RideRescue is a mobile app concept I developed as part of an online course on UX design. The goal of the course was to create an experience that improves upon a current product or service. I decided to address the difficulties in the car rental industry through a mobile app designed to help people in emergencies. My initial research reflected that many people who rent vehicles do so out of immediate necessity, particularly when they have just been in an accident. I sought to create a service that would unify a user’s rental options across companies and locations into one seamless experience, giving the user maximum control over their turbulent circumstances. Users could order basic, smaller vehicles to be delivered on-demand or coordinate with a nearby branch to arrange a pickup. Users also benefit from having a full survey of their options before making a decision.
The final product was an application that allowed users to control a car rental with the immediacy they've come to expect from other car sharing services. RideRescue allows users to arrange a delivery or pickup, update their payment settings, elect their vehicle, and manage rental terms all without needing to set foot in a rental branch.
Branding identity concept
Screen designs for based on delivery or pickup transactions
Styling of intefaces with type and color
Animation of interface behavior with user input


I started my career as a Junior Web Developer for voices.com. Reporting to the UX/UI architect to configure and elaborate programming environment following their setup protocols. Helped deployed online assists and worked with the marketing team to update severals cosmetics assets on the public portions of their website.
GQA Tech Quality Control. I processed data, and process street level imagery. Geocoding address data points identification of images for tax assessment, Use of proprietary software identifying and appropriately tag images. Perform processing work in other departments as required.
Maintained current and accurate project documentacion reflecting changes to the process, testing and procedures
During my time in Voices.com I was part of the the internal IT team responsible for the development of all online systems related to their main line of business - The world's largest online voice-over acting marktetplace. Reporting to the CTO and UX/UI Architect to configure and elaborate programming environment following their setup protocols. I also helped deployed online assets and worked with their marketing team to update severals cosmetic assets on the public portions of their web site. By the end of my internship I progressed to work with more complex elements of their system dealing with a significant user interface update which is fundamental to the company.
Apart from being a web developer, I'm also a Dj, it's a hobby that I love. When indoors, I like to play videogames and follow a number of sci-fi and fantasy genre movies and television shows and I spend a large amount of my free time exploring the latest technology advancements in the front-end web development world.
Download a copy of my Resumé


Well, it was finals week and I had a paper to write and two finals to study for so, I was really stressed out. My girlfriend had told me that alot of people in her school take adderall to focus and she's tried it a few times to study and it works. She gave me 3 pills for the week and told me to take one a day, maybe one and half if its not strong enough. This was my first experience with Adderall so I didn't know what to expect.
I took one 30mg pill, after about 30mins I really didn't feel much so I decided to take another 30mgs. About 30mins later I started feeling it. I felt very focused and I felt a cozy feeling. I felt nestled into my book and my work. I was actually excited and enthusiastic about doing my work. After about 2 hours or so of reading and typing I caught my self taking very slow deep breaths. I felt my pulse and my heart was beating very very slowly, unusually slow, then there was no beating for like 4 seconds and I began to panic knowing I was about to passout.
Then all of a sudden my heart began to race beating very quickly. I stood up from my chair and tried to regulate my breathing in the hopes to regulate my heart rate. It work for the time, so I continued to due my work but, all I could focus on is breathing and my heart rate. I tried to forget about it and just focus on my work. As I was typing I felt numbness and tingling in my fingers as if my finger fell asleep. I knew that my heart wasnít pumping enough blood to my finger so I got up and tried moving around alil to get my blood flowing. I also made a sandwich and drank alot of water hoping to flush out some of it.
That did work slightly so I just continued work hoping itíll go away on its own. My fingers got really cold and it became difficult for me to type because my fingers were so numb. They felt like if I had put them in ice water for an hour. The rest of the night I was just focused on checking my heart and breathing and trying not to pass out or anything. It was a very scary experience. I did not sleep at all. In the morning those negative affects wore off but, I felt hyper and exhausted at the same time. My mind was hyper but my body was tired.
This experience I had was actually two days ago. Last night I finally got a full night of sleep but, I still donít feel totally normal. It was a lot more powerful than I thought. I have experimented with drugs in the past. Iíd compare my feelings as similar to E without the emotional part. All in all I had a bad experience but I wrote a pretty good paper.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Layers
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Pre-Render in Game
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Ocala events
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Power King Tractor - Machinery - Alibaba.com
Enjoy Best Buy. And other Power King Tractor Products and Power King Tractor Suppliers for Your Choice. . Group Products by Supplier. Business Type .
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Europe News Headlines - Yahoo! News
Get the latest Europe news headlines from Yahoo! . Bank of England Governor Mervyn King speaks to a parliamentary committee on banking standards in .
http://news.yahoo.com/europe/ -
Mouse Wireless Power King - Alibaba.com
80 Products . China Mouse Wireless Power King,China Mouse Wireless Power KingSuppliers and Manufacturers Directory- Source a Large Selection of Mouse .
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Duty Free Retail: King Power monopoly bid | Bangkok Post: learning
Aug 30, 2012 . King Power's bid for duty free at Don Meuang Airport 55% more than . board - board of directors; the small group of people who make high .
http://www.bangkokpost.com/learning/learning-from-news/310031/duty-free-retail-king-power-monopoly-bidclothing 4 all inc riverside
98 people's favorite
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Popular Kids Higher Paid as Adults - Yahoo! News
Oct 24, 2012 . Nerd or Homecoming King: Which one will make more money as an adult? . finds that the more friends you had in high school, the greater will be your earning power later on in life. . A wide range of information about this group has been compiled from the inception of the study . User 4 mths ago .
http://news.yahoo.com/bad-news-nerds-popular-kids-higher-paid-adults-154315409--abc-news-money.html -
Tire Review: Power King Towmaster
Jan 9, 2013 . I have just experienced tire failure using Power King Towmaster . a group action please contact me at<EMAIL_ADDRESS>. If you are NOT a Facebook Users You will need to open an account at . Add to My Yahoo!
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Shows how to use multiple bss to implement a master-detail pattern exploiting the replication feature and the i index passed into the apply edi-function


SketchUp 2014 for Architectural Visualization
ISBN-13:<PHONE_NUMBER>414
ISBN-10:<PHONE_NUMBER>
Authors: Thomas Bleicher; Robin de Jongh
Edition: 2nd Revised ed.
Binding: Paperback
Publisher: Packt Publishing - ebooks Account
Published: May 2014
List Price: $49.99
Best Rental Prices |
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| #1 |
Free shipping Ready to ship |
Notes |


Attach the [ZoneHtml] attribute to elements that contain multiple content areas (zones) to place localizable Html within the specified zone.
The [ZoneHtml] attribute can be used on a layout to define the default content of a zone within the layout. It can also be used on a region or page that contains a layout to overide the layout and change the content of the zone for this instance of the layout.
Example usage:
[IsLayout("functional_area__list", "title,list")] [ZoneRegion("title", "layouts:null")] [ZoneRegion("list", "functional_area__list")] [ZoneHtml("title", "functional_area__list_heading", "
Functional areas
")] internal class FunctionalAreaList: ContentElement { }
The [ZoneHtml()] attribute has the following properties you can set.
This is the name of the zone within a layout where the Html should be rendered.
This must be set to a unique key that identifies this piece of text. The key will be used to find the locale specific version of the Html based on the browser's language settings.
This is the Html to use for all locales where there is no localization defined for this LocalizationId.


Calculate how much you can charge for any given ride based on its age, its stats, and certain game settings.


Real-World Jenkins
Antonio Beyah
Overview
- Introduction
- Welcome to Jenkins
- Jenkins Deep Dive
- Demo/Questions
Who I Am
- Cloud Engineer:
Software Engineer focused on cloud technologies, specializing in automation.
-
Primarily in retail industry (development and operations)
- Best Buy (Cashier, Geek Squad, Geeksquad.com, BestBuy.com)
- SPS Commerce (Cloud Engineering)
- Target (Independent Consultant, Cloud Platform Engineering)
- Long time Jenkins user
What The Presentation Is About
- Best Practices
- Common Pitfalls
- Administration
- Monitoring and Scaling
- Introduction to Jenkins Ecosystem
What This Talk Is Not About
- Code Deep Dive (Stapler, Remoting, Plugin Development)
- Specific Automation Tools (Chef, Puppet, Ansible, etc)
- Specific Cloud Platforms (AWS, GCP, esp. not Azure)
- Windows(Ok, maybe an honorable mention)
Welcome to Jenkins
What is Jenkins?
According to jenkins.io:
"The leading open source automation server, Jenkins provides hundreds of plugins to support building, deploying and automating any project."
According to me:
"A mature platform that allows you to get stuff done."
Use Cases
- Build Server: Checking out code from SCM, builds the pieces, runs the unit tests, reports feedback.
- CI/CD Server: Build Server + spinning up an environment and testing it
- Cron/Batch Replacement: Checking out code from SCM, builds the pieces, runs the unit tests, reports feedback.
- Excuse Eliminator: Independent, unbiased, and accepting of new work
Key Information / Architecture
- Written in Java, Heavily tied to Groovy
- Uses a Master and Agent model
- Master (connect)-> Agent (JNLP)
- Master (connect)-> Agent (SSH Slaves Plugin)
- Agent (connect)-> Master (JNLP)
- Recommended: Agent (connect)-> Master (JNLP via Swarm Plugin)
- Master can tell agent to do anything
- Agent can tell agent to do anything (default)
- Almost all functionality comes from plugins
Jenkins Deep Dive
- Managing Jobs
- Handling Credentials
- Evolution to Pipeline
- Cluster Monitoring and Maintenance
Managing Jobs:
Job Components
- Wrappers: Impacts the overall build
- Parameters: User Inputs
- Triggers: The way the job gets started (timer or hook)
- SCM: Integration with source code management (Git, SVN)
- Builders: The steps that occur
- Publishers: Actions that occur after all of the builders have finished
Managing Jobs:
Best Practices
- Use the Jenkins Job DSL
- It's a plugin for jenkins that provides a Groovy-based DSL for declaring jobs
- You can even use it to create other seed jobs


How does it work? Actually, if the help needs an explanation it's probably not intuitive enough for a user... But for you as a developer some background info might be in order.
There are 2 ways for the helpbox to show up. First, for text/number inputs like text/password/email etc. the box shows up when the focus event is triggered on the element. Second, for inputs like buttons and inputs whose values are changed when clicked, the helpbox shows up when the mouseover event is triggered on the element.
Invetek - Loran Kloeze | https://github.com/invetek/jquery-formhelp


It looks like you've never logged in before. Click here to set up a password.. {% endif %} {% if wrong_account %}
It looks like you may need another e-mail address to view that page, or the page may no longer be accessible.
{% endif %}{% extends "./wrapper.html" %} {% block content %}
{{ error_message }}
{% endif %} {% if invalid %}Incorrect e-mail or password.  Click here to set up a password.. {% endif %} {% if wrong_account %}
It looks like you may need another e-mail address to view that page, or the page may no longer be accessible.
{% endif %}


Label
Type
Address
Size
dns
power_settings_new
https
update
publish
clear
System update
Perform a system update. The board will choose the OTA partition automatically.
attach_file
Set as boot partition after upload
Reboot after upload
Upload
Erase
Are you sure you want to erase the partition
?
Erase
Cancel


Stand Out
Hello, Welcome! My name is Venesa Atieno. I am a Designer/ Front end developer living in Nairobi, Kenya. Check my work here
Get insights into who is browsing your site so that you can make smarter business decisions.
A responsive design makes your website accessible to all users, regardless of their device.
Looking to improve your page performance, SEO, or user experience? Request a free site audit.
Easily update content without knowing how to code.
Clean, modern designs - optimized for performance, search engines, and converting users to customers.


Nordstrom.
Full-stack UX Design
for PRICE TOOLS team
- UX Design
- Product Design
- UI Development
The problem you are trying to solve
Current "Competitive intelligence" price matching workflows involve several wells of individually-held tribal knowledge covering dozens of non-standardized process flows that ultimately culminate in the execution of macros to do some of the calculation and heavy lifting and eventual entry into RMS. That execution has a long processing time into RMS 9 which provides a major opportunity to deliver a web-based application with a measurable gain in perforance and processing time. To offload some of the burdens our QA’s and Buying Office experience every day by improving performance and simplyfying their workflow we proposed an entirely new workflow with CompIntel detailed below.
Quality Assurance Analysts (QA’s) and The Buying Office (BO's) spend countless hours each day running formulas in Excel and emailing each other back and forth as their general workflow. The third-party data [from Boomerang] is not always accurate and current process is intense and laborious in an already time-sensitive scenario (as the often-inaccurate Boomerang data refreshes before QAs can mitigate the thousands of items that must be manually clicked and checked including all size and color permutations that competitor retailers may host, rendering it untrustworthy). We are serving the customer by providing a more collaborative environment to perform their daily tasks and an expedited workflow with our amalgamation of tools to aggregate a massive amount of information into a succinct workflow that includes QA and BO interfaces, daily assignments, cadence, default cadence, admin, and super administrative task handling. Our eventual plan will be to remove the dependency on Boomerang saving millions of dollars and freeing up budgets for the company and “eventually building a robot that does it all.” A future enhancement (mid-late 2019) combines our tool with a companion application, a Chrome extension, to further expedite the QA validation process by guiding the QA and BO through the verification stage off-site instructing them where to click so we can prop-drill for all the sizes and colors and automate the process of verifying scores of otherwise buried data that requires manual clicks are sometimes dozens or hundreds of colors and size permutations article or item dependent. From this initial platform we seek to expand upon the infrastructure to provide the foundation for future ML/AI efforts and deliver a platform to connect previously disjointed aspects of the enterprise through this modernization effort.
This tool begins by enabling the delegation of the week’s work based on a pre-determined Division Priority and rotation of users. Using a single-entry point, we provide a roles-based division of labor between QA and BO where they perform the manual verification/modification of [untrustworthy and expensive] Boomerang Data that ensures data accuracy when setting the price (retail matching). Each QA is responsible for a swath of work that an administrator delegates via a drag and drop interface that tracks the progress and returns a feedback loop of graphical data. A single QA-person could be responsible for thousands of items that need to be QA’d in a given time frame. QA-people check other retailers by manually searching for items based on Boomerang Data to determinate that price, size, color, etc are accurate. Our tool provides all of this information in a single view vs. numerous spreadsheets. Items can be self-delegated, searched, reorganized, filtered, acted upon, etc inside our tool. The Nordstrom Item and the Other Retailer items are quickly referenceable and QA can begin the validation process immediately inside the tool. When they have confirmed matches, and made any necessary adjustments, each validated item is sent off to the Buying Office for review. BO confirms the matches provided by QA and puts a clearance plan into action. Admins determine the Users and Roles, the Labels and Categories for departments and divisions, and then assign those departments and divisions to users based on roles to then delegate and determine a cadence for the week’s work using a drag and drop interface.
Because we are passing state throughout the app we are able to capture all of the users' events and have a glimpse into everything that they are clicking, when they are clicking, where they are clicking, and their order of operations, and the time it takes to complete tasks, etc. Bottlenecks and painpoints will hopefully be surfaced and additional UX work can be performed to enhance our users' operation of the tool.
— How did the design work improve the user experience and benefit the business? —
Prior to the inception of CompIntel execution status for 3/4 of the users was below the target execution threshold of 80%. After CompIntel was unleashed: 100% of the users reached 100% execution status. Much higher than the target threshold of 80%!
This is our beta approach twards developing a more robust system of automation and continuing to build tools to harness the power that our users have within them. CompIntel presented the first opportunity to learn from our new modernization effort and build towards the CommonUI as this was a ground-up effort from the inside out. We completely owned the engineer and design from end to end in house. I have ideas to incentivize them to become more productive via gamification. I even tested Batman Fightwords on Kayla by having Micah meet her to A/B test her when he first started. He'd never met her before and I slipped the Bat Man Fight Words in there. She thought it was super cool. She's our most powerful user and the person who shared her workflow with us and gave us the most insight into what we needed to develop. Next is to create our robot army to assist and the Chrome extension.


We, at Guess the Date, don't store your data. Your data stays on your phone.
The only third party we use is Google AdMob. This is how Google uses information from apps.
The only third party we use is Google AdMob. This is how Google uses information from apps.


Comms Cookbook¶
In this section, we will see how to open a bidirectionnal communication between Python code and a notebook extension using Jupyter’s Comms. The main steps will be to open a Comms from jupyter notebook, connect with an nb_extension and have a bidirectionnal communication from notebook to front end. This documentation should be seen as a complement to Jupyter’s Notebook documentation on Comms. So make sure to read this documentation, and most specifically the section talking about opening communication from the kernel, which means that the front end should be ready to receive a connection demand.
JavaScript side¶
Let’s make a simple nb_extension as seen in nbextension cookbook. This extension will register a comm and define a callback function that will get executed when it receives messages. This function will log the message and send a message containing the string banana.
define([
'base/js/namespace'
], function(
Jupyter
) {
function load_extension() {
Jupyter.notebook.events.on("kernel_ready.Kernel", function() {
Jupyter.notebook.kernel.comm_manager.register_target(
"my_comm_target",
function (comm, msg) {
comm.on_msg(function(msg){
console.log("Received from Python:", msg.content.data)
comm.send("banana")
})
}
)
})
}
return {
load_ipython_extension: load_extension,
};
});
To test this, you have to create the comm_extension, and then install it, the same way as we did here nbextension cookbook.
Python side¶
Here is Python code that will connect to the comm target created by the JavaScript code above.
from ipykernel.comm import Comm
my_comm = Comm(target_name="my_comm_target")
items = ["apple", "orange"]
@my_comm.on_msg
def _recv(msg):
# Receive message from JavaScript and append it to items
data = msg["content"]["data"]
items.append(data)
Quite simple isn’t it? The only part that might be tricky to understand is the
@my_comm.on_msg
decorator. It aims to provide developers a way of defining a
callback function, which will be called every time my_comm
receives a
message.
Test¶
To test this, create a notebook, copy the python code into a cell, and run it. Now you should be able to send a message to JavaScript with a cell like this:
my_comm.send("Please send me more fruit!")
You should see a message logged in the JavaScript console showing that your
message has been received by the frontend. Now if you evaluate the items
variable in a new cell, you should see that it contains banana, which was
sent from JavaScript and then received and appended to the list by the _recv
function.
Notes: you will see that here the python side is used as it is, it is not a server_extension as I initially thought, which is a good thing, because it will be much easier to package and use.
What’s happening?¶
Well, you have to consider this piece of code as two code bases working
together. You have a front end part that loads when notebook starts, and waits
for an incoming connection, on a comm_channel called my_comm_target
. The
incoming connection can come from either a server_extension, or in this case,
directly from a notebook cell. The code in this cell creates my_comm
, a Comm
instance that connects to my_comm_target
and sends data through it. At the
same time, it define a callback function that is triggered everytime my_comm
receives messages.
This is how you can create a bridge between your python code, and a visual effect of it, which is written in javascript.
And this is what is happening under the hood for ipywidgets. It is nothing more than that. The really impressive improvement though on ipywidgets is the way communication is handled, the fact that their is an MVC framework in place with Backbone.js on the front end. The way they handle the print and the embeding is also very interesting. The limitation comes from the fact that to embed other libraries such as React, that brings a lot to the table, we’d have to embed it within Backbone.js and loose a lot of the React library capabilities.
To use React, it is really important to investigate on how to reproduce this AMD syntax with ES6 and Webpack to be able to use React or whatever other framework to create widgets. More on that matter in the next cookbook , keep reading !


Abstract
This section looks at the distribution of different survey parameters characterizing daily commuting trips for 6 LAC cities. The main characteristics analyzed are Socioeconomic strata (SES) of the individual performing the trip, mode used for the trip, gender of the individual, and age of the individual.
There is a clear and well documented difference in mode use by strata. Lower income groups walk and use more transit, and higher incomes rely on private vehicles.
Although there is not a clear cut common trend for all cities, Bogota, Mexico, Montevideo and Sao Paulo exhibit more larger trip durations (60 minutes or more) in lower income sectors. Conversely, Buenos aires, Montevideo and Santiago also show more participation of lower income groups in the shortest trips, below 15 minutes.
This aggregated version of socioeconomic strata shows the different trends by city, but with pockets of specific durations in some stratas.
Distance information is missing for Buenos Aires.
Partially explaining the distribution for trip durations, and showing most of the trips are below 20km in distance; except for the case of Mexico, where longer trips are a significant portion, specially for lower income groups.
Montevideo, SaoPaulo, Santiago show shorter trip duration for lower ses groups. Bogota shows a reversed trend, and mexico shows a high peak of longer distances in the lower groups. Santiago and Sao Paulo show higher degrees of noise in the tails of lower income groups, but not in higher ones.
Combining the trip duration and distance data, we obtain an average or “commercial” speed of the commute. Again, distance data is not present for Buenos Aires.
Similarly to before, Montevideo, Santiago, and Sao Paulo show higher incomes travelling faster (observe the diffusion of the peak for higher income groups). Bogota shows an opposite trend, with similar distributions for all sectors. Mexico exhibits the most distinct variation across groups.
Although not all surveys offer the same possible answers to the trip motive, these were aggregated to the 9 major categories outlined below.
Trends can be seen for studying and working, not equal across all cities. The match between Santiago, Sao Paulo and Montevideo here may be explaining the similarities observed before. Transporting someone, recreation, errands and shopping have varying degrees of relevance by city.
Using the post processing within the IDB that tried to identified patterns for trips that were outside the limits of the metropolitan area. These were distinguished as originating outside, or bound for outside of the city limits. Sao Paulo and Buenos Aires were excluded from this analysis due to the lack of geographical coordinates in the survey responses.
Trips originated and bound for within the metropolitan area were labelled endogenous and filtered out. Trips originated outside and inbound are labelled incoming, thost originated within and with outer destination are labelled outbound, and those originated outside and bound outside of the metropolitan area are labelled external. Endogenous trips are clearly the higher percentage of trips.
Probably the city size is influencing the values presented, and should be considered along with each city morphology.
Below we analyze the different trip characteristics by mode used for the trip.
The travel time pattern by mode is clear across the region. This could be due to higher speed of the mode, or due to shorter distances being performed.
Here we can conclude the time difference is owed to shorter distances being performed in private modes. Observe there is a higher spread here.
Reinforcing the previous diagrams, bus and mass transit perform longer trips, that take more time due to lower commercial speeds.
1: The role of motorized modes in working trips. 2: Taxi, walking, and private cars have more diverse roles
This section observes trends by gender of the individual performing the trip.
Perhaps the most outstanding pattern is the relation between male oriented private car and female dominated bus in all cities. Taxi and mass transit are also slightly female oriented, and motorcycle clearly male oriented.
Women favor shorter trips, but except for the case of Mexico, the differences do not look substantial. Trip time groups seem to be given by city characterstics and not individual gender patterns.
Men travel slightly faster.
Working is male dominated, and replaced, depending on the city, with activities like groceries, transporting someone, or errands.
Below are explored patterns by age group.


Chemist
[Chemist Documentation]
Chemist is an Elixir wrapper for the Riot API.
Preparation
You will need a Riot Api Key, which you can get by signing up at the [Riot Games Developer] site. Set the environmental variable “RIOT_API_KEY” to your key.
Sample standalone startup script:
#!/bin/bash
export RIOT_API_KEY="myriotkey"
iex -S mix
Installation
Add
chemist
to your list of dependencies inmix.exs
:def deps do [{:chemist, "~> 0.1.0"}] end
Ensure
chemist
is started before your application:def application do [applications: [:chemist]] end
Other implementations for the Riot API in Elixir
- [velkoz]
- [viktor]
[Chemist Documentation]:https://hexdocs.pm/chemist [Riot Games Developer]:https://developer.riotgames.com/ [velkoz]:https://github.com/Tim-Machine/velkoz [viktor]:https://github.com/josephyi/viktor


Tokyo stocks close down for third straight session
TOKYO, Nov 21 — Tokyo stocks fell for a third consecutive session today on revived concerns about a China-US trade deal after American lawmakers approved legislation to support Hong Kong civil rights.
The benchmark Nikkei 225 index lost 0.48 per cent, or 109.99 points, to close at 23,038.58, while the broader Topix dipped 0.10 per cent, or 1.73 points, to 1,689.38.
Tokyo stocks had opened lower following losses on Wall Street as US lawmakers yesterday approved legislation that supports human rights and democracy in Hong Kong and also backs the territory’s anti-China protesters, sending the measure opposed by Beijing to President Donald Trump.
“In addition to falls in US stocks, reports that US President Trump is expected to sign legislation passed by the Congress are seen damping the Japanese market,” Toshiyuki Kanayama, senior market analyst at Monex, said in a note.
The bill requires the US president to annually review the favourable trade position that Washington grants to Hong Kong, and threatens to revoke the coveted status the semi-autonomous Chinese territory enjoys with the United States if its freedoms are quashed.
The measure now heads to Trump, who has not said whether he will sign it.
“Negative news overwhelmed the positive,” said Shinichi Yamamoto, a broker at Okasan Securities in Tokyo.
“But this week’s fall can be seen as a pause following the recent strong buying sentiment,” Yamamoto told AFP. “Chances are still high that stocks climb up again after a short break.”
The dollar fetched ¥108.58 in Asian afternoon trade, down from ¥108.62 as traders fled to the safe-haven Japanese currency on renewed concerns over the trade deal.
In Tokyo, semiconductor-related shares were sharply lower, with chip-making equipment manufacturer Tokyo Electron trading down 3.44 per cent at ¥21,735 and chip-testing equipment maker Advantest off 3.88 per cent at ¥5,200.
Nissan fell 1.01 per cent to ¥664 but Toyota rose 0.36 per cent to ¥7,716 with Honda up 0.35 per cent at ¥3,103. — AFP


In the three years since hiring former player Jeff Boals as head coach, Ohio's men have gone 59-33 and won a MAC Championship, securing their first NCAA Tournament bid in nine years, where they upset defending national champions Virginia in the first round. They also had a player drafted for the first time in nearly 20 years when Jason Preston was selected 33rd overall by the Los Angeles Clippers in 2021.
On the women's side, Ohio recently saw a trio of incredible careers come to an end. First, 1,000-point scorers Erica Johnson and Gabby Burris hung up their jerseys this past season after leading Ohio to some of its best seasons ever, including its first-ever 30-win season in 2018-19.
Second, Cece Hooks finished one of the greatest careers in MAC history this year, graduating as the conference's all-time leader in points, steals and made field goals. She was also named MAC Defensive Player of the Year, All-MAC First Team and MAC All-Defensive Team four times each, and won MAC Player of the Year in 2021.
Despite all this recent success, both Ohio teams are at a bit of a crossroads, as many talented players departed from Athens this offseason. For the women's team, Hooks and Burris graduated and Johnson elected to forego her final year of eligibility to pursue her professional career. For the men, Jason Carter graduated and Mark Sears and Ben Vander Plas transferred to Alabama and Virginia, respectively.
Those six players accounted for 65.7% percent of all the points scored by both Ohio basketball teams last season. Additionally, five of the six players averaged more than 30 minutes per game, and the only one who didn't was Carter, who dealt with injuries for much of the season and still averaged 28.7.
There are undoubtedly massive holes to fill for both Ohio basketball teams, but if there is one thing that Boals and women's head coach Bob Boldon have proved, it is that they are capable of bringing talent to Athens in a variety of ways.
In just three years in Athens, Boals has already proven to be adept at utilizing the transfer portal, where he brought in Carter, as well as Tommy Schmock and Dwight Wilson III, as graduate transfers. He has also been active in that area this offseason, bringing in a pair of transfers, Jaylin Hunter from Old Dominion and Gabe Wiznitzer from Louisville.
He has also shown himself to be a skillful recruiter when it comes to both luring top talents to Athens and finding diamonds in the rough. He was able to bring in a high-profile recruit like Sears, as well as giving Preston one of his only college offers, both of which turned out to be pretty good moves for the program.
On the women's side, Boldon has spent his entire nine-year tenure with the Bobcats bringing in talented players. He coached six different 1000-point scorers: Hooks, Johnson and Burris, as well as Kiyanna Black, Quiera Lampkins and Amani Burke. Boldon is also the winningest coach in Ohio basketball history and won his 100th career MAC game this past season.
After a pair of disappointing finishes last season, both Ohio basketball teams are staring down the barrel of two very tough transitions, but Athens is home to two of the best coaches in the MAC, and Boals and Boldon now have yet another opportunity to show off their ability to bring talent to the Bobcats.


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Par date:
This feature was added here because jquerymobile does not support more than 5 items into the footer navbars.
See http://jquerymobile.com/demos/1.3.0/docs/widgets/navbar/
JQM expands collapsibles immediately below this their header and with a width equal to the header.
We tried to put a collapsible below this button, without header and having the width of the page, and then to programatically expand it - but this does not work yet.
Please expand the section presented below.


Mapping parameters
The following pages provide detailed explanations of the various mapping parameters that are used by field mappings:
The following mapping parameters are common to some or all field data types:
analyzer
|
Important
|
Only |
The analyzer
parameter specifies the analyzer used for
text analysis when indexing or searching a text
field.
Unless overridden with the search_analyzer
mapping
parameter, this analyzer is used for both index and
search analysis. See Specify an analyzer.
|
Tip
|
We recommend testing analyzers before using them in production. See Test an analyzer. |
search_quote_analyzer
The search_quote_analyzer
setting allows you to specify an analyzer for phrases, this is particularly useful when dealing with disabling
stop words for phrase queries.
To disable stop words for phrases a field utilising three analyzer settings will be required:
-
An
analyzer
setting for indexing all terms including stop words -
A
search_analyzer
setting for non-phrase queries that will remove stop words -
A
search_quote_analyzer
setting for phrase queries that will not remove stop words
PUT my-index-000001
{
"settings":{
"analysis":{
"analyzer":{
"my_analyzer":{ (1)
"type":"custom",
"tokenizer":"standard",
"filter":[
"lowercase"
]
},
"my_stop_analyzer":{ (2)
"type":"custom",
"tokenizer":"standard",
"filter":[
"lowercase",
"english_stop"
]
}
},
"filter":{
"english_stop":{
"type":"stop",
"stopwords":"_english_"
}
}
}
},
"mappings":{
"properties":{
"title": {
"type":"text",
"analyzer":"my_analyzer", (3)
"search_analyzer":"my_stop_analyzer", (4)
"search_quote_analyzer":"my_analyzer" (5)
}
}
}
}
PUT my-index-000001/_doc/1
{
"title":"The Quick Brown Fox"
}
PUT my-index-000001/_doc/2
{
"title":"A Quick Brown Fox"
}
GET my-index-000001/_search
{
"query":{
"query_string":{
"query":"\"the quick brown fox\"" (6)
}
}
}
-
my_analyzer
analyzer which tokens all terms including stop words -
my_stop_analyzer
analyzer which removes stop words -
analyzer
setting that points to themy_analyzer
analyzer which will be used at index time -
search_analyzer
setting that points to themy_stop_analyzer
and removes stop words for non-phrase queries -
search_quote_analyzer
setting that points to themy_analyzer
analyzer and ensures that stop words are not removed from phrase queries -
Since the query is wrapped in quotes it is detected as a phrase query therefore the
search_quote_analyzer
kicks in and ensures the stop words are not removed from the query. Themy_analyzer
analyzer will then return the following tokens [the
,quick
,brown
,fox
] which will match one of the documents. Meanwhile term queries will be analyzed with themy_stop_analyzer
analyzer which will filter out stop words. So a search for eitherThe quick brown fox
orA quick brown fox
will return both documents since both documents contain the following tokens [quick
,brown
,fox
]. Without thesearch_quote_analyzer
it would not be possible to do exact matches for phrase queries as the stop words from phrase queries would be removed resulting in both documents matching.
boost
Individual fields can be boosted automatically — count more towards the relevance score — at query time, with the boost
parameter as follows:
PUT my-index-000001
{
"mappings": {
"properties": {
"title": {
"type": "text",
"boost": 2 (1)
},
"content": {
"type": "text"
}
}
}
}
-
Matches on the
title
field will have twice the weight as those on thecontent
field, which has the defaultboost
of1.0
.
|
Note
|
The boost is applied only for term queries (prefix, range and fuzzy queries are not boosted). |
You can achieve the same effect by using the boost parameter directly in the query, for instance the following query (with field time boost):
POST _search
{
"query": {
"match": {
"title": {
"query": "quick brown fox"
}
}
}
}
is equivalent to:
POST _search
{
"query": {
"match": {
"title": {
"query": "quick brown fox",
"boost": 2
}
}
}
}
|
Warning
|
Why index time boosting is deprecated
We advise against using index time boosting for the following reasons:
|
coerce
Data is not always clean. Depending on how it is produced a number might be
rendered in the JSON body as a true JSON number, e.g. 5
, but it might also
be rendered as a string, e.g. "5"
. Alternatively, a number that should be
an integer might instead be rendered as a floating point, e.g. 5.0
, or even
"5.0"
.
Coercion attempts to clean up dirty values to fit the data type of a field. For instance:
-
Strings will be coerced to numbers.
-
Floating points will be truncated for integer values.
For instance:
PUT my-index-000001
{
"mappings": {
"properties": {
"number_one": {
"type": "integer"
},
"number_two": {
"type": "integer",
"coerce": false
}
}
}
}
PUT my-index-000001/_doc/1
{
"number_one": "10" (1)
}
PUT my-index-000001/_doc/2
{
"number_two": "10" (2)
}
-
The
number_one
field will contain the integer10
. -
This document will be rejected because coercion is disabled.
|
Tip
|
The coerce setting value can be updated on existing fields
using the PUT mapping API.
|
Index-level default
The index.mapping.coerce
setting can be set on the index level to disable
coercion globally across all mapping types:
PUT my-index-000001
{
"settings": {
"index.mapping.coerce": false
},
"mappings": {
"properties": {
"number_one": {
"type": "integer",
"coerce": true
},
"number_two": {
"type": "integer"
}
}
}
}
PUT my-index-000001/_doc/1
{ "number_one": "10" } (1)
PUT my-index-000001/_doc/2
{ "number_two": "10" } (2)
-
The
number_one
field overrides the index level setting to enable coercion. -
This document will be rejected because the
number_two
field inherits the index-level coercion setting.
copy_to
The copy_to
parameter allows you to copy the values of multiple
fields into a group field, which can then be queried as a single
field.
|
Tip
|
If you often search multiple fields, you can improve search speeds by using
copy_to to search fewer fields. See Search as few fields as possible.
|
For example, the first_name
and last_name
fields can be copied to
the full_name
field as follows:
PUT my-index-000001
{
"mappings": {
"properties": {
"first_name": {
"type": "text",
"copy_to": "full_name" (1)
},
"last_name": {
"type": "text",
"copy_to": "full_name" (1)
},
"full_name": {
"type": "text"
}
}
}
}
PUT my-index-000001/_doc/1
{
"first_name": "John",
"last_name": "Smith"
}
GET my-index-000001/_search
{
"query": {
"match": {
"full_name": { (2)
"query": "John Smith",
"operator": "and"
}
}
}
}
-
The values of the
first_name
andlast_name
fields are copied to thefull_name
field. -
The
first_name
andlast_name
fields can still be queried for the first name and last name respectively, but thefull_name
field can be queried for both first and last names.
Some important points:
-
It is the field value which is copied, not the terms (which result from the analysis process).
-
The original
_source
field will not be modified to show the copied values. -
The same value can be copied to multiple fields, with
"copy_to": [ "field_1", "field_2" ]
-
You cannot copy recursively via intermediary fields such as a
copy_to
onfield_1
tofield_2
andcopy_to
onfield_2
tofield_3
expecting indexing intofield_1
will eventuate infield_3
, instead use copy_to directly to multiple fields from the originating field.
doc_values
Most fields are indexed by default, which makes them searchable. The inverted index allows queries to look up the search term in unique sorted list of terms, and from that immediately have access to the list of documents that contain the term.
Sorting, aggregations, and access to field values in scripts requires a different data access pattern. Instead of looking up the term and finding documents, we need to be able to look up the document and find the terms that it has in a field.
Doc values are the on-disk data structure, built at document index time, which
makes this data access pattern possible. They store the same values as the
_source
but in a column-oriented fashion that is way more efficient for
sorting and aggregations. Doc values are supported on almost all field types,
with the notable exception of text
and annotated_text
fields.
All fields which support doc values have them enabled by default. If you are sure that you don’t need to sort or aggregate on a field, or access the field value from a script, you can disable doc values in order to save disk space:
PUT my-index-000001
{
"mappings": {
"properties": {
"status_code": { (1)
"type": "keyword"
},
"session_id": { (2)
"type": "keyword",
"doc_values": false
}
}
}
}
-
The
status_code
field hasdoc_values
enabled by default. -
The
session_id
hasdoc_values
disabled, but can still be queried.
dynamic
By default, fields can be added dynamically to a document, or to inner objects within a document, just by indexing a document containing the new field. For instance:
PUT my-index-000001/_doc/1 (1)
{
"username": "johnsmith",
"name": {
"first": "John",
"last": "Smith"
}
}
GET my-index-000001/_mapping (2)
PUT my-index-000001/_doc/2 (3)
{
"username": "marywhite",
"email"<EMAIL_ADDRESS>"name": {
"first": "Mary",
"middle": "Alice",
"last": "White"
}
}
GET my-index-000001/_mapping (4)
-
This document introduces the string field
username
, the object fieldname
, and two string fields under thename
object which can be referred to asname.first
andname.last
. -
Check the mapping to verify the above.
-
This document adds two string fields:
email
andname.middle
. -
Check the mapping to verify the changes.
The details of how new fields are detected and added to the mapping is explained in Dynamic Mapping.
The dynamic
setting controls whether new fields can be added dynamically or
not. It accepts three settings:
true
|
Newly detected fields are added to the mapping. (default) |
false
|
Newly detected fields are ignored. These fields will not be indexed so will not be searchable
but will still appear in the |
strict
|
If new fields are detected, an exception is thrown and the document is rejected. New fields must be explicitly added to the mapping. |
The dynamic
setting may be set at the mapping type level, and on each
inner object. Inner objects inherit the setting from their parent
object or from the mapping type. For instance:
PUT my-index-000001
{
"mappings": {
"dynamic": false, (1)
"properties": {
"user": { (2)
"properties": {
"name": {
"type": "text"
},
"social_networks": { (3)
"dynamic": true,
"properties": {}
}
}
}
}
}
}
-
Dynamic mapping is disabled at the type level, so no new top-level fields will be added dynamically.
-
The
user
object inherits the type-level setting. -
The
user.social_networks
object enables dynamic mapping, so new fields may be added to this inner object.
|
Tip
|
The dynamic setting can be updated on existing fields
using the PUT mapping API.
|
eager_global_ordinals
What are global ordinals?
To support aggregations and other operations that require looking up field
values on a per-document basis, OpenSearch uses a data structure called
doc values. Term-based field types such as keyword
store
their doc values using an ordinal mapping for a more compact representation.
This mapping works by assigning each term an incremental integer or 'ordinal'
based on its lexicographic order. The field’s doc values store only the
ordinals for each document instead of the original terms, with a separate
lookup structure to convert between ordinals and terms.
When used during aggregations, ordinals can greatly improve performance. As an
example, the terms
aggregation relies only on ordinals to collect documents
into buckets at the shard-level, then converts the ordinals back to their
original term values when combining results across shards.
Each index segment defines its own ordinal mapping, but aggregations collect data across an entire shard. So to be able to use ordinals for shard-level operations like aggregations, OpenSearch creates a unified mapping called 'global ordinals'. The global ordinal mapping is built on top of segment ordinals, and works by maintaining a map from global ordinal to the local ordinal for each segment.
Global ordinals are used if a search contains any of the following components:
-
Certain bucket aggregations on
keyword
,ip
, andflattened
fields. This includesterms
aggregations as mentioned above, as well ascomposite
,diversified_sampler
, andsignificant_terms
. -
Bucket aggregations on
text
fields that requirefielddata
to be enabled. -
Operations on parent and child documents from a
join
field, includinghas_child
queries andparent
aggregations.
|
Note
|
The global ordinal mapping uses heap memory as part of the field data cache. Aggregations on high cardinality fields can use a lot of memory and trigger the field data circuit breaker. |
Loading global ordinals
The global ordinal mapping must be built before ordinals can be used during a search. By default, the mapping is loaded during search on the first time that global ordinals are needed. This is is the right approach if you are optimizing for indexing speed, but if search performance is a priority, it’s recommended to eagerly load global ordinals eagerly on fields that will be used in aggregations:
PUT my-index-000001/_mapping
{
"properties": {
"tags": {
"type": "keyword",
"eager_global_ordinals": true
}
}
}
When eager_global_ordinals
is enabled, global ordinals are built when a shard
is refreshed — OpenSearch always loads them before
exposing changes to the content of the index. This shifts the cost of building
global ordinals from search to index-time. OpenSearch will also eagerly
build global ordinals when creating a new copy of a shard, as can occur when
increasing the number of replicas or relocating a shard onto a new node.
Eager loading can be disabled at any time by updating the eager_global_ordinals
setting:
PUT my-index-000001/_mapping
{
"properties": {
"tags": {
"type": "keyword",
"eager_global_ordinals": false
}
}
}
Avoiding global ordinal loading
Usually, global ordinals do not present a large overhead in terms of their loading time and memory usage. However, loading global ordinals can be expensive on indices with large shards, or if the fields contain a large number of unique term values. Because global ordinals provide a unified mapping for all segments on the shard, they also need to be rebuilt entirely when a new segment becomes visible.
In some cases it is possible to avoid global ordinal loading altogether:
-
The
terms
,sampler
, andsignificant_terms
aggregations support a parameterexecution_hint
that helps control how buckets are collected. It defaults toglobal_ordinals
, but can be set tomap
to instead use the term values directly. -
If a shard has been force-merged down to a single segment, then its segment ordinals are already 'global' to the shard. In this case, OpenSearch does not need to build a global ordinal mapping and there is no additional overhead from using global ordinals. Note that for performance reasons you should only force-merge an index to which you will never write to again.
enabled
OpenSearch tries to index all of the fields you give it, but sometimes you want to just store the field without indexing it. For instance, imagine that you are using OpenSearch as a web session store. You may want to index the session ID and last update time, but you don’t need to query or run aggregations on the session data itself.
The enabled
setting, which can be applied only to the top-level mapping
definition and to object
fields, causes OpenSearch to skip
parsing of the contents of the field entirely. The JSON can still be retrieved
from the _source
field, but it is not searchable or
stored in any other way:
PUT my-index-000001
{
"mappings": {
"properties": {
"user_id": {
"type": "keyword"
},
"last_updated": {
"type": "date"
},
"session_data": { (1)
"type": "object",
"enabled": false
}
}
}
}
PUT my-index-000001/_doc/session_1
{
"user_id": "bilbo",
"session_data": { (2)
"arbitrary_object": {
"some_array": [ "foo", "bar", { "baz": 2 } ]
}
},
"last_updated": "2015-12-06T18:20:22"
}
PUT my-index-000001/_doc/session_2
{
"user_id": "jpountz",
"session_data": "none", (3)
"last_updated": "2015-12-06T18:22:13"
}
-
The
session_data
field is disabled. -
Any arbitrary data can be passed to the
session_data
field as it will be entirely ignored. -
The
session_data
will also ignore values that are not JSON objects.
The entire mapping may be disabled as well, in which case the document is
stored in the _source
field, which means it can be
retrieved, but none of its contents are indexed in any way:
PUT my-index-000001
{
"mappings": {
"enabled": false (1)
}
}
PUT my-index-000001/_doc/session_1
{
"user_id": "bilbo",
"session_data": {
"arbitrary_object": {
"some_array": [ "foo", "bar", { "baz": 2 } ]
}
},
"last_updated": "2015-12-06T18:20:22"
}
GET my-index-000001/_doc/session_1 (2)
GET my-index-000001/_mapping (3)
-
The entire mapping is disabled.
-
The document can be retrieved.
-
Checking the mapping reveals that no fields have been added.
The enabled
setting for existing fields and the top-level mapping
definition cannot be updated.
Note that because OpenSearch completely skips parsing the field contents, it is possible to add non-object data to a disabled field:
PUT my-index-000001
{
"mappings": {
"properties": {
"session_data": {
"type": "object",
"enabled": false
}
}
}
}
PUT my-index-000001/_doc/session_1
{
"session_data": "foo bar" (1)
}
-
The document is added successfully, even though
session_data
contains non-object data.
format
In JSON documents, dates are represented as strings. OpenSearch uses a set of preconfigured formats to recognize and parse these strings into a long value representing milliseconds-since-the-epoch in UTC.
Besides the built-in formats, your own
custom formats can be specified using the familiar
yyyy/MM/dd
syntax:
PUT my-index-000001
{
"mappings": {
"properties": {
"date": {
"type": "date",
"format": "yyyy-MM-dd"
}
}
}
}
Many APIs which support date values also support date math
expressions, such as now-1m/d
— the current time, minus one month, rounded
down to the nearest day.
Custom date formats
Completely customizable date formats are supported. The syntax for these is explained DateTimeFormatter docs.
Built In Formats
Most of the below formats have a strict
companion format, which means that
year, month and day parts of the week must use respectively 4, 2 and 2 digits
exactly, potentially prepending zeros. For instance a date like 5/11/1
would
be considered invalid and would need to be rewritten to 2005/11/01
to be
accepted by the date parser.
To use them, you need to prepend strict_
to the name of the date format, for
instance strict_date_optional_time
instead of date_optional_time
.
These strict date formats are especially useful when date fields are dynamically mapped in order to make sure to not accidentally map irrelevant strings as dates.
The following tables lists all the defaults ISO formats supported:
epoch_millis
-
A formatter for the number of milliseconds since the epoch. Note, that this timestamp is subject to the limits of a Java
Long.MIN_VALUE
andLong.MAX_VALUE
. epoch_second
-
A formatter for the number of seconds since the epoch. Note, that this timestamp is subject to the limits of a Java
Long.MIN_VALUE
andLong. MAX_VALUE
divided by 1000 (the number of milliseconds in a second). date_optional_time
orstrict_date_optional_time
-
A generic ISO datetime parser, where the date must include the year at a minimum, and the time (separated by
T
), is optional. Examples:yyyy-MM-dd’T’HH:mm:ss.SSSZ
oryyyy-MM-dd
. strict_date_optional_time_nanos
-
A generic ISO datetime parser, where the date must include the year at a minimum, and the time (separated by
T
), is optional. The fraction of a second part has a nanosecond resolution. Examples:yyyy-MM-dd’T’HH:mm:ss.SSSSSSZ
oryyyy-MM-dd
. basic_date
-
A basic formatter for a full date as four digit year, two digit month of year, and two digit day of month:
yyyyMMdd
. basic_date_time
-
A basic formatter that combines a basic date and time, separated by a 'T':
yyyyMMdd’T’HHmmss.SSSZ
. basic_date_time_no_millis
-
A basic formatter that combines a basic date and time without millis, separated by a 'T':
yyyyMMdd’T’HHmmssZ
. basic_ordinal_date
-
A formatter for a full ordinal date, using a four digit year and three digit dayOfYear:
yyyyDDD
. basic_ordinal_date_time
-
A formatter for a full ordinal date and time, using a four digit year and three digit dayOfYear:
yyyyDDD’T’HHmmss.SSSZ
. basic_ordinal_date_time_no_millis
-
A formatter for a full ordinal date and time without millis, using a four digit year and three digit dayOfYear:
yyyyDDD’T’HHmmssZ
. basic_time
-
A basic formatter for a two digit hour of day, two digit minute of hour, two digit second of minute, three digit millis, and time zone offset:
HHmmss.SSSZ
. basic_time_no_millis
-
A basic formatter for a two digit hour of day, two digit minute of hour, two digit second of minute, and time zone offset:
HHmmssZ
. basic_t_time
-
A basic formatter for a two digit hour of day, two digit minute of hour, two digit second of minute, three digit millis, and time zone off set prefixed by 'T':
'T’HHmmss.SSSZ
. basic_t_time_no_millis
-
A basic formatter for a two digit hour of day, two digit minute of hour, two digit second of minute, and time zone offset prefixed by 'T':
'T’HHmmssZ
. basic_week_date
orstrict_basic_week_date
-
A basic formatter for a full date as four digit weekyear, two digit week of weekyear, and one digit day of week:
xxxx’W’wwe
. basic_week_date_time
orstrict_basic_week_date_time
-
A basic formatter that combines a basic weekyear date and time, separated by a 'T':
xxxx’W’wwe’T’HHmmss.SSSZ
. basic_week_date_time_no_millis
orstrict_basic_week_date_time_no_millis
-
A basic formatter that combines a basic weekyear date and time without millis, separated by a 'T':
xxxx’W’wwe’T’HHmmssZ
. date
orstrict_date
-
A formatter for a full date as four digit year, two digit month of year, and two digit day of month:
yyyy-MM-dd
. date_hour
orstrict_date_hour
-
A formatter that combines a full date and two digit hour of day:
yyyy-MM-dd’T’HH
. date_hour_minute
orstrict_date_hour_minute
-
A formatter that combines a full date, two digit hour of day, and two digit minute of hour:
yyyy-MM-dd’T’HH:mm
. date_hour_minute_second
orstrict_date_hour_minute_second
-
A formatter that combines a full date, two digit hour of day, two digit minute of hour, and two digit second of minute:
yyyy-MM-dd’T’HH:mm:ss
. date_hour_minute_second_fraction
orstrict_date_hour_minute_second_fraction
-
A formatter that combines a full date, two digit hour of day, two digit minute of hour, two digit second of minute, and three digit fraction of second:
yyyy-MM-dd’T’HH:mm:ss.SSS
. date_hour_minute_second_millis
orstrict_date_hour_minute_second_millis
-
A formatter that combines a full date, two digit hour of day, two digit minute of hour, two digit second of minute, and three digit fraction of second:
yyyy-MM-dd’T’HH:mm:ss.SSS
. date_time
orstrict_date_time
-
A formatter that combines a full date and time, separated by a 'T':
yyyy-MM-dd’T’HH:mm:ss.SSSZZ
. date_time_no_millis
orstrict_date_time_no_millis
-
A formatter that combines a full date and time without millis, separated by a 'T':
yyyy-MM-dd’T’HH:mm:ssZZ
. hour
orstrict_hour
-
A formatter for a two digit hour of day:
HH
hour_minute
orstrict_hour_minute
-
A formatter for a two digit hour of day and two digit minute of hour:
HH:mm
. hour_minute_second
orstrict_hour_minute_second
-
A formatter for a two digit hour of day, two digit minute of hour, and two digit second of minute:
HH:mm:ss
. hour_minute_second_fraction
orstrict_hour_minute_second_fraction
-
A formatter for a two digit hour of day, two digit minute of hour, two digit second of minute, and three digit fraction of second:
HH:mm:ss.SSS
. hour_minute_second_millis
orstrict_hour_minute_second_millis
-
A formatter for a two digit hour of day, two digit minute of hour, two digit second of minute, and three digit fraction of second:
HH:mm:ss.SSS
. ordinal_date
orstrict_ordinal_date
-
A formatter for a full ordinal date, using a four digit year and three digit dayOfYear:
yyyy-DDD
. ordinal_date_time
orstrict_ordinal_date_time
-
A formatter for a full ordinal date and time, using a four digit year and three digit dayOfYear:
yyyy-DDD’T’HH:mm:ss.SSSZZ
. ordinal_date_time_no_millis
orstrict_ordinal_date_time_no_millis
-
A formatter for a full ordinal date and time without millis, using a four digit year and three digit dayOfYear:
yyyy-DDD’T’HH:mm:ssZZ
. time
orstrict_time
-
A formatter for a two digit hour of day, two digit minute of hour, two digit second of minute, three digit fraction of second, and time zone offset:
HH:mm:ss.SSSZZ
. time_no_millis
orstrict_time_no_millis
-
A formatter for a two digit hour of day, two digit minute of hour, two digit second of minute, and time zone offset:
HH:mm:ssZZ
. t_time
orstrict_t_time
-
A formatter for a two digit hour of day, two digit minute of hour, two digit second of minute, three digit fraction of second, and time zone offset prefixed by 'T':
'T’HH:mm:ss.SSSZZ
. t_time_no_millis
orstrict_t_time_no_millis
-
A formatter for a two digit hour of day, two digit minute of hour, two digit second of minute, and time zone offset prefixed by 'T':
'T’HH:mm:ssZZ
. week_date
orstrict_week_date
-
A formatter for a full date as four digit weekyear, two digit week of weekyear, and one digit day of week:
xxxx-'W’ww-e
. week_date_time
orstrict_week_date_time
-
A formatter that combines a full weekyear date and time, separated by a 'T':
xxxx-'W’ww-e’T’HH:mm:ss.SSSZZ
. week_date_time_no_millis
orstrict_week_date_time_no_millis
-
A formatter that combines a full weekyear date and time without millis, separated by a 'T':
xxxx-'W’ww-e’T’HH:mm:ssZZ
. weekyear
orstrict_weekyear
-
A formatter for a four digit weekyear:
xxxx
. weekyear_week
orstrict_weekyear_week
-
A formatter for a four digit weekyear and two digit week of weekyear:
xxxx-'W’ww
. weekyear_week_day
orstrict_weekyear_week_day
-
A formatter for a four digit weekyear, two digit week of weekyear, and one digit day of week:
xxxx-'W’ww-e
. year
orstrict_year
-
A formatter for a four digit year:
yyyy
. year_month
orstrict_year_month
-
A formatter for a four digit year and two digit month of year:
yyyy-MM
. year_month_day
orstrict_year_month_day
-
A formatter for a four digit year, two digit month of year, and two digit day of month:
yyyy-MM-dd
.
ignore_above
Strings longer than the ignore_above
setting will not be indexed or stored.
For arrays of strings, ignore_above
will be applied for each array element separately and string elements longer than ignore_above
will not be indexed or stored.
|
Note
|
All strings/array elements will still be present in the _source field, if the latter is enabled which is the default in OpenSearch.
|
PUT my-index-000001
{
"mappings": {
"properties": {
"message": {
"type": "keyword",
"ignore_above": 20 (1)
}
}
}
}
PUT my-index-000001/_doc/1 (2)
{
"message": "Syntax error"
}
PUT my-index-000001/_doc/2 (3)
{
"message": "Syntax error with some long stacktrace"
}
GET my-index-000001/_search (4)
{
"aggs": {
"messages": {
"terms": {
"field": "message"
}
}
}
}
-
This field will ignore any string longer than 20 characters.
-
This document is indexed successfully.
-
This document will be indexed, but without indexing the
message
field. -
Search returns both documents, but only the first is present in the terms aggregation.
|
Tip
|
The ignore_above setting can be updated on
existing fields using the PUT mapping API.
|
This option is also useful for protecting against Lucene’s term byte-length
limit of 32766
.
|
Note
|
The value for ignore_above is the character count, but Lucene counts
bytes. If you use UTF-8 text with many non-ASCII characters, you may want to
set the limit to 32766 / 4 = 8191 since UTF-8 characters may occupy at most
4 bytes.
|
ignore_malformed
Sometimes you don’t have much control over the data that you receive. One
user may send a login
field that is a date
, and another sends a
login
field that is an email address.
Trying to index the wrong data type into a field throws an exception by
default, and rejects the whole document. The ignore_malformed
parameter, if
set to true
, allows the exception to be ignored. The malformed field is not
indexed, but other fields in the document are processed normally.
For example:
PUT my-index-000001
{
"mappings": {
"properties": {
"number_one": {
"type": "integer",
"ignore_malformed": true
},
"number_two": {
"type": "integer"
}
}
}
}
PUT my-index-000001/_doc/1
{
"text": "Some text value",
"number_one": "foo" (1)
}
PUT my-index-000001/_doc/2
{
"text": "Some text value",
"number_two": "foo" (2)
}
-
This document will have the
text
field indexed, but not thenumber_one
field. -
This document will be rejected because
number_two
does not allow malformed values.
The ignore_malformed
setting is currently supported by the following mapping types:
- Numeric field types
-
long
,integer
,short
,byte
,double
,float
,half_float
,scaled_float
- Date field type
-
date
- Date nanoseconds field type
-
date_nanos
- Geo-point field type
-
geo_point
for lat/lon points - Geo-shape field type
-
geo_shape
for complex shapes like polygons - IP field type
-
ip
for IPv4 and IPv6 addresses
|
Tip
|
The ignore_malformed setting value can be updated on
existing fields using the PUT mapping API.
|
Index-level default
The index.mapping.ignore_malformed
setting can be set on the index level to
ignore malformed content globally across all allowed mapping types.
Mapping types that don’t support the setting will ignore it if set on the index level.
PUT my-index-000001
{
"settings": {
"index.mapping.ignore_malformed": true (1)
},
"mappings": {
"properties": {
"number_one": { (1)
"type": "byte"
},
"number_two": {
"type": "integer",
"ignore_malformed": false (2)
}
}
}
}
-
The
number_one
field inherits the index-level setting. -
The
number_two
field overrides the index-level setting to turn offignore_malformed
.
Dealing with malformed fields
Malformed fields are silently ignored at indexing time when ignore_malformed
is turned on. Whenever possible it is recommended to keep the number of
documents that have a malformed field contained, or queries on this field will
become meaningless. OpenSearch makes it easy to check how many documents
have malformed fields by using exists
,term
or terms
queries on the special
_ignored
field.
Limits for JSON Objects
You can’t use ignore_malformed
with the following data types:
You also can’t use ignore_malformed
to ignore JSON objects submitted to fields
of the wrong data type. A JSON object is any data surrounded by curly brackets
"{}"
and includes data mapped to the nested, object, and range data types.
If you submit a JSON object to an unsupported field, OpenSearch will return an error
and reject the entire document regardless of the ignore_malformed
setting.
index
The index
option controls whether field values are indexed. It accepts true
or false
and defaults to true
. Fields that are not indexed are not queryable.
index_options
The index_options
parameter controls what information is added to the
inverted index for search and highlighting purposes.
|
Warning
|
The |
It accepts the following values:
docs
-
Only the doc number is indexed. Can answer the question Does this term exist in this field?
freqs
-
Doc number and term frequencies are indexed. Term frequencies are used to score repeated terms higher than single terms.
positions
(default)-
Doc number, term frequencies, and term positions (or order) are indexed. Positions can be used for proximity or phrase queries.
offsets
-
Doc number, term frequencies, positions, and start and end character offsets (which map the term back to the original string) are indexed. Offsets are used by the unified highlighter to speed up highlighting.
PUT my-index-000001
{
"mappings": {
"properties": {
"text": {
"type": "text",
"index_options": "offsets"
}
}
}
}
PUT my-index-000001/_doc/1
{
"text": "Quick brown fox"
}
GET my-index-000001/_search
{
"query": {
"match": {
"text": "brown fox"
}
},
"highlight": {
"fields": {
"text": {} (1)
}
}
}
-
The
text
field will use the postings for the highlighting by default becauseoffsets
are indexed.
index_phrases
If enabled, two-term word combinations ('shingles') are indexed into a separate
field. This allows exact phrase queries (no slop) to run more efficiently, at the expense
of a larger index. Note that this works best when stopwords are not removed,
as phrases containing stopwords will not use the subsidiary field and will fall
back to a standard phrase query. Accepts true
or false
(default).
index_prefixes
The index_prefixes
parameter enables the indexing of term prefixes to speed
up prefix searches. It accepts the following optional settings:
min_chars
|
The minimum prefix length to index. Must be greater than 0, and defaults to 2. The value is inclusive. |
max_chars
|
The maximum prefix length to index. Must be less than 20, and defaults to 5. The value is inclusive. |
This example creates a text field using the default prefix length settings:
PUT my-index-000001
{
"mappings": {
"properties": {
"body_text": {
"type": "text",
"index_prefixes": { } (1)
}
}
}
}
-
An empty settings object will use the default
min_chars
andmax_chars
settings
This example uses custom prefix length settings:
PUT my-index-000001
{
"mappings": {
"properties": {
"full_name": {
"type": "text",
"index_prefixes": {
"min_chars" : 1,
"max_chars" : 10
}
}
}
}
}
meta
Metadata attached to the field. This metadata is opaque to OpenSearch, it is only useful for multiple applications that work on the same indices to share meta information about fields such as units
PUT my-index-000001
{
"mappings": {
"properties": {
"latency": {
"type": "long",
"meta": {
"unit": "ms"
}
}
}
}
}
|
Note
|
Field metadata enforces at most 5 entries, that keys have a length that is less than or equal to 20, and that values are strings whose length is less than or equal to 50. |
|
Note
|
Field metadata is updatable by submitting a mapping update. The metadata of the update will override the metadata of the existing field. |
fields
It is often useful to index the same field in different ways for different
purposes. This is the purpose of multi-fields. For instance, a string
field could be mapped as a text
field for full-text
search, and as a keyword
field for sorting or aggregations:
PUT my-index-000001
{
"mappings": {
"properties": {
"city": {
"type": "text",
"fields": {
"raw": { (1)
"type": "keyword"
}
}
}
}
}
}
PUT my-index-000001/_doc/1
{
"city": "New York"
}
PUT my-index-000001/_doc/2
{
"city": "York"
}
GET my-index-000001/_search
{
"query": {
"match": {
"city": "york" (2)
}
},
"sort": {
"city.raw": "asc" (3)
},
"aggs": {
"Cities": {
"terms": {
"field": "city.raw" (3)
}
}
}
}
-
The
city.raw
field is akeyword
version of thecity
field. -
The
city
field can be used for full text search. -
The
city.raw
field can be used for sorting and aggregations
|
Note
|
Multi-fields do not change the original _source field.
|
|
Tip
|
New multi-fields can be added to existing fields using the PUT mapping API. |
Multi-fields with multiple analyzers
Another use case of multi-fields is to analyze the same field in different
ways for better relevance. For instance we could index a field with the
standard
analyzer which breaks text up into
words, and again with the english
analyzer
which stems words into their root form:
PUT my-index-000001
{
"mappings": {
"properties": {
"text": { (1)
"type": "text",
"fields": {
"english": { (2)
"type": "text",
"analyzer": "english"
}
}
}
}
}
}
PUT my-index-000001/_doc/1
{ "text": "quick brown fox" } (3)
PUT my-index-000001/_doc/2
{ "text": "quick brown foxes" } (3)
GET my-index-000001/_search
{
"query": {
"multi_match": {
"query": "quick brown foxes",
"fields": [ (4)
"text",
"text.english"
],
"type": "most_fields" (4)
}
}
}
-
The
text
field uses thestandard
analyzer. -
The
text.english
field uses theenglish
analyzer. -
Index two documents, one with
fox
and the other withfoxes
. -
Query both the
text
andtext.english
fields and combine the scores.
The text
field contains the term fox
in the first document and foxes
in
the second document. The text.english
field contains fox
for both
documents, because foxes
is stemmed to fox
.
The query string is also analyzed by the standard
analyzer for the text
field, and by the english
analyzer for the text.english
field. The
stemmed field allows a query for foxes
to also match the document containing
just fox
. This allows us to match as many documents as possible. By also
querying the unstemmed text
field, we improve the relevance score of the
document which matches foxes
exactly.
normalizer
The normalizer
property of keyword
fields is similar to
analyzer
except that it guarantees that the analysis chain
produces a single token.
The normalizer
is applied prior to indexing the keyword, as well as at
search-time when the keyword
field is searched via a query parser such as
the match
query or via a term-level query
such as the term
query.
A simple normalizer called lowercase
ships with OpenSearch and can be used.
Custom normalizers can be defined as part of analysis settings as follows.
PUT index
{
"settings": {
"analysis": {
"normalizer": {
"my_normalizer": {
"type": "custom",
"char_filter": [],
"filter": ["lowercase", "asciifolding"]
}
}
}
},
"mappings": {
"properties": {
"foo": {
"type": "keyword",
"normalizer": "my_normalizer"
}
}
}
}
PUT index/_doc/1
{
"foo": "BÀR"
}
PUT index/_doc/2
{
"foo": "bar"
}
PUT index/_doc/3
{
"foo": "baz"
}
POST index/_refresh
GET index/_search
{
"query": {
"term": {
"foo": "BAR"
}
}
}
GET index/_search
{
"query": {
"match": {
"foo": "BAR"
}
}
}
The above queries match documents 1 and 2 since BÀR
is converted to bar
at
both index and query time.
{
"took": $body.took,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped" : 0,
"failed": 0
},
"hits": {
"total" : {
"value": 2,
"relation": "eq"
},
"max_score": 0.4700036,
"hits": [
{
"_index": "index",
"_type": "_doc",
"_id": "1",
"_score": 0.4700036,
"_source": {
"foo": "BÀR"
}
},
{
"_index": "index",
"_type": "_doc",
"_id": "2",
"_score": 0.4700036,
"_source": {
"foo": "bar"
}
}
]
}
}
Also, the fact that keywords are converted prior to indexing also means that aggregations return normalized values:
GET index/_search
{
"size": 0,
"aggs": {
"foo_terms": {
"terms": {
"field": "foo"
}
}
}
}
returns
{
"took": 43,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped" : 0,
"failed": 0
},
"hits": {
"total" : {
"value": 3,
"relation": "eq"
},
"max_score": null,
"hits": []
},
"aggregations": {
"foo_terms": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "bar",
"doc_count": 2
},
{
"key": "baz",
"doc_count": 1
}
]
}
}
}
norms
Norms store various normalization factors that are later used at query time in order to compute the score of a document relatively to a query.
Although useful for scoring, norms also require quite a lot of disk (typically in the order of one byte per document per field in your index, even for documents that don’t have this specific field). As a consequence, if you don’t need scoring on a specific field, you should disable norms on that field. In particular, this is the case for fields that are used solely for filtering or aggregations.
|
Tip
|
Norms can be disabled on existing fields using the PUT mapping API. |
Norms can be disabled (but not reenabled after the fact), using the PUT mapping API like so:
PUT my-index-000001/_mapping
{
"properties": {
"title": {
"type": "text",
"norms": false
}
}
}
|
Note
|
Norms will not be removed instantly, but will be removed as old segments are merged into new segments as you continue indexing new documents. Any score computation on a field that has had norms removed might return inconsistent results since some documents won’t have norms anymore while other documents might still have norms. |
null_value
A null
value cannot be indexed or searched. When a field is set to null
,
(or an empty array or an array of null
values) it is treated as though that
field has no values.
The null_value
parameter allows you to replace explicit null
values with
the specified value so that it can be indexed and searched. For instance:
PUT my-index-000001
{
"mappings": {
"properties": {
"status_code": {
"type": "keyword",
"null_value": "NULL" (1)
}
}
}
}
PUT my-index-000001/_doc/1
{
"status_code": null
}
PUT my-index-000001/_doc/2
{
"status_code": [] (2)
}
GET my-index-000001/_search
{
"query": {
"term": {
"status_code": "NULL" (3)
}
}
}
-
Replace explicit
null
values with the termNULL
. -
An empty array does not contain an explicit
null
, and so won’t be replaced with thenull_value
. -
A query for
NULL
returns document 1, but not document 2.
|
Important
|
The null_value needs to be the same data type as the field. For
instance, a long field cannot have a string null_value .
|
|
Note
|
The null_value only influences how data is indexed, it doesn’t modify
the _source document.
|
position_increment_gap
Analyzed text fields take term positions
into account, in order to be able to support
proximity or phrase queries.
When indexing text fields with multiple values a "fake" gap is added between
the values to prevent most phrase queries from matching across the values. The
size of this gap is configured using position_increment_gap
and defaults to
100
.
For example:
PUT my-index-000001/_doc/1
{
"names": [ "John Abraham", "Lincoln Smith"]
}
GET my-index-000001/_search
{
"query": {
"match_phrase": {
"names": {
"query": "Abraham Lincoln" (1)
}
}
}
}
GET my-index-000001/_search
{
"query": {
"match_phrase": {
"names": {
"query": "Abraham Lincoln",
"slop": 101 (2)
}
}
}
}
-
This phrase query doesn’t match our document which is totally expected.
-
This phrase query matches our document, even though
Abraham
andLincoln
are in separate strings, becauseslop
>position_increment_gap
.
The position_increment_gap
can be specified in the mapping. For instance:
PUT my-index-000001
{
"mappings": {
"properties": {
"names": {
"type": "text",
"position_increment_gap": 0 (1)
}
}
}
}
PUT my-index-000001/_doc/1
{
"names": [ "John Abraham", "Lincoln Smith"]
}
GET my-index-000001/_search
{
"query": {
"match_phrase": {
"names": "Abraham Lincoln" (2)
}
}
}
-
The first term in the next array element will be 0 terms apart from the last term in the previous array element.
-
The phrase query matches our document which is weird, but its what we asked for in the mapping.
properties
Type mappings, object
fields and nested
fields
contain sub-fields, called properties
. These properties may be of any
data type, including object
and nested
. Properties can
be added:
-
explicitly by defining them when creating an index.
-
explicitly by defining them when adding or updating a mapping type with the PUT mapping API.
-
dynamically just by indexing documents containing new fields.
Below is an example of adding properties
to a mapping type, an object
field, and a nested
field:
PUT my-index-000001
{
"mappings": {
"properties": { (1)
"manager": {
"properties": { (2)
"age": { "type": "integer" },
"name": { "type": "text" }
}
},
"employees": {
"type": "nested",
"properties": { (3)
"age": { "type": "integer" },
"name": { "type": "text" }
}
}
}
}
}
PUT my-index-000001/_doc/1 (4)
{
"region": "US",
"manager": {
"name": "Alice White",
"age": 30
},
"employees": [
{
"name": "John Smith",
"age": 34
},
{
"name": "Peter Brown",
"age": 26
}
]
}
-
Properties in the top-level mappings definition.
-
Properties under the
manager
object field. -
Properties under the
employees
nested field. -
An example document which corresponds to the above mapping.
|
Tip
|
The properties setting is allowed to have different settings for fields
of the same name in the same index. New properties can be added to existing
fields using the PUT mapping API.
|
Dot notation
Inner fields can be referred to in queries, aggregations, etc., using dot notation:
GET my-index-000001/_search
{
"query": {
"match": {
"manager.name": "Alice White"
}
},
"aggs": {
"Employees": {
"nested": {
"path": "employees"
},
"aggs": {
"Employee Ages": {
"histogram": {
"field": "employees.age",
"interval": 5
}
}
}
}
}
}
|
Important
|
The full path to the inner field must be specified. |
search_analyzer
Usually, the same analyzer should be applied at index time and at search time, to ensure that the terms in the query are in the same format as the terms in the inverted index.
Sometimes, though, it can make sense to use a different analyzer at search
time, such as when using the edge_ngram
tokenizer for autocomplete.
By default, queries will use the analyzer
defined in the field mapping, but
this can be overridden with the search_analyzer
setting:
PUT my-index-000001
{
"settings": {
"analysis": {
"filter": {
"autocomplete_filter": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 20
}
},
"analyzer": {
"autocomplete": { (1)
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"autocomplete_filter"
]
}
}
}
},
"mappings": {
"properties": {
"text": {
"type": "text",
"analyzer": "autocomplete", (2)
"search_analyzer": "standard" (2)
}
}
}
}
PUT my-index-000001/_doc/1
{
"text": "Quick Brown Fox" (3)
}
GET my-index-000001/_search
{
"query": {
"match": {
"text": {
"query": "Quick Br", (4)
"operator": "and"
}
}
}
}
-
Analysis settings to define the custom
autocomplete
analyzer. -
The
text
field uses theautocomplete
analyzer at index time, but thestandard
analyzer at search time. -
This field is indexed as the terms: [
q
,qu
,qui
,quic
,quick
,b
,br
,bro
,brow
,brown
,f
,fo
,fox
] -
The query searches for both of these terms: [
quick
,br
]
See Index time search-as-you- type for a full explanation of this example.
|
Tip
|
The search_analyzer setting can be updated on existing fields
using the PUT mapping API.
|
similarity
OpenSearch allows you to configure a scoring algorithm or similarity per
field. The similarity
setting provides a simple way of choosing a similarity
algorithm other than the default BM25
, such as TF/IDF
.
Similarities are mostly useful for text
fields, but can also apply
to other field types.
Custom similarities can be configured by tuning the parameters of the built-in similarities. For more details about this expert options, see the similarity module.
The only similarities which can be used out of the box, without any further configuration are:
BM25
-
The Okapi BM25 algorithm. The algorithm used by default in OpenSearch and Lucene.
classic
-
Deprecated. The TF/IDF algorithm, the former default in OpenSearch and Lucene.
boolean
-
A simple boolean similarity, which is used when full-text ranking is not needed and the score should only be based on whether the query terms match or not. Boolean similarity gives terms a score equal to their query boost.
The similarity
can be set on the field level when a field is first created,
as follows:
PUT my-index-000001
{
"mappings": {
"properties": {
"default_field": { (1)
"type": "text"
},
"boolean_sim_field": {
"type": "text",
"similarity": "boolean" (2)
}
}
}
}
-
The
default_field
uses theBM25
similarity. -
The
boolean_sim_field
uses theboolean
similarity.
store
By default, field values are indexed to make them searchable, but they are not stored. This means that the field can be queried, but the original field value cannot be retrieved.
Usually this doesn’t matter. The field value is already part of the
_source
field, which is stored by default. If you
only want to retrieve the value of a single field or of a few fields, instead
of the whole _source
, then this can be achieved with
source filtering.
In certain situations it can make sense to store
a field. For instance, if
you have a document with a title
, a date
, and a very large content
field, you may want to retrieve just the title
and the date
without having
to extract those fields from a large _source
field:
PUT my-index-000001
{
"mappings": {
"properties": {
"title": {
"type": "text",
"store": true (1)
},
"date": {
"type": "date",
"store": true (1)
},
"content": {
"type": "text"
}
}
}
}
PUT my-index-000001/_doc/1
{
"title": "Some short title",
"date": "2015-01-01",
"content": "A very long content field..."
}
GET my-index-000001/_search
{
"stored_fields": [ "title", "date" ] (2)
}
-
The
title
anddate
fields are stored. -
This request will retrieve the values of the
title
anddate
fields.
|
Note
|
Stored fields returned as arrays
For consistency, stored fields are always returned as an array because there is no way of knowing if the original field value was a single value, multiple values, or an empty array. If you need the original value, you should retrieve it from the |
Another situation where it can make sense to make a field stored is for those
that don’t appear in the _source
field (such as copy_to
fields).
term_vector
Term vectors contain information about the terms produced by the analysis process, including:
-
a list of terms.
-
the position (or order) of each term.
-
the start and end character offsets mapping the term to its origin in the original string.
-
payloads (if they are available) — user-defined binary data associated with each term position.
These term vectors can be stored so that they can be retrieved for a particular document.
The term_vector
setting accepts:
no
|
No term vectors are stored. (default) |
yes
|
Just the terms in the field are stored. |
with_positions
|
Terms and positions are stored. |
with_offsets
|
Terms and character offsets are stored. |
with_positions_offsets
|
Terms, positions, and character offsets are stored. |
with_positions_payloads
|
Terms, positions, and payloads are stored. |
with_positions_offsets_payloads
|
Terms, positions, offsets and payloads are stored. |
The fast vector highlighter requires with_positions_offsets
.
The term vectors API can retrieve whatever is stored.
|
Warning
|
Setting with_positions_offsets will double the size of a field’s
index.
|
PUT my-index-000001
{
"mappings": {
"properties": {
"text": {
"type": "text",
"term_vector": "with_positions_offsets"
}
}
}
}
PUT my-index-000001/_doc/1
{
"text": "Quick brown fox"
}
GET my-index-000001/_search
{
"query": {
"match": {
"text": "brown fox"
}
},
"highlight": {
"fields": {
"text": {} (1)
}
}
}
-
The fast vector highlighter will be used by default for the
text
field because term vectors are enabled.


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Heinle Wischer and Partner Design a New Art Museum in Krakow, Poland
- Written by Christele Harrouk
- about 12 hours
Among 78 international participants, Heinle Wischer and Partner won the first prize in the design competition for the new building of the Stanisław Wyspiański Museum in Krakow, Poland. The project will be built, in the middle of the museum district of the city and next to the historic National Museum.
Estimated to open in 2025, the project will host the works of Stanisław Wyspiański, a dramatist and modernist artist, known in Poland, whose style is a synonym of “expressive figures and geometric compositions, interspersed with floral motifs.”
With a concept that represents the artist’s language of form, “a synthesis of simplicity, geometry, and nature”, the new museum “is a multifunctional, linear space, based on the play of light and shadow, set in a clear architectural form surrounded by greenery”. Just like Wyspiański's art, structure intersects with nature, with greenery on the roof terrace, visible in the circular atrium and the surroundings. The attic reveals a secret garden, surrounded by a perforated façade.
Inside of the building, a large circulation void, running through all levels of the museum, can be used for multimedia actions and installations. The composition of the space creates high exhibition rooms that “provide the necessary clarity to put the artworks in the spotlight”. Moreover, inspired by the artist's activity as a theatre reformer, the architects state that the curtains, sliding walls, and movable ceilings are used to provide flexibility in the use of space. The ceiling is filled with geometric drawings with recessed rails for the installation of sliding walls, curtains, and lighting.
Located at the gates of Krakow's Old Town, the art museum blends into the urban historical fabric, with a stone cladding façade, creating a contrast with the National Museum. The ground floor on another hand, in glass, offers “lightness and insights into the interior.”
- Project: Stanisław Wyspiański Museum, Krakow, Polen
- Architect: Heinle, Wischer und Partner, Wroclaw
- Team: Edzard Schultz (Partner in charge), Anna Stryszewska-Słońska, Katarzyna Skowronek, Marta Wróblewska, Łukasz Umiński, Łukasz Kaczmarek, Melina Haures
- Program: Permanent and temporary exhibition, rooms for outreach programs, museum shop, offices, cafés, gardens
- Estimated Completion: 2025


You can download the most recent version right here. Please visit the Getting Started section to get started with CreoChain.
Older versions of CreoChain can still be obtained on the CreoChain github page.