Friday Jul 24, 2015

What to do with optimizer statistics when upgrading to 12c to take advantage of Database In-Memory

Before most customers can take advantage of Database In-Memory they will need to navigate the tricky terrain of a database upgrade. One of the most challenging aspects of an upgrade is figuring out how to minimize performance regressions due to execution plan changes.

And if that wasn’t enough to handle, the introduction of Database In-Memory into a 12c environment has the potential to change even more of the execution plans. So, what should you do?

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Tuesday Jul 21, 2015

Oracle Database In-Memory Bundle Patch 10 Released

The latest Bundle Patch for Database In-Memory has been released. The Bundle Patch is 21188742 or 12.1.0.2 Bundle Patch 10 for Engineered Systems and DB In-Memory (July2015)). This Bundle Patch improves the performance of mixed workload environments (OLTP & DW workloads), as well as enhancing the performance of analytic queries with aggregation. More information on the latest Bundle Patch can be found in the MOS note 21188742.8 or in the Mos note: 12.1.0.2 Bundle Patches for Engineered Systems and DB In-Memory (Doc ID 1937782.1).

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Friday Jul 17, 2015

Star Schema Challenge - Part 3

In my previous posts on the Star Schema Challenge, I established baseline results for query performance with row store tables (that is, without using Database In-Memory). A 9 dimensional, 500 million row star schema supported 25 users with a median query time of 16.9 seconds. (With a median of 16.9 seconds, there was not much point in increasing the number of users with the row store tables). The same data in a 500 million row de-normalized table supported 25 users with a median query time of 33.5 seconds per query.

Remember all of these tests are being conducted on a 60 core Intel server with commodity disk and 1 TB of DRAM.

Row Store Query

Median query performance (in seconds) of the query workload with 25 users on row store tables.

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Friday Jul 10, 2015

Star Schema Challenge - Part 2.1

In Star Schema Challenge – Part 2 I revealed baseline results for my query workload running on a 500 million row star schema with all tables marked NO INMEMORY and In-Memory Aggregation prevented using the NO_VECTOR_TRANSFORM hint. With a median of 16.9 seconds per query (allowing for an average think time between queries of 5 seconds), I decided that 25 users were enough for a baseline.

After my first post in this series someone suggested that the best implementation might be a de-normalized table. I decided to satisfy this user’s curiosity and test a de-normalized table also, starting with NO INMEMORY test. (Vector Transform is not applicable to the de-normalized table because it does not join to another table.)

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Friday May 08, 2015

Getting started with Oracle Database In-Memory Part V - Controlling Access

I’m finally going to make good on a promise I made way back in part 3 of our getting started with In-Memory series, to explain how you could control which queries use the In-Memory column store (IM column store) and which don't.

As with all new query performances enhancing features in the Oracle Database, a number of initialization parameters and hints have been introduce that enable you to control when and how the IM column store will be used. This post provides information on the initialization parameter, while the details on the Optimizer hint that control the use of the IM column store can be found on the Optimizer blog.

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Thursday Apr 30, 2015

When to use Oracle Database In-Memory?

At this stage you've probably heard a lot about how Oracle Database In-Memory is an unprecedented breakthrough in Oracle database performance, offering incredible performance gains for a wide range of workloads. What you might not know is when and where it would be best to take advantage of this exciting new technology.

So, we've put together a new whitepaper to share some of the high-level use cases, and explain the scenarios under which Database In-Memory provides a performance benefit. The purpose of this paper is to give you some general guidelines so that you can determine whether your use case is a good match.

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Friday Apr 24, 2015

In-Memory Priority

I'd like to thank everyone who attended our two Hands On Lab sessions at Collaborate last week. We had a great time presenting them and we received some really good feedback. One of the questions that came up, and gets asked periodically, is how does In-Memory priority really work?

Many times people have the misconception that the In-Memory priority attribute affects more than just the order of population but that’s not the case. The only thing the priority affects is the order of population at database startup. We discussed population briefly some time ago, but I think it's worth a quick review.

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Friday Apr 17, 2015

CPU Efficient Query Processing with Database In-Memory

In my last post I talked about In-Memory Aggregation and mentioned that the vector transformation plan is more CPU efficient than alternative plans. In this post I’ll provide a few examples to illustrate just how effective a vector transformation plan can be.

Let’s consider a star schema, with one fact table and 9 dimension tables (time, customer, product, channel and demographic attributes such as age and income).

9 Dimensional Star

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Thursday Apr 09, 2015

Push-Down: Part 2

In our previous post we introduced the concept of push down. The ability to push predicates, aggregations and group-bys down into the scan of a column or columns, allowing Oracle to take full advantage of all the scan performance features of the In-Memory column store. We also illustrated how you could monitor the benefits of push down via session level statistics (v$mystat). What you might not have realized is that we can also see what where clause predicates get push via the execution plan.

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Tuesday Mar 31, 2015

Push-Down: Making Queries Fast!

In our previous posts we discussed the basic architecture of the In-Memory column store (IM column store) and now we want to drill down into some of the unique performance enhancing features. Push-down is one of the optimizations that makes scanning columns in the IM column store very efficient. Oracle Database In-Memory has the ability to push predicates, aggregations and group-bys down into the scan of a column or columns.  This ability to push-down allows us to take advantage of other performance enhancing features of Database In-Memory like SIMD vector processing and storage indexes.

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Monday Feb 16, 2015

Getting started with Oracle Database In-Memory Part V - Aggregation

When most people think about Oracle Database In-Memory (Database In-Memory), the first thing that comes to mind is super fast scanning and filtering operations. But what you may not know is Database In-Memory also includes many SQL optimizations designed to accelerate star and snowflake type queries. We refer to this collection of SQL optimizations as In-Memory Aggregation (IMA). IMA is typically 3-10x faster than ‘conventional’ plans, and that’s in addition to the improvements provided by scanning and filtering the data via the In-Memory column store (IM column store).

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Tuesday Jul 22, 2014

It's here! Oracle Database In-Memory is officially released

Oracle Database 12.1.0.2 was officially release today and is now available for download, which mean Oracle Database In-Memory is officially here!

Along with the software release comes a whole new set of collateral that explains in detail how all of the new Oracle Database In-Memory functionality works. The In-Memory page on Oracle.com has all of the juicy details including the official whitepaper.

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The Oracle Database In-Memory blog is written by the Oracle product management team and sheds light on all things In-Memory.

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