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# Z-Ordering Support

To improve query speed, Kyuubi supports Z-Ordering to optimize the layout of data
stored in all kind of storage with various data format.

Please check our benchmark report [here](z-order-benchmark.md).

## Introduction

The following picture shows the workflow of z-order.

![](../../../imgs/extension/zorder-workflow.png)

It contains three parties:
- Upstream

Due to the extra sort, the upstream job will run a little slower than before

- Table

  Z-order has the good data clustering, so the compression ratio can be improved

- Downstream

  Improve the downstream read performance benefit from data skipping. Since the parquet and orc file support collect data statistic automatically when you write data e.g. minimum and maximum values, the good data clustering let the pushed down filter more efficient

### Supported table format

| Table Format | Supported |
|--------------|-----------|
| parquet      | Y         |
| orc          | Y         |
| json         | N         |
| csv          | N         |
| text         | N         |

### Supported column data type

| Column Data Type | Supported |
|------------------|-----------|
| byte             | Y         |
| short            | Y         |
| int              | Y         |
| long             | Y         |
| float            | Y         |
| double           | Y         |
| boolean          | Y         |
| string           | Y         |
| decimal          | Y         |
| date             | Y         |
| timestamp        | Y         |
| array            | N         |
| map              | N         |
| struct           | N         |
| udt              | N         |

## How to use

This feature is inside Kyuubi extension, so you should apply the extension to Spark by following steps.

- add extension jar: `copy $KYUUBI_HOME/extension/kyuubi-extension-spark-3-1* $SPARK_HOME/jars/`
- add config into `spark-defaults.conf`: `spark.sql.extensions=org.apache.kyuubi.sql.KyuubiSparkSQLExtension`

Due to the extension, z-order only works with Spark-3.1 and higher version.

### Optimize history data

If you want to optimize the history data of a table, the `OPTIMIZE ...` syntax is good to go. Due to Spark SQL doesn't support read and overwrite same datasource table, the syntax can only support to optimize Hive table.

#### Syntax

```sql
OPTIMIZE table_name [WHERE predicate] ZORDER BY col_name1 [, ...]
```

Note that, the `predicate` only supports partition spec.

#### Examples

```sql
OPTIMIZE t1 ZORDER BY c3;

OPTIMIZE t1 ZORDER BY c1,c2;

OPTIMIZE t1 WHERE day = '2021-12-01' ZORDER BY c1,c2;
```

### Optimize incremental data

Kyuubi supports optimize a table automatically for incremental data. e.g., time partitioned table. The only things you need to do is adding Kyuubi properties into the target table properties:

```sql
ALTER TABLE t1 SET TBLPROPERTIES('kyuubi.zorder.enabled'='true','kyuubi.zorder.cols'='c1,c2');
```

- the key `kyuubi.zorder.enabled` decide if the table allows Kyuubi to optimize by z-order.
- the key `kyuubi.zorder.cols` decide which columns are used to optimize by z-order.

Kyuubi will detect the properties and optimize SQL using z-order during SQL compilation, so you can enjoy z-order with all writing table command like:

```sql
INSERT INTO TABLE t1 PARTITION() ...;

INSERT OVERWRITE TABLE t1 PARTITION() ...;

CREATE TABLE t1 AS SELECT ...;
```

