Re: Parallel Append implementation

Amit Khandekar <amitdkhan.pg@gmail.com>

From: Amit Khandekar <amitdkhan.pg@gmail.com>
To: Robert Haas <robertmhaas@gmail.com>
Cc: Ashutosh Bapat <ashutosh.bapat@enterprisedb.com>, pgsql-hackers <pgsql-hackers@postgresql.org>
Date: 2017-03-23T19:08:49Z
Lists: pgsql-hackers

Attachments

On 23 March 2017 at 16:26, Amit Khandekar <amitdkhan.pg@gmail.com> wrote:
> On 23 March 2017 at 05:55, Robert Haas <robertmhaas@gmail.com> wrote:
>> On Wed, Mar 22, 2017 at 4:49 AM, Amit Khandekar <amitdkhan.pg@gmail.com>
wrote:
>>> Attached is the updated patch that handles the changes for all the
>>> comments except the cost changes part. Details about the specific
>>> changes are after the cost-related points discussed below.
>>>
>>> For non-partial paths, I was checking following 3 options :
>>>
>>> Option 1. Just take the sum of total non-partial child costs and
>>> divide it by number of workers. It seems to be getting close to the
>>> actual cost.
>>
>> If the costs for all children are about equal, then that works fine.
>> But when they are very unequal, then it's highly misleading.
>>
>>> Option 2. Calculate exact cost by an algorithm which I mentioned
>>> before, which is pasted below for reference :
>>> Per­subpath cost : 20 16 10 8 3 1, with 3 workers.
>>> After 10 time units (this is minimum of first 3 i.e. 20, 16, 10), the
>>> times remaining are :
>>> 10  6  0 8 3 1
>>> After 6 units (minimum of 10, 06, 08), the times remaining are :
>>> 4  0  0 2 3 1
>>> After 2 units (minimum of 4, 2, 3), the times remaining are :
>>>  2  0  0 0 1 1
>>> After 1 units (minimum of 2, 1, 1), the times remaining are :
>>>  1  0  0 0 0 0
>>> After 1 units (minimum of 1, 0 , 0), the times remaining are :
>>>  0  0  0 0 0 0
>>> Now add up above time chunks : 10 + 6 + 2 + 1 + 1 = 20
>>
>
>> This gives the same answer as what I was proposing
>
> Ah I see.
>
>> but I believe it's more complicated to compute.
> Yes a bit, particularly because in my algorithm, I would have to do
> 'n' subtractions each time, in case of 'n' workers. But it looked more
> natural because it follows exactly the way we manually calculate.
>
>> The way my proposal would work in this
>> case is that we would start with an array C[3] (since there are three
>> workers], with all entries 0.  Logically C[i] represents the amount of
>> work to be performed by worker i.  We add each path in turn to the
>> worker whose array entry is currently smallest; in the case of a tie,
>> just pick the first such entry.
>>
>> So in your example we do this:
>>
>> C[0] += 20;
>> C[1] += 16;
>> C[2] += 10;
>> /* C[2] is smaller than C[0] or C[1] at this point, so we add the next
>> path to C[2] */
>> C[2] += 8;
>> /* after the previous line, C[1] is now the smallest, so add to that
>> entry next */
>> C[1] += 3;
>> /* now we've got C[0] = 20, C[1] = 19, C[2] = 18, so add to C[2] */
>> C[2] += 1;
>> /* final result: C[0] = 20, C[1] = 19, C[2] = 19 */
>>
>> Now we just take the highest entry that appears in any array, which in
>> this case is C[0], as the total cost.
>
> Wow. The way your final result exactly tallies with my algorithm
> result is very interesting. This looks like some maths or computer
> science theory that I am not aware.
>
> I am currently coding the algorithm using your method.

While I was coding this, I was considering if Path->rows also should
be calculated similar to total cost for non-partial subpath and total
cost for partial subpaths. I think for rows, we can just take
total_rows divided by workers for non-partial paths, and this
approximation should suffice. It looks odd that it be treated with the
same algorithm we chose for total cost for non-partial paths.

Meanwhile, attached is a WIP patch v10. The only change in this patch
w.r.t. the last patch (v9) is that this one has a new function defined
append_nonpartial_cost(). Just sending this to show how the algorithm
looks like; haven't yet called it.

Commits

  1. Update parallel.sgml for Parallel Append

  2. Support Parallel Append plan nodes.

  3. Remove BufFile's isTemp flag.

  4. Improve comments for parallel executor estimation functions.

  5. Separate reinitialization of shared parallel-scan state from ExecReScan.

  6. Eat XIDs more efficiently in recovery TAP test.

  7. Avoid syntax error on platforms that have neither LOCALE_T nor ICU.

  8. Preparatory refactoring for parallel merge join support.