kyuubi/docs/sql/z-order-benchmark.md

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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. one billion data and 10 hundred files（parquet files）: for smaller file(200k)

test env：
spark-3.1.2
hadoop-2.7.2
kyubbi-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 $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， sort column： src_ip, dst_ip and shuffle partition just as file numbers .

```
OPTIMIZE conn_zorder_only_ip ZORDER BY src_ip, dst_ip;
```

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

```
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 $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()

```

Optimize Sql:

```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;
```



Query Sql : 

```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
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 2000 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           | 43,170,600     | 2.668 s            | 99.568%                  |
| conn_zorder         | 174.0 M           | 54,841,302     | 3.207 s            | 99.451%                  |



**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%                  |