[ https://issues.apache.org/jira/browse/SPARK-11879?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Cheng Lian resolved SPARK-11879. -------------------------------- Resolution: Duplicate > Checkpoint support for DataFrame/Dataset > ---------------------------------------- > > Key: SPARK-11879 > URL: https://issues.apache.org/jira/browse/SPARK-11879 > Project: Spark > Issue Type: Improvement > Components: SQL > Reporter: Cristian Opris > > Explicit support for checkpointing DataFrames is need to be able to truncate > lineages, prune the query plan (particularly the logical plan) and > transparent failure recovery. > While for recovery saving to a Parquet file may be sufficient, actually using > that as a checkpoint (and truncating the lineage), requires reading the files > back. > This is required to be able to use DataFrames in iterative scenarios like > Streaming and ML, as well as for avoiding expensive re-computations in case > of executor failure when executing a complex chain of queries on very large > datasets. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org