starry

KO | EN

gitlite — search

#python #hacktoberfest #ai #llm #golang #android #deep-learning #nodejs #windows #kubernetes #awesome-list #openai #mcp #open-source

(★ 35)

fast spark local mode

starry Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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.

```scala
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.

```scala
//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：

```scala
// 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.

starry

starry Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

starry

starry Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README