---
title: "ANALYZE Statements"
weight: 8
type: docs
aliases:
  - /docs/sql/reference/analyze/
  - /dev/table/sql/analyze.html
---
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# 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/sql/interfaces/sql-client" >}}).

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 succeeded.
    
Flink SQL> ANALYZE TABLE Store COMPUTE STATISTICS FOR ALL COLUMNS;
[INFO] Execute statement succeeded.

Flink SQL> ANALYZE TABLE Store COMPUTE STATISTICS FOR COLUMNS location;
[INFO] Execute statement succeeded.
    
Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS;
[INFO] Execute statement succeeded.

Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS;
[INFO] Execute statement succeeded.

Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS;
[INFO] Execute statement succeeded.

Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR ALL COLUMNS;
[INFO] Execute statement succeeded.    

Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR ALL COLUMNS;
[INFO] Execute statement succeeded.
    
Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR ALL COLUMNS;
[INFO] Execute statement succeeded.
    
Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year='2022', sold_month='1', sold_day='10') COMPUTE STATISTICS FOR COLUMNS amount;
[INFO] Execute statement succeeded.
    
Flink SQL> ANALYZE TABLE Orders PARTITION (sold_year='2022', sold_month='1', sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product;
[INFO] Execute statement succeeded.
    
Flink SQL> ANALYZE TABLE Orders PARTITION(sold_year, sold_month, sold_day) COMPUTE STATISTICS FOR COLUMNS amount, product;
[INFO] Execute statement succeeded.
```
{{< /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 >}}