[pdal] [EXTERNAL] Re: Oracle PDAL queries not scaling

Tue Aug 11 15:30:51 PDT 2015

The statistics should actually auto gather (unless you've disabled that part). Its done as part of the DBMS Auto tasks and should gather when the tables are stale. Although this is more for a production type operation. If you are doing testing, you absolutely should make sure your statistics are up to date otherwise you will get bad access plans.

Its also recommended to set a unique index on your Block table on the Obj_ID/Blk_ID (although this is primarily used to individually select pointclouds).  The more info you can give the optimizer, the better your query will perform.

You can run (and should) explain plan's on your data access queries and see what estimates the optimizer returns. If they don't match what you expect, then you're probably feeding bad information to the optimizer.

Mike

----
Michael Smith
US Army Corps
Remote Sensing GIS/Center
michael.smith at usace.army.mil

From: <pdal-bounces at lists.osgeo.org<mailto:pdal-bounces at lists.osgeo.org>> on behalf of Oscar Martinez Rubi <o.rubi at esciencecenter.nl<mailto:o.rubi at esciencecenter.nl>>
Date: Tuesday, August 11, 2015 at 11:04 AM
To: Peter van Oosterom <P.J.M.vanOosterom at tudelft.nl<mailto:P.J.M.vanOosterom at tudelft.nl>>, Albert Godfrind <albert.godfrind at oracle.com<mailto:albert.godfrind at oracle.com>>, "pdal at lists.osgeo.org<mailto:pdal at lists.osgeo.org>" <pdal at lists.osgeo.org<mailto:pdal at lists.osgeo.org>>
Cc: Theo Tijssen <T.P.M.Tijssen at tudelft.nl<mailto:T.P.M.Tijssen at tudelft.nl>>, "mike.horhammer at oracle.com<mailto:mike.horhammer at oracle.com>" <mike.horhammer at oracle.com<mailto:mike.horhammer at oracle.com>>
Subject: [EXTERNAL] Re: [pdal] Oracle PDAL queries not scaling
Resent-From: Michael Smith <michael.smith at usace.army.mil<mailto:michael.smith at usace.army.mil>>

Hi,

What I did now is to force statistics gathering, so:

ANALYZE TABLE AHN_BLCK compute system statistics for table;
BEGIN
    dbms_stats.gather_table_stats('PDAL20M','AHN_BLCK',NULL,NULL,FALSE,'FOR ALL COLUMNS SIZE AUTO',8,'ALL');
END;

And after doing this the queries are scalable, so in this way I do not need to wait for the DB to learn...

Regards,

O.

On 11-08-15 16:51, Peter van Oosterom wrote:
Hi Oscar,

It feels like when you fetch the data, this is based on a query execution plan that does a full table scan to get the blocks. Even if there is an index, the database may not use this. However, the database may notice that the actual query execution was disappointing (collecting statistics), and that after repeating the same tests, the database behaviour changed its behaviour and does scale well.

[others: this was not in email of Oscar, but after repeating the test the data was fetched in about 0.02 seconds for all sizes 20M, 210M and 2201M. So, also the fetching in case of small dataset becomes significantly faster form 0.5 vs. 0.02 seconds.]

Would be good to see the actual query execution plain or force the database to use the index (with an hint). My hand-on practical Oracle syntax knowledge is too low to give exact hits how to do this, but perhaps others can help here.

Kind regards, Peter.

On 11-8-2015 16:26, Oscar Martinez Rubi wrote:
Hi,

I have investigated a bit more this issue.

I wanted to see what data does the OCI reader actually reads, so I executed the pre-selection query out of PDAL, in python. So I run the attached script (freshly after loading the data).

The script runs the same exact query as done in PDAL twice and prints for each run the number of returned blocks, time spent in the query and time spent to fetch the results.

the exact query is:

SELECT
    l."OBJ_ID", l."BLK_ID", l."BLK_EXTENT", l."BLK_DOMAIN", l."PCBLK_MIN_RES", l."PCBLK_MAX_RES", l."NUM_POINTS", l."NUM_UNSORTED_POINTS", l."PT_SORT_DIM",  l."POINTS", b.pc
FROM
    AHN_BLCK l, AHN_BASE b, QUERY_POLYGONS g
WHERE
    l.obj_id = b.id
    AND
    SDO_FILTER(l.blk_extent,g.geom) = 'TRUE' AND g.id = 1;

The results I get are:

20M run 1:
    #blocks: 12
    query time[s]: 0.113833904266
    fetch time[s]: 0.571593046188
20M run 2:
    #blocks: 12
    query time[s]: 0.000102996826172
    fetch time[s]: 0.500910997391
210M run 1:
    #blocks: 13
    query time[s]: 0.0586049556732
    fetch time[s]: 5.09832000732
210M run 2:
    #blocks: 13
    query time[s]: 0.000245094299316
    fetch time[s]: 5.05038785934
2201M run 1:
    #blocks: 13
    query time[s]: 0.070690870285
    fetch time[s]: 52.4960811138
2201M run 2:
    #blocks: 13
    query time[s]: 0.000225067138672
    fetch time[s]: 53.1006689072

So, even though the query times and the number of returned blocks are similar the fetch times are the not. We can see the scaling issue there. Somehow the fetching is much more expensive (10x) when points are 10x.

I also noticed that after a while doing queries the times get much better and scalable even when I do new queries with other polygons. So, the first queries suffer of scaling issues but later it gets better.

Any idea why?

Regards,

O.

On 10-08-15 18:40, Albert Godfrind wrote:
Like many tools that access an Oracle database, we lack the ability to see what actually happens in the database at a detailed level, i.e. which actual queries are sent, and how the database executes them in terms of CPU use, logical and physical I/Os, network throughput and latency.

So I think it is important to add some debugging / tracing facility to let me see what happens:

1) An option to make PDAL (actually the OCI driver here) log each SQL statement it executes, together with the elapsed time and the number of rows (blocks) fetched. Obviously we have that statement in the input XML file, but a trace would put everything in a single log and include proper measurements.

2) More important: an option to make the OCI driver enable SQL tracing at the database side. This is simple to do by just issuing an “ALTER SESSION …” statement before running the queries. The resulting trace will show all details about execution times as well as resource consumption (CPU and IO) and wait times. That could be added as an option in the XML file. Or maybe extend the XML file with the option to specify a SQ statement to be performed before each query (we could then use that to manually add the ALTER SESSION statement.

The resulting trace can help isolate the bottleneck as one of:

1) the I/Os in the database, to fetch the blocks from disk (mostly I/O)
2) the network time to pass the blocks to the PDAL client (network throughput and latency)
3) the time to process the blocks in the PDAL client (mostly CPU)

Albert

On 5-Aug-2015, at 12:26, Oscar Martinez Rubi <o.rubi at esciencecenter.nl<mailto:o.rubi at esciencecenter.nl>> wrote:

Hi,

I did a test to see how good Oracle with PDAL scale with bigger data sets. I had 3 datasets that are self-contained with 20M, 210M and 2201M points. I loaded them in different Oracle DBs with PDAL and laz-perf. And, for each of them I ran 7 queries (via a pdal pipeline that preselects blocks, applies a crop and then write to a LAS file)

The results are in the attached file.

Regarding the loading, for the 20M I only used one core (it is only one file) while for the others I used 16 cores, i.e. 16 simult. PDAL instances loading data to Oracle. I opened an issue in GitHub because I noticed that in some of the runs the size that I got was too large, and I do not know what caused that. The attached numbers are when everything seemed to work and the sizes were as expected.

This message, though, is about the queries. Each query is run twice in each DB. As you can see in the results file, for 10x more points in the data set the queries are 10x slower, at least for the first run (with the 2201M the second run is much faster but this does not happen with the 210M).

Find also attached one of the XML that i used for the queries (example is for query1). Note that the geometry is previously inserted in oracle so I can use to pre-filter blocks with the query option in oci reader

First I though that maybe the query option in the oci reader in the XML was ignored and that all the blocks of the dataset were being processed by PDAL (that would explain 10x more points 10x slower queries) but I ran a pdal pipeline for query1 with verbose and I saw that the crop filter "only" processed 120000 points which makes sense taking into account that region of query 1 only has 74818 points. Or maybe the crop still process all the blocks extents but only opens and decompress the points of the overlapping ones?

Any idea what is happening?

Regards,

O.
<results.txt><query1.xml>

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