MinIO Spark select enables retrieving only required data from an object using Select API.

This library requires

• Spark 2.3+
• Scala 2.11+

• S3 Select is supported with CSV, JSON and Parquet files using minioSelectCSV, minioSelectJSON and minioSelectParquet values to specify the data format.
• S3 Select supports select on multiple objects.
• S3 Select supports querying SSE-C encrypted objects.

• Spark CSV and JSON options such as nanValue, positiveInf, negativeInf, and options related to corrupt records (for example, failfast and dropmalformed mode) are not supported.
• Using commas (,) within decimals is not supported. For example, 10,000 is not supported and 10000 is.
• The following filters are not pushed down to MinIO:
  • Aggregate functions such as COUNT() and SUM().
  • Filters that CAST() an attribute. For example, CAST(stringColumn as INT) = 1.
  • Filters with an attribute that is an object or is complex. For example, intArray[1] = 1, objectColumn.objectNumber = 1.
  • Filters for which the value is not a literal value. For example, intColumn1 = intColumn2
  • Only Select Supported Data Types are supported with the documented limitations.

Include this package in your Spark Applications using:

> $SPARK_HOME/bin/spark-shell --packages io.minio:spark-select_2.11:2.1 

If you use the sbt-spark-package plugin, in your sbt build file, add:

spDependencies += "minio/spark-select:2.1" 

Otherwise,

libraryDependencies += "io.minio" % "spark-select_2.11" % "2.1" 

In your pom.xml, add:

<dependencies> <!-- list of dependencies --> <dependency> <groupId>io.minio</groupId> <artifactId>spark-select_2.11</artifactId> <version>2.1</version> </dependency> </dependencies>

Setup all required environment variables

NOTE: It is assumed that you have already installed hadoop-2.8.5, spark 2.3.1 at some locations locally.

export HADOOP_HOME=${HOME}/spark/hadoop-2.8.5/ export PATH=${PATH}:${HADOOP_HOME}/bin export SPARK_DIST_CLASSPATH=$(hadoop classpath) export SPARK_HOME=${HOME}/spark/spark-2.3.1-bin-without-hadoop/ export PATH=${PATH}:${SPARK_HOME}/bin export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64/ git clone https://github.com/minio/spark-select sbt assembly spark-shell --jars target/scala-2.11/spark-select-assembly-2.1.jar 

Once the spark-shell has been successfully invoked.

scala> :load examples/csv.scala Loading examples/csv.scala... import org.apache.spark.sql._ import org.apache.spark.sql.types._ schema: org.apache.spark.sql.types.StructType = StructType(StructField(name,StringType,true), StructField(age,IntegerType,false)) df: org.apache.spark.sql.DataFrame = [name: string, age: int] +-------+---+ | name|age| +-------+---+ |Michael| 31| | Andy| 30| | Justin| 19| +-------+---+ scala> 

spark .read .format("minioSelectCSV") // "minioSelectJSON" for JSON or "minioSelectParquet" for Parquet .schema(...) // mandatory .options(...) // optional .load("s3://path/to/my/datafiles")

read.df("s3://path/to/my/datafiles", "minioSelectCSV", schema) 

spark .read .format("minioSelectCSV") // "minioSelectJSON" for JSON or "minioSelectParquet" for Parquet .schema(...) // mandatory .options(...) // optional. Examples: // .options(Map("quote" -> "\'", "header" -> "true")) or // .option("quote", "\'").option("header", "true") .load("s3://path/to/my/datafiles") 

CREATE TEMPORARY VIEW MyView (number INT, name STRING) USING minioSelectCSV OPTIONS (path "s3://path/to/my/datafiles") 

The following options are available when using minioSelectCSV and minioSelectJSON. If not specified, default values are used.

There are no options needed with Parquet files.

Schema with two columns for CSV.

import org.apache.spark.sql._ import org.apache.spark.sql.types._ object app { def main(args: Array[String]) { val schema = StructType( List( StructField("name", StringType, true), StructField("age", IntegerType, false) ) ) val df = spark .read .format("minioSelectCSV") .schema(schema) .load("s3://sjm-airlines/people.csv") println(df.show()) println(df.select("*").filter("age > 19").show()) } }

With custom schema for JSON.

import org.apache.spark.sql._ import org.apache.spark.sql.types._ object app { def main(args: Array[String]) { val schema = StructType( List( StructField("name", StringType, true), StructField("age", IntegerType, false) ) ) val df = spark .read .format("minioSelectJSON") .schema(schema) .load("s3://sjm-airlines/people.json") println(df.show()) println(df.select("*").filter("age > 19").show()) } }

With custom schema for Parquet.

import org.apache.spark.sql._ import org.apache.spark.sql.types._ object app { def main(args: Array[String]) { val schema = StructType( List( StructField("name", StringType, true), StructField("age", IntegerType, false) ) ) val df = spark .read .format("minioSelectParquet") .schema(schema) .load("s3://sjm-airlines/people.parquet") println(df.show()) println(df.select("*").filter("age > 19").show()) } }

Schema with two columns for CSV.

from pyspark.sql import * from pyspark.sql.types import * if __name__ == "__main__": # create SparkSession spark = SparkSession.builder \ .master("local") \ .appName("spark-select in python") \ .getOrCreate() # filtered schema st = StructType([ StructField("name", StringType(), True), StructField("age", IntegerType(), False), ]) df = spark \ .read \ .format('minioSelectCSV') \ .schema(st) \ .load("s3://testbucket/people.csv") # show all rows. df.show() # show only filtered rows. df.select("*").filter("age > 19").show()

> $SPARK_HOME/bin/spark-submit --packages io.minio:spark-select_2.11:2.1 <python-file>