Reg: Error in parallel Hint-

Hi Experts,

I have a concern about the use of the parallel hint with DOP = 'AUTO '.

These errors during the ETL or tasks are performed with / * + PARALLEL (AUTO) * /.

JOB - 1
java.sql.SQLException: ORA-12853: insufficient memory for buffers PX: run 1159024K, max necessary K 11666304

ORA-04031: unable to allocate 65560 bytes of shared memory ('pool', 'unknown object', 'pool', 'Pool of msg PX')


JOB - 2

java.sql.SQLException: ORA-12801: error reported in the P256 parallel query server
ORA-12853: insufficient memory for buffers PX: run 1159024K, max necessary K 11666304
ORA-04031: unable to allocate 65560 bytes of shared memory ('pool', 'unknown object', 'pool', 'Pool of msg PX')

I tried google, but could not get a pattern appropriate for that and also correct.

1-do I need to increase the bound parameter buffer?

2 - is this 'AUTO' option the cause? And using a DOP = 3, 4, etc. guaranteed to solve this problem?

(I try this and check but want to know the exact reason, so that it causes no problems in Prod)

Guidance in this regard.

-Nordine

( on Oracle 11.2.0.3.0 )

nordine B wrote:

Before going deep into your words, had a quick question - despite having DOP as 'auto' in the suspicion, why is there an error on the buffers PX?

I was under the assumption that DOP setting on "automatic", the optimizer will take care of all resources and allocate the CPU threads accordingly. But then why this error is growing?

First, I already mentioned in my reply what treat buffers "PX": this is the memory that is required for communication between the servers in PX. Parallel execution comes with the need for a communication between the involved process layer and this layer of communication requires memory for exchanging data (and could even cross the interconnection for CARS cross the parallel execution of the instance). If you want to know more about it, you can watch my Webinar: http://allthingsoracle.com/analyzing-and-troubleshooting-oracle-parallel-execution/

So does not AUTO magic DOP, it is mainly about the following:

-L' optimizer uses a new method of calculation to arrive at a particular degree of parallelism (DOP). The basics, how is the calculation I have described in my post 'Cost Is Time' mentioned in my previous answer. Since this calculation is based on the estimated costs of the optimizer, it can happen to little all - adapted DOP, or may be a completely inappropriate DOP if the calculation of the underlying costs did not reflect the actual amount of work, or if the calibration i/o returned to the unreasonable values.

-In addition to this AUTO DOP code still uses other settings / settings / thresholds which can be used to control / limit the calculated value.

* The PARALLEL_MIN_TIME_THRESHOLD parameter can be used to control which * estimated time * must be exceeded before the parallel execution gets seen by AUTO DOP. Note also that it is based on estimates and therefore underlies the same problems as described above

* The PARALLEL_DEGREE_LIMIT can be used at the upper limit of the degree of "cap".

Currently Manager of used consumer resource group data can impose a different ceiling and will be considered in the calculation, too

-The PARALLEL_SERVERS_TARGET parameter can be used to control the simultaneous execution of running in parallel, resulting in a queue of executions if the threshold is exceeded

-The calibration of i/o resources provides an important factor which is the main engine for the calculation of the DOP

-Resource Manager can be used to implement fairly complex scenarios with different queues and the different settings by Consumer Group

So you can see there are some elements in AUTO DOP that may consider the consumption of resources, especially things of queues and advanced resource manager configurations, but stuff 'Pads PX' does not part of the calculation.

If you want to learn more about these settings and interaction with the resource manager, follow the links on the page "webinar" above - that it points to several very good articles published on the Oracle 'Data Warehouse' blog, a highly recommended resource if you regularly deal with a running in parallel.

Randolf

Tags: Database

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