Re: Creating table with data from a join

[Igor Stassiy <istassiy@xxxxxxxxx>]

David, I did something like this:

psql -f /dev/fd/3 3 << IN1 & psql -f /dev/fd/4 4 << IN2 ...

 INSERT INTO c SELECT * FRO a JOIN b ON a.ad=b.id WHERE a.id < 0.25th quantile

IN1

INSERT INTO c SELECT * FRO a JOIN b ON a.ad=b.id WHERE a.id < 0.5th quantile AND a.id >= 0.25th quantile

IN2

...

IN3

...

IN4

And quantiles were computed using the function:

SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY id) FROM a, and then once more for lower and upper halves. But unfortunately, I got only about 16% improvement from non-parallelized version of INSERT INTO .. SELECT ..

Marc, I am using postgres 9.4. I didn't benchmark, but intuitively the modulo operator will force traversing every record in table "a" 4 times, as it can't use an index.

Julien, my concern was why the option 3 (with parallel) is not the fastest. And now, even with parallel INSERT INTO .. SELECT its not the fastest. I can't really use the UNLOGGED table in this case. 

The following document summarises why is CREATE TABLE AS .. the fastest: 

14.4.7 http://www.postgresql.org/docs/current/static/populate.html#POPULATE-PITR

Basically CREATE TABLE AS .. just doesn't write to wal if the wal_level is minimal and hence cuts IO about in half.

On Tue, Jul 14, 2015 at 1:42 PM David Rowley <david.rowley@xxxxxxxxxxxxxxx> wrote:

On 14 July 2015 at 21:12, Igor Stassiy <istassiy@xxxxxxxxx> wrote:

Hello,

I am benchmarking different ways of putting data into table on table creation:

1. INSERT INTO c SELECT * FROM a JOIN b on a.id = b.id;

2. CREATE TABLE c AS SELECT * FROM a JOIN b on a.id = b.id;

3. psql -c "COPY (SELECT * FROM a JOIN b on a.id = b.id) TO STDOUT" | 

parallel --block 128M --jobs 4 --pipe psql -c "COPY c FROM STDIN";

(the parallel command is available as part of parallel deb package in Ubuntu for example, it splits the stdin by newline character and feeds it to the corresponding command)

Both tables a and b have ~16M records and one of the columns in a is geometry (ranging from several KB in size to several MB). Columns in b are mostly integers.

The machine that I am running these commands on has the following parameters:

default_statistics_target = 50 # pgtune wizard 2012-06-06 maintenance_work_mem = 1GB # pgtune wizard 2012-06-06 constraint_exclusion = on # pgtune wizard 2012-06-06 checkpoint_completion_target = 0.9 # pgtune wizard 2012-06-06 effective_cache_size = 48GB # pgtune wizard 2012-06-06

work_mem = 80MB # pgtune wizard 2012-06-06 

wal_buffers = 8MB # pgtune wizard 2012-06-06 

checkpoint_segments = 16 # pgtune wizard 2012-06-06 

shared_buffers = 16GB # pgtune wizard 2012-06-06 

max_connections = 400 # pgtune wizard 2012-06-06

One would expect the 3rd option to be faster than 1 and 2, however 2 outperforms both by a large margin (sometimes x2). This is especially surprising taking into account that COPY doesn't acquire a global lock on the table, only a RowExclusiveLock 

(according to http://www.postgresql.org/message-id/10611.1014867684@xxxxxxxxxxxxx)

So is option 2 a winner by design? Could you please suggest other alternatives to try (if there are any)? And what might be the reason that 3 is not outperforming the other 2?

I would imagine that the calling of the output function to translate the each value's internal representation to it's user visible/external representation plus all the overhead of sending results to the client would be a likely candidate of the slow down. In either case 3 would only be as fast as the query generating the output. With 1 and 2 all the tuple representations of each record stays in the internal format.

If you have some logical way to break the query down into parts, then maybe that would be a place to look.

For example:

INSERT INTO c SELECT * FROM a JOIN b on a.id = b.id WHERE a.id < 8000000;

INSERT INTO c SELECT * FROM a JOIN b on a.id = b.id WHERE a.id >= 8000000;

Of course, you'd need to be very careful to ensure that the results of each SELECT never overlap. It would be nice to invent some better way than this that divided the workload evenly even when the tables grow. 

Then you could run these concurrently.

Regards

David Rowley

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