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Spark 1.4 PermGenSize Error (ssimeonov)
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| /* Spark Shell Executed */ | |
| ./bin/spark-shell --master spark://servername:7077 --driver-class-path $CLASSPATH | |
| /* Output */ | |
| Welcome to | |
| ____ __ | |
| / __/__ ___ _____/ /__ | |
| _\ \/ _ \/ _ `/ __/ '_/ | |
| /___/ .__/\_,_/_/ /_/\_\ version 1.4.0 | |
| /_/ | |
| Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_79) | |
| Type in expressions to have them evaluated. | |
| Type :help for more information. | |
| Spark context available as sc. | |
| 15/07/06 18:39:40 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies) | |
| 15/07/06 18:39:40 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies) | |
| 15/07/06 18:39:42 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 0.13.1aa | |
| SQL context available as sqlContext. | |
| scala> val df = sqlContext.jsonFile("/data/ssimeonov/gz/apache-spark-failure-data-part-00000.gz") | |
| df: org.apache.spark.sql.DataFrame = [aac_brand: string, aag__id: bigint, aag_weight: bigint, aca_brand: string, aca_conversion_integration: boolean, aca_daily_budget: bigint, aca_hide_brand_from_publishers: boolean, aca_is_remnant: boolean, aca_short_name: string, accid: string, acr__id: bigint, acr_choices: array<struct<cta:string,headline:string,img:string,target:string>>, acr_cta: string, acr_description1: string, acr_description2: string, acr_destination: string, acr_displayUrl: string, acr_headline: string, acr_img: string, acr_isiUrl: string, acr_paramCTA: string, acr_paramName: string, acr_paramPlaceholder: string, acr_target: string, acr_type: string, acr_weight: bigint, agid: string, akw__id: bigint, akw_canonical_id: bigint, akw_criterion_type: string, akw_destination_url: st... | |
| scala> df.registerTempTable("training") | |
| scala> sqlContext.sql("select count(*) as cnt from training").collect().take(10).foreach(println) | |
| [88175] | |
| scala> import org.apache.spark.sql.hive.HiveContext | |
| import org.apache.spark.sql.hive.HiveContext | |
| scala> val ctx = new HiveContext(sc) | |
| ctx: org.apache.spark.sql.hive.HiveContext = org.apache.spark.sql.hive.HiveContext@21165c0e | |
| scala> import ctx.implicits._ | |
| import ctx.implicits._ | |
| scala> val df = ctx.jsonFile("/data/ssimeonov/gz/apache-spark-failure-data-part-00000.gz") | |
| java.lang.OutOfMemoryError: PermGen space | |
| Stopping spark context. | |
| Exception in thread "main" | |
| Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "main" |
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| /* Spark Shell Executed */ | |
| ./bin/spark-shell --master spark://servername:7077 --driver-class-path $CLASSPATH --conf "spark.driver.extraJavaOptions=-XX:MaxPermSize=256m" | |
| /* Output */ | |
| Welcome to | |
| ____ __ | |
| / __/__ ___ _____/ /__ | |
| _\ \/ _ \/ _ `/ __/ '_/ | |
| /___/ .__/\_,_/_/ /_/\_\ version 1.4.0 | |
| /_/ | |
| Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_79) | |
| Type in expressions to have them evaluated. | |
| Type :help for more information. | |
| Spark context available as sc. | |
| SQL context available as sqlContext. | |
| scala> val df = sqlContext.jsonFile("/data/ssimeonov/gz/apache-spark-failure-data-part-00000.gz") | |
| df: org.apache.spark.sql.DataFrame = [aac_brand: string, aag__id: bigint, aag_weight: bigint, aca_brand: string, aca_conversion_integration: boolean, aca_daily_budget: bigint, aca_hide_brand_from_publishers: boolean, aca_is_remnant: boolean, aca_short_name: string, accid: string, acr__id: bigint, acr_choices: array<struct<cta:string,headline:string,img:string,target:string>>, acr_cta: string, acr_description1: string, acr_description2: string, acr_destination: string, acr_displayUrl: string, acr_headline: string, acr_img: string, acr_isiUrl: string, acr_paramCTA: string, acr_paramName: string, acr_paramPlaceholder: string, acr_target: string, acr_type: string, acr_weight: bigint, agid: string, akw__id: bigint, akw_canonical_id: bigint, akw_criterion_type: string, akw_destination_url: st... | |
| scala> sqlContext.sql("select count(*) as cnt from training").collect().take(10).foreach(println) | |
| [88175] | |
| scala> import org.apache.spark.sql.hive.HiveContext | |
| import org.apache.spark.sql.hive.HiveContext | |
| scala> val ctx = new HiveContext(sc) | |
| ctx: org.apache.spark.sql.hive.HiveContext = org.apache.spark.sql.hive.HiveContext@1f5588c1 | |
| scala> import ctx.implicits._ | |
| import ctx.implicits._ | |
| scala> val df = ctx.jsonFile("/data/ssimeonov/gz/apache-spark-failure-data-part-00000.gz") | |
| df: org.apache.spark.sql.DataFrame = [aac_brand: string, aag__id: bigint, aag_weight: bigint, aca_brand: string, aca_conversion_integration: boolean, aca_daily_budget: bigint, aca_hide_brand_from_publishers: boolean, aca_is_remnant: boolean, aca_short_name: string, accid: string, acr__id: bigint, acr_choices: array<struct<cta:string,headline:string,img:string,target:string>>, acr_cta: string, acr_description1: string, acr_description2: string, acr_destination: string, acr_displayUrl: string, acr_headline: string, acr_img: string, acr_isiUrl: string, acr_paramCTA: string, acr_paramName: string, acr_paramPlaceholder: string, acr_target: string, acr_type: string, acr_weight: bigint, agid: string, akw__id: bigint, akw_canonical_id: bigint, akw_criterion_type: string, akw_destination_url: st... | |
| scala> df.registerTempTable("training") | |
| scala> val dfCount = ctx.sql("select count(*) as cnt from training") | |
| dfCount: org.apache.spark.sql.DataFrame = [cnt: bigint] | |
| scala> println(dfCount.first.getLong(0)) | |
| 88175 | |
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| /* Spark Shell Executed */ | |
| ./bin/spark-shell --master spark://servername:7077 --driver-class-path $CLASSPATH | |
| /* Output */ | |
| Welcome to | |
| ____ __ | |
| / __/__ ___ _____/ /__ | |
| _\ \/ _ \/ _ `/ __/ '_/ | |
| /___/ .__/\_,_/_/ /_/\_\ version 1.4.0 | |
| /_/ | |
| Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_45) | |
| Type in expressions to have them evaluated. | |
| Type :help for more information. | |
| Spark context available as sc. | |
| SQL context available as sqlContext. | |
| scala> val df = sqlContext.jsonFile("/data/ssimeonov/gz/apache-spark-failure-data-part-00000.gz") | |
| df: org.apache.spark.sql.DataFrame = [aac_brand: string, aag__id: bigint, aag_weight: bigint, aca_brand: string, aca_conversion_integration: boolean, aca_daily_budget: bigint, aca_hide_brand_from_publishers: boolean, aca_is_remnant: boolean, aca_short_name: string, accid: string, acr__id: bigint, acr_choices: array<struct<cta:string,headline:string,img:string,target:string>>, acr_cta: string, acr_description1: string, acr_description2: string, acr_destination: string, acr_displayUrl: string, acr_headline: string, acr_img: string, acr_isiUrl: string, acr_paramCTA: string, acr_paramName: string, acr_paramPlaceholder: string, acr_target: string, acr_type: string, acr_weight: bigint, agid: string, akw__id: bigint, akw_canonical_id: bigint, akw_criterion_type: string, akw_destination_url: st... | |
| scala> df.registerTempTable("training") | |
| scala> sqlContext.sql("select count(*) as cnt from training").collect().take(10).foreach(println) | |
| [88175] | |
| scala> import org.apache.spark.sql.hive.HiveContext | |
| import org.apache.spark.sql.hive.HiveContext | |
| scala> val ctx = new HiveContext(sc) | |
| ctx: org.apache.spark.sql.hive.HiveContext = org.apache.spark.sql.hive.HiveContext@38c408c7 | |
| scala> import ctx.implicits._ | |
| scala> val df = ctx.jsonFile("/data/ssimeonov/gz/apache-spark-failure-data-part-00000.gz") | |
| scala> df.registerTempTable("training") | |
| df: org.apache.spark.sql.DataFrame = [aac_brand: string, aag__id: bigint, aag_weight: bigint, aca_brand: string, aca_conversion_integration: boolean, aca_daily_budget: bigint, aca_hide_brand_from_publishers: boolean, aca_is_remnant: boolean, aca_short_name: string, accid: string, acr__id: bigint, acr_choices: array<struct<cta:string,headline:string,img:string,target:string>>, acr_cta: string, acr_description1: string, acr_description2: string, acr_destination: string, acr_displayUrl: string, acr_headline: string, acr_img: string, acr_isiUrl: string, acr_paramCTA: string, acr_paramName: string, acr_paramPlaceholder: string, acr_target: string, acr_type: string, acr_weight: bigint, agid: string, akw__id: bigint, akw_canonical_id: bigint, akw_criterion_type: string, akw_destination_url: st... | |
| scala> | |
| scala> val dfCount = ctx.sql("select count(*) as cnt from training") | |
| dfCount: org.apache.spark.sql.DataFrame = [cnt: bigint] | |
| scala> println(dfCount.first.getLong(0)) | |
| 88175 |
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