I’m using spark 2.3 with schema merge set to false. I don’t think spark is reading any file indeed but it tries to list them all one by one and it’s super slow on s3 !
Pointing to a single partition manually is not an option as it requires me to be aware of the partitioning in order to add it to the path and also, spark doesn’t include the partitioning column in that case. > Le 27 avr. 2018 à 16:07, Yong Zhang <java8...@hotmail.com> a écrit : > > What version of Spark you are using? > > You can search "spark.sql.parquet.mergeSchema" on > https://spark.apache.org/docs/latest/sql-programming-guide.html > > Starting from Spark 1.5, the default is already "false", which means Spark > shouldn't scan all the parquet files to generate the schema. > > Yong > Spark SQL and DataFrames - Spark 2.3.0 Documentation > spark.apache.org > Global Temporary View. Temporary views in Spark SQL are session-scoped and > will disappear if the session that creates it terminates. If you want to have > a temporary view that is shared among all sessions and keep alive until the > Spark application terminates, you can create a global temporary view. > > > > From: Walid LEZZAR <walez...@gmail.com> > Sent: Friday, April 27, 2018 7:42 AM > To: spark users > Subject: How to read the schema of a partitioned dataframe without listing > all the partitions ? > > Hi, > > I have a parquet on S3 partitioned by day. I have 2 years of data (-> about > 1000 partitions). With spark, when I just want to know the schema of this > parquet without even asking for a single row of data, spark tries to list all > the partitions and the nested partitions of the parquet. Which makes it very > slow just to build the dataframe object on Zeppelin. > > Is there a way to avoid that ? Is there way to tell spark : "hey, just read a > single partition and give me the schema of that partition and consider it as > the schema of the whole dataframe" ? (I don't care about schema merge, it's > off by the way) > > Thanks. > Walid.
