Re: [Spark Core] excessive read/load times on parquet files in 2.2 vs 2.0
any other feedback on this? On 9/8/17 11:00 AM, Neil Jonkers wrote: Can you provide a code sample please? On Fri, Sep 8, 2017 at 5:44 PM, Matthew Anthony <statm...@gmail.com <mailto:statm...@gmail.com>> wrote: Hi all - since upgrading to 2.2.0, we've noticed a significant increase in read.parquet(...) ops. The parquet files are being read from S3. Upon entry at the interactive terminal (pyspark in this case), the terminal will sit "idle" for several minutes (as many as 10) before returning: "17/09/08 15:34:37 WARN SharedInMemoryCache: Evicting cached table partition metadata from memory due to size constraints (spark.sql.hive.filesourcePartitionFileCacheSize = 20 bytes). This may impact query planning performance." In the spark UI, there are no jobs being run during this idle period. Subsequently, a short 1-task job lasting approximately 10 seconds runs, and then another idle time of roughly 2-3 minutes follows thereafter before returning to the terminal/CLI. Can someone explain what is happening here in the background? Is there a misconfiguration we should be looking for? We are using Hive metastore on the EMR cluster. - To unsubscribe e-mail: user-unsubscr...@spark.apache.org <mailto:user-unsubscr...@spark.apache.org>
Re: [Spark Core] excessive read/load times on parquet files in 2.2 vs 2.0
The code is as simple as calling `data = spark.read.parquet(address.)`. I can't give you the actual address I'm reading from for security reasons. Is there something else I can provide? We're using standard EMR images with Hive and Spark installed. On 9/8/17 11:00 AM, Neil Jonkers wrote: Can you provide a code sample please? On Fri, Sep 8, 2017 at 5:44 PM, Matthew Anthony <statm...@gmail.com <mailto:statm...@gmail.com>> wrote: Hi all - since upgrading to 2.2.0, we've noticed a significant increase in read.parquet(...) ops. The parquet files are being read from S3. Upon entry at the interactive terminal (pyspark in this case), the terminal will sit "idle" for several minutes (as many as 10) before returning: "17/09/08 15:34:37 WARN SharedInMemoryCache: Evicting cached table partition metadata from memory due to size constraints (spark.sql.hive.filesourcePartitionFileCacheSize = 20 bytes). This may impact query planning performance." In the spark UI, there are no jobs being run during this idle period. Subsequently, a short 1-task job lasting approximately 10 seconds runs, and then another idle time of roughly 2-3 minutes follows thereafter before returning to the terminal/CLI. Can someone explain what is happening here in the background? Is there a misconfiguration we should be looking for? We are using Hive metastore on the EMR cluster. - To unsubscribe e-mail: user-unsubscr...@spark.apache.org <mailto:user-unsubscr...@spark.apache.org>
[Spark Core] excessive read/load times on parquet files in 2.2 vs 2.0
Hi all - since upgrading to 2.2.0, we've noticed a significant increase in read.parquet(...) ops. The parquet files are being read from S3. Upon entry at the interactive terminal (pyspark in this case), the terminal will sit "idle" for several minutes (as many as 10) before returning: "17/09/08 15:34:37 WARN SharedInMemoryCache: Evicting cached table partition metadata from memory due to size constraints (spark.sql.hive.filesourcePartitionFileCacheSize = 20 bytes). This may impact query planning performance." In the spark UI, there are no jobs being run during this idle period. Subsequently, a short 1-task job lasting approximately 10 seconds runs, and then another idle time of roughly 2-3 minutes follows thereafter before returning to the terminal/CLI. Can someone explain what is happening here in the background? Is there a misconfiguration we should be looking for? We are using Hive metastore on the EMR cluster. - To unsubscribe e-mail: user-unsubscr...@spark.apache.org
