Did You Know #21 – Adaptive Features

When Oracle are adding a new feature to the database, they usually add a parameter to control it. Sometimes, after adding feature and the parameter, they realize that the parameter they chose for controlling the feature is not suitable. It might be confusing, or too general or something else. In these cases they change the … Continue reading Did You Know #21 – Adaptive Features

Free Webinar – How Oracle Works!

Next Tuesday (19th September) I am doing a free webinar for ProHuddle. It lasts under an hour and is an introduction to how some of the core parts of the Oracle RDBMS work, I call it “The Heart of Oracle: How the Core RDBMS Works”. Yes, I try and explain all of the core Oracle RDBMS in under an hour! I’m told I just about manage it. You can see details of the event and (more...)

The full table scan direct path read decision for version 12.2

This post is about the decision the Oracle database engine makes when it is using a full segment scan approach. The choices the engine has is to store the blocks that are physically read in the buffercache, or read the blocks into the process’ PGA. The first choice is what I refer to as a ‘buffered read’, which places the block in the database buffercache so the process itself and other processes can bypass the (more...)

ADF Client Side Validation with JavaScript

In my previous post I explained how to use JS client side formatter for ADF Faces input components - ADF Goes Client Side - UI Performance Boost with JavaScript. The same principle can be applied for client side validation. Most likely you are not going to implement complex validation rules on the client side, but for simple checks - it will be perfect. There will be no roundtrip to the server and this will allow (more...)

Parallel Reality


"Expect everything, I always say, and the unexpected never happens."
-- Norton Juster, The Phantom Tollbooth

The following question was recently posted in an Oracle forum:


hi Friends,
I see  this wait event latch: parallel query alloc buffer, when a job meant for doing some cleanup ran this query.
Why does this wait event come happen? , i searched google,MOS no exact hit for explanation of the exact same event.
Looking at query does  (more...)

Oracle Java Cloud Service – Scaling and Cluster Setup for ADF

Last couple of weeks I was busy preparing to my OOW'17 session about estimating Java Cloud Service performance for ADF application. I was running stress tests against various JCS instance configurations to be able to create performance estimation methodology. I will describe this methodology on OOW, but here today will list key steps required to scale up JCS instance.

Let's assume you are running single cloud node with 1 CPU and 7.5 GB RAM. (more...)

Friday Philosophy – Sometime The Solution Has To Not Only Match The Problem But Also…

…The People!

When you design a system for end users, a good designer/developer considers the “UX” – User eXperience. The system has to be acceptable to the end user. This is often expressed as “easy to use” or “fun” or “Quick”. But in reality, the system can fail in all sort of ways but still be a success if the end user gets something out of using it. I’ve said it before and I’ll say (more...)

ADF Goes Client Side – UI Performance Boost with JavaScript

If you would like to boost ADF UI performance, you should look into client side validation and formatting options possible to be done in ADF UI. Today I will describe how you can implement client side converter, to format number value on client side, without making request to the server. Same approach could be used to implement client side validators. You can raise error message and it will be assigned to UI field in the (more...)

The Slow Tires

Once there was a man with a car. On this car he had 4 tires.
As the car is a modern one it has a nice board computer which collects many measurements. One is the rotation per minute of the tires.
One day it took more time for the man to get home than usually. So he decided to check his car's computer for any values which could lead to that delay.
After some minutes (more...)

Validate Performance Improvement Using Query Folding Feature in Power BI

I’ve been using Power BI for a couple months now, not as a developer, but as a system architecture. I may not deal with dashboard and report development on a daily basis, however, I, as an end user, use Power BI extensively to monitor Azure and Power BI usage including audit and billing. I would like to learn more about this tool to its nuts and bolts. The intention of this blog series is to (more...)

Postgresql block internals

This blogpost is the result of me looking into how postgres works, and specifically the database blocks. The inspiration and essence of this blogpost comes from two blogs from Jeremiah Peschka: https://facility9.com/2011/03/postgresql-row-storage-fundamentals/ and https://facility9.com/2011/04/postgresql-update-internals/
I am using Oracle Linux 7u3 and postgres 9.6 (current versions when this blogpost was written).

Postgres is already installed, and a database cluster is already running. Let’s create a database ‘test’ for the sake of our (more...)

Diving into Spark and Parquet Workloads, by Example

Topic: In this post you can find a few simple examples illustrating important features of Spark when reading partitioned tables stored in Parquet, in particular with a focus on performance investigations. The main topics covered are:
  • Partition pruning
  • Column projection
  • Predicate/filter push-down
  • Tools for investigating Parquet metadata
  • Tools for measuring Spark metrics

Motivations: The combination of Spark and Parquet currently is a very popular foundation for building scalable analytics platforms. At least this is what (more...)

Reading AWR Report – Part 2

In the previous part I explained the top part of the report. In this part I’ll continue with the actual information about database activity, what we are looking for and other important things we can find in the report. Wait Events The table with the wait events is one of the most important in the … Continue reading Reading AWR Report – Part 2

OGh Tech Experience 2017 – recap

On June 15th and 16th 2017 the very first OGh Tech Experience was held. This 2-day conference was a new combination of the DBA Days and Fusion Middleware Tech Experience that were held in previous years. To summarize: OGh hit bullseye. It was two days packed with excellent in-depth technical sessions, good customer experiences and great networking opportunities.

The venue was well chosen. De Rijtuigenloods in Amersfoort is a former maintenance building of the Dutch (more...)

Unpivot

An interesting observation appeared recently as a side-channel on a question on the OTN database forum – how does Oracle execute an unpivot() operation. Here’s an example of such a query:

rem
rem     Script:         unpivot_2.sql
rem     Author:         Jonathan Lewis
rem     Dated:          June 2017
rem

create table t1( id, col1, col2, col3, col4, col5, padding )
cache
pctfree 95 pctused 5
-- compress for query low
as
select
        1, 100 , 200 , 300 ,  (more...)

Reading AWR Report – Part 1

Once in a while I get requests for some information about reading and analyzing an AWR report. I have been thinking for a long time about writing such a post, but always postponed it as it is a very tricky topic. The AWR (or statspack for that matter) report is huge and contains so much … Continue reading Reading AWR Report – Part 1

#Javaland 2017 wrap up

Yes – I did it again and attend Javaland conference in Phantasialand Brühl.

It was not easy this year to concentrate on the sessions because of the hottest march of the last 100 years. But the quality of the sessions beats the weather. Maybe again my invest in reading the abstracts and filter the sessions before the conference has payed off.

Day 1 Conference

Jens Schauderdocumentation & slides with AsciiDoc, Git, Gradle and Reveal.js

(more...)

On Measuring Apache Spark Workload Metrics for Performance Troubleshooting

Topic: This post is about measuring Apache Spark workload metrics for performance investigations. In particular you can find the description of some practical techniques and a simple tool that can help you with Spark workload metrics collection and performance analysis. The post is released with accompanying code on GitHub: sparkMeasure

Introduction to Spark performance instrumentation

  
From recent experience I find that scalability and performance are some of the key motivating factors that drive people (more...)

Business rules, common sense and query performance

Very often, significant performance benefits can be obtained by using some very basic knowledge of the application, its data and business rules. Sometimes even less than that: even if you are not familiar with the application logic at all, you can still use common sense to make some reasonable guesses that would get you a long way in improving query’s performance. Here is an example (based on an actual query that I had to tune (more...)

LNS and LOG_BUFFER

In a oracle data guard environment Log Network Server (LNS) process transports the redo from the primary to the standby site. The behavior of LNS process is different from SYNC and ASYNC mode replication. In ASYNC mode transport, LNS read the redo from log buffer and hand over it to the RFS process in the target site. It is not necessary the redo is always available in the buffer cache. If there is not enough (more...)