You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Xiao Li (JIRA)" <ji...@apache.org> on 2018/01/31 18:58:00 UTC
[jira] [Updated] (SPARK-21657) Spark has exponential time
complexity to explode(array of structs)
[ https://issues.apache.org/jira/browse/SPARK-21657?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiao Li updated SPARK-21657:
----------------------------
Component/s: (was: Spark Core)
> Spark has exponential time complexity to explode(array of structs)
> ------------------------------------------------------------------
>
> Key: SPARK-21657
> URL: https://issues.apache.org/jira/browse/SPARK-21657
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0, 2.1.0, 2.1.1, 2.2.0, 2.3.0
> Reporter: Ruslan Dautkhanov
> Assignee: Ohad Raviv
> Priority: Major
> Labels: cache, caching, collections, nested_types, performance, pyspark, sparksql, sql
> Fix For: 2.3.0
>
> Attachments: ExponentialTimeGrowth.PNG, nested-data-generator-and-test.py
>
>
> It can take up to half a day to explode a modest-sized nested collection (0.5m).
> On a recent Xeon processors.
> See attached pyspark script that reproduces this problem.
> {code}
> cached_df = sqlc.sql('select individ, hholdid, explode(amft) from ' + table_name).cache()
> print sqlc.count()
> {code}
> This script generate a number of tables, with the same total number of records across all nested collection (see `scaling` variable in loops). `scaling` variable scales up how many nested elements in each record, but by the same factor scales down number of records in the table. So total number of records stays the same.
> Time grows exponentially (notice log-10 vertical axis scale):
> !ExponentialTimeGrowth.PNG!
> At scaling of 50,000 (see attached pyspark script), it took 7 hours to explode the nested collections (\!) of 8k records.
> After 1000 elements in nested collection, time grows exponentially.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org