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From "Gabriel Reid (JIRA)" <>
Subject [jira] [Commented] (CRUNCH-294) Cost-based job planning
Date Sat, 16 Nov 2013 08:39:23 GMT


Gabriel Reid commented on CRUNCH-294:

I definitely like the idea, but I'm thinking the cost calculation might need to be a bit more
complex than what it currently is if we want to optimize for minimal IO. From what I see,
the cost is calculated independently for each NodePath within an Edge, but I'm thinking that
the costs for all splits within an edge should be considered as a whole. This can be an issue
if there are multiple NodePaths for a single Edge.

To illustrate, I make a mini test case that replicates the original issue on the mailing list.

This is the job plan produced by the current planner, without the patch. By writing S1 to
disk, the total write size is 8.

This is the job plan produced after the patch, with the same (default) scale factors. By writing
both S2 and S3 to disk, the total write size is 12, so this actually has a larger write footprint
than the version before the patch.

With the patch, if S2 and S3 have a large scale factor then S1 is written to disk, which is
indeed what we want if we're optimizing for minimal disk writes:

But if S3 has a large scale factor and S2 has a small scale factor, we serialize S1 and S2,
with a write size of 11.

I'm thinking that there must be some kind of clever method of doing this optimization without
considering all possible combinations of splits over all NodePaths in an edge, but it's not
clear to me what that method would be right now.

Also, sorry if the job plan images are all wacked in terms of size -- it seems the thumbnail
functionality isn't working, so I put custom sizes on all the images.

> Cost-based job planning
> -----------------------
>                 Key: CRUNCH-294
>                 URL:
>             Project: Crunch
>          Issue Type: Improvement
>          Components: Core
>            Reporter: Josh Wills
>            Assignee: Josh Wills
>         Attachments: CRUNCH-294.patch, jobplan-default-new.png, jobplan-default-old.png,
jobplan-large_s2_s3.png, jobplan-lopsided.png
> A bug report on the user list drove me to revisit some of the core planning logic, particularly
around how we decide where to split up DoFns between two dependent MapReduce jobs.
> I found an old TODO about using the scale factor from a DoFn to decide where to split
up the nodes between dependent GBKs, so I implemented a new version of the split algorithm
that takes advantage of how we've propagated support for multiple outputs on both the map
and reduce sides of a job to do finer-grained splits that use information from the scaleFactor
calculations to make smarter split decisions.
> One high-level change along with this: I changed the default scaleFactor() value in DoFn
to 0.99f to slightly prefer writes that occur later in a pipeline flow by default.

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