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From Padma Penumarthy <ppenumar...@mapr.com>
Subject Re: Drill query planning taking a LONG time
Date Fri, 17 Feb 2017 22:07:49 GMT
We have a JIRA for this issue that hopefully will be fixed in the next release.
https://issues.apache.org/jira/browse/DRILL-5089

Thanks,
Padma


On Feb 17, 2017, at 1:50 PM, David Kincaid <kincaid.dave@gmail.com<mailto:kincaid.dave@gmail.com>>
wrote:

My apologies for not following up sooner. Earlier this week our DevOps
engineer was looking into this problem as well and discovered the root
cause of our issue. We developed a custom storage provider that utilizes S3
as the pstore. We thought this was just storing configuration information
(esp. storage plugin config), but we discovered that it was spending a lot
of time reading files in a /temp/drill subdirectory of our S3 bucket. We
removed the custom plugin and things are running much better now.

I have one of our engineers working on this now to see where we went wrong.
My question for the list now is if you know what exactly it is doing. We
really want to be able to store the storage plugin config on S3 so that it
is persisted between restarts of the EMR cluster that we are running Drill
on. If you have any suggestions or advice it would be much appreciate.

I really appreciate all the time and patience you all showed helping us
troubleshoot this issue. I'm glad in the end that it really was something
on our end and not something more mysterious happening in Drill itself.

Thanks,

Dave

On Wed, Feb 15, 2017 at 12:37 PM, Jinfeng Ni <jni@apache.org<mailto:jni@apache.org>>
wrote:

Can you help try one more thing if you can?

Run jstack on the foreman Drillbit process while the query is doing
the query planning. Capture the jstack every one second or couple of
seconds consecutively for some time, by appending the jstack output
into one log file. Take a look at the stack trace for the forman
thread in the form of "275b623b-bb15-8bd8-fd29-f9a571a7534e:foreman"
(The first part is the query ID).  If foreman thread is stuck in one
method call, it  may show up in the log repeatedly.  In this way we
may have a better idea what's the cause of the problem.

Based on the tests you tried, the combination of the query / parquet
files probably hit a bug in the code that we are not aware of
currently. Without the parquet files to re-reproduce, it's hard to
debug the issue and find a possible fix.



On Wed, Feb 15, 2017 at 8:35 AM, David Kincaid <kincaid.dave@gmail.com<mailto:kincaid.dave@gmail.com>>
wrote:
I ran that EXPLAIN that you suggested against the entire 100 file table
and
it takes about 3 seconds. I will try to get a defect written up in the
next
few days.

- Dave

On Tue, Feb 14, 2017 at 9:06 PM, Jinfeng Ni <jni@apache.org<mailto:jni@apache.org>>
wrote:

>From the two tests you did, I'm inclined to think there might be some
special things in your parquet files. How do you generate these
parquet files? Do they contain normal data type (int/float/varchar),
or complex type (array/map)?

In our environment, we also have hundreds of parquet files, each with
size ~ hundreds of MBs.  A typical query (several tables joined) would
takes a couple of seconds in planning.

One more test if you can help run.

EXPLAIN PLAN FOR
SELECT someCol1, someCol2
FROM dfs.`parquet/transaction/OneSingleFile.parquet`;

The above query is simple enough that planner should not spend long
time in enumerating different choices. If it still takes long time for
query planning,  the more likely cause might be in parquet files you
used.



On Tue, Feb 14, 2017 at 1:06 PM, David Kincaid <kincaid.dave@gmail.com<mailto:kincaid.dave@gmail.com>>
wrote:
I will write up a defect. The first test you suggested below - running
the
query on just one of our Parquet files produces the same result (10-12
minute planning time). However, the second test - using
cp.`tpch/nation.parquet` - results in a planning time of only about a
minute. So, I'm not sure how to interpret that. What does that mean to
you
all?

- Dave

On Tue, Feb 14, 2017 at 12:37 PM, Jinfeng Ni <jni@apache.org<mailto:jni@apache.org>>
wrote:

Normally, the slow query planning could be caused by :

1. Some planner rule hit a bug when processing certain operators in
the query, for instance join operator, distinct aggregate.  The query
I tried on a small file seems to rule out this possibility.
2. The parquet metadata access time. According to the long, this does
not seem to be the issue.
3. Something we are not aware of.

To help get some clue, can you help do the following:
1. run the query over one single parquet files, in stead of 100
parquet files? You can change using
dfs.`parquet/transaction/OneSingleFile.parquet`. I'm wondering if
the
planning time is proportional to # of parquet files.

2. What if you try your query by replacing
dfs.`parquet/transaction/OneSingleFile.parquet` with
cp.`tpch/nation.parquet` which is a small tpch parquet file (you need
re-enable the storage plugin 'cp')? Run EXPLAIN should be fine. This
will tell us if the problem is caused by the parquet source, or the
query itself.

Yes, please create a defect in Drill JIRA.

On Tue, Feb 14, 2017 at 5:02 AM, David Kincaid <
kincaid.dave@gmail.com<mailto:kincaid.dave@gmail.com>>
wrote:
Thank you for the feedback. It seems there is nothing more I can do
on my
end. What are my next steps? Shall I create a defect in the Drill
Jira?

- Dave

On Mon, Feb 13, 2017 at 5:13 PM, Jinfeng Ni <jni@apache.org<mailto:jni@apache.org>>
wrote:

The size of parquet files will matter in terms of meta data access
time, which is just 212 ms according to your log file. My
understanding is it does not matter too much to the overall
planning
times. That's why it probably makes sense to try over such a small
toy
example.

Normally the planning time for such simple query should be much
shorter than 12 minutes.  It indicates it could be caused by a
code
bug, or something else that we are currently unaware of.






On Mon, Feb 13, 2017 at 2:47 PM, David Kincaid <
kincaid.dave@gmail.com<mailto:kincaid.dave@gmail.com>>
wrote:
The example in DRILL-5183 is just a very small toy example to
demonstrate
the bug with how Drill reads Parquet array fields. It doesn't
have
anything
to do with this planning issue (at least I don't think it does).
Sorry
if I
confused things with that reference.

I just tried running our query directly against the table at
dfs.`parquet/transaction` and get the same result (12 minutes of
planning
time). I disabled the cp and s3 storage plugins that were
enabled
so
that
only the dfs storage plugin is enabled and the result is the
same.

Is this expected for Drill to take this long in the planning
phase
for a
query? Is there anything else I can try or information I could
provide to
help identify the bug (seems like a bug to me)? I really
appreciate
you
guys helping out so quickly this afternoon.

- Dave

On Mon, Feb 13, 2017 at 4:13 PM, Jinfeng Ni <jni@apache.org<mailto:jni@apache.org>>
wrote:

I downloaded books.parquet from DRILL-5183, and created a view
on
top
of this single parquet file. Then, run EXPLAIN for the query,
and
it
completes within 1.2 seconds on Drill 1.8.0 release. (The # of
parquet
files would impact the time to fetch metadata. Since it's not
the
bottleneck in this case, it should not cause a big difference).

Do you see the long planning time issue for this query only,
or it
happens for other queries as well? Besides the possibility of
planning
rule bugs, we once saw another possible cause of long planning
issue.
In your storage plugin configuration, if you enable some other
storage
plugin (for instance, hbase, or hive etc) which are slow to
access,
then those un-relevant storage plugin might impact your query
as
well.
You may temporarily disable those storage plugins, and see if
it's
the
cause of the problem.

0: jdbc:drill:zk=local> explain plan for
. . . . . . . . . . . > select fltb1.sapId, yearmo,
. . . . . . . . . . . > COUNT(*) as totalcnt,
. . . . . . . . . . . > count(distinct(CASE
. . . . . . . . . . . >                WHEN
. . . . . . . . . . . >
(REPEATED_CONTAINS(fltb1.
classLabels,
. . . . . . . . . . . >
'Thing:Service:MedicalService:Diagnostic:Radiology:
Ultrasound.*'))
. . . . . . . . . . . >                THEN fltb1.invoiceId
. . . . . . . . . . . >                END)) as ultracount,
. . . . . . . . . . . > count(distinct (CASE
. . . . . . . . . . . >                 WHEN
. . . . . . . . . . . >
(REPEATED_CONTAINS(fltb1.
classLabels,
. . . . . . . . . . . >
'Thing:Service:MedicalService:Diagnostic:LaboratoryTest.*'))
. . . . . . . . . . . >                 THEN fltb1.invoiceId
. . . . . . . . . . . >                 END)) as labcount
. . . . . . . . . . . > from (
. . . . . . . . . . . >   select sapid, invoiceId,
. . . . . . . . . . . >         TO_CHAR(TO_TIMESTAMP(
transactionDate,
'YYYY-MM-dd HH:mm:ss.SSSSSS'), 'yyyy-MM') yearmo,
. . . . . . . . . . . >         classLabels
. . . . . . . . . . . >       from dfs.tmp.transactionView)
fltb1
. . . . . . . . . . . > group by fltb1.sapId, yearmo;
+------+------+
| text | json |
+------+------+
| 00-00    Screen
00-01      Project(sapId=[$0], yearmo=[$1], totalcnt=[$2],
ultracount=[$3], labcount=[$4])
....................................
00-09                SelectionVectorRemover
00-12                  Sort(sort0=[$0], sort1=[$1], dir0=[ASC],
dir1=[ASC])
00-15                    HashAgg(group=[{0, 1}],
totalcnt=[COUNT()])
................................
00-22                          Scan(groupscan=[
ParquetGroupScan
[entries=[ReadEntryWithPath [path=file:/tmp/parquet/
transaction]],
selectionRoot=file:/tmp/parquet/transaction, numFiles=1,
usedMetadataFile=false, columns=[`sapId`, `invoiceId`,
`transactionDate`, `classLabels`.`array`]]])

1 row selected (1.195 seconds)


On Mon, Feb 13, 2017 at 1:51 PM, David Kincaid <
kincaid.dave@gmail.com<mailto:kincaid.dave@gmail.com>>
wrote:
Here is the entire transactionView.view.drill file. As you
can
see
the
view
itself is very simple and is just wrapping a syntactic
problem
with
the
array field. That's an issue I reported in Jira under
DRILL-5183 (
https://issues.apache.org/jira/browse/DRILL-5183)

{
 "name" : "transactionView",
 "sql" : "SELECT `transactionRowKey`, `sapId`,
`practiceName`,
`practiceCity`, `practiceState`, `practicePostalCode`,
`animalId`,
`dateOfBirth`, `species`, `breed`, `gender`, `status`,
`ownerId`,
`itemType`, `classification`, `subclass`,
`practiceDescription`,
`clientDescription`, `invoiceId`, `unitOfMeasure`,
`vendorName`,
`vaccine`,
`rabies`, `vaccineType`, `price`, `quantity`,
`transactionDate`,
`visitReason`, `speciesCode`, `genderCode`,
`t`.`classLabels`['array'] AS
`classLabels`\nFROM `dfs`.`/parquet/transaction` AS `t`",
 "fields" : [ {
   "name" : "transactionRowKey",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "sapId",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "practiceName",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "practiceCity",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "practiceState",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "practicePostalCode",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "animalId",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "dateOfBirth",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "species",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "breed",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "gender",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "status",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "ownerId",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "itemType",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "classification",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "subclass",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "practiceDescription",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "clientDescription",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "invoiceId",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "unitOfMeasure",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "vendorName",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "vaccine",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "rabies",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "vaccineType",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "price",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "quantity",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "transactionDate",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "visitReason",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "speciesCode",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "genderCode",
   "type" : "ANY",
   "isNullable" : true
 }, {
   "name" : "classLabels",
   "type" : "ANY",
   "isNullable" : true
 } ],
 "workspaceSchemaPath" : [ ]
}

On Mon, Feb 13, 2017 at 3:47 PM, Jinfeng Ni <jni@apache.org<mailto:jni@apache.org>>
wrote:

Yes, the log confirmed that the planning, especially
physical
planning, is the one that took most of the time.

If the definition of view s3.cisexport.transactionView is
not
very
complicated (involves large # of tables), then it's possible
that
some
planner rules have a bug. (In the past, we once saw couple
of
planner
rules would be fired in a loop).

Is it possible that you can share the DDL of the view?  That
may
help
us re-produce the problem and take a look at the trace of
Calcite,
which Drill uses as the query planner.












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