEnv.executeSql(
+ "CREATE TABLE Orders (" +
+ " `id` BIGINT NOT NULl," +
+ " `product` VARCHAR(32)," +
+ " `amount` INT," +
+ " `sold_year` BIGINT", +
+ " `sold_month` BIGINT", +
+ " `sold_day` BIGINT" +
+ ") PARTITIONED BY (`sold_year`, `sold_month`, `sold_day`) "
+") with (...)");
+
+// 非分区表，收集表级别的统计信息(表的统计信息主要为行数(row count))。
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS");
+
+// 非分区表，收集表级别的统计信息和所有列的列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// 非分区表，收集表级别的统计信息和指定列(列: location)的列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS FOR COLUMNS location");
+
+
+// 假设分区表 “Orders” 有 4 个分区，分区信息如下：
+// Partition1 : (sold_year='2022', sold_month='1', sold_day='10')
+// Partition2 : (sold_year='2022', sold_month='1', sold_day='11')
+// Partition3 : (sold_year='2022', sold_month='2', sold_day='10')
+// Partition4 : (sold_year='2022', sold_month='2', sold_day='11')
+
+
+// 分区表，收集分区 Partition1 的表级别统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS");
+
+// 分区表，收集分区 Partition1 和 Partition2 的表级别统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS");
+
+// 分区表，为所有分区收集表级别统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS");
+
+// 分区表，收集分区 Partition1 的表级别统计信息和所有列的统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// 分区表，收集分区 Partition1 和 Partition2 的表级别统计信息和所有列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// 分区表，为所有分区收集表级别统计信息和所有列的统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// 分区表，收集分区 Partition1 的表级别统计信息和分区中指定列(列: amount)的列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR COLUMNS amount");
+
+// 分区表，收集分区 Partition1 和 Partition2 的表级别统计信息和分区中指定列(列: amount，列: product)的列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+
+// 分区表，收集所有分区的表级别统计信息和指定列(列: amount，列: product)的列统计信息。
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+```
+{{< /tab >}}
+{{< tab "Python" >}}
+```python
+table_env = TableEnvironment.create(...)
+
+# 注册名为 “Store” 的非分区表
+table_env.execute_sql(
+ "CREATE TABLE Store (" +
+ " `id` BIGINT NOT NULl," +
+ " `location` VARCHAR(32)," +
+ " `owner` VARCHAR(32)" +
+ ") with (...)");
+
+# 注册名为 “Orders” 的分区表
+table_env.execute_sql(
+ "CREATE TABLE Orders (" +
+ " `id` BIGINT NOT NULl," +
+ " `product` VARCHAR(32)," +
+ " `amount` INT," +
+ " `sold_year` BIGINT", +
+ " `sold_month` BIGINT", +
+ " `sold_day` BIGINT" +
+ ") PARTITIONED BY (`sold_year`, `sold_month`, `sold_day`) "
+") with (...)");
+
+# 非分区表，收集表级别的统计信息(表的统计信息主要为行数(row count))。
+table_env.execute_sql("ANALYZE TABLE Store COMPUTE STATISTICS");
+
+# 非分区表，收集表级别的统计信息和所有列的列统计信息。
+table_env.execute_sql("ANALYZE TABLE Store COMPUTE STATISTICS FOR ALL COLUMNS");
+
+# 非分区表，收集表级别的统计信息和指定列(列: location)的列统计信息。
+table_env.execute_sql("ANALYZE TABLE Store COMPUTE STATISTICS FOR COLUMNS location");
+
+
+# 假设分区表 “Orders” 有 4 个分区，分区信息如下：
+# Partition1 : (sold_year='2022', sold_month='1', sold_day='10')
+# Partition2 : (sold_year='2022', sold_month='1', sold_day='11')
+# Partition3 : (sold_year='2022', sold_month='2', sold_day='10')
+# Partition4 : (sold_year='2022', sold_month='2', sold_day='11')
+
+
+# 分区表，收集分区 Partition1 的表级别统计信息。
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS");
+
+# 分区表，收集分区 Partition1 和 Partition2 的表级别统计信息。
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS");
+
+# 分区表，为所有分区收集表级别统计信息。
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS");
+
+# 分区表，收集分区 Partition1 的表级别统计信息和所有列的统计信息。
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR ALL COLUMNS");
+
+# 分区表，收集分区 Partition1 和 Partition2 的表级别统计信息和所有列统计信息。
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+# 分区表，为所有分区收集表级别统计信息和所有列的统计信息。
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+# 分区表，收集分区 Partition1 的表级别统计信息和分区中指定列(列: amount)的列统计信息。
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR COLUMNS amount");
+
+# 分区表，收集分区 Partition1 和 Partition2 的表级别统计信息和分区中指定列(列: amount，列: product)的列统计信息。
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+
+# 分区表，收集所有分区的表级别统计信息和指定列(列: amount，列: product)的列统计信息。
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+```
+{{< /tab >}}
+{{< tab "SQL CLI" >}}
+```sql
+Flink SQL> CREATE TABLE Store (
+> `id` BIGINT NOT NULl,
+> `location` VARCHAR(32),
+> `owner` VARCHAR(32)
+> ) with (
+> ...
+> );
+[INFO] Table has been created.
+
+Flink SQL> CREATE TABLE Orders (
+> `id` BIGINT NOT NULl,
+> `product` VARCHAR(32),
+> `amount` INT,
+> `sold_year` BIGINT,
+> `sold_month` BIGINT,
+> `sold_day` BIGINT
+> ) PARTITIONED BY (`sold_year`, `sold_month`, `sold_day`)
+> ) with (
+> ...
+> );
+[INFO] Table has been created.
+
+Flink SQL> ANALYZE TABLE Store COMPUTE STATISTICS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Store COMPUTE STATISTICS FOR ALL COLUMNS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Store COMPUTE STATISTICS FOR COLUMNS location;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR ALL COLUMNS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR ALL COLUMNS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR ALL COLUMNS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR COLUMNS amount;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION (sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product;
+[INFO] Execute statement succeed.
+```
+{{< /tab >}}
+{{< /tabs >}}
+
+<a name="syntax"></a>
+
+## 语法
+
+```sql
+ANALYZE TABLE [catalog_name.][db_name.]table_name PARTITION(partcol1[=val1] [, partcol2[=val2], ...]) COMPUTE STATISTICS [FOR COLUMNS col1 [, col2, ...] | FOR ALL COLUMNS]
+```
+- 对于分区表， 语法中 PARTITION(partcol1[=val1] [, partcol2[=val2], ...]) 是必须指定的
+ - 如果没有指定某分区，则会收集所有分区的统计信息
+ - 如果指定了某分区，则只会收集该分区的统计信息
+ - 如果该表为非分区表，但语句中指定了分区，则会报异常
+ - 如果指定了某个分区，但是该分区不存在，则会报异常
+
+- 语法中，FOR COLUMNS col1 [, col2, ...] 或者 FOR ALL COLUMNS 也是可选的
+ - 如果没有指定某一列，则只会收集表级别的统计信息
+ - 如果指定的列不存在，或者该列不是物理列，则会报异常
+ - 如果指定了某一列或者某几列，则会收集列的统计信息
+ - 列级别的统计信息包括:
+ - ndv: 该列中列值不同的数量
+ - nullCount: 该列中空值的数量
+ - avgLen: 列值的平均长度
+ - maxLen: 列值的最大长度
+ - minValue: 列值的最小值
+ - maxValue: 列值的最大值
+ - valueCount: 该值只应用于 boolean 类型
+ - 对于列统计信息，支持类型和对应的列统计信息值如下表所示("Y" 代表支持，"N" 代表不支持):
+
+| 类型 | `ndv` | `nullCount` | `avgLen` | `maxLen` | `maxValue` | `minValue` | `valueCount` |
+|:---------------------------------|:-----:|:-----------:|:--------:|:--------:|:----------:|:----------:|:------------:|
+| `BOOLEAN` | N | Y | N | N | N | N | Y |
+| `TINYINT` | Y | Y | N | N | Y | Y | N |
+| `SMALLINT` | Y | Y | N | N | Y | Y | N |
+| `INTEGER` | Y | Y | N | N | Y | Y | N |
+| `FLOAT` | Y | Y | N | N | Y | Y | N |
+| `DATE` | Y | Y | N | N | Y | Y | N |
+| `TIME_WITHOUT_TIME_ZONE` | Y | Y | N | N | Y | Y | N |
+| `BIGINT` | Y | Y | N | N | Y | Y | N |
+| `DOUBLE` | Y | Y | N | N | Y | Y | N |
+| `DECIMAL` | Y | Y | N | N | Y | Y | N |
+| `TIMESTAMP_WITH_LOCAL_TIME_ZONE` | Y | Y | N | N | Y | Y | N |
+| `TIMESTAMP_WITHOUT_TIME_ZONE` | Y | Y | N | N | Y | Y | N |
+| `CHAR` | Y | Y | Y | Y | N | N | N |
+| `VARCHAR` | Y | Y | Y | Y | N | N | N |
+| `other types` | N | Y | N | N | N | N | N |
+
+*注意:* 对于数据值定长的类型(例如：`BOOLEAN`, `INTEGER`, `DOUBLE` 等)， Flink 不会去收集 `avgLen` 和 `maxLen` 值。
+
+{{< top >}}
diff --git a/docs/content.zh/docs/dev/table/sql/overview.md b/docs/content.zh/docs/dev/table/sql/overview.md
index 9c9f4e2a208..d4732f9be8d 100644
--- a/docs/content.zh/docs/dev/table/sql/overview.md
+++ b/docs/content.zh/docs/dev/table/sql/overview.md
@@ -34,6 +34,7 @@ under the License.
- [CREATE TABLE, CATALOG, DATABASE, VIEW, FUNCTION]({{< ref "docs/dev/table/sql/create" >}})
- [DROP TABLE, DATABASE, VIEW, FUNCTION]({{< ref "docs/dev/table/sql/drop" >}})
- [ALTER TABLE, DATABASE, FUNCTION]({{< ref "docs/dev/table/sql/alter" >}})
+- [ANALYZE TABLE]({{< ref "docs/dev/table/sql/analyze" >}})
- [INSERT]({{< ref "docs/dev/table/sql/insert" >}})
- [SQL HINTS]({{< ref "docs/dev/table/sql/queries/hints" >}})
- [DESCRIBE]({{< ref "docs/dev/table/sql/describe" >}})
@@ -63,7 +64,7 @@ under the License.
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, 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_LE [...]
+ 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, CHA [...]
{{< top >}}
diff --git a/docs/content/docs/dev/table/sql/analyze.md b/docs/content/docs/dev/table/sql/analyze.md
new file mode 100644
index 00000000000..20813bba2a0
--- /dev/null
+++ b/docs/content/docs/dev/table/sql/analyze.md
@@ -0,0 +1,382 @@
+---
+title: "ANALYZE Statements"
+weight: 8
+type: docs
+aliases:
+ - /dev/table/sql/analyze.html
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+# ANALYZE Statements
+
+`ANALYZE` statements are used to collect statistics for existing tables and store the result to catalog. Only
+`ANALYZE TABLE` statements are supported now, and need to be triggered manually instead of automatically.
+
+<span class="label label-danger">Attention</span> Currently, `ANALYZE TABLE` only supports in batch mode. Only existing
+table is supported, and an exception will be thrown if the table is a view or table not exists.
+
+
+## Run an ANALYZE TABLE statement
+
+{{< tabs "analyze table" >}}
+{{< tab "Java" >}}
+`ANALYZE TABLE` statements can be executed with the `executeSql()` method of the `TableEnvironment`.
+
+The following examples show how to run a `ANALYZE TABLE` statement in `TableEnvironment`.
+{{< /tab >}}
+{{< tab "Scala" >}}
+`ANALYZE TABLE` statements can be executed with the `executeSql()` method of the `TableEnvironment`.
+
+The following examples show how to run a `ANALYZE TABLE` statement in `TableEnvironment`.
+{{< /tab >}}
+{{< tab "Python" >}}
+
+`ANALYZE TABLE` statements can be executed with the `execute_sql()` method of the `TableEnvironment`.
+
+The following examples show how to run a `ANALYZE TABLE` statement in `TableEnvironment`.
+
+{{< /tab >}}
+{{< tab "SQL CLI" >}}
+
+`ANALYZE TABLE` statements can be executed in [SQL CLI]({{< ref "docs/dev/table/sqlClient" >}}).
+
+The following examples show how to run a `ANALYZE TABLE` statement in SQL CLI.
+
+{{< /tab >}}
+{{< /tabs >}}
+
+{{< tabs "a5de1760-e363-4b8d-9d6f-0bacb35b9dcf" >}}
+{{< tab "Java" >}}
+```java
+TableEnvironment tableEnv = TableEnvironment.create(...);
+
+// register a non-partition table named "Store"
+tableEnv.executeSql(
+ "CREATE TABLE Store (" +
+ " `id` BIGINT NOT NULl," +
+ " `location` VARCHAR(32)," +
+ " `owner` VARCHAR(32)" +
+ ") with (...)");
+
+// register a partition table named "Orders"
+tableEnv.executeSql(
+ "CREATE TABLE Orders (" +
+ " `id` BIGINT NOT NULl," +
+ " `product` VARCHAR(32)," +
+ " `amount` INT," +
+ " `sold_year` BIGINT", +
+ " `sold_month` BIGINT", +
+ " `sold_day` BIGINT" +
+ ") PARTITIONED BY (`sold_year`, `sold_month`, `sold_day`) "
+ ") with (...)");
+
+// Non-partition table, collect row count.
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS");
+
+// Non-partition table, collect row count and statistics for all columns.
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// Non-partition table, collect row count and statistics for column `location`.
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS FOR COLUMNS location");
+
+
+// Suppose table "Orders" has 4 partitions with specs:
+// Partition1 : (sold_year='2022', sold_month='1', sold_day='10')
+// Partition2 : (sold_year='2022', sold_month='1', sold_day='11')
+// Partition3 : (sold_year='2022', sold_month='2', sold_day='10')
+// Partition4 : (sold_year='2022', sold_month='2', sold_day='11')
+
+
+// Partition table, collect row count for Partition1.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS");
+
+// Partition table, collect row count for Partition1 and Partition2.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS");
+
+// Partition table, collect row count for all partitions.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS");
+
+// Partition table, collect row count and statistics for all columns on partition1.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// Partition table, collect row count and statistics for all columns on partition1 and partition2.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// Partition table, collect row count and statistics for all columns on all partitions.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// Partition table, collect row count and statistics for column `amount` on partition1.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR COLUMNS amount");
+
+// Partition table, collect row count and statistics for `amount` and `product` on partition1 and partition2.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+
+// Partition table, collect row count and statistics for column `amount` and `product` on all partitions.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+```
+{{< /tab >}}
+{{< tab "Scala" >}}
+```scala
+val tableEnv = TableEnvironment.create(...)
+
+// register a non-partition table named "Store"
+tableEnv.executeSql(
+ "CREATE TABLE Store (" +
+ " `id` BIGINT NOT NULl," +
+ " `location` VARCHAR(32)," +
+ " `owner` VARCHAR(32)" +
+ ") with (...)");
+
+// register a partition table named "Orders"
+tableEnv.executeSql(
+ "CREATE TABLE Orders (" +
+ " `id` BIGINT NOT NULl," +
+ " `product` VARCHAR(32)," +
+ " `amount` INT," +
+ " `sold_year` BIGINT", +
+ " `sold_month` BIGINT", +
+ " `sold_day` BIGINT" +
+ ") PARTITIONED BY (`sold_year`, `sold_month`, `sold_day`) "
+") with (...)");
+
+// Non-partition table, collect row count.
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS");
+
+// Non-partition table, collect row count and statistics for all columns.
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// Non-partition table, collect row count and statistics for column `location`.
+tableEnv.executeSql("ANALYZE TABLE Store COMPUTE STATISTICS FOR COLUMNS location");
+
+
+// Suppose table "Orders" has 4 partitions with specs:
+// Partition1 : (sold_year='2022', sold_month='1', sold_day='10')
+// Partition2 : (sold_year='2022', sold_month='1', sold_day='11')
+// Partition3 : (sold_year='2022', sold_month='2', sold_day='10')
+// Partition4 : (sold_year='2022', sold_month='2', sold_day='11')
+
+
+// Partition table, collect row count for Partition1.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS");
+
+// Partition table, collect row count for Partition1 and Partition2.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS");
+
+// Partition table, collect row count for all partitions.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS");
+
+// Partition table, collect row count and statistics for all columns on partition1.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// Partition table, collect row count and statistics for all columns on partition1 and partition2.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// Partition table, collect row count and statistics for all columns on all partitions.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+// Partition table, collect row count and statistics for column `amount` on partition1.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR COLUMNS amount");
+
+// Partition table, collect row count and statistics for `amount` and `product` on partition1 and partition2.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+
+// Partition table, collect row count and statistics for column `amount` and `product` on all partitions.
+tableEnv.executeSql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+```
+{{< /tab >}}
+{{< tab "Python" >}}
+```python
+table_env = TableEnvironment.create(...)
+
+# register a non-partition table named "Store"
+table_env.execute_sql(
+ "CREATE TABLE Store (" +
+ " `id` BIGINT NOT NULl," +
+ " `location` VARCHAR(32)," +
+ " `owner` VARCHAR(32)" +
+ ") with (...)");
+
+# register a partition table named "Orders"
+table_env.execute_sql(
+ "CREATE TABLE Orders (" +
+ " `id` BIGINT NOT NULl," +
+ " `product` VARCHAR(32)," +
+ " `amount` INT," +
+ " `sold_year` BIGINT", +
+ " `sold_month` BIGINT", +
+ " `sold_day` BIGINT" +
+ ") PARTITIONED BY (`sold_year`, `sold_month`, `sold_day`) "
+") with (...)");
+
+# Non-partition table, collect row count.
+table_env.execute_sql("ANALYZE TABLE Store COMPUTE STATISTICS");
+
+# Non-partition table, collect row count and statistics for all columns.
+table_env.execute_sql("ANALYZE TABLE Store COMPUTE STATISTICS FOR ALL COLUMNS");
+
+# Non-partition table, collect row count and statistics for column `location`.
+table_env.execute_sql("ANALYZE TABLE Store COMPUTE STATISTICS FOR COLUMNS location");
+
+
+# Suppose table "Orders" has 4 partitions with specs:
+# Partition1 : (sold_year='2022', sold_month='1', sold_day='10')
+# Partition2 : (sold_year='2022', sold_month='1', sold_day='11')
+# Partition3 : (sold_year='2022', sold_month='2', sold_day='10')
+# Partition4 : (sold_year='2022', sold_month='2', sold_day='11')
+
+
+# Partition table, collect row count for Partition1.
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS");
+
+# Partition table, collect row count for Partition1 and Partition2.
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS");
+
+# Partition table, collect row count for all partitions.
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS");
+
+# Partition table, collect row count and statistics for all columns on partition1.
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR ALL COLUMNS");
+
+# Partition table, collect row count and statistics for all columns on partition1 and partition2.
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+# Partition table, collect row count and statistics for all columns on all partitions.
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR ALL COLUMNS");
+
+# Partition table, collect row count and statistics for column `amount` on partition1.
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR COLUMNS amount");
+
+# Partition table, collect row count and statistics for `amount` and `product` on partition1 and partition2.
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+
+# Partition table, collect row count and statistics for column `amount` and `product` on all partitions.
+table_env.execute_sql("ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product");
+```
+{{< /tab >}}
+{{< tab "SQL CLI" >}}
+```sql
+Flink SQL> CREATE TABLE Store (
+> `id` BIGINT NOT NULl,
+> `location` VARCHAR(32),
+> `owner` VARCHAR(32)
+> ) with (
+> ...
+> );
+[INFO] Table has been created.
+
+Flink SQL> CREATE TABLE Orders (
+> `id` BIGINT NOT NULl,
+> `product` VARCHAR(32),
+> `amount` INT,
+> `sold_year` BIGINT,
+> `sold_month` BIGINT,
+> `sold_day` BIGINT
+> ) PARTITIONED BY (`sold_year`, `sold_month`, `sold_day`)
+> ) with (
+> ...
+> );
+[INFO] Table has been created.
+
+Flink SQL> ANALYZE TABLE Store COMPUTE STATISTICS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Store COMPUTE STATISTICS FOR ALL COLUMNS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Store COMPUTE STATISTICS FOR COLUMNS location;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR ALL COLUMNS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR ALL COLUMNS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR ALL COLUMNS;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR COLUMNS amount;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION (sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product;
+[INFO] Execute statement succeed.
+
+Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product;
+[INFO] Execute statement succeed.
+```
+{{< /tab >}}
+{{< /tabs >}}
+
+
+## Syntax
+
+```sql
+ANALYZE TABLE [catalog_name.][db_name.]table_name PARTITION(partcol1[=val1] [, partcol2[=val2], ...]) COMPUTE STATISTICS [FOR COLUMNS col1 [, col2, ...] | FOR ALL COLUMNS]
+```
+- PARTITION(partcol1[=val1] [, partcol2[=val2], ...]) is required for the partition table
+ - If no partition is specified, the statistics will be gathered for all partitions
+ - If a certain partition is specified, the statistics will be gathered only for specific partition
+ - If the table is non-partition table , while a partition is specified, an exception will be thrown
+ - If a certain partition is specified, but the partition does not exist, an exception will be thrown
+
+- FOR COLUMNS col1 [, col2, ...] or FOR ALL COLUMNS are optional
+ - If no column is specified, only the table level statistics will be gathered
+ - If a column does not exist, or column is not a physical column, an exception will be thrown.
+ - If a column or any column is specified, the column level statistics will be gathered
+ - the column level statistics include:
+ - ndv: the number of distinct values
+ - nullCount: the number of nulls
+ - avgLen: the average length of column values
+ - maxLen: the max length of column values
+ - minValue: the min value of column values
+ - maxValue: the max value of column values
+ - valueCount: the value count only for boolean type
+ - the supported types and its corresponding column level statistics are as following sheet lists("Y" means support, "N" means unsupported):
+
+| Types | `ndv` | `nullCount` | `avgLen` | `maxLen` | `maxValue` | `minValue` | `valueCount` |
+|:---------------------------------|:-----:|:-----------:|:--------:|:--------:|:----------:|:----------:|:------------:|
+| `BOOLEAN` | N | Y | N | N | N | N | Y |
+| `TINYINT` | Y | Y | N | N | Y | Y | N |
+| `SMALLINT` | Y | Y | N | N | Y | Y | N |
+| `INTEGER` | Y | Y | N | N | Y | Y | N |
+| `FLOAT` | Y | Y | N | N | Y | Y | N |
+| `DATE` | Y | Y | N | N | Y | Y | N |
+| `TIME_WITHOUT_TIME_ZONE` | Y | Y | N | N | Y | Y | N |
+| `BIGINT` | Y | Y | N | N | Y | Y | N |
+| `DOUBLE` | Y | Y | N | N | Y | Y | N |
+| `DECIMAL` | Y | Y | N | N | Y | Y | N |
+| `TIMESTAMP_WITH_LOCAL_TIME_ZONE` | Y | Y | N | N | Y | Y | N |
+| `TIMESTAMP_WITHOUT_TIME_ZONE` | Y | Y | N | N | Y | Y | N |
+| `CHAR` | Y | Y | Y | Y | N | N | N |
+| `VARCHAR` | Y | Y | Y | Y | N | N | N |
+| `other types` | N | Y | N | N | N | N | N |
+
+*NOTE:* For the fix length types (like `BOOLEAN`, `INTEGER`, `DOUBLE` etc.), we need not collect the `avgLen` and `maxLen` from the original records.
+
+{{< top >}}
\ No newline at end of file
diff --git a/docs/content/docs/dev/table/sql/overview.md b/docs/content/docs/dev/table/sql/overview.md
index 028dc4137b9..97fe751c16e 100644
--- a/docs/content/docs/dev/table/sql/overview.md
+++ b/docs/content/docs/dev/table/sql/overview.md
@@ -34,6 +34,7 @@ This page lists all the supported statements supported in Flink SQL for now:
- [CREATE TABLE, CATALOG, DATABASE, VIEW, FUNCTION]({{< ref "docs/dev/table/sql/create" >}})
- [DROP TABLE, DATABASE, VIEW, FUNCTION]({{< ref "docs/dev/table/sql/drop" >}})
- [ALTER TABLE, DATABASE, FUNCTION]({{< ref "docs/dev/table/sql/alter" >}})
+- [ANALYZE TABLE]({{< ref "docs/dev/table/sql/analyze" >}})
- [INSERT]({{< ref "docs/dev/table/sql/insert" >}})
- [DESCRIBE]({{< ref "docs/dev/table/sql/describe" >}})
- [EXPLAIN]({{< ref "docs/dev/table/sql/explain" >}})
@@ -62,7 +63,7 @@ For DDLs, we support full data types defined in page [Data Types]({{< ref "docs/
Although not every SQL feature is implemented yet, some string combinations are already reserved as keywords for future use. If you want to use one of the following strings as a field name, make sure to surround them with backticks (e.g. `` `value` ``, `` `count` ``).
- A, ABS, ABSOLUTE, ACTION, ADA, ADD, ADMIN, AFTER, ALL, ALLOCATE, ALLOW, ALTER, ALWAYS, AND, 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_LE [...]
+ 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, CHA [...]
{{< top >}}