Scala Client

support environment

Currently, Spark integration is only supported in On-Prem Cluster Mode and AWS environment

Installation

You can download the Jar from the link below

• Scala 2.11
• Scala 2.12

Quick Start

Setup Project

sbt

Folders

/canner-client-test
├──lib
│ └── canner-spark-client-assembly_<version>.jar
├── src/main/scala/test
│ └── QuickStart.scala
├── project
│ └── build.properties
└── build.sbt

Please make sure the downloaded Jar is placed under /lib

/build.sbt

name := "canner-client-test"

version := "0.1"

scalaVersion := "2.12.12"

mainClass in (Compile, run) := Some("test. QuickStart")

libraryDependencies += "com.canner" % "canner-spark-client" % "1.0.0" from "file:///<path-to-client-test>/canner-client-test/lib"

// https://mvnrepository.com/artifact/org.apache.spark/spark-core
libraryDependencies += "org.apache.spark" %% "spark-core" % "2.4.5"

// https://mvnrepository.com/artifact/org.apache.spark/spark-sql
libraryDependencies += "org.apache.spark" %% "spark-sql" % "2.4.5"

maven

Folders

/canner-client-test
├──lib
│ └── canner-spark-client-assembly_<version>.jar
├── src/main/scala/test
│ └── QuickStart.scala
└── pom.xml

Please make sure the downloaded Jar is placed under /lib

Create a local repo

You can use Maven to build a Local Repository to store Canner Enterprise Jar.

1. Create folder

mkdir local-repo

2. Use maven cmd to deploy jar

mvn deploy:deploy-file -DgroupId=com.canner -DartifactId=canner-spark-client -Dversion=1.0.0 -Durl=file:./local-repo/ -DrepositoryId=local-repo -DupdateReleaseInfo=true -Dfile= ${project-root}/lib/canner-spark-client-assembly_2.12-1.0.0.jar -Dpackaging=jar

pom.xml

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
        xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.canner</groupId>
    <artifactId>test</artifactId>
    <version>1</version>

    <properties>
        <scala.mainVersion>2.12</scala.mainVersion>
        <scala.version>${scala.mainVersion}.12</scala.version>
        <scala.compat.version>2.12</scala.compat.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${scala.mainVersion}</artifactId>
            <version>2.4.5</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.mainVersion}</artifactId>
            <version>2.4.5</version>
        </dependency>
        <dependency>
            <groupId>com.canner</groupId>
            <artifactId>canner-spark-client</artifactId>
            <version>1.0.0</version>
        </dependency>
    </dependencies>

    <repositories>
        <repository>
            <id>local-repo</id>
            <url>file:///${project.basedir}/local-repo</url>
        </repository>
    </repositories>
</project>

Count Sample Program

If you use Canner Enterprise distributed mode on AWS, you need to create an AWS User with permission to access Canner Enterprise's S3 Bucket, and then put the user's <AWS_ACCESS_KEY_ID>, <AWS_SECRET_ACCESS_KEY> into the code used in

If you use Standalone mode. Please ask the administrator to get <AWS_ACCESS_KEY_ID>, <AWS_SECRET_ACCESS_KEY>

/src/main/scala/test/QuickStart.scala

package test

import org.apache.spark.sql.SparkSession
import com.canner.Canner

object QuickStart {
   val TOKEN = "<CANNER_PERSONAL_ACCESS_TOKEN>"

   def main(args: Array[String]): Unit = {

     val spark = SparkSession. builder()
       // if you're using standalone, please specify the endpoint to object storage url.
       // if you're using ssl and encounter certification issue, please add `-Dcom.amazonaws.sdk.disableCertChecking=true` to avoid this problem
       //.config("spark.hadoop.fs.s3a.endpoint", "https://<my-endpoint-name>:9000")
       .config("spark.hadoop.fs.s3a.access.key", "<AWS_ACCESS_KEY_ID>")
       .config("spark.hadoop.fs.s3a.secret.key", "<AWS_SECRET_ACCESS_KEY>")
       .config("spark.hadoop.fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem").master("local[*]").getOrCreate()

     // entrypoint is the hostname of Canner
     val entrypoint = "https://test.canner.your.host.com"
     val workspace = "<workspace ID>"

     val canner = Canner(entrypoint, TOKEN, workspace, spark)

     // Executing a sql query on Canner and getting a `DataFrame` back
     val df = canner.genQuery("select * from lineitem")

     println("--------------------")
     print("df count: " + df.count())
     println("--------------------")

   }
}