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Last Commit
Aug. 13, 2018
Created
Jul. 16, 2018

What's Starry?

Starry brings amazingly 1-2ms response time to spark when you deploy spark application with local mode.

Why Starry

Since Spark supports complex SQL and if you want a memory db, with Starry, Spark will be a possible solution. We also use starry to deploy ML predict service.

maven repo

<dependency>
  <groupId>com.github.passionke</groupId>
  <artifactId>starry</artifactId>
  <version>1.0</version>
</dependency>

Quick tutorial

Starry has enhanced SparkContext and Spark SQL Engine, so you should use StarrySparkContext instead of original SparkContext.

import org.apache.spark.sql.{SparkSession, SparkSessionExtensions}
import org.apache.spark.sql.execution.LocalBasedStrategies

val sparkConf = new SparkConf()
  sparkConf.setMaster("local[*]")
  sparkConf.setAppName("aloha")
  sparkConf
    .set("spark.default.parallelism", "1")
    .set("spark.sql.shuffle.partitions", "1")
    .set("spark.sql.codegen.wholeStage", "false")
    .set("spark.sql.extensions", "org.apache.spark.sql.StarrySparkSessionExtension")
  
  val sparkContext = new StarrySparkContext(sparkConf)
  // now you have got one enhanced sparkSession. Using it just as usual.
  val sparkSession: SparkSession =
    SparkSession.builder
      .sparkContext(sparkContext)
      .getOrCreate
  // Starry also provide some extraStrategies optimized for local mode. 
  // Using LocalBasedStrategies to register.
  LocalBasedStrategies.register(sparkSession)  

Tips

Use createDataSet(Seq[T]) instead of createDataSet(RDD[T]) to build your data Eg.

//do like this:
val strList = JSONArray.fromObject(param("data", "[]")).map(f => StringFeature(f.toString))
import sparkSession.implicits._
val res = sparkSession.createDataset(strList).selectExpr(sql).toJSON.collect().mkString(",")

//do not like this:
val strList = JSONArray.fromObject(param("data", "[]")).map(f => StringFeature(f.toString))
val rdd = sparkSession.sparkContext.parallelize(strList, perRequestCoreNum)
import sparkSession.implicits._
val res = sparkSession.createDataset(rdd).selectExpr(sql).toJSON.collect().mkString(",")

If you want to load data from HDFS ,you can try code like following:

// unRegistering dynamically starry strategies from SparkSession
// then load them in memory.
val df = sparkSession.read.load(tablePath)
LocalBasedStrategies.unRegister(sparkSession)
val rows = df.collectAsList()

// Register starry strategies  again 
LocalBasedStrategies.register(sparkSession)
// Create DataFrame using List not RDD 
sparkSession.createDataFrame(rows, df.schema).createOrReplaceTempView(tableName)

When you query the data again, you will find it amazing fast.