--- layout: post title: Clean & Clear skin balancing moisturizer tags: - Clean & Clear status: publish type: post published: true meta: _edit_last: '2' _graphene_slider_img: global _graphene_slider_imgurl: '' _graphene_show_addthis: global _aioseop_keywords: Clean & Clear skin balancing moisturizer review, Clean & Clear skin balancing moisturizer, clean & clear review _aioseop_title: Clean & Clear skin balancing moisturizer _yoast_wpseo_linkdex: '0' _thumbnail_id: '787' --- Had picked this moisturizer for the first time during my college days.. when budget was limited. Have repurchased this product so many times over the years and still continue to use,
Price: Rs. 80 for 100ml Shelf Life: 37 months Product boasts of : The 100% oil free formulation balances skin moisture and soothes dry areas. Also helps prevent blemishes. Product contains : Water, propylene Glycol, C12-15 Alkyl Benzoate, Cetyl Alcohol, Staryl Alcohol, Cyclomethicone, Stearic Acid, Propylene Glycol Isostearate, Glyceryl Stearate, Salicyclic Acid, PEG-100 Stearate, Sodium Hydroxide, Sodium Isostearoyl Lactylate, Dimethicone, Disodium EDTA, Methylchloroisothiazolinone (and) Methylisothiazolinone, Fragrance. What I like about the product:


10+ Free Online Keyword Suggestion Tools
By John Negoita on Wednesday, Jun 6, 2018
Here’s the truth:
Doing keyword research for search engine optimization is crucial for getting a your website to rank higher. But what does keyword research actually mean?
Basically, it means trying to guess which keywords and combination of keywords will attract the most traffic. Since guessing is not a science and SEO is, let’s take a look at how to do keyword research like a PRO for free.
The truth is this,
If you only started blogging, or you don’t have a website that brings a substantial income, paying for an online keyword research tool is not an option.
I know that because I really can’t afford 100$ per month to spend for that. I post about once a week, so that would mean spending in average 25$ per post only for keyword research. Needless to say, that there are weeks when I don’t post, so that would be money down the drain.
Anyway, you are here because you are searching for free tools to do keyword research. And that’s what you will get below. Please note that most of the tools will try to sell you the extra features, and I encourage you to do so if you have the money.
But, if not, here’s what I would do:
First, I would bookmark this page, so I can come back to it whenever I want to do keyword research.
Then, I would combine as much as possible the results from these keyword research tools. As most of the free keyword tools will limit the number of queries you can do per day, it’s a good strategy to continously rotate the tool you use.
In time you will get used to all the tools and get better and better at quickly getting the best keywords for your posts.
Without further ado
The Best Free Online Keyword Suggestion Tools
In this list, I’ve gathered the best online keyword suggestion tools that you can use for free.
- Ubersuggest
- LSIGraph
- KWFinder
- AnswerThePublic - Search Intent Tool
- Keywords Everywhere keyword research tool
- Keyword.io - Google Autocomplete Longtail Keyword Tool
- Google AdWords > Keyword Planner Tool
- Google Correlate
- Keyword Keg
- Wordtracker
- SEMrush
Basically they are Google keyword volume checker tools. Using them you will be able to find the best keywords for your posts and improve your Google ranking in the search results.
So, which is the best keyword suggestion tool for free online use? I will let you decide.
1. Ubersuggest (Neil Patel) - Free Keyword Tool, Generate More Suggestions
UPDATE: In March of 2019 Ubersuggest updated with some fantastic tools. Ubersuggest Backlinks shows backlinks of domain and URL for free. Also, the Keywords Traffic Analyzer Tool got even better.
UPDATE: As of October 2018, Ubersuggest has changed its interface and the way it works. I can’t really tell if the changes made are final or if the online keyword tool is in the final version.
The New Ubersuggest 2018 (and 2019)
The new Ubersuggest online keyword suggestion and SEO tool offers 3 main features:
- Keyword Overview - SEO search volume and PPC estimations overview
- Keyword Suggestions - From head terms to long-tail phrases you’ll get hundreds of suggestions from our free keyword tool. You’ll also see volume, the competition, and even seasonal trends for each keyword.
- Keyword Difficulty - Estimate how hard it’s going to be to rank organically. Check competitive keywords and phrases.
Here’s a quick view of how the SEO Keywords Ideas Report looks like in the new Ubersuggest tool 2018:
The Old Ubersuggest
It’s not Ubbersuggest, it’s Ubersuggest!
Ubersuggest is an amazing online keyword tool.
Neil Patel (one of my favorite SEO gurus) has purchased this tool a while back and placed it on his site. You can use the tool for free. This free online keyword suggestion tool allows you to do keyword research without even logging in.
What can I say, it’s simply the best.
Rumour has it that Neil Patel is preparing a huge blow to the online keyword suggestion tools and SEO tools in general. Neil is preparing to create a full suite of free online SEO tools. Since none of the big players on this market has clearly been set as a leader, and the top tools are now extremly expensive (starting at around 199$/month), Neil’s move can potentially be a game changer in the world of SEO online tools.
Virtually, he can take over the entire market in one move. But, until then, let’s take a look at Ubersuggest.
How does Ubersuggest work?
You key in a keyword or phrase that you want to use, and the tool will suggest (or ubersuggest) alternative keyword ideas.
Not only that, but it will also give you some info regarding:
- Estimated Search Volume
- CPC (Cost Per Click in Google AdWords)
- Competitive Level
Apparently, the results are compiled combination Google Keyword Planner and Google suggested keywords.
You can export the results in CSV and start choosing your focus keywords that will work best for you.
2. LSI Graph - Latent Semantic Indexing Or Related Searches
LSI Graph is a LSI keyword tool.
What Does LSI Keywords Mean?###
Latent semantic indexing (LSI) is a system search engines use to analyze the other words people use surrounding a given topic. LSI keywords are words and phrases with a high degree of correlation to your target topic.
In short, if you look at the end of Google search results page “Searches related to” that’s what LSI is. LSI keywords are keywords in the same family as the the topic of the keyword you input. That is determined by what other people search for in relation to their initial search.
LSI Graph takes in a keyword and provides a list of related keywords.
Here’s how the results look like
3. KWFinder - Find Long Tail Keywords With Low SEO Difficulty##
KWFinder is a nifty SEO tool for long tail keywords research.
It provides quite an extensive report showing:
- Estimated Search Volume
- Search Volume Trend (last 12 months)
- CPC (Cost Per Click in Google AdWords)
- SEO Difficulty Index
- Detailed Overview Of The Competition (including domain and page authority, number of inbound links, SERP)
Here’s how the results look like
4. AnswerThePublic - Search Intent Tool
AnswerThePublic is probably one of my favorites. It shows keyword suggestions in the form of questions. This gives a great indication on not only which topics you should cover in an article about something, but also real keywords that you should probably use in your titles or Hx tags.
Here’s how the results look like
5. Keywords Everywhere - Chrome Extension Keyword Research Tool
Keywords Everywhere is a Chrome and Firefox extension that will show keyword traffic, CPC and competition right in the browser when you do a search.
The websites that are supported by Keywords Everywhere :
- Google.com - the data is shown right under the search textbox
- Google Search Console - the data is shown in the Search Analytics page
- Google Analytics - data shown in the Organic and Search Engine Optimization -> Queries pages
- Google Trends - data shown in the queries widget
- Google Keyword Planner - data shown in a new column
- Google Search - data shown under main keyword as well as related searches
- YouTube - data shown under the searchbox
- Amazon - data shown under the searchbox
- eBay - data shown under the searchbox
- Etsy - data shown under the searchbox
- UberSuggest - data shown alongside keywords in this awesome keyword suggestion tool
- AnswerThePublic.com - data shown in popup on each spoke of the wheel, data also shown in the alphabetic listings
- Soovle.com - data shown next to each keyword all over the page
- KeywordShitter.com - data shown under the main text area next to keywords
- Majestic - Anchor Text Report
- Moz Open Site Explorer - Anchor Text Report
When you do a search on Google for seo tips, here’s how the results look like:
6. Keyword.io - Google Autocomplete Longtail Keyword Tool
Keyword.io is a keyword research tool that grabs autocomplete keywords from sources like Google autocomplete, Amazon autocomplete, Ebay autocomplete, but also other sources like: Bing, Yandex, Wikipedia, Alibaba, YouTube, Fiverr, Google Play Store, Fotolia, Pond5, etc.
Let’s see an example.
When you research keywords for car insurance, you’ll certainly not want to miss keywords like auto policy limits, young drivers or hail damage for content and marketing ideas.
Keyword.io related keyword tool finds thousands of those for you, including search volume, Adwords prices and competition.
Here’s how the results look like:
7. Google AdWords > Keyword Planner - Use Google For Keyword Research
Google AdWords > Keyword Planner Tool is probably one of the best known tool for keyword research. I left it at the end especially because it’s so well known.
Google AdWords is mainly for creating Google ads, not doing SEO or keyword research. That said, there is a strong overlap between keywords advertisers are focusing and what people are searching.
Because of that, this is considered the main Google keyword volume checker. The Google AdWords Keyword Research Tool will give you an insight into Google search terms right from the source.
The way to use it has changed several times during the last 10 years. I’m describing how Google AdWords Keyword Planner Tool looks like in 2018 and how to use it.
To open Google AdWords Keyword Planner simply go to the Keyword Planner in the Tools menu:
Then, you will see 2 options:
- Find Keywords - this is actually the way to get keywords suggestion similar to your focus keywords. This is pretty similar to results offered by other SEO research tools.
- Get Search Volume & Forecast - the keyword volume checker is given as an interval, which is not very exact. This doesn’t make Google AdWords the best kewyword research tool, but you can get a pretty good idea on the search volume.
Google AdWords > Keyword Planner > Find Keywords
Simply key in your chosen keywords and the tool will suggest similar keywords. Here’s a sample of how that looks like:
Google AdWords > Keyword Planner > Get Search Volume & Forecast
Here’s how the tool provides insight regarding search volume and competition on the selected keywords.
8. Google Correlate - Statistical Correlations Between Search Keywords
Google Correlate is a less known tool from Google. It does statistic correlations between keywords searched.
Key in a keyword combination and the tool will give you other combinations that the same user will be most likely to search for.
Here’s how the results look like
9. Keyword Keg - Discover Top Long-tail Keywords Using Data From 11 Suggest APIs##
Keyword Keg is another free online keyword suggestion tool. There’s also a permium version that you have to pay for, but the free version has already some very interesting keyword suggestion features.
IMPORTANT: when doing keyword research as part of a SEO session for pages that want to eventually sell products or services, you should pay attention to the commercial intent of keywords.
What’s that?
Keywords commercial intent is an indicator of whether the person doing the search with that keyword intends to buy something or not.
I’ll give you an example:
The keywords “free online keyword suggestion tool” has less commercial intent (to none) than “premium keyword suggestion tool”. For the former, obviously the intent is to find something free for keyword suggestion, while the latter shows the willingness of the searcher to buy access to a keyword suggestion tool.
Keyword Keg like other keyword suggestion tools in this list provides an indicator of commercial intent in the form of CPC (cost-per-click). CPC is what advertisers pay for ads with certain keywords. If they are willing to pay for those ads, it means that for sure there are people willing to buy.
The higher the CPC, the higher the probable commercial intent of the keywords.
Here’s how the Keyword Keg results look like
10. Wordtracker - Free Keyword Research Tool
Wordtracker is a free online keyword research tool that provides in-depth reports on search volume for related search strings.
It combines competitors’ keywords, in-depth PPC and SEO insights, data from Google and presents them in a report that looks something like this:
11. SEMrush - Service For Competitors Research, Shows Organic And Ads Keywords For Any Site Or Domain
Wordtracker is a free online keyword research tool. It does require you to create an account and there are limitations on the number of queries you can make in a day. But, the reports offered are among the most extensive SEO reports I could find in any of the other keyword research tools. Besides keyword research you can do other SEO tasks like backlink listing, competition analysis and more.
Here’s how SEMrush presents the information regarding keyword search volumes:


Founder of Moblize Love and City Impact Conference
Kyle really is the microphone to our brand, story, and communication. He helped set us up for success, and to continue to tell story to inspire.Director at Crazy Love
Kyle's heart, and passion was evident in what he does. That reflects for all of Allsprout, and really connected with our heart at Crazy Love.Creative Director at SF Worship Center
Kyle's faithfulness, and integrity in what he does is evident throughout his character, and work. I'm happy with what Allsprout has done and I would definitely recommend working with them.


This controls where SCM polling is performed.
If this is not selected, SCM polling will be performed on the agent that handled the latest build.
If this is selected, SCM polling will be performed on the controller. This allows polling to be done even when no suitable agent is present; a prerequisite when using on-demand provisioned agents. However, the Plastic SCM command line client must be present and configured on the controller.
Note that Jenkins stores this setting at each build, and SCM polling uses the setting from the latest build. Changes to this setting will only take effect after you also have triggered a build.


---
title: Walking a model tree
---
A model is a tree structure which may contain paths, and it may also contain other models in a heirachy.
You can traverse the entire tree by calling makerjs.model.walk with your model and an object with these optional properties:
- onPath: function(walkPath object) - called for every path (in every model) in your tree.
- beforeChildWalk: function(walkModel) - called for every model in your tree, prior to diving deeper down the tree. Return false if you wish to not dive deeper.
- afterChildWalk: function(walkModel) - called for every model in your tree, after returning from a deep dive down the tree.
walkPath object
A walkPath object has these properties:
- layer: the layer name (if any) containing this path.
- modelContext: the model containing this path.
- offset: the absolute coordinates from [0, 0] where this path is located.
- pathContext: the path itself.
- pathId: the id of this path in its parent model.paths container.
- route: array of property names to locate this path from the root of the tree.
- routeKey: a string representation of the route which may safely be used as a unique key identifier for this path.
walkModel object
A walkModel object has these properties:
- childId: the id of this model in its parent model.models container.
- childModel: the model itself
- layer: the layer name (if any) containing this path.
- offset: the absolute coordinates from [0, 0] where this model is located.
- parentModel: the model containing this model.
- route: array of property names to locate this model from the root of the tree.
- routeKey: a string representation of the route which may safely be used as a unique key identifier for this model.


| MUPD-Music Pedagogy |
| NR |
12153 |
MUPD |
2190 |
001 |
24M |
2.000 |
Diction for Singers II (Italian) |
MW |
01:50 pm-02:45 pm |
20 |
6 |
14 |
0 |
0 |
0 |
Joshua T Zink (P) |
01/17-05/04 |
SFA 208 |
Credit from 4-Year College |
| NR |
12154 |
MUPD |
2210 |
001 |
24M |
2.000 |
Diction for Singers IV (French) |
MW |
01:50 pm-02:45 pm |
10 |
4 |
6 |
0 |
0 |
0 |
Christine C Isley-Farmer (P) |
01/17-05/04 |
SFA 205 |
Credit from 4-Year College |
| C |
12361 |
MUPD |
3050 |
001 |
24M |
2.000 |
Jazz Pedagogy |
TR |
01:00 pm-01:55 pm |
26 |
27 |
-1 |
0 |
0 |
0 |
William M Parkinson (P) |
01/17-05/04 |
SFA 101 |
Credit from 4-Year College and Upper-Division Work |
| NR |
17525 |
MUPD |
4140 |
002 |
24M |
2.000 |
Advanced Woodwind Pedagogy |
|
TBA |
5 |
1 |
4 |
0 |
0 |
0 |
Todd H Waldecker (P) |
01/17-05/04 |
TBA TBA |
Credit from 4-Year College and Upper-Division Work |
| NR |
14089 |
MUPD |
4310 |
001 |
24M |
2.000 |
Vocal Pedagogy |
TR |
02:40 pm-03:35 pm |
15 |
4 |
11 |
0 |
0 |
0 |
Dina Sandra Cancryn (P) |
01/17-05/04 |
SFA 301 |
Credit from 4-Year College and Upper-Division Work |
| NR |
17169 |
MUPD |
5130 |
001 |
24M |
2.000 |
Advanced String Pedagogy |
|
TBA |
5 |
1 |
4 |
0 |
0 |
0 |
Kevin Bate (P) |
01/17-05/04 |
TBA TBA |
|
| NR |
15799 |
MUPD |
6330 |
001 |
24M |
3.000 |
Jazz Pedagogy and Program Administration |
|
TBA |
5 |
1 |
4 |
0 |
0 |
0 |
James Raymond Simmons (P) |
01/17-05/04 |
TBA TBA |
|


Please select a .bin file you want to flash to a Telink BLE device.
Select Firmware:
Status: waiting for you to connect a device
Temp/Humi: waiting for data, this will not change on devices with custom firmware, only on stock firmware
#enable-experimental-web-platform-features may be needed to read MAC
Device known id:
Mi Token:
Mi Bind Key:
When doing an activation here the device is needed to be activated in the Mi app again when wanted to use there.
Send settings to custom firmware:
Smiley:
Advertising Type:
Sensor display:
Show battery in LCD:
Advertising interval:
Measurement step (in advertising intervals):
RF TX Power:
Sensor offset:
To set a precise offset use the text input field with FA+offset for Temp and FB+offset for Humiditiy convert an int8 to hex.
Sensor instant Advertising:
To set other values use the text input with FC+temp alarm or FD+humi alarm with uint8_t values.
This can be used to get instant advertising if the sensor data changes to fast. Temp divided by 10


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D&D Database
Character Insertion Form
Character Name:
Student ID:
Class:
Background:
Race:
Alignment:
Experience Points:
Strength:
Value:
Saving Throw:
Dexterity:
Value:
Saving Throw:
Constitution:
Value:
Saving Throw:
Intellegence:
Value:
Saving Throw:
Wisdom:
Value:
Saving Throw:
Charisma:
Value:
Saving Throw:
Inspiration:
Proficiency Bonus:
Gold:
HitPointMax:
Personality Traits:
Ideals:
Bonds:
Flaws:
Other Info:
.


This page refers to v0.12 documentation
Getting Started
The following sections walk through steps to have HAProxy Ingress working, watching ingress resources and exposing services.
Prerequisites
HAProxy Ingress needs a running Kubernetes cluster. Controller version v0.12 needs Kubernetes 1.18 or newer, see other supported versions in the README file. HAProxy Ingress also works fine on local k8s deployments like minikube, kind or k3d.
An ingress controller works exposing internal services to the external world, so another pre-requisite is that at least one cluster node is accessible externally. On cloud environments, a cloud load balancer can be configured to reach the ingress controller nodes.
HAProxy Ingress uses TLS SNI extension and the Host header to associate requests and ingress’ host
s. The easiest way to accomplish this on local environment is using nip.io. A production environment should consider a dynamic DNS solution or a wildcard DNS record.
Installation
HAProxy Ingress uses Helm chart to install and configure the controller. See below some deployment instructions:
1) Install helm
, HAProxy Ingress requires version 3. See the installation instructions here.
2) Add the HAProxy Ingress’ Helm repository. This will instruct Helm to find all available packages:
$ helm repo add haproxy-ingress https://haproxy-ingress.github.io/charts
3) Check if kubeconfig points to the right cluster:
$ kubectl cluster-info
The default cluster can be changed either via kubectl config set-context <cluster-context>
or adding --kube-context <cluster-context>
in the helm command-line options.
Note that the user needs administrative privileges in the cluster to properly installs the controller.
4) Create a haproxy-ingress-values.yaml
file with custom parameters:
controller:
hostNetwork: true
HAProxy Ingress chart documentation has all the available options. See also further documentation in the default values file.
5) Install HAProxy Ingress using haproxy-ingress
as the release name and haproxy-ingress-values.yaml
file as the custom parameters:
$ helm install haproxy-ingress haproxy-ingress/haproxy-ingress\
--create-namespace --namespace ingress-controller\
--version 0.12.18\
-f haproxy-ingress-values.yaml
Note
The commandinstall
above can be changed to upgrade
to start a rolling update of HAProxy Ingress version or configuration. template
can be used instead to generate the manifests without installing them - add either a redirect ... >haproxy-ingress-install.yaml
to save the output, or --output-dir output/
command line option to save one file per manifest.
The controller should be running in a few seconds. There are three important customizations made in the example above:
- version: a good practice, this will ensure that you’ll have the same version installed even if a new release issued.
- namespace: we’re instructing helm to install HAProxy Ingress in the
ingress-controller
namespace. This namespace will be created if it does not exist yet. The default behavior, if namespace is not provided, is to deploy the controller in the kubectl’s current namespace. - hostNetwork: we’re configuring the deployment to expose haproxy in the host network, which means bind all haproxy ports, including but not limited to 80 and 443, in the node’s IPs. Maybe this isn’t a proper configuration for your production - it depends on the options you have to expose a Kubernetes’ service, but doing so we’ll be able to send http/s requests on local development environments, or even baremetal and on premise deployments that doesn’t have a fronting router or load balancer to expose the controller. In any case a service is also configured in the
ingress-controller
namespace which tries to expose haproxy.
HAProxy Ingress’ Helm chart has a few more configuration options, see all of them in the chart documentation and in the default values file.
Deploy and expose
The following steps deploy an echoserver image and exposes it in the current namespace.
1) Create the echoserver’s deployment and service:
$ kubectl create deployment echoserver --image k8s.gcr.io/echoserver:1.3
$ kubectl expose deployment echoserver --port=8080
2) Check if echoserver is up and running:
$ kubectl get pod -w
NAME READY STATUS RESTARTS AGE
echoserver-5b6fb6dd96-68jwp 1/1 Running 0 27s
3) Make HAProxy Ingress exposes the echoserver service. Change HOST
value in the example below to a hostname that resolves to an ingress controller node.
Obs.: nip.io
is a convenient service which converts a valid domain name to any IP, either public or local. See here how it works.
$ HOST=echoserver.<IP_ADDRESS>.nip.io
$ kubectl create -f - <<EOF
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
metadata:
annotations:
kubernetes.io/ingress.class: haproxy
name: echoserver
spec:
rules:
- host: $HOST
http:
paths:
- backend:
serviceName: echoserver
servicePort: 8080
path: /
tls:
- hosts:
- $HOST
EOF
4) Send a request to our echoserver.
$ curl -k https://echoserver.<IP_ADDRESS>.nip.io
$ wget -qO- --no-check-certificate https://echoserver.<IP_ADDRESS>.nip.io
What’s next
Learn more about Ingress and IngressClass resources:
- Ingress and IngressClass resources from Kubernetes docs
HAProxy Ingress has lots of configuration options. See the following tips to get started faster:
- Follow some configuration instruction from the examples page
- See how HAProxy Ingress uses ingress objects: configuration keys
- Get started with all the configuration options: configuration


aria-describedby Test
First button in the navigation order
Button One
Second button in the navigation order
Button 2
aria-describedby test. How long did VoiceOver wait to read this?


Internationalization of a Spincast application is now very easy.
You can read everything about this topic in the Dictionary section of the documentation.
Not only can you now translate your application, but you can also override the messages provided by Spincast and the various plugins.
Finally, a Pebble function is also
provided to get localized messages from your templates (your HTML
templates, for example).


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Tyler Estes
Mechanical Engineer // Developer // Maker
Tyler Estes
Mechanical Engineer // Developer // Maker


I am currently studying at Acadia Unviersity, Canada. I am majoring in Computer Science and I have completed all courses. I am working on my master thesis on topic of EEG(electroencephalo-graph) data compression. Hopefully, I will graduate before May, 2016.
Since I came Canada 2 years ago, I am really enjoy the life here. I would like to have an opptunity to work and live here and I won't consider the specific location.
I love travel, watching sports game and playing chess. Also, I spent most of my spare time to read coding books and surf on some technique Website. I am a newbee for career, but I wont be that too long. Everyweek, I always select one or two hockeygames or A.M football games to watch.
EEG is the abbreviation of electroencephalography, which is a monitoring scheme for the recording of brain waves. With the multiple electrodes placed on the scalp, EEG data measures the change of voltage fluctuation and detecting the brain neurons electric current flow. Some diagnostic applications and devices invented by the principle of EEG signals, which helped doctors or scientists could observe the signals by vision.
With the rise of the Wearable medical idea. People start to research the EEG signals, which is the abbreviation of the electroencephalograph. However, during its multiple electrodes and complex signal waves, EEG equipment always generate huge amount of datas. Therefore, scientists are consider how to transmit this magnitudes of data from internet and computer. Data compression is an inevitable problem during the whole information spreading process. In this paper, we are try to discover the performance of audio compression techniques to compress the EEG data. In the first stage, we test the difference between audio compression techniques and the other lossless techniques. In the second stage, audio compression method, especially FLAC (Free Lossless Audio Dodec) tools are implemented several times in my experiments and some results relate to the parameters of FLAC has been founded.
When I do the research on my thesis, I use C language to do experiments. I have solid understanding about Data Structure and Algorithm. I am also keeping high interest on web construct and Database build. Therefore I have read several PHP & MYSQL books, I bulid several Admin System for myself and my friend.
Contact me and I'll get back to you within 24 hours.
10 Summer St, Po Box 2638, Wolfville, Ns, Canada
+1<PHONE_NUMBER>
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no warnings when compiling /W3 · nothings/stb@a0b521f compiling all test cases and compilers in test.sbm Compilers: 32-bit: VS2015 VS2013 VS2008 VC6 (1998) clang-cl 9.0.1 64-bit VS2015 c...
PostgreSQL Tools for the Visually Inclined I started my career on the Microsoft stack building forms and websites using drag and drop tools. Over time that became a punchline, which is unfortunate because honestly, the productivity was insa…
Martin needs to watch Trainspotting · Issue #2 · martinwoodward/martin_public Version Used: Martin Woodward, Jan 22 2017 Steps to Reproduce: Find Martin Talk about movies Mention the worst toilet in Scotland Expected Behavior: "Yeah, that was a bloody good moment in Tra...
How to play 21 against 22 Learn how to play 21 against 22 (or any polyrhythm). It's not only possible, but it's not as difficult as you might think. In this video, we look at a few ways to play polyrhythms -- including Jacob Collier's famous 21:22 -- as well as some musical appli...
7:11 Polyrhythms This video teaches you exactly how to break down a 7 against 11 polyrhythm, and play it. Why? Because memes can be a way to musical aptitude. It should have been sponsored by 7-Eleven, but wasn't. Oh well. If you want to download the 7:11 tune, its he...
Exploring the Docker Extension for VS Code and .NET Core - DEV Community 👩💻👨💻 The Docker extension for VS Code was recently updated to better support .NET ... Tagged with docker, vscode, dotnetcore, azure.
Hardware intrinsic in .NET Core 3.0 - Introduction In the past two years, I worked for Intel Corporation as a compiler engineer. The major project I worked during that period of time is to design and implement the x86 hardware intrinsic for .NET Core.
Efficient params and string formatting by jaredpar · Pull Request #2293 · dotnet/csharplang This proposal is being discussed at LDM on 3/4/2019.
IIS Hosting for ASP .NET Core Web Apps This is the ninth of a series of posts on ASP .NET Core in 2019. In this series, we’ll cover 26 topics over a span of 26 weeks from January through June 2019, titled A-Z of ASP .NET Core! A – Z of…
puns.dev - The Worst Computer Puns on the Internet A curated selection of the worst computer puns from around the Internet
The Latest in Creepy Spyware - Schneier on Security The Nest home alarm system shipped with a secret microphone, which -- according to the company -- was only an accidental secret:
CoreCLR’s environment is not your environment CoreCLR maintains its own private copy of environment variables
How to make your game run at 60fps Here’s the deal. You got a game, and you want to run it at 60 fps on a 60hz monitor. Your computer is fast enough that both rendering and…
CognitiveComplexity - Plugins | JetBrains This plugin implements live calculation of the Cognitive Complexity metric, which was proposed by G.
The Orville Prequel / Old Parody of Star Trek TOS footage was originally found by this channel: https://www.youtube.com/channel/UCsU6mvoJAhMTqJnuZXCwFBQ sci fi reviews channel: https://www.youtube.com/channel/UCrLsxBysUHnpSKRpXMbMVzg All parody Edits : https://www.youtube.com/channel/UCnnRzi7q1YTRFY...
dotnet/coreclr CoreCLR is the runtime for .NET Core. It includes the garbage collector, JIT compiler, primitive data types and low-level classes. - dotnet/coreclr
dotnet/coreclr CoreCLR is the runtime for .NET Core. It includes the garbage collector, JIT compiler, primitive data types and low-level classes. - dotnet/coreclr
Inline IL ASM in C# with Roslyn | xoofx Source code for Roslyn changes is available on github Source code of the example program is available in this gist In the past years I have been using and abusing a lot patching assemblies for...


--- layout: post title: Il paradosso dell'arciere date: '2008-04-16T12:15:00.000+02:00' author: Luca Ferrari tags: - arco modified_time: '2008-04-16T12:47:02.200+02:00' blogger_id: tag:blogger.com,1999:blog-1836481905487384887.post-9116226434729287974 blogger_orig_url: http://fluca1978.blogspot.com/2008/04/il-paradosso-dellarciere.html permalink: /:year/:month/:day/:title.html ---


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Ceramic is the general term for pottery and porcelain. It can be said that an important part of the history of the Chinese nation is the history of ceramic development, the achievements of the Chinese people in science and technology, and the pursuit and shaping of the United States. In many ways, it is reflected through ceramic production and forms a very typical technical and artistic feature of various eras. It is universally acknowledged that porcelain was invented by the Chinese. The invention of porcelain is based on the continuous development and improvement of pottery technology. The White pottery of the Shang Dynasty was made of porcelain clay(Kaolin) as a raw material, and the firing temperature was above 1000 °C. It was the basis for the appearance of original porcelain.


fun copy(): Block
Returns the array of all words in the block. Used when the block is dirty and in the least recently used block, so the data can be written back to memory. This allocates new space for other data.


{% extends "_layouts/base.html" %} {% block head %} {% endblock %} {% block content %}
Please provide admin user details.


CodeDeploy DeploymentGroup Ec2TagFilters
Ec2TagFilters
is a property of the AWS::CodeDeploy::DeploymentGroup resource that specifies which EC2
instances to associate with the deployment group.
For information on using tags and tag groups to help manage your Amazon EC2 instances and on-premises instances, see Tagging Instances for Deployment Groups in AWS CodeDeploy in the AWS CodeDeploy User Guide.
Syntax
Properties
Key
-
Filter instances with this key.
Required: No
Type: String
Type
-
The filter type. For example, you can filter instances by the key, tag value, or both. For valid values, see EC2TagFilter in the AWS CodeDeploy API Reference.
Required: Yes
Type: String
Value
-
Filter instances with this tag value.
Required: No
Type: String


Zoho hits 50 million business users, launches WorkDrive
by Larry DignanWith the launch of WorkDrive, Zoho is aiming to bridge its apps with a unified search, single storage repository and integration with encryption, virus detection, imaging processing and artificial intelligence.
Zoho said it has hit the 50 million business user mark and launched WorkDrive, a unified document management system across its portfolio of 45 applications.
The company, which offers a suite of enterprise applications that adds up to be a business operating system, has rapidly expanded with 10 data centers around the world.
With the launch of WorkDrive, Zoho is aiming to bridge its apps with a unified search, single storage repository and integration with encryption, virus detection, imaging processing and artificial intelligence.
The unified file system behind WorkDrive is designed to enable more integrated business processes by connecting documents and context. WorkDrive's role is akin to how Google Drive operates within G Suite.
Storage, servers and more: We found 24 cloud services for your business | What's the best cloud storage for you?
WorkDrive includes:
- Integration with the Zoho Office suite;
- Team Folders for collaboration and sharing;
- Image, video and document conversion services;
- Analytics for administrators and access controls;
- External collaboration features.
In addition to WorkDrive, Zoho said it revamped its Workplace suite of productivity apps including Sheet, Mail, Meeting and Writer to name a few. Zoho added an integrated app dashboard with widgets of information from the nine apps in Workplace. Each app in Workplace has added features and integration with the Zia AI assistant.
Also: How to sync multiple cloud storage services for free TechRepublic
WorkDrive is available starting at $2 per user per month and Workplace is $3 per user per month.
Related:


define(["d3", "model", "lodash", "codemirror/lib/codemirror", "codemirror/mode/javascript/javascript", "inlet"], function (d3, Model, _, CodeMirror) {
return function ConfigEditor(runtime) {
var model = Model({
A JSON configuration editor plugin.
This uses CodeMirror to provide a live editing environment for the runtime configuration. This allows users to edit the configuration JSON interactively and see it change while the system is running.
Created by Curran Kelleher Feb 2015
define(["d3", "model", "lodash", "codemirror/lib/codemirror", "codemirror/mode/javascript/javascript", "inlet"], function (d3, Model, _, CodeMirror) {
return function ConfigEditor(runtime) {
var model = Model({
The hidden
boolean property triggers the layout
to recalculate to show and hide the editor.
publicProperties: [ "hidden" , "size"],
size: "400px"
});
Append a div to contain the editor to the runtime div.
Use CSS position: absolute;
so setting left
and top
CSS
properties later will position the SVG relative to the runtime div.
var div = d3.select(runtime.div)
.append("div")
.style("position", "absolute");
Append an instance of CodeMirror to the div.
var editor = CodeMirror(div.node(), {
This ensures that all the text is displayed after resizing. See http://codemirror.net/demo/resize.html
viewportMargin: Infinity,
This makes the editor display syntax-highlighted JSON.
mode: "javascript"
});
Keeps track of the config string from the CodeMirror editor that was the last to be parsed and set as the runtime config.
var oldConfigStr;
These flags are used to stop circular updates. var ignoreChangeFromCodemirror = false; var ignoreChangeFromRuntime = false;
Edit the text when the runtime config updates.
runtime.when("config", function(config){
var newConfigStr = JSON.stringify(config, null, 2);
if(newConfigStr !== oldConfigStr){
editor.setValue(newConfigStr);
}
});
Update the runtime config when text is edited.
editor.on("change", _.throttle(function(){
oldConfigStr = editor.getValue();
runtime.config = JSON.parse(oldConfigStr);
Throttle by 33 ms so the frequent updates induced by Inlet widgets propagates through the system at most 30 frames per second (1000 / 30 = 33.333).
}, 33));
When the size of the visualization is set by the layout plugin,
model.when("box", function (box) {
set the CSS (left, top, width, height) properties to move and position the editor relative to the runtime div.
div
.style("left", box.x + "px")
.style("top", box.y + "px")
.style("width", box.width + "px")
.style("height", box.height + "px");
Update the height of the CodeMirror editor.
div.select(".CodeMirror")
.style("height", box.height + "px");
});
Clean up the DOM elements when the component is destroyed.
model.destroy = function(){
TODO test this TODO remove config listener
runtime.div.removeChild(textarea.node());
};
Augment the editor using Inlet, which gives the user a color picker for colors and a slider for numbers.
Inlet(editor);
return model;
};
});


The cardinality indicator (0..1, 1..1, 1..*, etc.) specifies the allowable occurrences within a document instance. The cardinality indicators are interpreted with the following format “m…n” where m represents the least and n the most:
When a constraint has subordinate clauses, the scope of the cardinality of the parent constraint must be clear. In the next figure, the constraint says exactly one participant is to be present. The subordinate constraint specifies some additional characteristics of that participant.
Constraints Format - only one allowed
In the next figure, the constraint says only one participant “like this” is to be present. Other participant elements are not precluded by this constraint.
Constraints Format—only one like this allowed


One of the Danish Frisian Islands, Rømø lies just north of the German island of Sylt. A causeway links this island to the Danish mainland.
Access to the harbour, which is on the SE tip, is possible in all tides. Yachts berth in about 4m, on the N side of the inner harbour. It is also possible to anchor outside, just N of the entrance.


Hi, I am working in a scheduling problem and I need to post a constraint x[z]<=y, where x is an IntVarArray, z is an IntVar and y is a constant (but in general could be another variable). It is close to the element constraint which allows to post the constraint x[z]==y. What is the best way to do it? best, Javier Larrosa


Instantly see which nswers you get correct. When you miss a challenge, we'll quickly show you how to improve.
We motivates you to stay on track by recording how many days in a row you spend learning a language.
Hearts keep your lessons alive! You lose them when you answer incorrectly. When you're out of hearts, start over and try again.
Learning is an emotional roller coaster mixed with amazing highs, frustrating plateaus, and the feeling like you’re not making any progress at all. It’s so easy to become discouraged and want to throw in the towel which is why I’ve collated a list of inspiring language quotes from history’s great linguistics and authors as well as beautiful proverbs that will give you a much-needed boost of morale.
motivating your students
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maximizing the joy, and minimizing the pain, of the classroom experience.
Believe, Trust and Speak
Each lesson includes a variety of speaking, listening, translation, and multiple choice challenges
Instantly see which answers you get correct. When you miss a challenge, we'll quickly show you how to improve.
Boost your brain cells getting more favorite quotes and like them, we will show you more.
SY motivates you to stay on track by recording how many days in a row you spend learning a language


Policy Routing Settup Enter the policy name, policies, and WAN interface then click "Apply/Save" to add the entry to the policy routing table. Note: If selected "IPoE" as WAN interface, default gateway must be configured. Policy Name: Interface Group: <%ejGet(lanlistpr)%> Source IP: Use Interface: Default Gateway IP:
Policy Routing Settup Enter the policy name, policies, and WAN interface then click "Apply/Save" to add the entry to the policy routing table. Note: If selected "IPoE" as WAN interface, default gateway must be configured.


Hands-on Lab: Using Oracle Database 12c as a JSON document store
Mark Drake
Manager, Product Management
Oracle XML DB & JSON
Contents
Using Oracle Database 12 as a JSON document store
JSON (JavaScript Object Notation)
Using SQL Developer to run SQL scripts
Set up SQL Developer’s connection to the database
Storing JSON documents in Oracle Database 12c
Create a simple table to store JSON documents.
Loading JSON Documents into the database
Accessing JSON documents stored in Oracle Database 12c
Accessing scalar values using JSON_VALUE
Accessing objects and arrays using JSON_QUERY
Relational access to JSON content using JSON_TABLE
Creating Relational views of JSON content
Filtering result sets using JSON_EXISTS
Indexing JSON documents stored in Oracle Database 12c
This Lab will walk you through the basics of using Oracle Database as a JSON document store.
Approximately 60 minutes.
The following description of JSON is taken from the web site http://www.json.org:
JSON is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.
JSON is built on two structures:
· A collection of name/value pairs. In various languages, this is realized as an object, record, struct, dictionary, hash table, keyed list, or associative array.
· An ordered list of values. In most languages, this is realized as an array, vector, list, or sequence.
These are universal data structures. Virtually all modern programming languages support them in one form or another. It makes sense that a data format that is interchangeable with programming languages also be based on these structures.
See http://www.json.org for more details.
A simple JSON document representing a PurchaseOrder can be seen below.
"PONumber": 1600,
"Reference": "ABULL-20140421",
"Requestor": "Alexis Bull",
"User": "ABULL",
"CostCenter": "A50",
"ShippingInstructions": {
"name": "Alexis Bull",
"Address": {
"street": "200 Sporting Green",
"city": "South San Francisco",
"state": "CA",
"zipCode": 99236,
"country": "United States of America"
},
"Phone": [
{
"type": "Office",
"number": "909-555-7307"
}, {
"type": "Mobile",
"number": "415-555-1234"
}
]
},
"SpecialInstructions": null,
"AllowPartialShipment": false,
"LineItems": [
{
"ItemNumber": 1,
"Part": {
"Description": "One Magic Christmas",
"UnitPrice": 19.95,
"UPCCode":<PHONE_NUMBER>9
},
"Quantity": 9.0
}, {
"ItemNumber": 2,
"Part": {
"Description": "Lethal Weapon",
"UnitPrice": 19.95,
"UPCCode":<PHONE_NUMBER>7
},
"Quantity": 5.0
}
]
}
JSON documents contain scalar values, arrays and objects. Scalar values can be strings, numbers, booleans or null. Objects consist of zero or more key-value pairs. The value can be a scalar, an array or an object. Arrays do not need to be homogeneous e.g. each item in an array can be of a different type.
|
|
|
|
|
|
|
"ABULL-20120421" |
|
|
|
99236 |
|
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|
{ "street": "200 Sporting Green", "city": "South San Francisco", "state": "CA", "zipCode": 99236, "country": "United States of America" } |
|
|
|
[ { "ItemNumber" : 1, "Part" : { "Description" : "One Magic Christmas", "UnitPrice" : 19.95, "UPCCode" :<PHONE_NUMBER>9 }, "Quantity" : 9 }, { "ItemNumber" : 2, "Part" : { "Description" : "Lethal Weapon", "UnitPrice" : 19.95, "UPCCode" :<PHONE_NUMBER>7 }, "Quantity" : 5 } ] |
|
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|
{ "ItemNumber" : 2, "Part" : { "Description" : "Lethal Weapon", "UnitPrice" : 19.95, "UPCCode" :<PHONE_NUMBER>7 }, "Quantity" : 5 }
|
|
|
|
[13131092899,<PHONE_NUMBER>7] |
All of the scripts required for this laboratory have been pre-loaded in the virtual machine and can be run via SQL Developer. To run scripts in SQL Developer:
1. Click the Files tab in the SQL Developer navigator window. If the Files tab is not present, open the View menu and click Files.
2. Expand the folder tree in the Files table and locate the folder containing the SQL script files, The folder can found by selecting MyComputer -> Desktop -> DatabaseTrack -> JSON -> sql
3. Click the + button to show the list of files in the SQL folder.
4.
Open the script by moving the mouse over the required script and
then selecting OPEN from the right mouse button menu.
1. Open
a command prompt by right clicking anywhere on the desktop and selecting the
“Open in Terminal” option from the right click menu.
2. When
the terminal window opens, set the current directory to the home directory and run
the reset script by executing the command ./reset_JSON at the command
prompt.
3. Once the script has completed, type CTRL+D to close the terminal window.
1. Click
the SQL Developer icon on the desktop to start the application.
2. Create
a database connection as user JSON by performing the following steps.
3. In the Connections tab, right-click Connections and select New Connection.
The New / Select Database Connection window opens.
4. Enter
the following details:
Connection Name: JSON
UserName: JSON1
Password:oracle
Hostname: localhost
Port: 1521
Service name: ORCL
5. Click Test to make sure that the connection has been set correctly. Select the Save Password check box, to ensure that the password is saved. After the test status shows success, click Save to save the connection, then click Connect.
1. Go to Tools -> Preferences.
Expand Database, and select
the Autotrace/Explain Parameters option.
2. Make sure to select the following check boxes
Object_Name
Cost
Cardinality
Predicates
Click OK.
JSON support is added to Oracle Database 12c starting with release Oracle Database <IP_ADDRESS>.0.
In Oracle there is no dedicated JSON data type. JSON documents are stored in the database using standard Oracle data types such as VARCHAR2, CLOB and BLOB. VARCHAR2 can be used where the size of the JSON document will never exceed 4K or 32K, when LONG VARCHAR support is enabled. Larger documents are stored using CLOB or BLOB data types.
In order to ensure that the content of the column is valid JSON data, a new constraint IS JSON, is provided that can be applied to a column. This constraint returns TRUE if the content of the column is well formatted JSON and FALSE otherwise.
Open the script 1.0 CreateTable.sql. This script shows how to create a table to store JSON documents and how to use external tables to load JSON documents into the table.
This first statement in this script creates a table which will be used to contain JSON documents.
create table
J_PURCHASEORDER (
ID RAW(16) NOT NULL,
DATE_LOADED TIMESTAMP(6) WITH TIME ZONE,
PO_DOCUMENT CLOB CHECK (PO_DOCUMENT IS JSON)
)
/
This statement creates a very simple table, J_PURCHASEORDER. The table has a column PO_DOCUMENT of type CLOB. The IS JSON constraint is applied to the column PO_DOCUMENT, ensuring that the column can store only well formed JSON documents.
JSON documents can come from a number of different sources. Since Oracle stores JSON data using standard SQL data types, all of the popular Oracle APIs can be used to load JSON documents into the database. JSON documents contained in files can be loaded directly into the database using External Tables.
The second statement creates a simple external table that can read JSON documents from a dump file generated by a typical No-SQL style database. In this case, the documents are contained in the file PurchaseOrders.dmp. The SQL directory object ORDER_ENTRY points to the folder containing the dump file, and the SQL directory object JSON_LOADER_OUTPUT points to the database’s trace folder which will contain any ‘log’ or ‘bad’ files generated when the table is processed.
CREATE TABLE
DUMP_FILE_CONTENTS(
PO_DOCUMENT CLOB
)
ORGANIZATION EXTERNAL(
TYPE ORACLE_LOADER
DEFAULT DIRECTORY ORDER_ENTRY
ACCESS PARAMETERS (
RECORDS DELIMITED BY 0x'0A'
BADFILE JSON_LOADER_OUTPUT: 'JSON_DUMPFILE_CONTENTS.bad'
LOGFILE JSON_LOADER_OUTPUT: 'JSON_DUMPFILE_CONTENTS.log'
FIELDS(
JSON_DOCUMENT CHAR(5000)
)
)
LOCATION (
ORDER_ENTRY:'PurchaseOrders.dmp'
)
)
PARALLEL
REJECT LIMIT UNLIMITED
/
The third statement copies the JSON documents from the file underlying the external table into the J_PURCHASEORDER table.
insert into J_PURCHASEORDER
select SYS_GUID(), SYSTIMESTAMP, JSON_DOCUMENT
from DUMP_FILE_CONTENTS
where PO_DOCUMENT IS JSON
/
commit
/
The IS JSON condition is used to ensure that the insert operation takes place only for well formed documents. Make sure that the commit statement is executed after the insert statement has completed. SYS_GUID and SYSTIMESTAMP are used to populate columns ID and DATE_LOADED.
Oracle 12c allows a simple ‘dotted’ notation to be used to perform a limited set of operations on columns containing JSON. It also introduces a set of SQL operators that allow the full power of the JSON path language to be used with JSON. This is similar to the way in which the database allows XQuery to be used to access the contents of XML documents stored in the database.
Accessing JSON using simplified syntax
The dotted notation can be used to perform basic operations on JSON stored in Oracle 12c. Using the dotted notation you can access the value of any keys contained in the JSON document. In order to use the dotted notation, a table alias must be assigned to the table in the FROM clause, and any reference to the JSON column must be prefixed with the assigned alias. All data is returned as VARCHAR2(4000).
Open the script 1.1 SIMPLE_QUERIES.sql. This script demonstrates how to
use the simplied syntax to extract values from an JSON document and how to
filter a result set based on the content of a JSON document.
The first query shows the count of PurchaseOrder documents by cost center:
select
j.PO_DOCUMENT.CostCenter, count(*)
from J_PURCHASEORDER j
group by j.PO_DOCUMENT.CostCenter
order by j.PO_DOCUMENT.CostCenter
/
The second query shows how to fetch the JSON document where the PONumber key has the value 1600:
select
j.PO_DOCUMENT
from J_PURCHASEORDER j
where j.PO_DOCUMENT.PONumber = 1600
/
The third query shows how to fetch data from any document where the key PONumber contains the value 1600. The statement returns the values of the keys Reference, Requestor and CostCenter as well as the value of the key city which is a property of the object identified by the key Address which in turn is a property of the object identified by the key ShippingInstructions.
select
j.PO_DOCUMENT.Reference,
j.PO_DOCUMENT.Requestor,
j.PO_DOCUMENT.CostCenter,
j.PO_DOCUMENT.ShippingInstructions.Address.city
from J_PURCHASEORDER j
where j.PO_DOCUMENT.PONumber = 1600
/
The fourth query shows how to fetch the content of the Address object:
select
j.PO_DOCUMENT.ShippingInstructions.Address
from J_PURCHASEORDER j
where j.PO_DOCUMENT."PONumber" = 1600
/
The Address object is returned as JSON text in a VARCHAR2(4000).
The JSON_VALUE operator uses a JSON path expression to
access a single scalar value. It is typically used in the select list to return
the value associated with the JSON path expression or in the WHERE clause to
filter a result set based on the content of a JSON document.
JSON_VALUE takes two arguments, a JSON column and a JSON path expression. It provides a set of modifiers that allow control over the format in which the data is returned and how to handle any errors encountered while evaluating the JSON path expression.
Open the script 2.0 JSON_VALUE.sql. This script demonstrates how to use the JSON_VALUE operator to extract scalar values from a JSON document using JSON path expressions and to filter a result set based on the content of a JSON document.
The first query shows the count of PurchaseOrder documents by cost center:
select
JSON_VALUE(PO_DOCUMENT ,'$.CostCenter'), count(*)
from J_PURCHASEORDER
group by JSON_VALUE(PO_DOCUMENT ,'$.CostCenter')
/
The query uses the JSON_VALUE operator to return the value of the CostCenter key from each document in the J_PURCHASEORDER table. The SQL Count and Group operators are then applied to the values returned, to generate the desired result. This shows how by storing JSON documents in the Oracle Database makes it possible to bring the full power of SQL to bear on JSON content without giving up any of the advantages of the JSON paradigm.
The second query shows how to access a value from within an array. This example uses index 0 to access the value of the UPCCode from the Part object inside the first member of the LineItems array. This query also shows how to use JSON_VALUE as a filter in the WHERE clause of the SQL statement, so that the JSON_VALUE operator in the select list is executed only for those rows where the JSON_VALUE based condition in the WHERE clause evaluates to TRUE.
select JSON_VALUE(PO_DOCUMENT ,'$.LineItems[0].Part.UPCCode')
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
JSON_VALUE also provides control over the value returned. You can specify size when returning a VARCHAR2 value, and size and precision when returning a NUMBER value. The third query shows how to specify the SQL data type that is returned by JSON_VALUE. In this case, the value is returned as a NUMBER(5,3) value.
select JSON_VALUE(
PO_DOCUMENT ,
'$.LineItems[0].Part.UnitPrice'
returning NUMBER(5,3)
)
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
The JSON_VALUE function also provides options for handling errors that might be encountered when applying the JSON PATH expression to a JSON document. Options available include:
·
NULL ON ERROR: The default. If an error is encountered while
applying a JSON path expression to a JSON document the result is assumed to be SQL
NULL and no error is raised.
·
ERROR ON ERROR: An error is raised in the event that an error is
encountered while applying a JSON path expression to a JSON document.
·
DEFAULT on ERROR: The developer specifies a literal value that is
returned in the event that an error is encountered while applying a JSON path
expression to a JSON document.
The most common source of an error with JSON_VALUE is that the JSON path expression resolves to something other than a scalar value. JSON, by its nature is highly flexible, which raises the possibility of having a small number of documents that do not conform to a particular pattern. The NULL ON ERROR behavior ensures that a single error caused by one outlier does not abort an entire operation.
The fourth query shows the default behavior for JSON_VALUE in the event of an error during JSON path evaluation.
select
JSON_VALUE(PO_DOCUMENT ,'$.ShippingInstruction.Address')
from J_PURCHASEORDER
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
In this example, there is an error in the first key in the JSON path expression. The first key should be ShippingInstructions, not ShippingInstruction. Since the JSON path expression does match a scalar value, the NULL ON ERROR behavior kicks in and the JSON_VALUE operator returns NULL.
The fifth query demonstrates the DEFAULT on ERROR behavior
select
JSON_VALUE(
PO_DOCUMENT ,
'$.ShippingInstruction.Address'
DEFAULT 'N/A' ON ERROR
)
from J_PURCHASEORDER
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
Since the JSON path expression does not evaluate to a scalar value, when the JSON path expression is evaluated the DEFAULT on ERROR behavior kicks in, and the JSON_VALUE operator returns “N/A”.
The sixth query demonstrates the ERROR ON ERROR behavior.
select
JSON_VALUE(
PO_DOCUMENT ,
'$.ShippingInstruction.Address'
ERROR ON ERROR
)
from J_PURCHASEORDER
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
Since the JSON path expression does not evaluate to a scalar value, when the JSON path expression is evaluated the ERROR ON ERROR behavior kicks in and results in error “ORA-40462: JSON_VALUE evaluated to no value” being raised.
The seventh query is another example of the ERROR ON ERROR behavior.
select JSON_VALUE(
PO_DOCUMENT ,
'$.ShippingInstructions.Address'
ERROR ON ERROR
)
from J_PURCHASEORDER
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
In this case, the JSON path expression does evaluate to a key in the document; however the value associated with the key is an object, not a scalar. Since JSON_VALUE can only return a scalar value, when the JSON path expression is evaluated the ERROR ON ERROR behavior kicks in and results in error “ORA-40456: JSON_VALUE evaluated to non-scalar value” being raised.
The eighth query shows that the error handling clauses only apply to runtime errors that arise when the JSON path expression is applied to a set of JSON documents:
select
JSON_VALUE(
PO_DOCUMENT ,
'$.ShippingInstructions,Address'
NULL ON ERROR
)
from J_PURCHASEORDER
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
In this example, the error is that a comma, rather than a period, has been used as the separator between the first and second keys in the JSON path expression. Despite the fact that NULL ON ERROR semantics were requested, when the statement is executed error “ORA-40442: JSON path expression syntax error” is raised due to the fact that an invalid JSON path expression is a compile time error rather than a runtime error.
JSON_QUERY is the complement of JSON_VALUE. Whereas JSON_VALUE can return only a scalar value, JSON_QUERY can return only an object or an array. Like JSON_VALUE, JSON_QUERY takes two arguments, a JSON column and a JSON path expression. It provides a set of modifiers that allow control over the format in which the data is returned and how to handle any errors encountered while evaluating the JSON path expression. The following examples show the use of JSON_QUERY.
Open the script 3.0 JSON_QUERY.sql. This script demonstrates how to use the JSON_QUERY operator to extract objects and arrays from a JSON document using JSON path expressions.
The first query shows how to return the contents of the key ShippingInstructions:
select
JSON_QUERY(PO_DOCUMENT ,'$.ShippingInstructions')
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
Since the contents of ShippingInstructions is an object, the value is returned as a JSON object delimited by curly braces ‘{‘ and ‘}’.
The second query shows how to return the contents of the key LineItems:
select
JSON_QUERY(PO_DOCUMENT ,'$.LineItems')
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
Since the contents of LineItems is an array, the value is returned as a JSON array delimited by square brackets ‘[‘ and ‘]’.
By default, for maximum efficiency, objects and arrays are
printed with the minimal amount of whitespace. This minimizes the number of
bytes needed to represent the value, and is fine when the object will be consumed
by a computer. However, sometimes it is necessary for the output of a JSON_QUERY
operation to be human readable. JSON_QUERY provides the keyword PRETTY for this
purpose.
The third query shows the use of the PRETTY keyword to format the output of the JSON_QUERY operator.
select
JSON_QUERY(PO_DOCUMENT ,'$.LineItems' PRETTY)
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
The array is output as neatly indented JSON, making it much easier for a human to understand. However this comes at the cost of increasing the number of bytes needed to represent the content of the array.
The fourth query shows how to use an array offset to access one item from an array of objects:
select
JSON_QUERY(PO_DOCUMENT ,'$.LineItems[0]' PRETTY)
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
Since the selected item is a JSON object, it is delimited by curly braces ‘{‘ and ‘}’.
The fifth query shows how to access the value of a key from one specific member of an array of objects.
select
JSON_QUERY(
PO_DOCUMENT ,
'$.LineItems[0].Part
)
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
Since the contents of Part is an object, the value is returned as a JSON object delimited by curly braces ‘{‘ and ‘}’.
The JSON_QUERY function also provides options for handling errors that might be encountered when applying the JSON path expression to a JSON document. Options available include:
·
NULL ON ERROR: The default. If an error is encountered while
applying a JSON path expression to a JSON document, the result is assumed to be
NULL and no error condition is raised.
·
ERROR ON ERROR: An error is raised in the event that an error is
encountered while applying a JSON path expression to a JSON document.
· EMPTY ON ERROR: An empty array, “[]”, is returned in the event that an error is encountered while applying a JSON path expression to a JSON document.
The most common source of an error with JSON_QUERY is that the JSON path expression resolves to something other than an object or array. The NULL ON ERROR behavior ensures that a single error caused by one outlier does not abort an entire operation.
The sixth query shows the default behavior for JSON_QUERY in the event of an error during JSON path evaluation.
select
JSON_QUERY(
PO_DOCUMENT ,
'$.LineItems[0].Part.UPCCode'
)
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
In this example the error is that the JSON path expression maps to a scalar value rather than an object or an array. Since JSON_QUERY can only return an object or an array, the NULL ON ERROR handling kicks in and results in the query returning NULL.
The seventh query shows the ERROR ON ERROR behavior for JSON_QUERY in the event of an error during JSON path evaluation.
select
JSON_QUERY(
PO_DOCUMENT ,
'$.LineItems[0].Part.UPCCode'
ERROR ON ERROR
)
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
Since the result of the JSON path expression is a scalar value, the ERROR ON ERROR handling kicks in and results in an “ORA-40480: result cannot be returned without array wrapper” error being raised.
The eighth query shows the use of the EMPTY ON ERROR behavior
select
JSON_QUERY(
PO_DOCUMENT ,
'$.LineItems[0].Part.UPCCode'
EMPTY ON ERROR
)
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
Since the result of the JSON path expression is a scalar value, the EMPTY ON ERROR handling kicks in and the result of the JSON_QUERY operation is an empty array, “[]”.
The ninth query shows how to use the “WITH CONDITIONAL ARRAY WRAPPER” option to avoid a JSON_QUERY error when a JSON path expression evaluates to a scalar value. When this option is specified, a JSON path expression that evaluates to a scalar value is returned as an array. The array consists of one element containing a scalar value.
select
JSON_QUERY(
PO_DOCUMENT ,
'$.LineItems[0].Part.UPCCode'
WITH CONDITIONAL ARRAY WRAPPER
)
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
Since the result of the JSON path expression is a scalar value, JSON_QUERY automatically converts the result into an array with that one item and returns the array.
The tenth query shows how to use WITH ARRAY WRAPPER to force JSON_QUERY to always return the result as an array. In this example, the index is an asterisk, indicating that all members of the LineItems array should be processed, and the last key in the JSON path expression is also an asterisk, indicating that all children of the Part key should be processed.
select
JSON_QUERY(
PO_DOCUMENT ,
'$.LineItems[*].Part.*'
WITH ARRAY WRAPPER
)
from J_PURCHASEORDER p
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
The result of executing this query is an array with 6 items. The first 3 items come from the Description, UnitPrice and UPCCode associated with the Part key of the first member of the LineItems array. The second 3 come from the Description, UnitPrice and UPCCode associated with the Part key of the second member of the LineItems array. Also note that since Description and UPCCode are strings, and UnitPrice is a numeric, the resulting array is heterogeneous in nature, containing a mixture of string and numeric values.
JSON_TABLE is used in the FROM clause of a SQL statement. It enables the creation of an inline relational view of JSON content. The JSON_TABLE operator uses a set of JSON path expressions to map content from a JSON document into columns in the view. Once the contents of the JSON document have been exposed as columns, all of the power of SQL can be brought to bear on the content of JSON document.
The input to the JSON_TABLE operator is a JSON object or a JSON array. The JSON_TABLE operator is driven by a row pattern which consists of a JSON path expression. The row pattern determines how many rows the JSON_TABLE operator will generate. It will generate 1 row from each key that matches the supplied row pattern. If the row pattern matches a single key then the JSON_TABLE operator will generate exactly one row. If the row pattern references one or more arrays then it will generate a row from each item in the deepest array.
The rows output by a JSON_TABLE operator are laterally joined to the row that generated them. There is no need to supply a WHERE clause to join the output of the JSON_TABLE operator with the table containing the JSON document.
Columns are defined by one or more column patterns that appear after the COLUMNS keyword. The inline view contains 1 column for each entry in the columns clause. Each column pattern consists of a column name, a SQL data type and a JSON path expression. The column name determines the SQL name of the column, the SQL data type determines the data type of the column and the JSON path expression maps a value from the document to the column. The JSON path expressions in the columns clause are relative to the row pattern.
Open the script 4.0 JSON_TABLE.sql. This script shows how to use JSON_TABLE to generate inline relational views of JSON documents.
The first query shows how to project a set of columns from values that occur at most once in the document. The values can come from any level of nesting, as long as they do not come from keys that are part of, or descended from an array, unless an index is used to identify one item in the array.
select M.*
from J_PURCHASEORDER p,
JSON_TABLE(
p.PO_DOCUMENT ,
'$'
columns
PO_NUMBER NUMBER(10) path '$.PONumber',
REFERENCE VARCHAR2(30 CHAR) path '$.Reference',
REQUESTOR VARCHAR2(32 CHAR) path '$.Requestor',
USERID VARCHAR2(10 CHAR) path '$.User',
COSTCENTER VARCHAR2(16 CHAR) path '$.CostCenter',
TELEPHONE VARCHAR2(16 CHAR) path '$.ShippingInstructions.Phones[0].number’
) M
where PO_NUMBER between 1600 and 1605
/
This example generates a view with 5 columns, REFERENCE, REQUESTOR, USERID, COSTCENTER and TELEPHONE. The use of ‘$’ as the JSON path expression for the ROW pattern means the entire JSON document. This means that all column patterns are descended directly from the top level object.
The second query shows how to
work with arrays. In order to expose the contents of an array as a set of rows,
the array must be processed using the NESTED PATH syntax. When a JSON_TABLE
operator contains a NESTED PATH clause it will output one row for each member
of the array referenced by the deepest NESTED PATH clause. The row will contain
all of the columns defined by each level of the JSON_TABLE expression.
select D.*
from J_PURCHASEORDER p,
JSON_TABLE(
p.PO_DOCUMENT ,
'$'
columns(
PO_NUMBER NUMBER(10) path '$.PONumber',
REFERENCE VARCHAR2(30 CHAR) path '$.Reference',
REQUESTOR VARCHAR2(32 CHAR) path '$.Requestor',
USERID VARCHAR2(10 CHAR) path '$.User',
COSTCENTER VARCHAR2(16) path '$.CostCenter',
NESTED PATH '$.LineItems'
columns(
ITEMNO NUMBER(16) path '$.ItemNumber',
DESCRIPTION VARCHAR2(32 CHAR) path '$.Part.Description',
UPCCODE VARCHAR2(14 CHAR) path '$.Part.UPCCode',
QUANTITY NUMBER(5,4) path '$.Quantity',
UNITPRICE NUMBER(5,2) path '$.Part.UnitPrice'
)
)
) D
where PO_NUMBER between 1600 and 1605
/
The NESTED PATH option simplifies the processing of nested collections. The NESTED PATH clause removes the need to define columns whose sole purpose is to pass a collection from one operator to the next, making it possible to use a “*” for the select list since the collections are no longer emitted by the JSON_TABLE operator.
One common use of JSON_TABLE is to create relational views of JSON content which can then be operated on using standard SQL syntax. This has the advantage of allowing programmers and tools that have no concept of JSON data and JSON path expressions to work with JSON documents that have been stored in the Oracle Database.
In Oracle <IP_ADDRESS>.0 it highly recommended to avoid joins between these views. A fully expanded detail view will provide much better performance than defining a master view and a detail view and then attempting to write a query that returns a result set consisting of columns selected from both the master view and the detail view.
Open the script 5.0 REALTIONAL_VIEWS.sql. This script shows how to use JSON_TABLE to create relational views of JSON content that can be queried using standard SQL.
The first statement creates a relational view called PURCHASEORDER_MASTER_VIEW which exposes values that occur at most once in each document.
create or replace view PURCHASEORDER_MASTER_VIEW
as
select m.*
from J_PURCHASEORDER p,
JSON_TABLE(
p.PO_DOCUMENT ,
'$'
columns
PO_NUMBER NUMBER(10) path
'$.PONumber',
REFERENCE VARCHAR2(30 CHAR) path '$.Reference',
REQUESTOR VARCHAR2(128 CHAR ) path '$.Requestor',
USERID VARCHAR2(10 CHAR) path '$.User',
COSTCENTER VARCHAR2(16) path '$.CostCenter',
SHIP_TO_NAME VARCHAR2(20 CHAR) path '$.ShippingInstructions.name',
SHIP_TO_STREET VARCHAR2(32 CHAR) path
'$.ShippingInstructions.Address.street',
SHIP_TO_CITY VARCHAR2(32 CHAR path
'$.ShippingInstructions.Address.city',
SHIP_TO_COUNTY VARCHAR2(32 CHAR) path
'$.ShippingInstructions.Address.county',
SHIP_TO_POSTCODE VARCHAR2(32 CHAR path
'$.ShippingInstructions.Address.postcode',
SHIP_TO_STATE VARCHAR2(2 CHAR) path
'$.ShippingInstructions.Address.state',
SHIP_TO_PROVINCE VARCHAR2(2 CHAR) path
'$.ShippingInstructions.Address.province',
SHIP_TO_ZIP VARCHAR2(8 CHAR) path
'$.ShippingInstructions.Address.zipCode',
SHIP_TO_COUNTRY VARCHAR2(32 CHAR) path
'$.ShippingInstructions.Address.country',
SHIP_TO_PHONE VARCHAR2(24 CHAR) path '$.ShippingInstructions.Phones[0].number',
INSTRUCTIONS VARCHAR2(2048 CHAR) path '$.SpecialInstructions'
) m
/
As can be seen from the output of a describe operation, this view looks like any other relational view. The JSON operators and JSON path expressions are hidden away in the DDL statement that created the view.
The second statement creates a relational view called PURCHASEORDER_DETAIL_VIEW that exposes the contents of the LineItems array as a set of rows.
create or replace view PURCHASEORDER_DETAIL_VIEW
as
select D.*
from J_PURCHASEORDER p,
JSON_TABLE(
p.PO_DOCUMENT ,
'$'
columns (
PO_NUMBER NUMBER(10) path
'$.PONumber',
REFERENCE VARCHAR2(30 CHAR) path '$.Reference',
REQUESTOR VARCHAR2(128 CHAR) path '$.Requestor',
USERID VARCHAR2(10 CHAR) path '$.User',
COSTCENTER VARCHAR2(16) path '$.CostCenter',
SHIP_TO_NAME VARCHAR2(20 CHAR) path
'$.ShippingInstructions.name',
SHIP_TO_STREET VARCHAR2(32 CHAR) path
'$.ShippingInstructions.Address.street',
SHIP_TO_CITY VARCHAR2(32 CHAR) path
'$.ShippingInstructions.Address.city',
SHIP_TO_COUNTY VARCHAR2(32 CHAR) path
'$.ShippingInstructions.Address.county',
SHIP_TO_POSTCODE VARCHAR2(10 CHAR) path
'$.ShippingInstructions.Address.postcode',
SHIP_TO_STATE VARCHAR2(2 CHAR) path
'$.ShippingInstructions.Address.state',
SHIP_TO_PROVINCE VARCHAR2(2 CHAR) path
'$.ShippingInstructions.Address.province',
SHIP_TO_ZIP VARCHAR2(8 CHAR) path '$.ShippingInstructions.Address.zipCode',
SHIP_TO_COUNTRY VARCHAR2(32 CHAR) path
'$.ShippingInstructions.Address.country',
SHIP_TO_PHONE VARCHAR2(24 CHAR) path '$.ShippingInstructions.Phones[0].number',
INSTRUCTIONS VARCHAR2(2048 CHAR) path '$.SpecialInstructions',
NESTED PATH '$.LineItems[*]'
columns (
ITEMNO NUMBER(38) path '$.ItemNumber',
DESCRIPTION VARCHAR2(256 CHAR) path '$.Part.Description',
UPCCODE VARCHAR2(14 CHAR) path '$.Part.UPCCode',
QUANTITY NUMBER(12,4) path '$.Quantity',
UNITPRICE NUMBER(14,2) path '$.Part.UnitPrice'
)
)
) D
/
The following statements show how, once the relational
views have been created, the full power of SQL can now be applied to JSON
content, without requiring any knowledge of the structure of the JSON or how to
manipulate JSON using SQL.
Relational views effectively provide schema-on-query semantics; making it
possible to define multiple views of the JSON content. Application developers
are still free to evolve the content of the JSON as required. New variants of
the JSON can be stored without affecting applications that rely on the existing
views.
As can be seen from the queries used, all the power of the SQL language can be used to access the JSON data. These views allow developers and, more importantly, tools that understand only the relational paradigm to work with JSON content.
The JSON_EXISTS operator is used in the WHERE clause of a SQL statement. It is used to tell whether or not a JSON document matches a JSON path expression. JSON_EXISTS takes two arguments, a JSON column and a JSON path expression. It returns TRUE if the document contains a key that matches the JSON path expression, FALSE otherwise. JSON_EXISTS provides a set of modifiers that provide control over how to handle any errors encountered while evaluating the JSON path expression. The following examples show the use of JSON_EXISTS.
Open the script 6.0 JSON_EXISTS.sql.
The first query counts the number of JSON documents that contain a ShippingInstructions key with an Address key that contains a state key:
select count(*)
from J_PURCHASEORDER
where JSON_EXISTS(PO_DOCUMENT ,'$.ShippingInstructions.Address.state')
/
The next three queries show the difference between using JSON_EXISTS to test for the presence of a key and using JSON_VALUE to check if a key is null or empty. The first query simply shows the set of possible values for the county key in the Address object.
select
JSON_VALUE(PO_DOCUMENT ,'$.ShippingInstructions.Address.county')
count(*)
from J_PURCHASEORDER
group by JSON_VALUE(PO_DOCUMENT ,'$.ShippingInstructions.Address.county')
/
The results show that there are two possible values for the county key; it is either NULL or contains the value “Oxon.”. The problem with this is that we cannot differentiate between cases where JSON_VALUE returned NULL because the key did not exist in the document and the cases where it returned NULL because the key contains a null or empty value.
The JSON_EXISTS operator makes it possible to differentiate between the case where the key is not present and the case where the key has a null or empty value. The following query shows how this is done:
select
JSON_VALUE(PO_DOCUMENT ,'$.ShippingInstructions.Address.county')
count(*)
from J_PURCHASEORDER
where JSON_EXISTS(PO_DOCUMENT ,'$.ShippingInstructions.Address.county')
group by JSON_VALUE(PO_DOCUMENT ,'$.ShippingInstructions.Address.county')
/
The results
show that there are 91 documents where the key is present but empty.
Oracle Database 12c includes support for indexing JSON documents. There are two approaches to indexing JSON content. The first is to create functional indexes based on the JSON_VALUE operator. The second is to leverage Oracle’s full-text indexing capabilities to create an inverted list index on the JSON documents.
JSON_VALUE can be used to create an index on any key in a JSON document as long as the following two conditions are met.
· The target of the JSON path expression must be a scalar value.
· The target of the JSON path expression must occur at most once in each document.
Indexes created using JSON_VALUE can be conventional B-TREE indexes or BITMAP indexes.
Open the script 7.0 JSON_INDEXING.sql.
The first statement shows how to create a unique B-Tree index on the contents of the PoNumber key.
create unique
index PO_NUMBER_IDX
on J_PURCHASEORDER (
JSON_VALUE(
PO_DOCUMENT,'$.PONumber' returning NUMBER(10) ERROR ON ERROR
)
)
/
The second statement shows how to create a BITMAP index on the contents of the CostCenter key
create bitmap
index COSTCENTER_IDX
on J_PURCHASEORDER (JSON_VALUE(PO_DOCUMENT ,'$.CostCenter'))
/
The third statement shows how to create a B-Tree index on the contents of the zipCode key
create index
ZIPCODE_IDX
on J_PURCHASEORDER(
JSON_VALUE(
PO_DOCUMENT ,
'$.ShippingInstructions.Address.zipCode')
returning NUMBER(5)
)
/
Note the use of the returning clause in the JSON_VALUE
operator to create a numeric index.
The fourth statement calculates the total cost of all items ordered on a particular PurchaseOrder. The JSON_VALUE operator is used in the WHERE clause to identify which document is to be processed.
select sum(QUANTITY
* UNITPRICE) TOTAL_COST
from J_PURCHASEORDER,
JSON_TABLE(
PO_DOCUMENT ,
'$.LineItems[*]'
columns(
QUANTITY NUMBER(12,4) path '$.Quantity',
UNITPRICE NUMBER(14,2) path '$.Part.UnitPrice'
)
)
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
After running the query click the explain plan icon () on the SQL Worksheet toolbar. This will show the execution plan for the query in the explain plan tab of the output window. The explain plan output shows that the unique index PO_NUMBER_IDX is used to determine which document to process.
The fifth statement returns the distinct set of requestors for PurchaseOrders from cost center “A110”. The JSON_VALUE operator is used in the WHERE clause to identify which document is to be processed.
select distinct
JSON_VALUE(PO_DOCUMENT ,'$.Requestor')
from J_PURCHASEORDER
where JSON_VALUE(PO_DOCUMENT ,'$.CostCenter') = 'A110'
/
After running the query, click the explain plan icon (). This will show the execution plan for the query in the explain plan tab of the output window. The explain plan output shows that the bitmap index COSTCENTER_IDX is used to determine which documents to process.
The sixth statement shows the count of orders by cost center for orders where the shipping address zipcode is greater than 90000. The JSON_VALUE operator is used in the WHERE clause to identify which document is to be processed.
select
JSON_VALUE(PO_DOCUMENT ,'$.CostCenter' returning VARCHAR2(10)),
count(*)
from J_PURCHASEORDER p
where JSON_VALUE(
PO_DOCUMENT ,
'$.ShippingInstructions.Address.zipCode'
returning NUMBER(5)
) > 90000
group by JSON_VALUE(PO_DOCUMENT ,'$.CostCenter' returning VARCHAR2(10))
/
After running the query click the explain plan icon () on the SQL Worksheet toolbar. This will show the execution plan for the query in the explain plan tab of the output window. The explain plan output shows that ZIPCODE_IDX is used to determine which documents to process.
Oracle Database 12c can also index a complete a JSON document, enabling index-backed searching of JSON content even in cases where the search criteria are not known until runtime. Document-level, or “Search” indexing is based on Oracle’s full-text indexing technology. Currently the JSON search index is capable of optimizing JSON_EXISTS operations and certain classes of JSON_VALUE operations.
1. Open the script 8.0 PO_DOCUMENT _INDEXING.sql.
The first statement creates the JSON Search index.
create index PO_DOCUMENT
_INDEX
on J_PURCHASEORDER(PO_DOCUMENT )
indextype is ctxsys.context
parameters('section group CTXSYS.JSON_SECTION_GROUP SYNC (ON COMMIT)')
/
The index is created using the predefined index preference CTXSYS.JSON_SECTION_GROUP. This has been optimized for JSON search indexing. SYNC (ON COMMIT) ensures that inserts and update operations are indexed at commit time.
The first query performs a query on the PONumber key.
select count(*),
sum(QUANTITY * UNITPRICE) TOTAL_COST
from J_PURCHASEORDER,
JSON_TABLE(
PO_DOCUMENT ,
'$.LineItems[*]'
columns(
QUANTITY NUMBER(12,4) path '$.Quantity',
UNITPRICE NUMBER(14,2) path '$.Part.UnitPrice'
)
where JSON_VALUE(PO_DOCUMENT ,’$.PONumber’ returning NUMBER(10)) = 1600
/
After running the query click the explain plan icon () on the SQL Worksheet toolbar. This will show the execution plan for the query in the explain plan tab of the output window. The explain plan output shows that the existing B-Tree index is used, in preference to the search index. Since both indexes are capable of optimizing the query, it is worth considering whether the B-Tree index on PONumber is still required once the search index has been created.
The second query performs a query based on the city key.
select count(*),
sum(QUANTITY * UNITPRICE) TOTAL_COST
from J_PURCHASEORDER,
JSON_TABLE(
PO_DOCUMENT ,
'$.LineItems[*]'
columns(
QUANTITY NUMBER(12,4) path '$.Quantity',
UNITPRICE NUMBER(14,2) path '$.Part.UnitPrice'
)
)
where
JSON_VALUE(PO_DOCUMENT ,'$.ShippingInstructions.Address.city') = 'Seattle'
/
After running the query click the explain plan icon () on the SQL Worksheet toolbar. This will show the execution plan for the query in the explain plan tab of the output window. The explain plan output shows that the search index is used to resolve the query. This demonstrates the big advantage of the search index, it is 100% schema agnostic. With the search index it is not necessary to have prior knowledge of the JSON path expressions that will be used when querying the JSON documents. The search index automatically indexes all possible JSON path expressions.
The third query performs a query based on city and cost center.
select count(*),
sum(QUANTITY * UNITPRICE) TOTAL_COST
from J_PURCHASEORDER,
JSON_TABLE(
PO_DOCUMENT ,
'$.LineItems[*]'
columns(
QUANTITY NUMBER(12,4) path '$.Quantity',
UNITPRICE NUMBER(14,2) path '$.Part.UnitPrice'
)
)
where
JSON_VALUE(PO_DOCUMENT ,'$.ShippingInstructions.Address.city') = 'Seattle'
and
JSON_VALUE(PO_DOCUMENT ,'$.CostCenter') = 'A90'
/
After running the query click the explain plan icon () on the SQL Worksheet toolbar. This will show the execution plan for the query in the explain plan tab of the output window. The explain plan output shows that both the search index and the bitmap index on cost center are used to resolve the query.
This lab provides an introduction to using Oracle Database 12c to store, query and index JSON documents. On successful completion of this lab you should be able to
· Understand the basics of the JSON data model
· Understand the basics of the JSON path language
· Create a table which can store well-formed JSON documents
· Extract scalar values from JSON documents, using the JSON_VALUE operator
· Extract objects and arrays from JSON documents, using the JSON_QUERY operator
· Test whether or not a JSON document contains a particular key using the JSON_EXISTS operator
· Expose JSON content in a relational manner using the JSON_TABLE operator
· Index the content of specific JSON keys using functional indexes
· Index JSON Documents using the JSON search index.


TabStrip / RTL
{{tab.name}} Content
This sample shows how to set the TabStrip in right-to-left layout. All groups, their header and content, are displayed in right-to-left layout. By clicking on the checkbox in control panel on the right, you can switch back to the default left-to-right layout setting.


Your format is not listed here or more complex? Feel free to contact us so we can assist you with the setup. ✌🏼


require(Prefer single-quoted strings when you don't need string interpolation or special symbols.
"csim")
S = SwitchBank.new
A = AndGate.new
#A.join(Tester.new("A"))
B = OrGate.new
#B.join(Tester.new("B"))
C = XorGate.new
#C.join(Tester.new("C"))
L = LED.new('Result')Do not use semicolons to terminate expressions.
;
S.join(A)
S.join(A)
A.join(B)
S.join(B)
B.join(C)
S.join(C)
C.join(L)
for x in (0..15)
for s in [3Space missing after comma.
,2Space missing after comma.
,1Space missing after comma.
,0]
print(...) interpreted as grouped expression.
(xSurrounding space missing for operator >>.
>>s) & 1
end
print Prefer single-quoted strings when you don't need string interpolation or special symbols.
" => "
S.value = x
end


Microservice Resiliency
Eureka
https://github.com/jeffgbutler/microservice-workshop-java/
https://jeffgbutler.github.io/microservice-workshop-java/
What is Eureka?
- Eureka is a service discovery system
- Eureka originated at Netflix
- Spring Cloud Netflix has integrated Eureka into Spring
- Web services register themselves with a Eureka Server
- Web service clients look up service addresses from the Eureka server
- This solves the problem of hard-coded URLs in the Feign clients
Configuring a Eureka Server
- "Eureka Server" is a dependency in the Spring Initializr
- Build a new project with only the "Eureka Server" dependency
- Add
@EnableEurekaServer
to the main Spring Boot class
- That's, basically, it
- Eureka server is also built into Pivotal Cloud Foundry with the Spring Cloud Services tile
Configuring Eureka Clients
- "Eureka Discovery" is the dependency in the Spring Initializr
- Web services are automatically registered with a Eureka server
- Feign clients will lookup endpoints from Eureka if we simply remove the
url
attribute of
@FeignClient
- That's, basically, it
Service Names and Granularity
- The name of the service is taken from the
spring.application.name
property. This property
is specified in bootstrap.yml
by convention
- The
name
attribute of a @FeignClient
annotation specifies which service to lookup
- This is a lookup for a base URL for all services in an application - it is not a granular service catalog
What is Ribbon?
- Ribbon, another Netflix project, is a client side load balancer
- Ribbon is a transitive dependency of Eureka Discovery (it comes along for free)
- Multiple service instances can register themselves in Eureka with the same name
- When there are multiple service instances, Ribbon will load balance between them
- If you have another load balancing strategy (like PCF scaling),
then you can disable Ribbon on the clients
- Ribbon is in maintenance mode currently and will be replaced by Spring Cloud Loadbalancer.
This should be transparent to most users
Exercise
- Create a Eureka Server
- Alter the services so they will register with the Eureka server
- Alter the aggregator service so it will lookup endpoints from the Eureka server


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Calculator (class)
Calculator::Calculator (function)
Calculator::CALCULATOR (C++ type)
Calculator::const_pointer (C++ type)
Calculator::const_reference (C++ type)
Calculator::drop (function)
,
[1]
Calculator::exist (function)
,
[1]
Calculator::Func1 (C++ type)
Calculator::Func2 (C++ type)
Calculator::get (function)
,
[1]
Calculator::get_value (function)
Calculator::init (function)
Calculator::map_fun1 (C++ type)
Calculator::map_fun1_const_iterator (C++ type)
Calculator::map_fun1_iterator (C++ type)
Calculator::map_fun2 (C++ type)
Calculator::map_fun2_const_iterator (C++ type)
Calculator::map_fun2_iterator (C++ type)
Calculator::map_real (C++ type)
Calculator::map_real_const_iterator (C++ type)
Calculator::map_real_iterator (C++ type)
Calculator::no_error (function)
Calculator::parse (function)
,
[1]
Calculator::parse_file (function)
,
[1]
Calculator::pointer (C++ type)
Calculator::print (function)
Calculator::reference (C++ type)
Calculator::report_error (function)
Calculator::set (function)
,
[1]
Calculator::set_binary_fun (function)
,
[1]
Calculator::set_unary_fun (function)
,
[1]
Calculator::value_type (C++ type)
Calculator::variables_map (function)
Calculator::variables_merge (function)
Calculator::~Calculator (function)
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Dependency Manager (Singleton)
The Dependency Manager is used to keep track which dependencies are needed to load every plastic element on the page It can (lazy)load those dependencys on demand and manages the according events.
Members
-
staticplastic.modules.dependencyManager.registry
-
Registry Object that collects all extnal dependencies by name, and ressources
If a module is added that has a new external dependency it can either be added here or be added via setDependency() at runtime. If "test" is given, plastic.js will look if the given global variable exists. If it does, it will not be loaded.
-
staticplastic.modules.dependencyManager.usedDeps
-
Object of all Ressources that have to be loaded
Methods
-
staticplastic.modules.dependencyManager.add(dependencyArray)
modules/dependencyManager.js, line 93 -
Add a dependency that has to be loaded
Name Type Description dependencyArray
Array Array of dependency names (has to fit with the registry above)
-
staticplastic.modules.dependencyManager.fetch()
modules/dependencyManager.js, line 117 -
Fetches all external dependencies asyncronally
Dependencies to load have to be added first via .add(dependencies) Triggers plastic events (loaded-)
- TODO
-
- Dependency Caching?
-
staticplastic.modules.dependencyManager.getDependency(dep){Object}
modules/dependencyManager.js, line 83 -
Gets a dependency Object by name
Name Type Description dep
string -
staticplastic.modules.dependencyManager.missingDependency(test){boolean}
modules/dependencyManager.js, line 159 -
Checks if Dependency is already loaded. Supports three types of checks:
- Global Browser Functions
- jQuery Global Functions
- jQuery Element Functions
Name Type Description test
string -
staticplastic.modules.dependencyManager.setDependency(depName, jsArray, cssArray)
modules/dependencyManager.js, line 64 -
Sets a new dependency (that isn't registered yet)
Name Type Description depName
string Dependency Name
jsArray
Array optional Array of JavaScript Files to load (optional)
cssArray
Array optional Array of CSS Files to load (optional)


--- layout: post title: Q Learning Maze Game categories: - Programming tags: [] comments: [] ---
I've been learning about reinforcement learning. The simplest version I've heard of is called Q learning. The key to this method is maintaing a Q matrix, which stores information about the quality each state-action pair. Once trained, a player can simply look up their state and take the action with the highest value.
The goal is obviously to train this Q matrix to accurately reflect the game. This can be done by taking moves and updating Q each time in the following way:
As you can see below, when my QLearner is training, it takes random moves every time. I think the more common way to do it is to sometimes take random moves and sometimes take moves according to the current version of Q. That kind of weights the learner toward moves that seem better. It doesn't matter in this case, though. The maze problem is so easy that anything would work.
Here, the reward is simply zero unless the player reaches the goal. At first I set the discount factor, gamma, to be 1. This did not work. Since the random player will always eventually finish the maze, this resulted in every square eventually getting a value of 1. The discount factor means that faster routes to the goal are favored.
{% highlight python %} import numpy as np from random import randint class Maze: def __init__(self): self.w = 5 self.h = 5 p = Tile.player b = Tile.block e = Tile.empty g = Tile.goal self.map = [ [e, e, e, b, g], [e, b, e, b, e], [e, b, e, e, e], [e, b, b, b, b], [e, e, e, e, e], ] self.reset() def __repr__(self): returnStr = "" for i in range(self.h): for j in range(self.w): if (j, i) == self.playerPos: returnStr += str(Tile.player) else: returnStr += str(self.map[i][j]) returnStr += "\n" return returnStr def tileAt(self, (x, y)): return self.map[y][x] def isValidPos(self, (x, y)): return all([x >= 0, y >= 0, x < self.w, y < self.h]) def reset(self): self.playerPos = (0, 4) self.gameWon = False def move(self, dir): movePos = addTuple(self.playerPos, dir) if self.isValidPos(movePos): if self.tileAt(movePos) == Tile.empty: self.playerPos = movePos return 0 elif self.tileAt(movePos) == Tile.goal: self.playerPos = movePos self.gameWon = True return 1 else: return 0 else: return 0 class QLearner: def __init__(self, maze): self.maze = maze self.Q = np.zeros((self.maze.w, self.maze.h, 4)) self.alpha = 0.1 self.gamma = 0.9 def learn(self, n): for r in range(n): while self.maze.gameWon == False: i = randint(0,3) self.updateQ(i) self.maze.reset() def updateQ(self, i): x0, y0 = self.maze.playerPos moveDir = Dir.moveOptions[i] reward = self.maze.move(moveDir) x, y = self.maze.playerPos maxQ = max(self.Q[x, y, [0,1,2,3]]) self.Q[x0, y0, i] = self.Q[x0, y0, i] + (self.alpha * (reward + (self.gamma * maxQ) - self.Q[x0, y0, i])) def getBestMove(self, (x, y)): bestMove = Dir.moveOptions[np.argmax(self.Q[x, y, [0,1,2,3]])] return bestMove def addTuple((a1, a2), (b1, b2)): return (a1 + b1, a2 + b2) class Dir: UP = (0,-1) DOWN = (0, 1) LEFT = (-1, 0) RIGHT = (1, 0) moveOptions = [UP, RIGHT, DOWN, LEFT] class Tile: player = 'p' goal = 'g' block = 'X' empty = ' ' maze = Maze() learner = QLearner(maze) learner.learn(1000) print learner.Q maze.reset() while maze.gameWon == False: maze.move(learner.getBestMove(maze.playerPos)) print maze {% endhighlight %}


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