Top 60 Oracle Blogs

Recent comments

Parallel Execution

12c Parallel Execution New Features: Hybrid Hash Distribution - Part 2

In the second part of this post (go to part 1) I want to focus on the hybrid distribution for skewed join expressions.

2. Hybrid Distribution For Skewed Join Expressions

The HYBRID HASH distribution allows to some degree addressing data distribution skew in case of HASH distributions, which I've described in detail already in the past.

12c Parallel Execution New Features: Hybrid Hash Distribution - Part 1

In this blog post I want to cover some aspects of the the new HYBRID HASH adaptive distribution method that I haven't covered yet in my other posts.As far as I know it serves two purposes for parallel HASH and MERGE JOINs, adaptive broadcast distribution and hybrid distribution for skewed join expressions. In the first part of this post I want to focus on former one (goto part 2).

1. Adaptive Broadcast Distribution For Small Left Row Sources

It allows the PX SEND / RECEIVE operation for the left (smaller estimated row source) of the hash join to decide dynamically at runtime, actually at each execution, if it should use either a BROADCAST or HASH distribution, and correspondingly for the other row source to use then either a ROUND-ROBIN or a HASH distribution, too.

Parallel rownum

It’s easy to make mistakes, or overlook defects, when constructing parallel queries – especially if you’re a developer who hasn’t been given the right tools to make it easy to test your code. Here’s a little trap I came across recently that’s probably documented somewhere, which could be spotted easily if you had access to the OEM SQL Monitoring screen, but would be very easy to miss if you didn’t check the execution plan very carefully. I’ll start with a little script to generate some data:

Parallel Execution 12c New Features Overview

Oracle 12c is the first release since a couple of years that adds significant new functionality in the area of Parallel Execution operators, plan shapes and runtime features. Although 11gR2 added the new Auto DOP feature along with In-Memory Parallel Execution and Statement Queueing, the 12c features are more significant because they introduce new operators that can change both execution plan shape and runtime behaviour.

Here is a list of new features that are worth to note (and not necessarily mentioned in the official documentation and white papers by Oracle):

- The new HYBRID HASH adaptive distribution method, that serves two purposes for parallel HASH and MERGE JOINs:

New Version Of XPLAN_ASH Utility - In-Memory Support

A new version 4.21 of the XPLAN_ASH utility is available for download. I publish this version because it will be used in the recent video tutorials explaining the Active Session History functionality of the script.

As usual the latest version can be downloaded here.

This is mainly a maintenance release that fixes some incompatibilities of the 4.2 version with less recent versions (10.2 and

As an extra however, this version now differentiates between general CPU usage and in-memory CPU usage (similar to Real-Time SQL Monitoring). This is not done in all possible sections of the output yet, but the most important ones are already covered.


Now that I've shown in the previous post in general that sometimes Parallel Execution plans might end up with unnecessary BUFFER SORT operations, let's have a look what particular side effects are possible due to this.

What would you say if someone tells you that (s)he just did a simple, straightforward "SELECT * FROM TABLE" that took several minutes to execute without returning, only to then error out with "ORA-01652 unable to extend temp segment", and the TABLE in question is actually nothing but a simple, partitioned heap table, so no special tricks, no views, synonyms, VPD etc. involved, it's really just a plain simple table?

Unnecessary BUFFER SORT Operations - Parallel Concatenation Transformation

When using Parallel Execution, depending on the plan shape and the operations used, Oracle sometimes needs to turn non-blocking operations into blocking operations, which means in this case that the row source no longer passes its output data directly to the parent operation but buffers some data temporarily in PGA memory / TEMP. This is either accomplished via the special HASH JOIN BUFFERED operation, or simply by adding BUFFER SORT operations to the plan.The reason for such a behaviour in parallel plans is the limitation of Oracle Parallel Execution that allows only a single data redistribution to be active concurrently.

New Version Of XPLAN_ASH Utility

A new version 4.2 of the XPLAN_ASH utility is available for download.

As usual the latest version can be downloaded here.

There were no too significant changes in this release, mainly some new sections related to I/O figures were added.

One thing to note is that some of the sections in recent releases may require a linesize larger than 700, so the script's settings have been changed to 800. If you use corresponding settings for CMD.EXE under Windows for example you might have to adjust accordingly to prevent ugly line wrapping.

Here are the notes from the change log:

- New sections "Concurrent activity I/O Summary based on ASH" and "Concurrent activity I/O Summary per Instance based on ASH" to see the I/O activity summary for concurrent activity

Parallel Costs

While creating a POC of a SQL rewrite recently I received a little surprise as I switched my query from serial execution to parallel execution and saw the optimizer’s estimated cost increase dramatically. I’ll explain why in a moment, but it made me think it might be worth setting up a very simple demonstration of the anomaly. I created a table t1 by copying view all_source – which happened to give me a table with about 100,000 rows and 1117 blocks – and then ran the query ‘select max(line) from t1;’ repeating the query with a /*+ parallel(t1 2) */ hint. From here are the two execution plans I got:

Parallel Fun

As I write, there’s an ongoing thread on Oracle-L that started with the (paraphrased) question: “I’ve got this query that returns 7 million rows; when I change it to ‘select count(*)’ it returns in 4 seconds but when we display the full result set on screen it takes hours, and every second or two the screen pauses; how do I make it go faster.”

The general rapid response was: “You shouldn’t be running 7M rows to a screen – the time is the time for the network traffic and display.”