Showing posts with label in-memory. Show all posts
Showing posts with label in-memory. Show all posts

Friday, June 17, 2022

My Speaking Schedule at IDUG Db2 Tech Conference in Boston (2022)

 Just a quick note to let everybody who is coming to Boston in July for IDUG know what I will be speaking about and when my presentations are scheduled!

First of all, my regular IDUG session this year is titled "Things Your DBAs Hear... and how to stop making them crazy!" This session is based on my decades of experience as a DBA and as a consultant. This session walks you through interactions between developers and DBAs, in a light-hearted way. All of them are real-life examples of actual conversations I've been in (or observed).  Attend this session to learn what frustrates DBAs and how improving your communication can improve your relationship with your DBAs... and therefore improve your development  efforts!  This is session E11, and it will be delivered on Wednesday, July 13 at 11:30 AM.

I will also be presenting at two different VSP sessions, one on Tuesday and another on Wednesday (this is the 10:15 AM time slot on both days).

On Tuesday, I will be presenting with InfoTel on the topic "To Protect and Preserve: Treat Your Data Properly or Pay the Consquences." This session will discuss vital data management issues such as data archiving and data protection (my portion), as well as some products that can help you manager your data better (the InfoTel portion). 

On Wednesday, I will be presenting "How to Accelerate Db2 SQL Workloads... Without Db2!" for Log-On Software. This session takes a look at in-memory trends and issues, and shines a light on how QuickSelect can improve the performance of SQL queries.

I hope to see you at this year's IDUG North American conference the week of July 11, 2022. If you are there, come see one (or all) of my sessions... and be sure to say "Howdy!"

Monday, October 19, 2020

Improving Mainframe Performance with In-Memory Techniques

 A recent, recurring theme of my blog posts has been the advancement of in-memory processing to improve the performance of database access and application execution. I wrote an in-depth blog post, The Benefits of In-Memory Processing, back in September 2020, and I definitely recommend you take a moment or two to read through that to understand the various ways that processing data in-memory can provide significant optimization.

There are multiple different ways to incorporate in-memory techniques into your systems ranging from system caching to in-memory tables to in-memory database systems and beyond. These techniques are gaining traction and being adopted at increasingly higher rates because they deliver better performance and better transaction throughput.

Processing in-memory instead of on disk can have a measurable impact on not just the performance of you mainframe applications and systems, but also on your monthly software bill. If you reduce the time it takes to process your mainframe workload by more effectively using memory, you can reduce the number of MSUs you consume to process your mission-critical applications. And depending upon the type of mainframe pricing model you deploy you can either be saving now or be planning to save in the future as you move to Tailored-Fit Pricing.

So it makes sense for organizations to look for ways to adopt in-memory techniques. With that in mind, I recommend that you plan to attend this upcoming IBM Systems webinar titled The benefits and growth of in-memory database and data processing to be held Tuesday, October 27, 2020 at 12:00 PM CDT.

This presentation features two great speakers: Nathan Brice, Program Director at IBM for IBM Z AIOps, and Larry Strickland, Chief Product Officer at DataKinetics.

In this webinar Nathan and Larry will take a look at the industry trends moving to in-memory, help to explain why in-memory is gaining traction, and review some examples of in-memory databases and alternate in-memory techniques that can deliver rapid transaction throughput. And they’ll also look at the latest Db2 for z/OS features like FTBs, contiguous buffer pools, fast insert and more that have caused analysts to call Db2 an in-memory database system.

Don’t miss this great session if you are at all interested in better performance, Db2’s in-memory capabilities, and a discussion of other tools that can aid you in adopting an in-memory approach to data processing.

Register today by clicking here!

Wednesday, September 02, 2020

The Benefits of In-Memory Processing

One area that most organizations can benefit from is by better using system memory more effectively. This is so because accessing and manipulating data in memory is more efficient than doing so from disk.

Think about it… There are three aspects of computing that impact the performance and cost of applications: CPU usage, I/O, and concurrency. When the same amount of work is performed by the computer using fewer I/O operations, CPU savings occur and less hardware is needed to do the same work. A typical I/O operation (read/write) involves accessing or modifying data on disk systems; disks are mechanical and have latency – that is, it takes time to first locate the data and then read or write it.

There are many other factors involved in I/O processing that involve overhead and can increase costs, all depending upon the system and type of storage you are using. For example, each I/O consists of a multitude of background system processes, all of which contribute to the cost of an I/O operation (as highlighted in Figure 1 below). It is not my intent to define each of these processes but to highlight the in-depth nature of the processing that goes on behind-the-scenes that contributes to the costly nature of an I/O operation.


Figure 1. The Cost of an I/O

So, you can reduce the time it takes to process your mainframe workload by more effectively using memory. You can take advantage of things like increased parallelism for sorts and improve single-threaded performance of complex queries when you have more memory available to use. And for OLTP workloads, large memory provides substantial latency reduction, which leads to significant response time reductions and increased transaction rates.

The most efficient way to access data is, of course, in-memory access. Disk access is orders-of-magnitude less efficient than access data from memory. Memory access is usually measured in microseconds, whereas disk access is measured in milliseconds. (Note that 1 millisecond equals 1000 microseconds.)

The IBM z15 has layers of on-chip and on-board cache that can improve the performance of your application workloads. We can view memory usage on the mainframe as a pyramid, from the slowest to the fastest, as shown in Figure 2. As we go up the pyramid, performance improves; from the slowest techniques (like tape) to the fastest (core cache). But this diagram drives home our core point even further: that system memory is faster than disk and buffering techniques.


Figure 2. The Mainframe Memory Pyramid

So how can we make better use of memory to avoid disk processing and improve performance? Although there are several different ways to adopt in-memory processing for your applications, one of the best methods can be to utilize a product. One such product is the IBM Z Table Accelerator.

 IBM Z Table Accelerator is an in-memory table accelerator that can improve application performance and reduces operational cost by utilizing system memory. Using it can help your organization to focus development efforts more on revenue-generating business activity, and less on other less efficient methods of optimizing applications. It is ideal for organizations that need to squeeze every ounce of power from their mainframe systems to maximize performance and transaction throughput while minimizing system resource usage at the application level. You can use it to optimize the performance of all types of data, whether from flat files, VSAM, Db2, or even IMS.

 So how does it work? Well, typically a small percentage of your data is accessed and used a large percentage of the time. Think about it in terms of the 80/20 Rule (or the Pareto Principle).  About 80% of your data is accessed only 20% of the time, and 20% of your data is accessed 80% of the time.

The data that you are accessing most frequently is usually reference data that is used by multiple business transactions. By focusing on this data and optimizing it you can gain significant benefits. This is where the IBM Z Table Accelerator comes into play. By copying some of the most often accessed data into the accelerator, which uses high-performance in-memory tables, significant performance gains can be achieved. That said, it is only a small portion of the data that gets copied from the system of record (e.g. Db2, VSAM, etc.) into the accelerator.

High-performance in-memory technology products -- such as IBM Z Table Accelerator -- use system memory. Sometimes, if the data is small enough, it can make it into the L3-L4 cache. This can be hard to predict, but when it occurs things get even faster.

Every customer deployment is different, but using IBM Z Table Accelerator to optimize in-memory data access can provide a tremendous performance boost.

A Use Case: Tailor-Fit Pricing

Let’s pause for a moment and consider a possible use case for IBM Z Table Accelerator.

In 2019, IBM announced Tailored Fit Pricing (TFP), with the goal of simplifying mainframe software pricing and billing. IBM designed TFP as a more predictable, cloud-like pricing model than its traditional pricing based on a rolling-four-hour-average of usage. Without getting into all of the details, TFP eliminates tracking and charging based on monthly usage and instead charges a consistent monthly bill based on the previous year’s usage (plus growth).

It is important to note that last point: TFP is based on last year’s usage. So you can reduce your bill next year by reducing your usage this year, before you convert to TFP. Therefore, it makes a lot of sense to reduce your software bills to the lowest point possible the year before the move to TFP.

 

So what does this have to do with IBM Z Table Accelerator? Well, adopting techniques to access data in-memory can lower MSU usage – and therefore your monthly software bill. Using IBM Z Table Accelerator to optimize your reference data in-memory before moving to TFP can help you to lower your software bills and better prepare you for the transition to Tailored Fit Pricing.

 

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 If you’d like to learn more about IBM Z Table Accelerator there is an upcoming SHARE webinar on September 15, 2020, that goes into some more details about the offering. It is titled Digital Transformation IncludesGetting The Most Out of Your Mainframe: click the link for details and to register to attend.


 

 

 

 

 

Monday, June 22, 2015

The DBMS Market Circa 2015

Today's blog post is to call attention to a series of articles and product overviews I have been writing for the TechTarget SearchDataManagement portal on Database Management Systems (DBMS).

Firstly, I wrote a 7 part series of articles reviewing the DBMS technology circa 2015. This series spans relational, NoSQL and in-memory database technology and here are the links to each of the seven articles:


Now you may be asking, why would I provide links to these articles on a DB2 blog? Good question. The answer is that it behooves you to keep up to date on the latest breakthroughs and offerings in the world of data management. Sure, we all can agree that DB2 is great and should be used by everybody! But let's face it, our organizations are going to have data-related projects where DB2 is not the primary DBMS... so read through those articles and get up to speed on the new NoSQL and in-memory database offerings out there.


I have also been writing a series of DBMS product overview documents that briefly review and highlight the features and capabilities of many popular DBMSes. I won't share all of them with you here today (if you are interested, they will all be linked to, over time, on my web site at http://mullinsconsulting.com/articles.html.  I will, though, share the link for the TechTarget product overview I wrote of DB2: IBM DB2 relational DBMS overview.

That's all for today... thanks for reading!