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{% if authorization_request.status == "Request Sent" %} Authorization Request {% else %} {{ authorization_request.linked_docname }} {% endif %}
Dear {{ linked_doc.party_name }},
Dear {{ linked_doc.employee_name }},
{{ company }} has sent you a contract.
Your signature is required for this agreement to be completed.
Review & Sign
Your contract with {{ company }} is now complete.
The signed copy of the agreement is attached for your records. No further action is required from you.
Thank you!
Regards,
Bloomstack Team
Our support address is:
support@bloomstack.com


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2020
Programs coordinator Shehacks Kenya Coordinating programs for the organization
I’m a user experience consultant working with teams around the world to help create outstanding websites. I’ve been a developer since I started my career 5 years ago, and have always taken a deep interest in making websites the best they can be for everyone using them. Now I design and develop beautiful websites which focus on providing the best experience for everyone using them, ensuring they are easy for your team to edit while also delivering the best experience for your users.
You have an idea and need somebody to help create it? I’ll listen to what you’re trying to achieve and make it as close to your vision as possible, with suggestions along the way to create the best product possible.
A team working on a project but need a boost of support?I’m flexible enough to work with any team, even if they’re part way through a project. Just let me know how to get started.
I work with certain design tools to create high-fidelity designs and prototypes. I design accessible and usable products which aid business growth.
Check This OutI use various web technologies to develop attractive websites which converts visitors to customers. I develop creative and responsive website layouts.
Check This OutI identify and evaluates new digital technologies and use web analytics tools to measure site traffic to better optimize marketing campaigns.
Check This OutPrograms coordinator Shehacks Kenya Coordinating programs for the organization
cybersecurity analyst at Ihub Kenya Worked on cybersecurity projects for Ihub and CChub
Freelance web designer Upwork Worked as a freelance designer


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POST http://<IP_ADDRESS>:8000/post?username=UUQxLTBYRg%3D%3D
User-Agent: python-requests/2.22.0
Accept-Encoding: gzip, deflate
Accept: */*
Connection: keep-alive
X-Shared: Common-Header-Value
Content-Type: application/json
X-Correlation-Id: 2ZyOl_WO6_2AV
Content-Length: 115
{
"name": "Jamie Munoz",
"amount": {
"value": 35,
"currency": "GBP"
},
"references": [
"Tax",
"VAT"
],
"properties": []
}
HTTP 200 (347 ms)
server: gunicorn/19.9.0
date: Fri, 06 Dec 2019 08:12:47 GMT
connection: close
content-type: application/json
content-length: 857
access-control-allow-origin: *
access-control-allow-credentials: true
{
"args": {
"username": "UUQxLTBYRg=="
},
"data": "{\"name\": \"Jamie Munoz\", \"amount\": {\"value\": 35, \"currency\": \"GBP\"}, \"references\": [\"Tax\", \"VAT\"], \"properties\": []}",
"files": {},
"form": {},
"headers": {
"Accept": "*/*",
"Accept-Encoding": "gzip, deflate",
"Connection": "keep-alive",
"Content-Length": "115",
"Content-Type": "application/json",
"Host": "<IP_ADDRESS>:8000",
"User-Agent": "python-requests/2.22.0",
"X-Correlation-Id": "2ZyOl_WO6_2AV",
"X-Shared": "Common-Header-Value"
},
"json": {
"amount": {
"currency": "GBP",
"value": 35
},
"name": "Jamie Munoz",
"properties": [],
"references": [
"Tax",
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},
"origin": "<IP_ADDRESS>",
"url": "http://<IP_ADDRESS>:8000/post?username=UUQxLTBYRg%3D%3D"
}


Some Key Values
Nov 2, 2014
Going through DBC's suggested values to consider, I can say that the below are especially important to me. I've included the scale rating recommended by DBC, which maps to the importance of living up to each value. 1 implies that I consider myself flexible on that value, 5 that I would feel awful having compromised it.
| Value | Strength of Scruple |
|---|---|
| Adventure | 4 |
| Affection | 4 |
| Ambition | 5 |
| Compassion | 5 |
| Confidence | 4 |
| Coolness under fire | 2 |
| Creativity | 5 |
| Effectiveness | 3 |
| Enthusiasm | 2 |
| Environmentalism | 3 |
| Excitement | 5 |
| Freedom | 5 |
| Health | 3 |
| Helping society | 4 |
| Hospitality | 5 |
| Independence | 3 |
| Insight | 3 |
| Intellectual status | 4 |
| Knowledge | 4 |
| Meaningful work | 5 |
| Music | 2 |
| Nature | 4 |
| Peace | 5 |
| Pleasure | 4 |
| Romance | 4 |
| Self-knowledge | 5 |
| Sophistication | 3 |
| Truth | 4 |
| Wisdom | 5 |
It's obvious that if I am going to expound on one of these, I should pick a 5. That said, I'd like to point out that the lower scores only denote that these are lower on the scale of what I know I am willing to make day-to-day compromises on. Everything on this table is of major import to me.
But I'll delve a bit further into Ambition. So many of the above values are tied up with each other, but for me Amibition conflates most with Meaningful work and Independence.
One of my good friends recently shared a very specific realization with me. It gets at what I talk about when I talk ambition. Over the past few years, he came to see that he overwhelmingly dates women who work in healthcare in some way. His words: "It's because they want to fix the world but they understand they have to contribute to a giant system to do that."
I value the brand of ambition that my friend prefers in the people he dates. An individual's life work is a substantial thing. I think people should always be sure that what they are pouring their efforts into, day in and day out, contributes to making life better for others. Healthcare is a very direct way to care for others; the benefit you provide patients with is inarguable. It's the second part of the statement, about contributing to a giant system, that is all the more interesting.
Ambition generally associated with being in a position of power. We should consider that it's nearly as ambitious to spend your efforts contributing to a cause that you think can make big changes, as it is to be one of the people running that cause.
Independence still comes into this in a major way. There are so many opportunities for people to choose careers that produce money and not much else. Truly ambitious people resist that obvious (and wonderful) benefits of personal wealth, and instead try to contribute to the betterment of society while keeping themselves secure enough.
Ambition is spending a lifetime working at bringing things into being that didn't exist before, or making what you found a little bit more ideal.
Ambition and Stereotype Threat
Oh damn: Am I an upper middle class white dude blogging about how ambition is such a social good? The idea smells off-puttingly Ayn Randian when I think about it that way. All I can offer in my defense is that the kind of ambition I support begins with an independent thought: "What do I want to contribute to?" There are an infinite amount of asnwers to this question. The most popular is the most boring and least ambitious, and it has to do with the size of one's checking account.
Advice
The last topic someone asked me to help on was about a job change. He was debating between two offers, an offer at a new company that he really wanted to work at and a counteroffer from his current job to pay him more to keep it. The current job had more money tied to it. Partially because this guy already made far more money than me--and therefore the lower pay already seemed stellar--I told him to evaluate whether the difference in pay between staying and going was higher than the difference between liking what he did every day. We talked it over, and he said he'd weigh it. He ended up staying, which was a surprise to me, but he did end up leaving for an even better combination of great job and great pay about six months later.


Brazil president's son faces fresh corruption probe: state prosecutors
RIO DE JANEIRO (Reuters) - The son of Brazilian President Jair Bolsonaro is under a new investigation by Rio de Janeiro state prosecutors for allegedly hiring fake employees during his time as a state lawmaker, the state prosecutor’s office said on Saturday.
This is the second state-level probe into Flavio Bolsonaro, who became a federal senator this year. A previous probe, also relating to alleged irregularities over the payment of his state-level staff, was shelved by the supreme court in a decision that is being appealed.
“The present investigation has a different and independent object,” to the previous one, the state prosecutor’s office said in its statement.
Frederick Wassef, Flavio Bolsonaro’s lawyer, denied any impropriety, saying his client was innocent.
“At the end of the whole process it will be proved that there was no illegality,” said Wassef.
The hiring of fake employees to enable lawmakers to pocket their salaries is a well-known scam in Brazilian politics.


{% include 'templates/head.html' %}
{% include 'templates/nav.html' %}
Quizzes
-
8 in class quizzes this quarter, which will cover the
content in a video that you are to watch before class
or videos that we have discussed in previous
lectures. Although we will generally stick to content
from the last week we do reserve the right to ask
about past material, so if you miss a class please
make sure to watch the video.
-
You will have 5 minutes to complete the quiz, which
will consist of true/false and multiple choice
questions.
-
Our plan is to make the quizzes easy. The hope is that
it's enough of an incentive to get people to watch the
videos ahead of time. If it isn't we'll have to come
up with another plan.
-
Quizzes are pop quizzes, meaning that we won't
announce when we plan to give them. The first lecture
that we might give a quiz is 4/8/2020.
{% include 'templates/footer.html' %}


Note
Archon depends on Web3 1.0. This version of web3
is still in Beta so usage may change unexpectedly and/or bugs may arise until a stable version of Web3 1.0 is released.
IPFS Links and Validation¶
Many users will want to use decentralized file storage for their arbitrable DApps
and will store Evidence
and MetaEvidence
on IPFS
.
There are several things to keep in mind when using IPFS
with Archon
IPFS Gateways¶
Most browsers do not currently support interacting with the ipfs
network directly.
Therefore, in order to fetch data from the ipfs
network, we need to use an IPFS Gateway.
Gateways are “standard” http
URI’s that return data from the ipfs
network based on the ipfs path.
It is important that you choose a gateway that you trust, as a malicious gateway can return tampered data. Please heed the warning below:
Warning
Archon considers data returned directly from a valid ipfs
URI pre-validated. This is because hash validation is built into the protocol. As we need to use gateways to interact with the ipfs
network at this time, MAKE SURE YOU SET A GATEWAY THAT YOU TRUST, or otherwise re-validate your ipfs
hashes yourself.
The default gateway set is https://gateway.ipfs.io
. This is the gateway provided by the Protocol Labs staff.
To set a custom gateway, use archon.setIpfsGateway(uri)
.
See documentation on setting a gateway here
IPFS URIs¶
IPFS
URIs are recognized in any of these formats:
/ipfs/Qm...../foo/bar
ipfs:/ipfs/Qm...../foo/bar
fs:/ipfs/Qm...../foo/bar
Gateway URI’s == Bad¶
It is not recommended that you link directly to an IPFS
gateway as the fileURI
in Evidence or MetaEvidence. It will not be able to be verified using Archon without
a custom hashing function. The IPFS
protocol does transformations on the data before hashing,
therefore a hash can not be verified naively using the resulting IPFS
multihash
and the original file contents. In addition IPFS
data should be able to be fetched
by any gateway or node in the network, and a user should be able to use any gateway or node
that they trust. Therefore hardcoding a gateway, which may not be trusted by the consumer,
would require additional validation regardless.
Archon treats all http(s)://
URI’s the same, and will try to validate the hash
as if they were standard hashes.
Bad:
const evidenceJSON = {
fileURI: "https://gateway.ipfs.io/ipfs/QmQhJRhvwgBPRdee18pK6QDECysFPuvuDLZMSeexdUfEiH"
}
archon.utils.validateFileFromURI(evidenceJSON.fileURI).then(data => {console.log(data.isValid)})
> false
Good:
const evidenceJSON = {
fileURI: "/ipfs/QmQhJRhvwgBPRdee18pK6QDECysFPuvuDLZMSeexdUfEiH"
}
archon.setIpfsGateway('https://gateway.ipfs.io')
archon.utils.validateFileFromURI(evidenceJSON.fileURI).then(data => {console.log(data.isValid)})
> true
Not Recommended:
const ipfsHasher = data => {...}
const evidenceJSON = {
fileURI: "https://gateway.ipfs.io/ipfs/QmQhJRhvwgBPRdee18pK6QDECysFPuvuDLZMSeexdUfEiH"
}
archon.utils.validateFileFromURI(
evidenceJSON.fileURI,
{ customHashFn: ipfsHasher }
).then(data => {
console.log(data.isValid)
})
> true


Permute Class
Randomizer to allow random ordering of a list of frames. Intended to be useful for e.g. randomly permuting the order of particular stimuli used during a set of trials (although frames need not be of the same kind to permute).
To use, define a frame with "kind": "choice" and "sampler": "permute", as shown below, in addition to the parameters described under 'properties'.
"frames": {
"test-trials": {
"sampler": "permute",
"kind": "choice",
"commonFrameProperties": {
"showPreviousButton": false
},
"frameOptions": [
{
"blocks": [
{
"emph": true,
"text": "Let's think about hippos!",
"title": "hippos!"
},
{
"text": "Some more about hippos..."
}
],
"kind": "exp-lookit-text"
},
{
"blocks": [
{
"emph": false,
"text": "Let's think about dolphins!",
"title": "dolphins!"
}
],
"kind": "exp-lookit-text"
}
]
}
}
Item Index
Properties
Data collected
Properties
commonFrameProperties
Object
Object describing common parameters to use in EVERY frame created by this randomizer. Parameter names and values are as described in the documentation for the frameType used.
frameOptions
Object[]
List of frames to be created by this randomizer. Each frame is an object with any necessary frame-specific properties specified. The 'kind' of frame can be specified either here (per frame) or in commonFrameProperties. If a property is defined for a given frame both in this frame list and in commonFrameProperties, the value in the frame list will take precedence.
(E.g., you could include 'kind': 'normal-frame' in commmonFrameProperties, but for a single frame in frameOptions, include 'kind': 'special-frame'.)
orderedFrameOptions
Object[]
List of sets of frame properties of the same length as frameOptions. The order
of this list will be preserved; the properties in orderedFrameOptions[0] will be added to the
frame shown first, the properties in orderedFrameOptions[1] will be added to the
frame shown second, etc. Properties are applied in this order:
commonFrameProperties, frameOptions, orderedFrameOptions
so orderedFrameOptions properties will take priority over regular frameOptions.
This allows you to, for instance, do something different during the first or last
trial (e.g., a practice/training trial or a debriefing trial).
If parameterSets
is included as one of the properties in orderedFrameOptions[n],
the values will be added to any parameterSets property on the existing frame
(value-by-value, iterating through corresponding parameterSets)
rather than overwriting the whole property.
Data keys collected
These are the fields that will be captured by this frame and sent back to the Lookit server. Each of these fields will correspond to one row of the CSV frame data for a given response - the row will havekey
set to the data key name, and value
set to the value for this response.
Equivalently, this data will be available in the exp_data
field of the response JSON data.


FIWARE Data Model List No Data Model Group Data Model Name Link /Subscribe - - - 1 Test Room /Test 2 Car /Test 3 Farm Farm /Farm/Farm 4 Building /Farm/Building 5 Pen /Farm/ Pen 6 Pig /Farm/Pig 7 SlaughteredPig /Farm/SlaughteredPig 8 Slaughterhouse /Farm/Slaughterhouse 9 FarmrEntityList /Farm/FarmrEntityList 10 Alert Alert /Alert 11 Building Building /Building/Building 12 BuildingOperation /Building/BuildingOperation 13 CivicIssueTracking Open311ServiceRequest /CivicIssueTracking/Open311ServiceRequest 14 Open311ServiceType /CivicIssueTracking/Open311ServiceType 15 Device Device /Device/Device 16 DeviceModel /Device/DeviceModel 17 Environment AeroAllergenObserved /Environment/AeroAllergenObserved 18 AirQualityObserved /Environment/AirQualityObserved 19 NoiseLevelObserved /Environment/NoiseLevelObserved 20 WaterQualityObserved /Environment/WaterQualityObserved 21 Indicator KeyPerformanceIndicator /Indicator/KeyPerformanceIndicator 22 Parking OffStreetParking /Parking/OffStreetParking 23 OnStreetParking /Parking/OnStreetParking 24 ParkingAccess /Parking/ParkingAccess 25 ParkingGroup /Parking/ParkingGroup 26 ParkingSpot /Parking/ParkingSpot 27 ParksAndGardens FlowerBed /ParksAndGardens/FlowerBed 28 Garden /ParksAndGardens/Garden 29 GreenspaceRecord /ParksAndGardens/GreenspaceRecord 30 Park /ParksAndGardens/Park 31 PointOfInterest PointOfInterest /PointOfInterest/PointOfInterest 32 Beach /PointOfInterest/Beach 33 Museum /PointOfInterest/Museum 34 TouristInformationCenter /PointOfInterest/TouristInformationCenter 35 StreetLighting Streetlight /StreetLighting/Streetlight 36 StreetlightControlCabinet /StreetLighting/StreetlightControlCabinet 37 StreetlightGroup /StreetLighting/StreetlightGroup 38 StreetlightModel /StreetLighting/StreetlightModel 39 Transportation Road /Transportation/Road 40 TrafficFlowObserved /Transportation/TrafficFlowObserved 41 Vehicle /Transportation/Vehicle 42 VehicleModel /Transportation/VehicleModel 43 WasteManagement WasteContainer /WasteManagement/WasteContainer 44 WasteContainerIsle /WasteManagement/WasteContainerIsle 45 WasteContainerModel /WasteManagement/WasteContainerModel 46 Weather WeatherAlarm /Weather/WeatherAlarm 47 WeatherForecast /Weather/WeatherForecast 48 WeatherObserved /Weather/WeatherObserved


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{% extends 'dataloaderinterface/base.html' %} {% load staticfiles %} {% block styles %} {{ block.super }} {% endblock %} {% block page_title %}
The WikiWatershed Data Portal supports multiple types of water quality data.
EnviroDIY is a community of enthusiasts sharing do-it-yourself ideas for environmental science and monitoring.
The Leaf Pack Network is an international network of teachers, students, and citizen monitors investigating their local stream ecosystems.


SCORE: 0%
1. Which of the following personalities gave ‘The Laws of Heredity’?
2. Which of the following is NOT the language enshrined in the eighth schedule of the Indian Constitution, as the language of the state?
3. World Trade Organization came into existence in……
4.According to the Constitution of India, which of the following is NOT one of the main organs of the Government?


{% extends "base.html" %}
{% block title %}{% endblock %}
{% block content %}
On Earth, an ocean is one of the major conventional divisions of the World Ocean, which occupies two-thirds of the planet's surface. These are, in descending order by area, the Pacific, Atlantic, Indian, Southern, and Arctic Oceans. The word sea is often used interchangeably with "ocean" in American English but, strictly speaking, a sea is a body of saline water (generally a division of the world ocean) partly or fully enclosed by land. This should make you hire us! Runs: {{campaign.start_date}} - {{campaign.end_date}} {{campaign.description}}
{% if campaign.isRunning %}
View Campaign Webpage
{% else %}
This Campaign is Not RunningAbout Oceans
Our Campaigns
{% for campaign in campaigns %}
{{campaign.name}}


Reflexology, Crystal Healing, Access Bars, CPD Courses for Reflexologists and Animal Communication.
Please note that Val is now based near Welshpool on the Shropshire/Mid Wales border. However she continues to see clients in Surrey/Sussex about once a month. She also travels to teach groups.
For appointments please email<EMAIL_ADDRESS>or call 07722 646581
Val Winchester began her training in 1996 at the Maureen Burgess School of Reflexology at Roehampton where she also qualified in Advanced Reflexology. Her interest in this therapy was sparked when some family members had become ill and she had watched a nurse perform some massage techniques which were helping tremendously.
Val then went on to study at the International College of Crystal Healing where she qualified as a Crystal Therapist and learned meditation. Val has run Meditation Groups and workshops for many years which she enjoys immensely.
As Val continued to see clients, she realised there was a further need in people as many of her clients were going through stressful times and indeed trauma. This led to her taking a Diploma in Spiritual Counselling and a Diploma in Hypnotherapy at the Holistic Healing College in London and the London College of Hypnotherapy. Val also trained in Ireland with Doreen Virtue as an Angel Therapy Practitioner and gives Angel Card Readings regularly.
In March 2016 Val renewed her qualification in Reflexology for Fertility and now offers Pregnancy Reflexology to offer the full complement of Reflexology treatments in pregnancy and post-partum.
Val qualified as an Access Consciousness Bars Practitioner in July 2015 and then in January 2016 she qualified as an Access Bars Facilitator which allows Val to teach Bars classes. She has also completed the Access Energetic Facelift, Access Body Processes and the Access Foundation course as well which has given her more tools to work with during the Access Bars treatment.
Val has a love of animals and has trained in Animal Communication and natural horsemanship techniques.
Reflexology is based on the principle that there are reflex points on the feet and hands and that by applying pressure to these points it can:
It is a very relaxing treatment which involves a beautiful massage of the feet combined with pressure being applied to the reflex points in a way that is tailored to each individual.
When the use of crystals is combined with the Reflexology session, the whole treatment is enhanced considerably. Many clients who find it difficult to relax or switch off, have found that they have a much deeper experience when the crystals are used.
Find out more about ReflexologyDuring a session of crystal therapy, crystals may be placed on the body, around the body or under the couch. This is mostly a “hands off” therapy where healing energies are then transmitted through the crystals. This is a very relaxing treatment and balances the energy field of the Client.
Please contact Val if you wish to discuss specific requirements.
Find out more about Crystal TherapyThe Bars are a series of 32 points on the head, which when lightly touched release old energies stuck in the brain and the body, allowing for tremendous and easy change.
The Bars takes all the considerations, thoughts, feelings, emotions, decisions, judgments, and beliefs from any lifetime, that are electrically stored in the brain and releases them. This release occurs when the bars are touched; it allows that stored junk to be released.
Find out more about Access BarsVal also runs CPD (Continued Professional Development) Courses for Reflexologists and Trainee Reflexologists. If you take the one day course, you will be awarded with 12 CPD points.
The CPD Course covers the following topics:
Find out more about CPD or to book a CPD Course, please contact Val.
Val's basic course is available to learn on-line as well. This is a shorter course, best suited for those who cannot attend the course in-person Learn online
Val qualified as an ACT2 Animal Communicator with James French in 2009. Since then, she has used it to help many horses, dogs and cats. It can help with behavioural problems, anxiety and illness and pain in the animal's body. It can also help the animal to pass over when the time comes.
Find out more about Animal Communication"Meeting Val Winchester has changed my life. In March 2016, just after I launched my fitness business, I met Val by chance and she offered me the opportunity to hire her yurt for my Pilates classes. That was the start of a magical journey of self-discovery and life changing events - my Pilates classes have grown, Val introduced me to Access Bars Consciousness and facilitated my practitioner course in June 2016 and Val's amazing Mediation and Healing groups has given me inner peace and calmness. I also highly recommend Val's Reflexology treatment, which is not only a wonderful massage it also leaves you having a total body and mind connection."
"Val's presence, healing energy, compassion, love and sense of humour leaves you feeling relaxed and a total sense of being. Thank you Val for being in my life."
"I have known Valerie Winchester for at least 15 years and have found her meditation circles and
other healing practices to be excellent.Valerie is a diligent and caring person in all that she does,
always ready to help any who have questions, experiences etc.,
Valerie is herself highly experienced in this field as I have attended many of her meditation groups
where she has been professional in her conduct, knowing how to assist those with any issues that may
arise and very discreet in her ability to do so.
I have no hesitation in recommending Valerie Winchester to run meditation courses and/or other healing
practices she is so capable of." - D Summer
"I absolutely love Val's meditation groups! She manages to hold a relaxing, open and gentle space for everyone no matter what comes up. I've been attending Val's groups for many years and I swear by them as I always come away feeling clearer and energised." - Eleonora
"I've been going to Val's meditation group for quite some time.
What works for me is the gentleness and caring that I feel. I've been to quite a few other groups
but never felt so looked after. I never want to miss it. She makes sure we are comfortable, the
temperature is right, we have blankets, cushions if our feet don't touch the floor and water to drink.
I like the quiet atmosphere that Val creates. There is some sharing, which is minimal, as the whole
point for me is to sit and be with stillness. But it's good to hear ourselves speak our names and a
brief where we are.
Every time we meet the meditations are different because of where I am and what's going on, So even
if my mind has been having a field day I know my body has been resting. Sometimes I am in such a lovely
peaceful place its a challenge to open my eyes and come back.
Val tunes in usually to see what the group needs and her meditation flows from that. It always seems
more powerful in a group than sitting at home on my own.
Music is often used and can really help soothe and uplift
We pick an Angel card at the beginning which can help focus, sometimes we also pick a crystal to hold.
At the end we often have a message from another card which we share, that's really helpful too hearing
all the card's messages.
Val is very particular about closing down and she introduces it so gently its like having another mini
quiet time. It is something I often forget to do after leaving a group and sometimes when I am out I
remember some of Val's words.
If Val is going to do another meditation group, let me know where!" - M Weatherall
"I have been taking part in Val's Meditation group for over ten years. Every session has a different
theme using soothing music, crystals and / or Angel Cards. She leads us excellently into the
meditations and guides us back, offering possible reasons for our individual experiences and allowing
discussion within the group.
Val ensures we drink plenty of water and is always calm,caring and considerate towards each member of
the group.
I have found an oasis from the hubbub of everyday life and take away a feeling tranquility each time I
visit Val's meditation group . I highly recommend her Meditation Sessions." - Sue Towersey


OData - the fine print
For an upcoming event I had to dig a little deeper into OData and learned a few things along the lines.
Same but different
OData's current version is 4.0 and that's the only version I will focus on in this article.
OData is the love child of an effort that joined Microsoft and SAP at the hip to create an universal internet data access standard, that on first sight has a lot of NIH issues to sort out. But I'm sure on a second (or third or forth) look it will all make sense.
Key components of OData
- a service document in
JSON
format. It describes what data endpoints are available and where. Depending on the requested scope (more on that later) that JSON contains different levels of details - a meta data document. That document is referenced in the service document. The format is
XML
. It describes the data format found in each record, relations to other record and actions that can be taken. Looks like XML Schema, but not quite. Record is used loosely here, data can be hierarchical and nested - a query language squeezed into an URL, so data can be fetched using
HTTP GET
without using a body submitted. Query allows data selections, filtering, output size and sorting to be specified - Query results in JSON format enhanced with OData specific Metadata
What I liked a lot: OData.org offers a rich set of documentation and tutorials, including a Postman collection to learn
Happy soup
So dealing with OData you can enjoy a mix of technologies:
- JSON for service document and payloads
- XML for meta data
- a dot notation (like dotNet or Java) for property values like data types (go learn about it). E.g.
Edm.String
defines a String value orOrg.OData.Core.V1.ResourcePath
the path to data - another query language
- individual items by default get addressed in an uncommon format:
serviceurl/People('johndoe')
. Luckily this is optional and you can use the more common format for a route:serviceurl/People/johndoe
. This seems to be born out of squezing the square pin URL into the round hole of database schemas and flexible primary keys
On the upside: Your Salesforce and Excel users will love it. Import and CRUD operations are well supported.
Initially I was looking at Apache Olingo to implement my needs. Unfortunately the library is too close to the Java servlet model and static defined data models (like your one application, not your data platform), so I disected my needs the hard way.
Lessons learned along the way
- A OData client (think Excel, Tableau, Salesforce) will initially connect to the service document url. It sends a
GET
request. To specify what level of detail is required theACCEPT
header is used. So when testing your provider implementation you need to take that into account - Excel sends
ACCEPT application/json;odata.metadata=minimal
- Salesforce sends
ACCEPT application/json;odata.metadata=full
- OpenAPI can specify the top level
application/json
or the full qualified content type in the response. If you fully qualify it, you might end up with a 406 and wonder what happens - The meta data url gets specified in your service document and is usually the service document url extended by
/$metadata
- I ran into a potential Salesforce bug, where the meta data URL was ignored and the service document URL extended by
$metadata
. Note the missing/
. Caused a bit of grief since Excel does/$metadata
- Everything is case sensitive, which makes sense, but is fun in Domino that treats item names case insensitive
- I couldn't get Salesforce to play nice until I added a field
ExternalId
to my form. Might be my lack of understanding how to change the primary key Salesforce will play nice with or something missing in my Metadata
Same, same, but different
Swap out the GET for a POST and the XML for something not-quite-JSON and you get GraphQL that tries to address a similar set of functionalities. We really love wheels reinvented
More lessons to be learned, stay tuned!
Posted by Stephan H Wissel on 04 March 2020 | Comments (0) | categories: OpenSource WebDevelopment


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Auto-selected this repository because it
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--- layout: home search_exclude: true ---
Welcome! gradHEP is a group of people who are interested in high-energy physics (HEP) analysis that can be done in a *differentiable* way. This is just jargon for wanting to optimize the analysis directly with respect to the physics goals of interest using gradient-based methods, such as gradient descent. We can make this possible if we keep track of the derivatives of each step (i.e. line of code) of the analysis with respect to its inputs. While that may sound like a harsh requirement, this is made pretty simple thanks to the magic of *automatic differentiation*, or ‘autodiff’ for short. If you code up a program using a library that supports autodiff, the library will keep track of the gradients throughout your program by stepping through each elementary operation -- e.g. addition, multiplication, log, etc., which have known differentiation rules -- and calculate the gradients using the chain rule. Of course, this is not without its caveats, as not all lines of code are necessarily differentiable. In particular, common operations in HEP like binning a set of data or making a cut do not vary smoothly with respect to their inputs. That’s why there’s an ongoing effort by this group to provide drop-in replacements for these operations that are differentiable, as well as entire differentiable analysis ‘blocks’, such as statistical model building using HistFactory, or inference using the profile likelihood as a test statistic. # Read more:


This is heading bcc215b8-0a37-40ab-8aae-68b0c92cca5e
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Dean Wampler, Typesafe
<EMAIL_ADDRESS>@deanwampler
Typesafe
This workshop/tutorial takes you through the basic principles of writing data analysis applications with Scalding, a Scala API that wraps Cascading. Expect this workshop to take 3 hours or more, if you do all parts of the exercises. I certainly encourage you to experiment.
I assume you have already completed the setup instructions in the README. These instructions walk you through a series of exercises. The exercises have a corresponding Scalding script (Scala source file). I used a convention of adding a number suffix to the name to indicate the order of the exercises. Note that some of these exercises are adapted from the Tutorial examples that are part of the Scalding Github repo, where noted.
This document will explain many features of the Scalding and Cascading. The scripts themselves contain additional details. The Scalding and Cascading documentation has more information than we can cover here:
api.zip
. Unzip the file and open the index.Feedback, patches, suggested additions are welcome! Fork me.
Let's start with a very brief synopsis of key Cascading concepts useful for understanding Scalding. Not all Cascading features are wrapped with Scalding APIs. In some cases, equivalent Scala idioms are used, even though the implementations may delegate to Cascading equivalents.
See the Cascading User Guide for more details.
A common data structure in many programming languages, a tuple is a grouping of a fixed number of fields, where each element has a specific type, the types of the different fields can be different and the fields can have names. It is analogous to a SQL record, a struct
in C, and an object in object-oriented languages.
A Tap is a data source (for reading) or sink (for writing), corresponding to a file on the local file system, Hadoop Distributed File System (HDFS), or Amazon S3. You instantiate an Hfs
instance for Hadoop or S3 file systems, and a FileTap
instance for local file system. There are also more specialized versions for particular scenarios, like specifying a "template" for file or directory naming conventions.
The Scheme encapsulates the file format. There are several supported natively by Cascading. The corresponding Java class names are used in the following subsections.
When read, each line of text in the file is returned, with no attempt to tokenize it into fields. The position, byte offset or line number, in the file for the line is also returned. In the Hadoop model of key-value data pairs, the offset is the key and the line is the value.
When written, tuple fields are serialized to text and separated by tabs.
Available for both local file systems and Hadoop.
Handles data where each line is a tuple with fields separated by delimiters, such as tabs and commas. Quoted strings with nested delimiters and files with column headers can be handled. Fields can be cast to primitive types.
Available for both local file systems and Hadoop.
A binary, Hadoop-only data format.
A more efficient implementation of SequenceFile.
Pipes are units of processing through which tuples are streamed. They are composed into Assemblies. Pipes are provided to merge and join streams, split them into separate streams, group them, filter them, etc.
A Flow is created whenever a Tap is connected to a Pipe. Flows can also be composed.
A Cascade joins flows and supports a model where a flow is only executed if the target output doesn't exist is older than the input data, analogous to build tools like make
.
Each section introduces one or more features for data manipulation, most of which are analogous to features found in SQL, Pig (the Hadoop data flow tool), and other systems.
First, the README tells you to run a SanityCheck0.scala
Scalding script as a sanity check to verify that your environment is ready to go.
Using bash
:
cd $HOME/fun/scalding-workshop
./run scripts/SanityCheck0.scala
On Windows:
cd C:\fun\scalding-workshop
scala run scripts/SanityCheck0.scala
From now on, I'll assume you are working in the scalding-workshop
directory, unless otherwise noted. Also, I'll just show the bash
versions of the subsequent run
commands. Finally, because I'm lazy ;), I'll sometimes drop the .scala
extension from script names when I discuss them in the text.
Run these commands again and verify that they run without error. The output is written to output/SanityCheck0.txt
. What's in that file?
It contains the contents of SanityCheck0.scala
, but each line is now numbered.
The essence of this script is the following three lines:
val in = TextLine("scripts/SanityCheck0.scala")
val out = TextLine("output/SanityCheck0.txt")
in.read.write(out)
It reads each line and writes it back out. The TextLine
format has an implicit "schema" for each line, the line number, called the 'offset
, which it adds, and the line itself, called 'line
. This naming convention that uses Scala symbols is a Scalding convention. So, the reason we have the line numbers in the output file is because they were added by TextLine
.
By default, when you create a new field in a pipeline, Cascading adds the field to the existing fields. All the fields together constitute a tuple.
When you write a SQL SELECT
statement like the following, you are projecting out the fields/columns or calculated values that you want, and discarding the rest of the fields.
SELECT name, age FROM employees;
Scalding also has a project
method for the same purpose. Let's modify SanityCheck0
to project out just the line we read from the file, discarding the line number. Scripts/Project1.scala
has this change near the end of the file:
in
.read
.project('line)
.write(out)
This expression is a sequence of Cascading Pipes. However, there is no write
method defined on the Pipe
class. Scalding uses a feature in Scala called implicit conversions to wrap Pipe
with a Scalding-specific type called com.twitter.scalding.RichPipe
. It provides most of the methods we'll actually use, like write
.
There are also comments in this script and the ones that follow about specific Scalding and Cascading features that I won't cover in these notes.
Run the script thusly:
./run scripts/Project1.scala
Now, if you look at the output in output/Project1.txt
, you'll see just the original lines from scripts/Project1.scala
. That is, running a diff
command on the input and output files should show no differences.
This exercise introduces several new concepts and implements the famous hello world! of Hadoop programming: word count. In word count, a corpus of documents is read, the content is tokenized into words, and the total count for each word over the entire corpus is computed.
First, we'll use two new invocation command options:
--input file
specifies the input file.--output file
specifies the output file.We are running in "local" mode, using the
--local
command option. In this case, we have to specify input and output files. If we were running in a Hadoop cluster, we would specify directories instead and Hadoop tasks (separate JVM processes) would be started to read each file, etc. This is the normal way of working with the Hadoop MapReduce API, and other tools like Hive and Pig, too.
Run the script with the following command. From now on, when the commands are too long to fit easily on one line, I'll show you the command twice. First, I'll show a nicely-formatted command line that's easy to read, with line-continuation marks "\" separating the lines. Then, I'll show the command all on one line, which is easier to copy and paste:
Nicely Formatted:
./run scripts/WordCount2.scala \
--input data/shakespeare/plays.txt \
--output output/shakespeare-wc.txt
Copy and Paste Version:
./run scripts/WordCount2.scala --input data/shakespeare/plays.txt --output output/shakespeare-wc.txt
The output should be identical to the contents of data/shakespeare-wc/simple/wc.txt
. Using a diff
command, should show no differences:
diff data/shakespeare-wc/simple/wc.txt output/shakespeare-wc.txt
(Or you may see a different count for the first line, the amount of whitespace seen...)
The script uses two new data transformation features to compute the word count, flatMap
and groupBy
.
When you apply a map
operation to a collection, each element is passed to a function that returns a new element, perhaps of a completely different type. For example, mapping a collection of integers to a collection of their string representations. A crucial feature of map
is that the process is one-to-one. Each input element has a corresponding output element and the sizes of the input and output collections are the same.
The flatMap
operation is similar, but now the output of the function called for each element is a collection of zero or more new elements. These output collections from each function call are flattened into a single collection. So, a crucial difference compared to map
is that the process is one-to-many, where many could be zero!
WordCount2
uses flatMap
to convert each line of input text into many words:
.flatMap('line -> 'word){
line : String => line.toLowerCase.split(tokenizerRegex)
}
where tokenizerRegex
is "\\s+"
.
A bit of Scala syntax; there are two argument lists passed to flatMap
. The first, ('line -> 'word)
specifies the field(s) in the tuple to pass to the mapping function, shown on the left-hand side of the arrow-like ->
, and it names the output field(s) the function will return, the single 'word
in this case.
The second function argument list is { line : String => line.toLowerCase.split(tokenizerRegex)}
. Scala lets you substitute curly braces {...}
for parentheses (...)
for function argument lists, which is most useful when the content of the "block-like" structure is a single function literal (a.k.a. anonymous function).
The line : String
is the argument list passed to the anonymous function, a single parameter named line
of type String
. On the right-hand side of the arrow-like =>
is the body of the anonymous function. In this case it converts line
to lower case and splits it on whitespace into an array of words.
Once we have a stream of individual words, we want to count the occurrences of each word. To do that, we need to group together all occurrences of the same words. The groupBy
operation is used by WordCount2
to do this.
.groupBy('word){group => group.size('count)}
The calling syntax is similar to flatMap
. The first argument list specifies one or more fields to group over, forming the "key". The second argument is a function literal. It takes a single argument of type com.twitter.scalding.GroupBuilder
that gives us a hook to the constructed group of words so we can compute what we need from it. In this case, all we care about is the size of the group, which we name 'count
.
Try these additional "mini-exercises" to explore what Scalding and Cascading are doing.
Look at the output and you'll notice that the tokenization is rather poor. How can you improve the value defined in tokenizerRegex
? Can you pass in the regular expression as an argument to the program?
Instead of projecting out 'line
, project out 'num
, the line number. (The output is boring, but now you know the name of this field!)
Try these additional "mini-exercises" to explore what Scalding and Cascading are doing.
Comment out the groupBy
line so that the raw results of flatMap
are written to the output instead of the word count output. Note the fields that are written.
You'll see the line number, the whole line, and an individual word from the line. Note that the line number and line are repeated for each word in the line.
Now restore the groupBy
line, and after it, add this line:
.groupBy('count){ group => group.mkString('word -> 'words, "\t") }
The output lines will be extremely long at the beginning of the file, but very short at the end. This second groupBy
regroups the 'word
and 'count
output from the previous pipe. It groups by count so we now have all the words with one occurrence on a line, followed by all the words with two occurrences, etc. At the end of the output, which words have the most occurrences? What are those "words"?
You probably noticed that simply splitting on whitespace is not very good, as punctuation is not removed. There are several ways it can be improved. First, replacing the definition of tokenizerRegex
, which is "\\s+"
, with "\\W+"
will treat all runs of non-alphanumeric characters as word separators. This improves the result considerably (although it's still not perfect...).
For a more complete tokenizer, refactor line.toLowerCase.split(tokenizerRegex)
into a tokenize
function. Then implement tokenize
to remove punctuation, etc. An example implementation can be found in the Scalding README.
The very first line in the output is an empty word and a count of approximately 49,000! These are blank lines in the text. The implementation removes all other whitespace, but as written, it still returns an empty word for blank lines. Adding a filter clause will remove these lines. We'll see how below, but you can search for that section now if you want to try it.
Let's do a similar groupBy
operation, this time to compute the average of Apple's (AAPL) closing stock price year over year (so you'll know what entry points you missed...). Also, in this exercise we'll solve a common problem; the input data is in an unsupported format.
Nicely Formatted:
./run scripts/StockAverages3.scala \
--input data/stocks/AAPL.csv \
--output output/AAPL-year-avg.txt
Copy and Paste Version:
./run scripts/StockAverages3.scala --input data/stocks/AAPL.csv --output output/AAPL-year-avg.txt
You should get the following output (the input data ends in early 2010):
1984 80 2.918625000000001 0.17609474544971507
1985 253 2.3041501976284584 0.5150177183048612
1986 253 3.7039130434782592 0.6311900614112455
1987 253 8.90608695652174 1.9436409195268336
1988 253 9.564703557312258 0.5662800839386863
1989 252 9.684563492063495 0.9768865589941155
1990 253 8.826126482213441 1.0871208010962554
1991 253 12.469169960474305 1.66952305050656
1992 254 13.130669291338577 1.7661116441071
1993 253 9.920395256916992 3.1660729659295854
1994 252 8.369880952380953 1.0934696061063884
1995 252 10.075198412698407 1.0061968512619912
1996 254 6.229881889763783 0.8162148485315347
1997 253 4.491818181818182 0.7140447712304852
1998 252 7.641666666666666 1.6581179568203712
1999 252 14.443214285714282 5.433605126282854
2000 252 22.856230158730177 8.415990854209504
2001 248 10.109758064516127 1.2389205523420814
2002 252 9.569920634920635 2.150379256336458
2003 252 9.272619047619047 1.6510305480966423
2004 252 17.763888888888886 6.577299642773537
2005 252 46.67595238095237 11.4046392452893
2006 251 70.81063745019917 9.507687243758655
2007<PHONE_NUMBER>043824701 35.17547139617391
2008<PHONE_NUMBER>118577075 33.66549448302255
2009<PHONE_NUMBER>2698412706 39.731840611338804
2010 25 204.7216 7.454055905344417
Note that as I write this (September 2012), AAPL is currently trading at ~$700/share! 1
By the way, here's the same query written using Hive, where we first define an "external table" that uses the same file:
CREATE EXTERNAL TABLE IF NOT EXISTS stocks(
ymd STRING,
price_open FLOAT,
price_high FLOAT,
price_low FLOAT,
price_close FLOAT,
volume INT,
price_adj_close FLOAT
) LOCATION 'path/to/data/AAPL.csv';
SELECT year(s.ymd), avg(s.price_close)
FROM stocks s
WHERE s.symbol = 'AAPL' AND s.exchange = 'NASDAQ'
GROUP BY year(s.ymd);
The query is a little more compact, in part because we can handle all issues of record parsing, etc. when we set up Hive tables, etc. However, Scalding gives us more flexibility when our SQL dialect and built-in functions aren't flexible enough for our needs.
Here's what the corresponding Pig script looks like (see scripts/StockAverages3.pig
):
aapl = load 'data/stocks/AAPL.csv' using PigStorage(',') as (
ymd: chararray,
price_open: float,
price_high: float,
price_low: float,
price_close: float,
volume: int,
price_adj_close: float);
by_year = group aapl by SUBSTRING(ymd, 0, 4);
year_avg = foreach by_year generate group, AVG(aapl.price_close);
-- You always specify output directories:
store year_avg into 'output/AAPL-year-avg-pig';
If you have Pig installed, you can run this script (from this directory) with the following command:
pig -x local scripts/StockAverages3.pig
The -x local
option means that Pig will treat the paths as references to the local file system, not the Hadoop Distributed File System (HDFS).
StockAverages3.scala
uses an anonymous function that takes a tuple
argument, which results in slightly ugly syntax. We can't use PartialFunction
, e.g.,
{ case (ymd, close) => ... }
But we can do a match on the tuple argument instead!
Nicely Formatted:
./run scripts/StockAverages3a.scala \
--input data/stocks/AAPL.csv \
--output output/AAPL-year-avg.txt
Copy and Paste Version:
./run scripts/StockAverages3a.scala --input data/stocks/AAPL.csv --output output/AAPL-year-avg.txt
Try these additional "mini-exercises" to learn more.
Try projecting averages for one or more other fields.
If you have Pig installed, try the Pig script. Compare the performance of the Pig vs. Scalding script, but keep in mind that because we're running in local mode, the performance comparison won't mean as much as when you run in a Hadoop cluster.
If you have Hive installed, try the Hive query shown above. You'll need to create a table that uses the data files first. Compare the performance of the Hive vs. Scalding script, keeping in mind the caveats mentioned for Pig.
What if some of the input records are bad. This is actually common in real-world data analytics. Let's adapt StockAverages3
to handle the case where invalid entries for the date or closing price are encountered. That is, they don't successfuly parse the year as an integer or the closing price as a double. We'll split the stream into "good" and "bad" data, writing the bad data to a separate "errors" output for subsequent inspection, clean-up, or whatever.
The script is very similar to the previous one, so we'll just call it StockAverages3b
. (The comments in the script describe the implementation differences, as usual...) Note that we need to specify a different input file, where we've introduced 5 bad records, and an errors
argument for the errors stream, which will contain 5 errors records after the script has finished:
Nicely Formatted:
./run scripts/StockAverages3b.scala \
--input data/stocks/AAPL-with-errors.csv \
--output output/AAPL-year-avg.txt \
--errors output/AAPL-errors.txt
Copy and Paste Version:
./run scripts/StockAverages3b.scala --input data/stocks/AAPL-with-errors.csv --output output/AAPL-year-avg.txt --errors output/AAPL-errors.txt
Try these additional "mini-exercises" to learn more.
Run the previous command replacing StockAverages3b
with StockAverages3
. (Omit the --errors
argument or leave it, in which case it will be ignored by StockAverages3
.) Notice the exceptions that are thrown when you hit the first bad input record.
When you look at the content of output/AAPL-errors.txt
, it isn't very helpful for debugging the problem because it only shows a bad year and/or closing price value, not the full record that was bad. At least having the full dates would help track down the bad records.
We're using mapTo
to discard the fields we no longer need, rather than map
, which would append the new fields to the existing fields.
Fortunately, all you need to do is modify the script to use map
instead, because the subsequent groupBy
will discard the unwanted fields anyway. (In fact, the project
we do early on isn't really necessary, although it probably improves performance by reducing network I/O to shuffle larger records around the cluster...) We don't use groupBy
on the error stream, so it keeps the data we want in that output.
Change mapTo
to map
, rerun the script, and look at the new content of output/AAPL-errors.txt
. Did output/AAPL-year-avg.txt
change?
A final note, if we didn't do a grouping operation of some kind, you could follow the map
step with a project
on the good
pipe to keep just the final year
and close
fields, etc. Using mapTo
is more efficient than separate map
and project
(or discard
) steps.
Let's join stocks and dividend data. To join two data sources, you set up to pipe assemblies and use one of the join operations.
scripts/StocksDividendsJoin4
performs an inner join of stock and dividend records. Let's invoke for Apple data (yes, although Apple only recently announced that it would pay a dividend, Apple paid dividends back in the late 80s and early 90s.):
Nicely Formatted:
./run scripts/StocksDividendsJoin4.scala \
--stocks data/stocks/AAPL.csv \
--dividends data/dividends/AAPL.csv \
--output output/AAPL-stocks-dividends-join.txt
Copy and Paste Version:
./run scripts/StocksDividendsJoin4.scala --stocks data/stocks/AAPL.csv --dividends data/dividends/AAPL.csv --output output/AAPL-stocks-dividends-join.txt
Note that we need to two input sources, so we use flags --stocks
and --dividends
for them, instead of --input
.
Try these additional "mini-exercises" to learn more.
Change joinWithSmaller
to leftJoinWithSmaller
to perform a left-outer join. (Also change the output file name to something else). You have to scroll a ways into file to find dividends. See also the next mini-exercise.
Sometimes you want to filter records, say to limit the output. Add the following filter clause to limit the records to 1988:
.filter('symd){ ymd: String => ymd.startsWith("1988")}
Try moving it to different positions in the pipe assembly and see if the execution times change. However, the data set is small enough that you might not notice a difference.
Recall in the WordCount2
exercise that we had thousands of blank lines that got counted. Add a filter
before the groupBy
that keeps only those words whose lengths are greater than zero.
CoGroups in Scalding are used internally to implement joins of two pipe assemblies. Clients can also use them to implement joins of three or more pipe assemblies, so-called star joins. You should always use the largest data stream as the first one in the join, because the Cascading implementation is optimized for this scenario.
However, in this exercise, we'll do a four-way self-join of the data files for the four stocks symbols I provided, AAPL, INTC, GE, and IBM.
For this script, the --input
flag is used to specify the directory where the stocks files are located.
Nicely Formatted:
./run scripts/StockCoGroup5.scala \
--input data/stocks \
--output output/AAPL-INTC-GE-IBM.txt
Copy and Paste Version:
./run scripts/StockCoGroup5.scala --input data/stocks --output output/AAPL-INTC-GE-IBM.txt
When you look at the implementation, it is not obvious how to use the CoGroup feature. You could do pair-wise joins, which would be conceptually easier perhaps, but offer poor performance in a large MapReduce job, as each pair would require a separate MapReduce Job. The CoGroup feature tries to do as many joins at one as possible.
For comparison, here is the equivalent Hive join.
SELECT a.ymd, a.symbol, a.price_close, b.symbol, b.price_close,
c.symbol, c.price_close, d.symbol, d.price_close
FROM stocks a
JOIN stocks b ON a.ymd = b.ymd
JOIN stocks c ON a.ymd = c.ymd
JOIN stocks d ON a.ymd = d.ymd
a.symbol = 'AAPL' AND
b.symbol = 'INTC' AND
c.symbol = 'GE' AND
d.symbol = 'IBM'
Note that because a.ymd
appears in all ON
clauses, Hive will perform this four-way join in a single MapReduce job.
Try implementing the same four-way join doing a sequence of pair-wise joins. Compare the complexity of the code and the performance of the join with the CoGroup implementation. The performance would be much slower in MapReduce, where each pair-wise join would require a separate MapReduce job.
This exercise shows how to split a data stream and use various features on the splits, including finding unique values.
Nicely Formatted:
./run scripts/Twitter6.scala \
--input data/twitter/tweets.tsv \
--uniques output/unique-languages.txt \
--count-star output/count-star.txt \
--count-star-100 output/count-star-100.txt
Copy and Paste Version:
./run scripts/Twitter6.scala --input data/twitter/tweets.tsv --uniques output/unique-languages.txt --count-star output/count-star.txt --count-star-100 output/count-star-100.txt
The output in output/unique-languages.txt
is the following:
en
es
id
ja
ko
pt
ru
There are seven languages in the tweet records (although they aren't necessary an accurate representation of the text of the tweet). The script filtered out an invalid value that looks vaguely like a null \\N
! The null "language" occurs for for messages in the stream that aren't tweets, but the results of other user-invoked actions.
The output in output/count-star.txt
is a single line with the value 889; there are 889 lines that have the non-null values for the language. Similarly, output/count-star-100.txt
should contain the value of 100, reflecting the limit(100)
step added to its dataflow.
Note that the implementations use groupAll
, then count the elements in the single group, via the GroupBuilder
object. (The count
method requires that we specify a field. We arbitrarily picked tweet_id
.)
By the way, this pattern is exactly how Pig implements COUNT(*)
. For example:
grouped = group tweets all;
count = foreach grouped generate COUNT(tweets);
Now you're a Pig programmer. You're welcome...
Here, tweets
would be the equivalent of a Pipe and grouped
is the name of a new Pipe created by grouping all records together into one new record. The foreach ... generate
statement iterates through this single record and projects the COUNT
of the group contents (named tweets
after the original relation).
Note, the limit(...)
feature appeared to have a bug that caused a divide by zero error in earlier versions of Scalding that only appeared when running in local mode, but not when running Hadoop jobs. (Or, it might have been a Cascading bug...)
Add the debug
pipe to the pipe assembly. How does it change the console output? This is a very useful feature when you're learning or debugging problems.
Add a filter
method call that removes these "bad" records. Hint: You'll want to remove all tuples where the language value is """\N"""
. Without the triple quotes, you would have to write "\\N"
.
Let's return to the Shakespeare data to compute context ngrams, a common natural language processing technique, where we provide a prefix of words and find occurrences of the prefix followed by an additional word. The ngrams are returned in order of frequency, descending.
Nicely Formatted:
./run scripts/ContextNGrams7.scala \
--input data/shakespeare/plays.txt \
--output output/context-ngrams.txt \
--ngram-prefix "I love" \
--count 10
Copy and Paste Version:
./run scripts/ContextNGrams7.scala --input data/shakespeare/plays.txt --output output/context-ngrams.txt --ngram-prefix "I love" --count 10
The output is the list containing each ngram along with a count of its occurrences. Note that the list is actually written to the console, as well as to the output location. We added a debug
step to the dataflow that dumps the tuples to the console.
The data set isn't large enough to find a lot of examples for many possible ngrams.
Try other prefixes of different lengths. You don't have to specify a two-word prefix!
Run the script on other large text files you have.
Context ngrams are a special case of ngrams, where you just find the most common n-length phrases. Put another way, "regular" ngrams are like context ngrams with no prefix. Write a script to compute the most common ngrams.
Let's revisit the exercise to join stock and dividend records and generalize it to read in multiple sets of data, for different companies, and process them as one stream. A complication is that the data files don't contain the stock ("instrument") symbol, so we'll see another way to add data to tuples.
Nicely Formatted:
./run scripts/StocksDividendsRevisited8.scala \
--stocks-root-path data/stocks/ \
--dividends-root-path data/dividends/ \
--symbols AAPL,INTC,GE,IBM \
--output output/stocks-dividends-join.txt
Copy and Paste Version:
./run scripts/StocksDividendsRevisited8.scala --stocks-root-path data/stocks/ --dividends-root-path data/dividends/ --symbols AAPL,INTC,GE,IBM --output output/stocks-dividends-join.txt
The Matrix API is relatively new and facilities many important machine learning algorithms.
Adjacency matrices are used to record the similarities between two things. For example, the "things" might be users who have rated movies and the adjacency might be how many movies they have reviewed in common. Higher adjacency numbers indicate more likely similarity of interests. Note that this simple representation says nothing about whether or not they both rated the movies in a similar way.
Once you have adjacency data, you need a similarity measure to determine how similar to things (e.g., people) really are. One is Jaccard Similarity:
This is set notation; the size of the intersection of two sets over the size of the union. It can be generalized and is similar to the cosine of two vectors normalized by length. Note that the distance would be 1 - similarity.
Run the script this way on a small matrix:
Nicely Formatted:
./run scripts/MatrixJaccardSimilarity9.scala \
--input data/matrix/graph.tsv \
--output output/jaccardSim.tsv
Copy and Paste Version:
./run scripts/MatrixJaccardSimilarity9.scala --input data/matrix/graph.tsv --output output/jaccardSim.tsv
TFIDF is a widely used Natural Language Processing tool to analyze text. It's useful for indexing documents, e.g., for web search engines. Naively, you might calculate the frequency* of words in a corpus of documents and assume that if a word appears more frequently in one document, then that document is probably a "definitive" place for that word, such as the way you search for web pages on a particular topic. Similarly, the most frequent words indicate the primary topics for a document.
There's a problem, though. Very common words, e.g., articles like "the", "a", etc. will appear very frequently, undermining results. So we want to remove them so how. Fortunately, they tend to appear frequently in every document, so you can reduce the ranking of a particular word if you divide its frequency in a given document by its frequency in all documents. That's the essence of TF*IDF.
For more information, see the Wikipedia page.
Run the script this way on a small matrix:
Nicely Formatted:
./run scripts/TfIdf10.scala \
--input data/matrix/docBOW.tsv \
--output output/featSelectedMatrix.tsv \
--nWords 300
Copy and Paste Version:
./run scripts/TfIdf10.scala --input data/matrix/docBOW.tsv --output output/featSelectedMatrix.tsv --nWords 300
So far, we have been using the original Fields-Based API, which emphasizes naming fields and uses a relatively dynamic approach to typing. This is consistent with Cascading's model.
There is newer, more experimental Type-Safe API that attempts to more fully exploit the type safety provided by Scala. It's not as well documented, but the place to start is the Type-Safe API Reference page.
Here is Twitter6
ported to this API. The comments in the script explain what's different. The output is the same for output/unique-languages.txt
and output/count-star.txt
. However, instead of just counting 100 lines, this time we write the 100 lines (and we also changed the command-line option):
Nicely Formatted:
./run scripts/Twitter6Typed.scala \
--input data/twitter/tweets.tsv \
--uniques output/unique-languages.txt \
--count-star output/count-star.txt \
--first-100 output/first-100.txt
Copy and Paste Version:
./run scripts/Twitter6Typed.scala --input data/twitter/tweets.tsv --uniques output/unique-languages.txt --count-star output/count-star.txt --first-100 output/first-100.txt
A great feature of Cascading, which Scalding exploits, is the ability to test locally before running on Hadoop. This improves the iterative development and feedback cycle.
I've provided a bash shell script, run11.sh
to run the job on Hadoop, but first, let's discuss Scalding's own approach.
Once you're ready to try it in Hadoop, the "official" Scalding way is to use the scripts/scald.rb
script in the Scalding distribution. For example, assuming that you cloned the Scalding repo into $SCALDING_HOME
, here is a command to run src/main/scala/HadoopTwitter11.scala
, which is actually identical to Twitter6
that we ran previously, except for comments. (I put it in src/main/scala
so the sbt build adds it to the assembly.):
 Also, replace --local
with --hdfs
so that HDFS is actually used. For this to work, you'll need to copy the data/twitter
directory to your HDFS home directory (i.e., /user/$USER
) and also create an output
directory there.
Finally, when using HDFS, the values specified for output using the --uniques
, count-star
, and count-star-limit
flags will be used as directories, not files, which is why we omitted the .txt
suffixes used before. This follows conventional Hadoop practice, where the parallel processes might result in multiple, concurrently-written output files.
To simplify matters, such as removing the need for you to install the Scalding distribution, I've provided a bash script, run11.sh
, to run this exercise. You'll need access to a computer with Hadoop and bash installed. You can install Hadoop on a Mac using HomeBrew, on Linux using the appropriate package installer, and Microsoft has recently released a port of Hadoop for Windows. However, the easiest way to play with Hadoop is to install a VMWare or VirtualBox runner and download a completely configured image file from Cloudera, MapR, or Hortonworks.
I'll just demonstrate using run11.sh
in the Workshop. Using all the default settings, just run:
run11.sh
This runs in local mode using the local file system, the MapReduce APIs, but not the full set of Hadoop services and HDFS. For help on the options to change the defaults:
run11.sh --help
The output will be identical to what you saw for the previous Twitter exercise.
In September, Twitter unveiled a new Scala API called Summingbird. There is also a tutorial from LambdaJam 2013 by one of the project creators.
Summingbird is designed to support a variety of backends, so that logic can be written once and executed on different systems. The initial release supports Hadoop through Scalding, Storm, an event processing system that Twitter and many other organizations use to complement Hadoop's batch-mode capabilities. There is also an in-memory mode that uses memcached, designed for testing, but potentially also for working with smaller data sets where the scalability of Hadoop isn't needed.
Twitter is encouraging others to contribute backends for additional systems.
Because additional libraries are required for running Summingbird apps, we'll just look at our Word Count example ported to Summingbird. See scripts/SummingbirdWordCount12.scala
.
It's interesting to contrast Scalding with other tools.
Because Scala is a functional programming language with excellent support for DSL (domain-specific language) creation, using Scalding is much more concise than the Java-based Cascading itself, because Scalding programs are more concise and intuitive, fully exploiting FP idioms for data manipulation. For more on this comparison, see this talk that Dean Wampler gave recently at the Chicago Hadoop Users Group and Big Data Techcon Boston, 2013.
This Clojure dialect written by Nathan Marz also benefits from the functional nature and concision of Clojure. Nathan has also built in the logic-based query model of Datalog.
Pig has very similar capabilities, with notable advantages and disadvantages.
fields
on Pipes to get the field names, but it's less convenient to use, especially in interactive scenarios.Hive is ideal when your problem fits the SQL model for queries. It's less useful for complex transformations. Also, like Pig, extensions must be written in another language.
When I work in Hadoop, my two tools for almost all work are Hive and Scalding. To learn more about Hive, see Programming Hive.
But when I’ve periodically refined these notes since then, the price fluctuated between $400 and $550 per share. ↩


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Type Aliases
The following type aliases are available globally.
-
The elements generated during streaming. These are very light-weight and are the same as those used as an intermediate representation when building an
AbstractSyntaxTree
Declaration
Swift
public typealias StreamedToken = AbstractSyntaxTreeConstructor.IntermediateRepresentationNode
-
A closure that captures all the required behaviour for rule evaluation (but not the parser logic surrounding it. If the rule is not satisfied an
Error
should be thrown. The return value simply indicates wether or not a token should be created.Declaration
Swift
public typealias CustomRuleClosure = (_ lexer:LexicalAnalyzer) throws -> Bool
Parameters
lexer
The
LexcicalAnalyzer
to user for scanningReturn Value
Wether or not a token should be created, so
true
if the rule was satisfied and a token should be created,false
if it was satisfied but a token should not be created
-
A dictionary of annotations and their values
Declaration
Swift
public typealias RuleAnnotations = [RuleAnnotation : RuleAnnotationValue]


public abstract class Map<K,V>
extends java.lang.Object
Map
is a data structure consisting of a set of keys and values in which each key is mapped to a single
value. The class of the objects used as keys is declared when the Map
is declared, as is the class of the
corresponding values.
A Map
provides helper methods to iterate through all of the keys contained in it, as well as various methods
to access and update the key/value pairs.
Changes from the java.util version:
Does not implement Serializable nor clone()
This class is an abstract implementation of the Map
interface. This default implementation does not support
adding. A subclass must implement the abstract method entrySet().| Modifier | Constructor and Description |
|---|---|
protected |
Map()
Constructs a new instance of this
AbstractMap . |
| Modifier and Type | Method and Description |
|---|---|
abstract void |
clear()
Removes all elements from this
Map , leaving it empty. |
abstract boolean |
containsKey(K key)
Returns whether this
Map contains the specified key. |
abstract boolean |
containsValue(V value)
Returns whether this
Map contains the specified value. |
abstract Set<MapEntry<K,V>> |
entrySet()
Returns a
Set containing all of the mappings in this Map . |
boolean |
equals(java.lang.Object object)
By default, equals isn't supported for maps or other collections, though subclasses can choose to override this
behavior if they want.
|
abstract V |
get(K key)
Returns the value of the mapping with the specified key
|
int |
hashCode()
Returns the hash code for this object.
|
abstract boolean |
isEmpty()
Returns whether this map is empty.
|
abstract Set<K> |
keySet()
Returns a set of the keys contained in this
Map . |
abstract V |
put(K key,
V value)
Maps the specified key to the specified value.
|
abstract void |
putAll(Map<? extends K,? extends V> map)
Copies every mapping in the specified
Map to this Map . |
abstract V |
remove(K key)
Removes a mapping with the specified key from this
Map . |
abstract int |
size()
Returns the number of mappings in this
Map . |
java.lang.String |
toString()
Returns the string representation of this map.
|
abstract Collection<V> |
values()
Returns a
Collection of the values contained in this Map . |
public abstract Set<MapEntry<K,V>> entrySet()
Set
containing all of the mappings in this Map
. Each mapping is an instance of MapEntry
. As the Set
is backed by this Map
, changes in one will be reflected in the other.public boolean equals(java.lang.Object object)
equals
in class java.lang.Object
object
- the object to compare to this object.true
if the specified object is equal to this list, false
otherwise; though by default
this method throws an exception and developers generally shouldn't use itpublic int hashCode()
hashCode
in class java.lang.Object
equals(Object)
public java.lang.String toString()
toString
in class java.lang.Object
public abstract void clear()
Map
, leaving it empty.public abstract boolean containsKey(K key)
Map
contains the specified key.key
- the key to search for.true
if this map contains the specified key, false
otherwise.public abstract boolean containsValue(V value)
Map
contains the specified value.value
- the value to search for.true
if this map contains the specified value, false
otherwise.public abstract V get(K key)
key
- the key.null
/ default value for non-nullable value
type if no mapping for the specified key is found.public abstract boolean isEmpty()
true
if this map has no elements, false
otherwise.size()
public abstract Set<K> keySet()
Map
. The Set
is backed by this Map
so changes
to one are reflected by the other. The Set
does not support adding.public abstract V put(K key, V value)
key
- the key.value
- the value.null
/ default value for non-nullable
value type if there was no mapping.java.lang.UnsupportedOperationException
- if adding to this Map
is not supported.java.lang.ClassCastException
- if the class of the key or value is inappropriate for this Map
.java.lang.IllegalArgumentException
- if the key or value cannot be added to this Map
.java.lang.NullPointerException
- if the key or value is null
and this Map
does not support
null
keys or values.public abstract void putAll(Map<? extends K,? extends V> map)
Map
to this Map
.map
- the Map
to copy mappings from.java.lang.UnsupportedOperationException
- if adding to this Map
is not supported.java.lang.ClassCastException
- if the class of a key or a value of the specified Map
is
inappropriate for this Map
.java.lang.IllegalArgumentException
- if a key or value cannot be added to this Map
.java.lang.NullPointerException
- if a key or value is null
and this Map
does not support
null
keys or values.public abstract V remove(K key)
Map
.key
- the key of the mapping to remove.null
/ default value for non-nullable value type if no
mapping for the specified key was foundjava.lang.UnsupportedOperationException
- if removing from this Map
is not supported.public abstract int size()
Map
.Map
.public abstract Collection<V> values()
Collection
of the values contained in this Map
. The Collection
is backed by
this Map
so changes to one are reflected by the other. The Collection
supports Collection.remove(E)
, Collection.removeAll(jsimple.util.Collection<E>)
, Collection.retainAll(jsimple.util.Collection<E>)
, and Collection.clear()
operations, and it does not support Collection.add(E)
or Collection.addAll(jsimple.util.Collection<? extends E>)
operations.
This method returns a Collection
which is the subclass of Collection
. The Collection.iterator()
method of this subclass returns a "wrapper object" over the iterator of this Map
's
entrySet()
. The Collection.size()
method wraps this Map
's size()
method and the
Collection.contains(E)
method wraps this Map
's containsValue(V)
method.
The collection is created when this method is called at first time and returned in response to all subsequent
calls. This method may return different Collection when multiple calls to this method, since it has no
synchronization performed.


We are more than just your average marketing agency, we care about our customers, we want them to be successful. We are passionate to generate profit though websites, social media and in any digital platform
Quality Digital Group treats all of thier clients, with the maximum customer service. We are going to do everything online with the max marketing efforts. Businesses leverage digital channels such as Google search, social media, email, and our clients websites is design to connect with their current and prospective customers.
Come talk to us for free or email us. Our clients are our friends. We want to help the small business owners to understand that the online world is a fundamental key for any business to be successful.
Prior to Quality Digital Group. He studied Marketing at the University of Central Florida. When not working, Jose is either at the crosssfit gym or playing basketball.
Prior to Quality Digital Group, Elton co-founded Promethium Labs. He studied Economics at the University of Central Florida and he's a full stack-developer from the same university. When not hacking, Elton is either at the gym or traveling.


Many sports. I love to play golf; though, I am no good. I am a giant fan of all Atlanta sports teams. Most notably, I love my Falcons most of all. Outside of sports, I enjoy spending time with family and friends having great food, shooting firearms, and simply talking. I enjoy playing games such as Zelda and Fallout. Another fun time I like to have is watching television shows. My favorites are Game of Thrones, Rick and Morty, American Horror Story, and some anime shows.


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We have 1, 2, and 3 year options for subscribing to Fiction International. Prices vary with each subscription option.
Issues are published once a year.