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https://issues.apache.org/jira/browse/SPARK-6695?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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uncleGen updated SPARK-6695:
----------------------------
Description:
In practical use, we usually need to create a big iterator, which means too big
in `memory usage` or too long in `array size`. On the one hand, it leads to too
much memory consumption. On the other hand, one `Array` may not hold all the
elements, as java array indices are of type 'int' (4 bytes or 32 bits). So,
IMHO, we may provide a `collector`, which has a buffer, 100MB or any others,
and could spill data into disk. The use case may like:
{code: borderStyle=solid}
rdd.mapPartition { it =>
...
val collector = new ExternalCollector()
collector.collect(a)
...
collector.iterator
}
{code}
I have done some related works, and I need your opinions, thanks!
was:
In practical use, we usually need to create a big iterator, which means too big
in `memory usage` or too long in `array size`. On the one hand, it leads to too
much memory consumption. On the other hand, one `Array` may not hold all the
elements, as java array indices are of type 'int' (4 bytes or 32 bits). So,
IMHO, we may provide a `collector`, which has a buffer, 100MB or any others,
and could spill data into disk. The use case may like:
```
rdd.mapPartition { it =>
...
val collector = new ExternalCollector()
collector.collect(a)
...
collector.iterator
}
```
I have done some related works, and I need your opinions, thanks!
> Add an external iterator: a hadoop-like output collector
> --------------------------------------------------------
>
> Key: SPARK-6695
> URL: https://issues.apache.org/jira/browse/SPARK-6695
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
> Reporter: uncleGen
>
> In practical use, we usually need to create a big iterator, which means too
> big in `memory usage` or too long in `array size`. On the one hand, it leads
> to too much memory consumption. On the other hand, one `Array` may not hold
> all the elements, as java array indices are of type 'int' (4 bytes or 32
> bits). So, IMHO, we may provide a `collector`, which has a buffer, 100MB or
> any others, and could spill data into disk. The use case may like:
> {code: borderStyle=solid}
> rdd.mapPartition { it =>
> ...
> val collector = new ExternalCollector()
> collector.collect(a)
> ...
> collector.iterator
> }
>
> {code}
> I have done some related works, and I need your opinions, thanks!
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