Ok, thanks. On Thu, Jun 9, 2016, 12:51 PM Jasleen Kaur <jasleenkaur1...@gmail.com> wrote:
> The github repo is https://github.com/datastax/spark-cassandra-connector > > The talk video and slides should be uploaded soon on spark summit website > > > On Wednesday, June 8, 2016, Chanh Le <giaosu...@gmail.com> wrote: > >> Thanks, I'll look into it. Any luck to get link related to. >> >> On Thu, Jun 9, 2016, 12:43 PM Jasleen Kaur <jasleenkaur1...@gmail.com> >> wrote: >> >>> Try using the datastax package. There was a great talk on spark summit >>> about it. It will take care of the boiler plate code and you can focus on >>> real business value >>> >>> On Wednesday, June 8, 2016, Chanh Le <giaosu...@gmail.com> wrote: >>> >>>> Hi everyone, >>>> I tested the partition by columns of data frame but it’s not good I >>>> mean wrong. >>>> I am using Spark 1.6.1 load data from Cassandra. >>>> I repartition by 2 field date, network_id - 200 partitions >>>> I reparation by 1 field date - 200 partitions. >>>> but my data is data of 90 days -> I mean if we reparation by date it >>>> will be 90 partitions. >>>> >>>> val daily = sql >>>> .read >>>> .format("org.apache.spark.sql.cassandra") >>>> .options(Map("table" -> dailyDetailTableName, "keyspace" -> reportSpace)) >>>> .load() >>>> .repartition(col("date")) >>>> >>>> >>>> >>>> I mean It doesn’t change the way I put the columns to repartition. >>>> >>>> Does anyone has the same problem? >>>> >>>> Thank in advance. >>>> >>>
