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# Z-order Benchmark

Z-order is a technique that allows you to map multidimensional data to a single dimension. We did a performance test.

For this test ,we used aliyun Databricks Delta test case
https://help.aliyun.com/document_detail/168137.html?spm=a2c4g.11186623.6.563.10d758ccclYtVb.

Prepare data for the three scenarios:

1. 10 billion data and 2 hundred files (parquet files): for big file(1G)
2. 10 billion data and 1 thousand files (parquet files): for medium file(200m)
3. 1 billion data and 10 thousand files (parquet files): for smaller file(200k)

Test env:
spark-3.1.2
hadoop-2.7.2
kyuubi-1.4.0

Test step:

Step1: create hive tables.

```scala
spark.sql(s"drop database if exists $dbName cascade")
spark.sql(s"create database if not exists $dbName")
spark.sql(s"use $dbName")
spark.sql(s"create table $connRandomParquet (src_ip string, src_port int, dst_ip string, dst_port int) stored as parquet")
spark.sql(s"create table $connOrderbyOnlyIp (src_ip string, src_port int, dst_ip string, dst_port int) stored as parquet")
spark.sql(s"create table $connOrderby (src_ip string, src_port int, dst_ip string, dst_port int) stored as parquet")
spark.sql(s"create table $connZorderOnlyIp (src_ip string, src_port int, dst_ip string, dst_port int) stored as parquet")
spark.sql(s"create table $connZorder (src_ip string, src_port int, dst_ip string, dst_port int) stored as parquet")
spark.sql(s"show tables").show(false)
```

Step2: prepare data for parquet table with three scenarios,
we use the following code.

```scala
def randomIPv4(r: Random) = Seq.fill(4)(r.nextInt(256)).mkString(".")
def randomPort(r: Random) = r.nextInt(65536)

def randomConnRecord(r: Random) = ConnRecord(
  src_ip = randomIPv4(r), src_port = randomPort(r),
  dst_ip = randomIPv4(r), dst_port = randomPort(r))
```

Step3: do optimize with z-order only ip and do optimize with order by only ip, sort column: src_ip, dst_ip and shuffle partition just as file numbers.

```
INSERT overwrite table conn_order_only_ip select src_ip, src_port, dst_ip, dst_port from conn_random_parquet order by src_ip, dst_ip;
OPTIMIZE conn_zorder_only_ip ZORDER BY src_ip, dst_ip;
```

Step4: do optimize with z-order and do optimize with order by, sort column: src_ip, src_port, dst_ip, dst_port and shuffle partition just as file numbers.

```
INSERT overwrite table conn_order select src_ip, src_port, dst_ip, dst_port from conn_random_parquet order by src_ip, src_port, dst_ip, dst_port;
OPTIMIZE conn_zorder ZORDER BY src_ip, src_port, dst_ip, dst_port;
```

The complete code is as follows:

```shell
./spark-shell
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession

case class ConnRecord(src_ip: String, src_port: Int, dst_ip: String, dst_port: Int)

val  conf  = new SparkConf().setAppName("zorder_test")
val spark = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate()
import spark.implicits._

val sc = spark.sparkContext
sc.setLogLevel("WARN")
//ten billion rows and two hundred files
val numRecords = 10*1000*1000*1000L
val numFiles = 200

val dbName = s"zorder_test_$numFiles"
val baseLocation = s"hdfs://localhost:9000/zorder_test/$dbName/"
val connRandomParquet = "conn_random_parquet"
val connZorderOnlyIp = "conn_zorder_only_ip"
val connZorder = "conn_zorder"
spark.conf.set("spark.sql.shuffle.partitions", numFiles)
spark.conf.get("spark.sql.shuffle.partitions")
spark.conf.set("spark.sql.hive.convertMetastoreParquet",false)
spark.sql(s"drop database if exists $dbName cascade")
spark.sql(s"create database if not exists $dbName")
spark.sql(s"use $dbName")
spark.sql(s"create table $connRandomParquet (src_ip string, src_port int, dst_ip string, dst_port int) stored as parquet")
spark.sql(s"create table $connOrderbyOnlyIp (src_ip string, src_port int, dst_ip string, dst_port int) stored as parquet")
spark.sql(s"create table $connOrderby (src_ip string, src_port int, dst_ip string, dst_port int) stored as parquet")
spark.sql(s"create table $connZorderOnlyIp (src_ip string, src_port int, dst_ip string, dst_port int) stored as parquet")
spark.sql(s"create table $connZorder (src_ip string, src_port int, dst_ip string, dst_port int) stored as parquet")
spark.sql(s"show tables").show(false)

import scala.util.Random
// Function for preparing Zorder_Test data
def randomIPv4(r: Random) = Seq.fill(4)(r.nextInt(256)).mkString(".")
def randomPort(r: Random) = r.nextInt(65536)

def randomConnRecord(r: Random) = ConnRecord(
src_ip = randomIPv4(r), src_port = randomPort(r),
dst_ip = randomIPv4(r), dst_port = randomPort(r))

val df = spark.range(0, numFiles, 1, numFiles).mapPartitions { it =>
val partitionID = it.toStream.head
val r = new Random(seed = partitionID)
Iterator.fill((numRecords / numFiles).toInt)(randomConnRecord(r))
}

df.write
.mode("overwrite")
.format("parquet")
.insertInto(connRandomParquet)

spark.read.table(connRandomParquet)
.write
.mode("overwrite")
.format("parquet")
.insertInto(connZorderOnlyIp)

spark.read.table(connRandomParquet)
.write
.mode("overwrite")
.format("parquet")
.insertInto(connZorder)
spark.stop()

```

Z-order Optimize statement:

```sql
set spark.sql.hive.convertMetastoreParquet=false;

OPTIMIZE conn_zorder_only_ip ZORDER BY src_ip, dst_ip;

OPTIMIZE zorder_test.conn_zorder ZORDER BY src_ip, src_port, dst_ip, dst_port;
```

ORDER BY statement:

```
INSERT overwrite table conn_order_only_ip select src_ip, src_port, dst_ip, dst_port from conn_random_parquet order by src_ip, dst_ip;

INSERT overwrite table conn_order select src_ip, src_port, dst_ip, dst_port from conn_random_parquet order by src_ip, src_port, dst_ip, dst_port;
```

Query statement:

```sql
set spark.sql.hive.convertMetastoreParquet=true;

select count(*) from conn_random_parquet where src_ip like '157%' and dst_ip like '216.%';

select count(*) from conn_zorder_only_ip where src_ip like '157%' and dst_ip like '216.%';

select count(*) from conn_zorder where src_ip like '157%' and dst_ip like '216.%';
```

## Benchmark result

We have done two performance tests: one is to compare the efficiency of  Z-order Optimize and Order by Sort,
and the other is to query based on the optimized Z-order by data and Random data.

### Efficiency of Z-order Optimize and Order-by Sort

**10 billion data and 1000 files and Query resource: 200 core 600G memory**

Z-order by or order by only ip:

|        Table        |   row count    | optimize  time |
|---------------------|----------------|----------------|
| conn_order_only_ip  | 10,000,000,000 | 1591.99 s      |
| conn_zorder_only_ip | 10,000,000,000 | 8371.405 s     |

Z-order by or order by all columns:

|    Table    |   row count    | optimize  time |
|-------------|----------------|----------------|
| conn_order  | 10,000,000,000 | 1515.298 s     |
| conn_zorder | 10,000,000,000 | 11057.194 s    |

### Z-order by benchmark result

By querying the tables before and after optimization, we find that:

**10 billion data and 200 files and Query resource: 200 core 600G memory**

|        Table        | Average File Size | Scan row count | Average query time | row count Skipping ratio |
|---------------------|-------------------|----------------|--------------------|--------------------------|
| conn_random_parquet | 1.2 G             | 10,000,000,000 | 27.554 s           | 0.0%                     |
| conn_zorder_only_ip | 890 M             | 43,170,600     | 2.459 s            | 99.568%                  |
| conn_zorder         | 890 M             | 54,841,302     | 3.185 s            | 99.451%                  |

**10 billion data and 1000 files and Query resource: 200 core 600G memory**

|        Table        | Average File Size | Scan row count | Average query time | row count Skipping ratio |
|---------------------|-------------------|----------------|--------------------|--------------------------|
| conn_random_parquet | 234.8 M           | 10,000,000,000 | 27.031 s           | 0.0%                     |
| conn_zorder_only_ip | 173.9 M           | 53,499,068     | 3.120 s            | 99.465%                  |
| conn_zorder         | 174.0 M           | 35,910,500     | 3.103 s            | 99.640%                  |

**1 billion data and 10000 files and Query resource: 10 core 40G memory**

|        Table        | Average File Size | Scan row count | Average query time | row count Skipping ratio |
|---------------------|-------------------|----------------|--------------------|--------------------------|
| conn_random_parquet | 2.7 M             | 1,000,000,000  | 76.772 s           | 0.0%                     |
| conn_zorder_only_ip | 2.1 M             | 406,572        | 3.963 s            | 99.959%                  |
| conn_zorder         | 2.2 M             | 387,942        | 3.621s             | 99.961%                  |