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https://issues.apache.org/jira/browse/SPARK-19145?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-19145:
------------------------------------
Assignee: Apache Spark
> Timestamp to String casting is slowing the query significantly
> --------------------------------------------------------------
>
> Key: SPARK-19145
> URL: https://issues.apache.org/jira/browse/SPARK-19145
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: gagan taneja
> Assignee: Apache Spark
>
> i have a time series table with timestamp column
> Following query
> SELECT COUNT(*) AS `count`
> FROM `default`.`table`
> WHERE `time` >= '2017-01-02 19:53:51'
> AND `time` <= '2017-01-09 19:53:51' LIMIT 50000
> is significantly SLOWER than
> SELECT COUNT(*) AS `count`
> FROM `default`.`table`
> WHERE `time` >= to_utc_timestamp('2017-01-02 19:53:51','YYYY-MM-DD
> HH24:MI:SS−0800')
> AND `time` <= to_utc_timestamp('2017-01-09 19:53:51','YYYY-MM-DD
> HH24:MI:SS−0800') LIMIT 50000
> After investigation i found that in the first query time colum is cast to
> String before applying the filter
> However in the second query no such casting is performed and its a filter
> with long value
> Below are the generate Physical plan for slower execution followed by
> physical plan for faster execution
> SELECT COUNT(*) AS `count`
> FROM `default`.`table`
> WHERE `time` >= '2017-01-02 19:53:51'
> AND `time` <= '2017-01-09 19:53:51' LIMIT 50000
> == Physical Plan ==
> CollectLimit 50000
> +- *HashAggregate(keys=[], functions=[count(1)], output=[count#3290L])
> +- Exchange SinglePartition
> +- *HashAggregate(keys=[], functions=[partial_count(1)],
> output=[count#3339L])
> +- *Project
> +- *Filter ((isnotnull(time#3314) && (cast(time#3314 as string)
> >= 2017-01-02 19:53:51)) && (cast(time#3314 as string) <= 2017-01-09
> 19:53:51))
> +- *FileScan parquet default.cstat[time#3314] Batched: true,
> Format: Parquet, Location:
> InMemoryFileIndex[hdfs://10.65.55.220/user/spark/spark-warehouse/cstat],
> PartitionFilters: [], PushedFilters: [IsNotNull(time)], ReadSchema:
> struct<time:timestamp>
> SELECT COUNT(*) AS `count`
> FROM `default`.`table`
> WHERE `time` >= to_utc_timestamp('2017-01-02 19:53:51','YYYY-MM-DD
> HH24:MI:SS−0800')
> AND `time` <= to_utc_timestamp('2017-01-09 19:53:51','YYYY-MM-DD
> HH24:MI:SS−0800') LIMIT 50000
> == Physical Plan ==
> CollectLimit 50000
> +- *HashAggregate(keys=[], functions=[count(1)], output=[count#3238L])
> +- Exchange SinglePartition
> +- *HashAggregate(keys=[], functions=[partial_count(1)],
> output=[count#3287L])
> +- *Project
> +- *Filter ((isnotnull(time#3262) && (time#3262 >=
> 1483404831000000)) && (time#3262 <= 1484009631000000))
> +- *FileScan parquet default.cstat[time#3262] Batched: true,
> Format: Parquet, Location:
> InMemoryFileIndex[hdfs://10.65.55.220/user/spark/spark-warehouse/cstat],
> PartitionFilters: [], PushedFilters: [IsNotNull(time),
> GreaterThanOrEqual(time,2017-01-02 19:53:51.0),
> LessThanOrEqual(time,2017-01-09..., ReadSchema: struct<time:timestamp>
> In Impala both query run efficiently without and performance difference
> Spark should be able to parse the Date string and convert to Long/Timestamp
> during generation of Optimized Logical Plan so that both the query would have
> similar performance
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