提供 24 關攻略
Protect the peasant village of Plainswood from the ogres.
# Build two fences on the marks to keep the villagers safe!
# Hover your mouse over the world to get X,Y coordinates.
hero.moveUp()
hero.buildXY("fence", 40, 52)
hero.moveDown(2)
hero.buildXY("fence", 40, 20)
Stay alive and navigate through the forest!
# Go to the end of the path and build a fence on the X
# Use your moveXY(x, y) function.
# It's the first point of the path.
hero.moveXY(36, 59)
# Move at the next points of the path.
hero.moveXY(37, 13)
# Build a fence to stop the ogre.
hero.buildXY("fence", 72, 24)
Navigate through the Backwoods Forest, but check every corner to ensure your safety.
# Navigate through the woods, but be on the lookout!
# These forest cubbies may contain ogres!
hero.moveXY(19, 33)
enemy = hero.findNearestEnemy()
# The if-statement will check if a variable has an ogre.
if enemy:
hero.attack(enemy)
hero.attack(enemy)
hero.moveXY(49, 51)
enemy = hero.findNearestEnemy()
if enemy:
# Attack the enemy here:
hero.attack(enemy)
hero.attack(enemy)
# `pass` statement is a placeholder. It does nothing and helps close if-statements.
pass
hero.moveXY(58, 14)
enemy = hero.findNearestEnemy()
# Use an if-statement to check if enemy exists:
if enemy:
# If enemy exists, attack it:
hero.attack(enemy)
hero.attack(enemy)
Collect all nine gems using at most four moveXY commands.
# Collect all the gems in 4 moveXY's or less!
# Programmers need to think creatively!
hero.moveXY(32, 48)
hero.moveXY(67, 48)
hero.moveXY(32, 15)
hero.moveXY(32, 48)
Surrounded on all sides by incoming ogres! Wait for the right time to attack, then strike!
# Defeat the ogres from within their own camp!
while True:
enemy = hero.findNearestEnemy()
# Use an if-statement to check if an enemy exists:
if enemy:
# Attack the enemy if it exists:
hero.attack(enemy)
hero.attack(enemy)
Patrol the village entrances, but stay defensive.
# Stay in the middle and defend!
while True:
enemy = hero.findNearestEnemy()
if enemy:
# Either attack the enemy...
hero.attack(enemy)
hero.attack(enemy)
pass
else:
# ... or move back to your defensive position.
hero.moveXY(40, 35)
pass
Use your new cleave ability to fend off munchkins.
# Use your new "cleave" skill as often as you can.
hero.moveXY(23, 23)
while True:
enemy = hero.findNearestEnemy()
if hero.isReady("cleave"):
# Cleave the enemy!
hero.cleave(enemy)
pass
else:
# Else (if cleave isn't ready), do your normal attack.
hero.attack(enemy)
pass
Battle head-to-head against another hero in this basic beginner combat arena.
# Defeat the enemy hero in a duel!
while True:
# Find and attack the enemy inside a loop.
# When you're done, submit to the multiplayer ladder!
enemy = hero.findNearestEnemy()
if hero.isReady("cleave"):
hero.cleave(enemy)
else:
hero.attack(enemy)
Yell and cleave your way to victory, like a real barbarian.
# Munchkins are attacking!
# The swarms will come at regular intervals.
# Whenever you can, cleave to clear the mass of enemies.
while True:
enemy = hero.findNearestEnemy()
# Use an if-statement with isReady to check "cleave":
if hero.isReady("cleave"):
# Cleave!
hero.cleave(enemy)
# Else, if cleave is not ready:
else:
# Attack the nearest ogre!
hero.attack(enemy)
Find the distance to targets. Artillery will do the rest!
# Ogres are scouting the forest!
# Use the distanceTo method to find where the enemies are.
# Say the distance for each enemy to tell the artillery where to fire!
enemy1 = "Gort"
distance1 = hero.distanceTo(enemy1)
hero.say(distance1)
enemy2 = "Smasher"
distance2 = hero.distanceTo(enemy2)
# Say the distance2 variable!
hero.say(distance2)
# Don't shoot at your friends!
friend3 = "Charles"
# The last ogre.
enemy4 = "Gorgnub"
# Find and say the distance to enemy4:
distance4 = hero.distanceTo(enemy4)
hero.say(distance4)
Stay close to Victor.
while True:
enemy = hero.findNearestEnemy()
distance = hero.distanceTo(enemy)
if distance < 10:
# Attack if they get too close to the peasant.
hero.attack(enemy)
pass
# Else, stay close to the peasant! Use else.
else:
hero.moveXY(40, 37)
Defend yourself while munchkins attack! Break open a chest and take the spoils.
# Another chest in the field for the hero to break open!
# Attack the chest to break it open.

# Defend yourself when a munchkin gets too close.
while True:
enemy = hero.findNearestEnemy()
distance = hero.distanceTo(enemy)
if hero.isReady("cleave"):
# First priority is to cleave if it's ready:
hero.cleave(enemy)
pass
elif distance < 5:
# Attack the nearest munchkin that gets too close:
hero.attack(enemy)
pass
else:
# Otherwise, try to break open the chest:
# Use the name of the chest to attack: "Chest".
hero.attack("Chest")
pass
Only agile heroes can pass! Use nested if statements to dodge the fireballs.
# In this level the evilstone is bad! Avoid them, walking the other direction.
while True:
evilstone = hero.findNearestItem()
if evilstone:
pos = evilstone.pos
if pos.x == 34: # == means "is equal to"
# If the evilstone is on the left, go to the right side.
hero.moveXY(46, 22)
pass
else:
# If the evilstone is on the right, go to the left side.
hero.moveXY(34, 22)
pass
else:
# If there's no evilstone, go to the middle.
hero.moveXY(40, 22)
pass
Patrolling the village can get repetitive... functions make it easier!
# This defines a function called findAndAttackEnemy
def findAndAttackEnemy():
enemy = hero.findNearestEnemy()
if enemy:
hero.attack(enemy)
# This code is not part of the function.
while True:
# Now you can patrol the village using findAndAttackEnemy
hero.moveXY(35, 34)
findAndAttackEnemy()
# Now move to the right entrance.
hero.moveXY(60, 31)
# Use findAndAttackEnemy
findAndAttackEnemy()
Use a custom function with parameters to simplify your code!
# Use the checkAndAttack function to make your code easy to read.
# This function has a parameter.
# An parameter is a way of passing information into a function.
def checkAndAttack(target):
# The 'target' parameter is just a variable!
# It contains the argument when the function was called.
if target:
hero.attack(target)
hero.moveXY(43, 34)
while True:
hero.moveXY(58, 52)
topEnemy = hero.findNearestEnemy()
# Using the checkAndAttack function with the topEnemy variable.
checkAndAttack(topEnemy)
# Move to the bottom X mark.
hero.moveXY(58, 16)
# Create a variable named bottomEnemy and find the nearest enemy.
bottomEnemy = hero.findNearestEnemy()
# Use the checkAndAttack function, and include the bottomEnemy variable.
checkAndAttack(bottomEnemy)
Defend the homestead with function parameters.
# This shows how to define a function called cleaveWhenClose
# The function defines a parameter called `target`
def cleaveWhenClose(target):
if hero.distanceTo(target) < 5:
pass
# Put your attack code here
# If cleave is ready, then cleave target
if hero.isReady("cleave"):
hero.cleave(target)
# else, just attack `target`!
else:
hero.attack(target)
# This code is not part of the function.
while True:
enemy = hero.findNearestEnemy()
if enemy:
# Note that inside cleaveWhenClose, we refer to the `enemy` as `target`.
cleaveWhenClose(enemy)
A brawl in the woods! Pick out friend from foe and prepare for a battle.
# Peasants and peons are gathering in the forest.
# Command the peasants to battle and the peons to go away!
while True:
friend = hero.findNearestFriend()
if friend:
hero.say("To battle, " + friend.id + "!")
# Now find the nearest enemy and tell them to go away.
enemy = hero.findNearestEnemy()
if enemy:
hero.say("Go away, " + enemy.id + "!")
Use mathematical programming to find the door to the wizards' secret lair.
# Move to 'Laszlo' and get his secret number.
hero.moveXY(30, 13)
las = hero.findNearestFriend().getSecret()
# Add 7 to 'Laszlo's number to get 'Erzsebet's number.
# Move to 'Erzsebet' and say her magic number.
erz = las + 7
hero.moveXY(17, 26)
hero.say(erz)
# Divide 'Erzsebet's number by 4 to get 'Simonyi's number.
# Go to 'Simonyi' and tell him his number.
sim = erz / 4
hero.moveXY(30, 39)
hero.say(sim)
# Multiply 'Simonyi's number by 'Laszlo's to get 'Agata's number.

# Follow the trail of coins to the red X at the exit.
while True:
# This finds the nearest item.
item = hero.findNearestItem()
if item:
# This stores the item's pos, or position in a variable.
itemPosition = item.pos
# Put the X and Y coordinates of the item into variables.
itemX = itemPosition.x
itemY = itemPosition.y
# Now, use moveXY to move to itemX and itemY:
hero.moveXY(itemX, itemY)
Hunt ogres in the woods, but watch out for lumbering beasts.
# Only attack enemies of type "munchkin" and "thrower".
# Don't attack a "burl". Run away from an "ogre"!
while True:
enemy = hero.findNearestEnemy()
# Remember: don't attack type "burl"!
if enemy.type == "burl":
hero.say("I'm not attacking that Burl!")
# The "type" property tells you what kind of creature it is.
if enemy.type == "munchkin":
hero.attack(enemy)
# Use "if" to attack a "thrower".
if enemy.type == "thrower":
hero.attack(enemy)
# If it's an "ogre", use moveXY to run away to the village gate!
if enemy.type == "ogre":
hero.moveXY(41, 48)
Stay alive for one minute in this tranquil forest. It gets harder (and more lucrative) each time you win! But if you lose, you must wait a day to resubmit.
# Stay alive for one minute.
# If you win, it gets harder (and more rewarding).
# If you lose, you must wait a day before you can resubmit.
# Remember, each submission gets a new random seed.
while True:
enemy = hero.findNearestEnemy()
if enemy:
if hero.isReady("cleave"):
hero.cleave(enemy)
else:
hero.attack(enemy)
We are ready to shoot! Distance - 300 kilometres! Direction angle - ... Who cares about direction!?
# Tell the wizard the distance to the coming ogres.
# This function finds the nearest enemy and returns the distance to it.
# If there is no enemy, the function returns 0.
def nearestEnemyDistance():
enemy = hero.findNearestEnemy()
result = 0
if enemy:
result = hero.distanceTo(enemy)
return result
while True:
# Call nearestEnemyDistance() and
# save the result in the variable enemyDistance.
enemyDistance = nearestEnemyDistance()
# If the enemyDistance is greater than 0:
if enemyDistance > 0:
# Say the value of enemyDistance variable.
hero.say(enemyDistance)
Ogres are so different.
# Attack munchkins, call brawlers and ignore burls.
# This function defines the hero's behaviour about enemies.
def dealEnemy(enemy):
# If enemy.type is "munchkin":
if enemy.type == "munchkin":
# Then attack it:
hero.attack(enemy)
# If the enemy's type is "brawler":
if enemy.type == "brawler":
# Then say something to call the brawler:
hero.say("Stay away from me!")
pass
while True:
enemy = hero.findNearestEnemy()
if enemy:
dealEnemy(enemy)
else:
hero.moveXY(30, 34)
The trapped predator still is a dangerous predator.
# You are trapped. Don't move, it'll be painful.
# This function checks if the enemy is in your attack range.
def inAttackRange(enemy):
distance = hero.distanceTo(enemy)
# Almost all swords have attack range of 3.
if distance <= 3:
return True
else:
return False
# Attack ogres only when they're within reach.
while True:
# Find the nearest enemy and store it in a variable.
enemy = hero.findNearestEnemy()
# Call inAttackRange(enemy), with the enemy as the argument
# and save the result in the variable canAttack.
canAttack = inAttackRange(enemy)
# If the result stored in canAttack is True, then attack!
if canAttack:
hero.attack(enemy)
pass


{% extends "base.html" %} {% block title %}Simulation Databases{% endblock %} {% block pageheader %}
The following database backends are defined in this application:


The Universal Story (1996)
Grade: 71
This is a movie about the movies: a documentary about Universal Studios. It is narrated by Richard Dreyfuss and features the most famous scenes of the most famous Universal movies, shown in roughly chronological order. Universal is one of the oldest, most prolific and most comercially successful of all movie studios.
The first part of the documentary is the most interesting. It covers the early years of Universal, when it was owned by Carl Laemmle, and the survival of the studio depended upon the box office success of its next picture. Universal, known then by a different name, made its first film, Hiawatha, a one-reeler silent epic, in 1909. We learn back stage gossip of Laemmle and early directors and producers, and glimpse scenes from long-forgotten silent films. The Universal Studios lot was opened in 1915, and its first film recognizable to today's audience was filmed the previous year, The Phantom of Opera starring Lon Chaney.
Talkies began production in 1929, with the then-current Universal production Show Boat hastily modified to part silent, part talky. "All Quiet On The Western Front" became one of the first great talkies upon release in 1930.
Universal became known for its horror films in the thirties, giving us Bela Lugosi as "Dracula" and Boris Karloff as "Frankenstein".
The great depression cost Laemmle his beloved studio in 1935, and it has been corporate-owned since that time. Until this point, the documentary has been dominated by gossip about Laemmle and his feuds with directors. Such back-stage talk disappears with the new owners.
Universal was, as always, prolific and profitable during the 1940s through the 1950s. But most of these films are not notable: big box office winners came from characters such as Abbott & Costello, Francis the Talking Mule, and Ma and Pa Kettle. Other profitable but forgettable films were produced by Ross Hunter, starring the likes of Rock Hudson and Doris Day. There were good films made, of course. We see scenes from James Stewart films such as "Destry Rides Again", "Harvey" and "Winchester '73". Hitchcock directed "Saboteur" and "Shadow of a Doubt". Orson Welles directed "A Touch of Evil", a weird, dark film that is unlike any Universal production before or since.
Television cut into movie-going audiences in the 1950s. If you can't beat them, join them. The Universal lot was bought by MCA, then a talent agency with ties to television. Universal became known for its television programs and later, TV movies, as much as for its theatrical releases.
Universal shed its schlocky image as it released better "A" pictures during the 1960s, with Stanley Kubrick's "Spartacus" and Ross Hunter's "Airport". Hitchcock began working exclusively with Universal beginning with "The Birds" (1963) but unfortunately his work was in decline by then.
Starting in the 1970s, the documentary's focus is purely on famous directors and their blockbusters at "Universal". Three of the biggest grossing films of their era, "Jaws", "E.T." and "Jurassic Park" are Steven Spielberg productions at Universal. We see clips from the likes of "American Grafitti", "The Deer Hunter", "Back To The Future", "Apollo 13", "Out of Africa", "Born On The Fourth of July", "Field of Dreams" and the great "Shindler's List". We don't hear about any colossal bombs the studio made during this time. Although well-made, this documentary could almost be seen as studio promotional material made for potential film investors.
The presentation is well-organized by category and chronological, with the only obvious miscue following the studio's first full-length animated motion picture "An American Tail" (1986) with Woody Woodpecker shorts from the 1940s.
http://members.tripod.com/~Brian_Koller/movies.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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| PREV NEXT | FRAMES NO FRAMES |
| Packages that use fsb.ast | |
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| fsb.ast | |
| fsb.explore | |
| fsb.semantics | |
| fsb.semantics.eagerpso | |
| fsb.semantics.empeagerpso | |
| fsb.semantics.naivepso | |
| fsb.semantics.pso | |
| fsb.semantics.sc |
| Classes in fsb.ast used by fsb.ast | |
|---|---|
| AritExpr
|
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| AritExpr.AXType
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| Assert
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| AssertAritExpr
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| AssertBoolExpr
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| BoolExpr
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| Declarations
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|---|---|
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| MProgram
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| Assign
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| Assume
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| AtomicDec
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| BeginAtomic
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| Branch
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| ChooseAssign
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evothings. easyble
Source: evothings-libraries/
Library for making BLE programming easier.
An all-in-one file with this library and helper libraries included is available in file easyble.dist.js. Include this file in index.html (recommended).
If you include easyble.js
rather than easyble.dist.js
it is safe practise to call function evothings.scriptsLoaded to ensure dependent libraries are loaded before calling
functions in this library (in this case you also need to have the dependent library folders).
Namespaces
Property
Methods
Namespaces
EasyBLEDevice
error
Property
printObject
Prints and object for debugging purposes.
Methods
closeConnectedDevices()
Disconnect and close all connected BLE devices.
Example
evothings.easyble.closeConnectedDevices();
filterDevicesByService(services)
Set to an Array of UUID strings to enable filtering of devices found by startScan().
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
services |
|
|
Array of UUID strings. Set to false to disable filtering. The default is no filtering. An empty array will cause no devices to be reported. |
reportDeviceOnce(reportOnce)
Deprecated. Set whether to report devices once or continuously during scan. The default is to report continously.
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
reportOnce |
boolean |
|
Set to true to report found devices only once. Set to false to report continuously. |
- Deprecated
- Use the options parameter evothings.easyble.ScanOptions in function evothings.easyble.startScan.
reset()
Reset the BLE hardware. Can be time consuming.
startScan(success, fail[, options])
Start scanning for devices. Note that the optional parameter serviceUUIDs has been deprecated. Please use the options parmameter evothings.easyble.ScanOptions instead to specify any specific service UUID to scan for.
Example
// Scan for all services.
evothings.easyble.startScan(
function(device)
{
console.log('Found device named: ' + device.name);
},
function(errorCode)
{
console.log('startScan error: ' + errorCode);
}
);
// Scan for specific service.
evothings.easyble.startScan(
function(device)
{
console.log('Found device named: ' + device.name);
},
function(errorCode)
{
console.log('startScan error: ' + errorCode);
},
// Eddystone service UUID specified in options.
{ serviceUUIDs: ['0000FEAA-0000-1000-8000-00805F9B34FB'] }
);
Parameters
| Name | Type | Optional | Description |
|---|---|---|---|
|
success |
|
Success function called when a device is found. Format: success(evothings.easyble.EasyBLEDevice). |
|
|
fail |
|
Error callback: fail(error). |
|
|
options |
Yes |
Object with scan options. |
stopScan()
Stop scanning for devices.
Example
evothings.easyble.stopScan();
Abstract types
AdvertisementData Object
Information extracted from a scanRecord. Some or all of the fields may be undefined. This varies between BLE devices. Depending on OS version and BLE device, additional fields, not documented here, may be present.
Properties
| Name | Type | Optional | Description |
|---|---|---|---|
|
kCBAdvDataLocalName |
string |
|
The device's name. Use this field rather than device.name, since on iOS the device name is cached and changes are not reflected in device.name. |
|
kCBAdvDataTxPowerLevel |
number |
|
Transmission power level as advertised by the device. |
|
kCBAdvDataIsConnectable |
boolean |
|
True if the device accepts connections. False if it doesn't. |
|
kCBAdvDataServiceUUIDs |
array |
|
Array of strings, the UUIDs of services advertised by the device. Formatted according to RFC 4122, all lowercase. |
|
kCBAdvDataServiceData |
object |
|
Dictionary of strings to strings. The keys are service UUIDs. The values are base-64-encoded binary data. |
|
kCBAdvDataManufacturerData |
string |
|
Base-64-encoded binary data. This field is used by BLE devices to advertise custom data that don't fit into any of the other fields. |
connectCallback(device)
Called when successfully connected to a device.
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
device |
|
EasyBLE devices object. |
dataCallback(data)
Function when data is available.
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
data |
ArrayBuffer |
|
The data is an array buffer. Use an ArrayBufferView to access the data. |
emptyCallback()
This callback indicates that an operation was successful, without specifying and additional information.
failCallback(errorString)
This function is called when an operation fails.
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
errorString |
string |
|
A human-readable string that describes the error that occurred. |
NotificationOptions Object
Options object for functions evothings.easyble.EasyBLEDevice#enableNotification and evothings.easyble.EasyBLEDevice#disableNotification.
Property
| Name | Type | Optional | Description |
|---|---|---|---|
|
writeConfigDescriptor |
boolean |
|
Supported on Android, ignored on iOS. Set to false to disable automatic writing of notification or indication config descriptor value. This is useful in special cases when full control of writing the config descriptor is needed. |
ReadServicesOptions Object
Options object for function evothings.easyble.EasyBLEDevice#readServices
Property
| Name | Type | Optional | Description |
|---|---|---|---|
|
serviceUUIDs |
array |
|
Array of service UUID strings. |
rssiCallback(rssi)
Called with RSSI value.
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
rssi |
number |
|
A negative integer, the signal strength in decibels. This value may be 127 which indicates an unknown value. |
scanCallback(device)
Called during scanning when a BLE device is found.
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
device |
|
EasyBLE device object found during scanning. |
ScanOptions Object
Options for function {evothings.easyble.startScan}
Properties
| Name | Type | Optional | Description |
|---|---|---|---|
|
serviceUUIDs |
array |
|
Array with strings of service UUIDs to scan for. When providing one service UUID, behaviour is the same on Android and iOS, when providing multiple UUIDs behaviour differs between platforms. On iOS multiple UUIDs are scanned for using logical OR operator, any UUID that matches any of the UUIDs adverticed by the device will count as a match. On Android, multiple UUIDs are scanned for using AND logic, the device must advertise all of the given UUIDs to produce a match. Leaving out this parameter or setting it to null will scan for all devices regardless of advertised services (default behaviour). |
|
allowDuplicates |
boolean |
|
If true same device will be reported repeatedly during scanning, if false it will only be reported once. Default is true. |
servicesCallback(device)
Called when services are successfully read.
Parameter
| Name | Type | Optional | Description |
|---|---|---|---|
|
device |
|
EasyBLE devices object. |


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break
Syntax
break
Description
On systems with a debugger, the environment "pauses" when execution reaches the break statement. While "pausing" is environment specific, in general, the program stops execution until the user presses the "Resume" or "Continue" button. While paused, the program variables can be inspected, stack traces done, etc.
Example
for i : 1 .. 100
put i
break
end for


At the core of every web application there’s data. Data can take many forms, but its main purpose is always to display information to the user. With web apps becoming increasingly interactive and complex, how the user accesses and interacts with information is now a key part of web development.
In this lesson, we’ll see how to fetch data from a server asynchronously, and use this data to display information on a web page without reloading the HTML.
You need to have built the Login and Registration Form part of the web app for this lesson. You also need to install Node.js and run the server API locally so you get account data.
You can test that the server is running properly by executing this command in a terminal:
Traditional web sites update the content displayed when the user selects a link or submits data using a form, by reloading the full HTML page. Every time new data needs to be loaded, the web server returns a brand new HTML page that needs to be processed by the browser, interrupting the current user action and limiting interactions during the reload. This workflow is also called a Multi-Page Application or MPA.
When web applications started to become more complex and interactive, a new technique called AJAX (Asynchronous JavaScript and XML) emerged. This technique allows web apps to send and retrieve data from a server asynchronously using JavaScript, without having to reload the HTML page, resulting in faster updates and smoother user interactions. When new data is received from the server, the current HTML page can also be updated with JavaScript using the DOM API. Over time, this approach has evolved into what is now called a Single-Page Application or SPA.
When AJAX was first introduced, the only API available to fetch data
asynchronously was
XMLHttpRequest
. But modern browsers now also implement the more convenient and powerful
Fetch
API, which uses promises and is better suited to manipulate JSON data.
While all modern browsers support the
Fetch API
, if you want your web application to work on legacy or old browsers it’s always a good idea to check the compatibility table on caniuse.com first.
In the previous lesson we implemented
the registration form to create an account. We’ll now add code to login
using an existing account, and fetch its data. Open the
app.js
file and add a new login
function:
async function login() {
const loginForm = document.getElementById("loginForm");
const user = loginForm.user.value;
}
Here we start by retrieving the form element with
getElementById()
, and then we get the username from the input
with loginForm.user.value
. Every form control can be accessed
by its name (set in the HTML using the name
attribute) as a
property of the form.
In a similar fashion to what we did for the registration, we’ll create another function to perform a server request, but this time for retrieving the account data:
async function getAccount(user) {
try {
const response = await fetch(
"//localhost:5000/api/accounts/" + encodeURIComponent(user)
);
return await response.json();
} catch (error) {
return { error: error.message || "Unknown error" };
}
}
We use the fetch
API to request the data asynchronously from
the server, but this time we don’t need any extra parameters other than
the URL to call, as we’re only querying data. By default,
fetch
creates a
GET
HTTP request, which is what we are seeking here.
✅ encodeURIComponent()
is a function that escapes special
characters for URL. What issues could we possibly have if we do not call
this function and use directly the user
value in the URL?
Let’s now update our login
function to use
getAccount
:
async function login() {
const loginForm = document.getElementById("loginForm");
const user = loginForm.user.value;
const data = await getAccount(user);
if (data.error) {
return console.log("loginError", data.error);
}
account = data;
navigate("/dashboard");
}
First, as getAccount
is an asynchronous function we need to
match it with the await
keyword to wait for the server
result. As with any server request, we also have to deal with error cases.
For now we’ll only add a log message to display the error, and come back
to it later.
Then we have to store the data somewhere so we can later use it to display
the dashboard informations. Since the account
variable does
not exist yet, we’ll create a global variable for it at the top of our
file:
After the user data is saved into a variable we can navigate from the
login page to the dashboard using the
navigate()
function we already have.
Finally, we need to call our login
function when the login
form is submitted, by modifying the HTML:
Test that everything is working correctly by registering a new account and trying to login using the same account.
Before moving on to the next part, we can also complete the
register
function by adding this at the bottom of the
function:
✅ Did you know that by default, you can only call server APIs from the
same domain and port than the web page you are viewing? This is
security mechanism enforced by browsers. But wait, our web app is running
on localhost:3000
whereas the server API is running on
localhost:5000
, why does it work? By using a technique called
Cross-Origin Resource Sharing (CORS), it is possible to perform cross-origin HTTP requests if the server adds
special headers to the response, allowing exceptions for specific domains.
Learn more about APIs by taking this lesson
Now that we have the user data, we have to update the existing HTML to
display it. We already know how to retrieve an element from the DOM using
for example document.getElementById()
. After you have a base
element, here are some APIs you can use to modify it or add child elements
to it:
Using the
textContent
property you can change the text of an element. Note that changing
this value removes all the element’s children (if there’s any) and
replaces it with the text provided. As such, it’s also an efficient
method to remove all children of a given element by assigning an empty
string ''
to it.
Using
document.createElement()
along with the
append()
method you can create and attach one or more new child elements.
✅ Using the
innerHTML
property of an element it’s also possible to change its HTML contents, but
this one should be avoided as it’s vulnerable to
cross-site scripting (XSS)
attacks.
Before moving on to the dashboard screen, there’s one more thing we should do on the login page. Currently, if you try to login with a username that does not exist, a message is shown in the console but for a normal user nothing changes and you don’t know what’s going on.
Let’s add a placeholder element in the login form where we can display an
error message if needed. A good place would be just before the login
<button>
:
This <div>
element is empty, meaning that nothing will
be displayed on the screen until we add some content to it. We also give
it an id
so we can retrieve it easily with JavaScript.
Go back to the app.js
file and create a new helper function
updateElement
:
function updateElement(id, text) {
const element = document.getElementById(id);
element.textContent = text;
}
This one is quite straightforward: given an element id and
text, it will update the text content of the DOM element with the
matching id
. Let’s use this method in place of the previous
error message in the login
function:
Now if you try to login with an invalid account, you should see something like this:
Now we have error text that shows up visually, but if you try it with a screen reader you’ll notice that nothing is announced. In order for text that is dynamically added to a page to be announced by screen readers, it will need to use something called a Live Region. Here we’re going to use a specific type of live region called an alert:
Implement the same behavior for the register
function errors
(don’t forget to update the HTML).
Using the same techniques we’ve just seen, we’ll also take care of displaying the account information on the dashboard page.
This is what an account object received from the server looks like:
{
"user": "test",
"currency": "$",
"description": "Test account",
"balance": 75,
"transactions": [
{ "id": "1", "date": "2020-10-01", "object": "Pocket money", "amount": 50 },
{ "id": "2", "date": "2020-10-03", "object": "Book", "amount": -10 },
{ "id": "3", "date": "2020-10-04", "object": "Sandwich", "amount": -5 }
]
}
Note: to make your life easier, you can use the pre-existing
test
account that’s already populated with data.
Let’s start by replacing the “Balance” section in the HTML to add placeholder elements:
We’ll also add a new section just below to display the account description:
✅ Since the account description functions as a title for the content underneath it, it is marked up semantically as a heading. Learn more about how heading structure is important for accessibility, and take a critical look at the page to determine what else could be a heading.
Next, we’ll create a new function in app.js
to fill in the
placeholder:
function updateDashboard() {
if (!account) {
return navigate("/login");
}
updateElement("description", account.description);
updateElement("balance", account.balance.toFixed(2));
updateElement("currency", account.currency);
}
First, we check that we have the account data we need before going
further. Then we use the updateElement()
function we created
earlier to update the HTML.
To make the balance display prettier, we use the method
toFixed(2)
to force displaying the value with 2 digits after the decimal point.
Now we need to call our updateDashboard()
function everytime
the dashboard is loaded. If you already finished the
lesson 1 assignment this
should be straighforward, otherwise you can use the following
implementation.
Add this code to the end of the updateRoute()
function:
And update the routes definition with:
const routes = {
"/login": { templateId: "login" },
"/dashboard": { templateId: "dashboard", init: updateDashboard },
};
With this change, every time the dashboard page is displayed, the function
updateDashboard()
is called. After a login, you should then
be able to see the account balance, currency and description.
In the first lesson we used
HTML templates along with the
appendChild()
method to implement the navigation in our app. Templates can also be
smaller and used to dynamically populate repetitive parts of a page.
We’ll use a similar approach to display the list of transactions in the HTML table.
Add a new template in the HTML <body>
:
This template represents a single table row, with the 3 columns we want to populate: date, object and amount of a transaction.
Then, add this id
property to the
<tbody>
element of the table within the dashboard
template to make it easier to find using JavaScript:
Our HTML is ready, let’s switch to JavaScript code and create a new
function createTransactionRow
:
function createTransactionRow(transaction) {
const template = document.getElementById("transaction");
const transactionRow = template.content.cloneNode(true);
const tr = transactionRow.querySelector("tr");
tr.children[0].textContent = transaction.date;
tr.children[1].textContent = transaction.object;
tr.children[2].textContent = transaction.amount.toFixed(2);
return transactionRow;
}
This function does exactly what its names implies: using the template we
created earlier, it creates a new table row and fills in its contents
using transaction data. We’ll use this in our
updateDashboard()
function to populate the table:
const transactionsRows = document.createDocumentFragment();
for (const transaction of account.transactions) {
const transactionRow = createTransactionRow(transaction);
transactionsRows.appendChild(transactionRow);
}
updateElement("transactions", transactionsRows);
Here we use the method
document.createDocumentFragment()
that creates a new DOM fragment on which we can work, before finally
attaching it to our HTML table.
There’s still one more thing we have to do before this code can work, as
our updateElement()
function currently supports text content
only. Let’s change its code a bit:
function updateElement(id, textOrNode) {
const element = document.getElementById(id);
element.textContent = ""; // Removes all children
element.append(textOrNode);
}
We use the
append()
method as it allows to attach either text or
DOM Nodes to
a parent element, which is perfect for all our use cases.
If you try using the test
account to login, you should now
see a transaction list on the dashboard 🎉.
Work together to make the dashboard page look like a real banking app. If you already styled your app, try to use media queries to create a responsive design working nicely on both desktop and mobile devices.
Here’s an example of a styled dashboard page:


Your community has a heart beat
Every community has leaders, supporters, and hard workers. Sometimes a community has members that don't contribute helpfully, or worse are trying to damage what's being done. Sometimes you can't control who comes into your community.
Make sure that heart is healthy
SocialKardia is a set of digital social network awareness tools, much like developer CI tools. SocialKardia gives you a chance to know how your community is hurting and why it's hurting. SocialKardia gives you insight into people involved in your social network from Twitter, IRC, Reddit, or more.
-
Scout
Scout is a passive analysis of social network profiles and personas. It can answer the following questions about an individual:
- Does this person interact with toxic people?
- How toxic is this person?
- Has this person changed over time?
-
Radar
Radar is a passive toxicity detection system for group discussion. It can be used as an alert system or simply as a histographic tool for these types of situations:
- Someone in your group is being extra toxic.
- The discussion tempature is rising.
- Someone is continually being a toxic community member.
-
Backtrace
When alerts do trigger you'll use this tool to find out why and fine tune the array of methodologies we have at our disposal. This is also useful for catching red herring or false positives. It gives you these utilities:
- Determine the toxicity of a piece of text.
- Store information about a particular user.
- Find the cause of spike points


Education
D.Phil in Engineering Science, Department of Engineering Science, University of Oxford, UK
Thesis: Self-Supervised Learning Using Motion and Visualizing Convolutional Neural Networks
Oct. 2014 - Dec. 2018
MS in Robotics, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
Aug. 2013 - Dec. 2013 GPA: 4.11/4.33
B. Tech in Computer Science and Engineering, International Institute of Information Technology Hyderabad, India
Aug. 2008 - Jun. 2012 GPA: 9.81/10
Work Experience
Research Scientist (Feb. 2019 - Current)
Google Germany GmbH, Brain Team, Berlin
Research Associate (Jan. - June 2014)
Carnegie Mellon University, PA, USA with Dr. Abhinav Gupta
Research Assistant (Jan. 2013 - Dec. 2013)
Carnegie Mellon University, PA, USA with Dr. Steve Smith and Dr. Martial Hebert
Short Term Scholar (May - July 2011)
University of Southern California, CA, USA with Dr. Gaurav Sukhatme and Dr. Maja Matarić
Academic Services
Reviewer for CVPR, ECCV, BMVC, WACV, NeurIPS, PAMI, NNLS, IJCV.
Lab Demonstrator (University of Oxford) Lab demonstrator for B-16 Structure Programming Lab, Department of Engineering Science
Teaching Assitant (IIIT Hyderabad) Teaching Assistant for undergraduate course on Artificial Intelligence at IIIT Hyderabad


"Here are some possibilities!"
An app to experience creating and deleting items using a database on heroku.
Github linkA repo I use to store an app to create readme Files.
Github linkI eventually will have a full working recipe and cooking application allowing the user to search up not only what to cook but how to cook.
Github linkA fully functional weather application allowing the user either using their own location or specifying another one to see the daily and weekly forecast of the weather using a weather API.
Github linkIn this application not only did I get to work with another API(aplication Programming Interface) I got to experiment with more Javascipt and high order array functions.. a.k.a list's of data.
Github linkWe begin diving deeper into javascript and actually writing code that does something. I am still working on this application.
Github linkI had to design a small pop quiz application that will test your knowledge on a few random topics. Who wants to be a millionaire!?
Github linkAfter a few weeks of understanding HTML and CSS we had to dive into Javascript and how to functionality to a website. In this case you will be guided through a couple window prompts to generate your own unique password, just don't forget to write it down!
Github linkOne of my very first projects I ever worked on with absolutly no experience writing code other than a little python that I had written here and there.
Github link


(Assuming There is a Tenant All Week)
Calculated by comparing the five closest houses to the specified coordinates and taking an average
(By Attracting as Many Tenants as Possible at a Competing Price)
Calculated by estimating total amount of revenue made based on frequency of reviewers and price per night to determine the most profitable house over time. Once the most profitable house is identified, slightly lower our price from that house to incentivise oursleves over our competition
We can assume the median price of a house in San Francisco is $1,236,700 (Zillow.com), So therefore we can purchase roughly 80 houses. By determining the most profitable houses based on price per night and frequency of tenants (determined using number of reviews per month), we can determine our income per year with these 80 profitable houses. Assuming the tenants stay the average 3.5 days, the total income per year would be roughly $8,202,694.08. This would mean it would take 12+ years to break even. Click Here for a csv file listing the coordinates and yearly income of houses to be purchased.


CHARLIE CHAN AT MONTE CARLO (director: Eugene J. Forde; screenwriters: Jerry Cady/Charles S. Belden/from a story by Robert Ellis and Helen Logan; cinematographer: Daniel Clark; editor: Nick De Maggio; cast: Warner Oland (Charlie Chan), Keye Luke (Lee Chan), Virginia Field (Evelyn Grey), Sidney Blackmer (Victor Karnoff), Edward Raquello (Paul Savarin), Harold Huber (French Police Inspector, Jules Joubert), Kay Linaker (Joan Karnoff), Robert Kent (Gordon Chase), George Lynn (Al Rogers), John Bleifer (Ludwig), Georges Renavent (Renault); Runtime: 71; Twentieth Century-Fox; 1937)
Reviewed by Dennis Schwartz
Warner Oland's final appearance as Charlie Chan is marred because this is one of the weaker episodes in the long running series. The story was too muddled and not too crisply executed. Charlie and number one son, Lee (Luke), are in Monte Carlo for a brief gambling vacation before going to Paris, where the son's art work will be in an exhibition contest. When their taxi breaks down on the way to the train station to catch the train to Paris and they are forced to walk, they come across a luxury car with a dead bank messenger (Renavent), and there's also a missing chauffeur, Ludwig (Bleifer), and a sports car speeding away from the crime scene. Charlie on further investigation finds a rhinestone by the car and footprints in the sand rubbed out.
In the events taking place on the night the murder was committed, Gordon Chase (Kent), the brother of Joan Karnoff (Linaker) and secretary to her wealthy financier husband Victor (Blackmer), learns that $25,000 in securities is missing from Victor's safe. He tells his sister to get it back to him immediately, since Victor is dumping these metallurgic securities on the market to cause financial woes on his main rival Paul Savarin (Raquello). Joan was once married to an American, Al Rogers (Lynn), who has a history as a petty thief back in Chicago and is working as a bartender in the Hotel Imperial. She was never divorced from him and is now being blackmailed for the bonds. In exchange for the bonds, he won't tell Victor. He only wants cash in return for them and refuses to turn them over to her when Joan pleads that she must return them to Victor by tonight. Joan will then pawn her jewelry for the money and gets them to Gordon just in time for her husband to give them to the messenger.
Victor tells them the dead messenger was carrying a million in bonds and accuses Savarin of the crime, but he also says he's not worried because he was insured. They also discover that Rogers tried to sell $25,000 in bonds that were Victor's. Charlie's friend, the police chief, Jules Joubert (Huber), finds the owner of the sports car is Evelyn Gray (Virginia Field), and Charlie discovers it was her rhinestone that fell off her high heel shoe. On a police check, they find Evelyn is a 24-year-old Londoner with no source of income but living in luxury in one of Monaco's best hotels, dressed in furs, and driving an expensive sports car. Upon questioning her, she admits she saw the dead man but did not kill him. Soon a report comes that Ludwig was found dead in a marsh near the crime scene. Charlie and Jules then discover Roger's body when they go to question him, as the murderer tried to make it look like a suicide and left the bag with the stolen bonds in his room. But Charlie notes there are $200,000 in bonds missing and brings all the suspects together to tell them that the one who took those missing bonds and then put them back in Victor's safe, is the killer of three people and is presently in the room.
In the room all the suspects reveal how they could be the murderer: Savarin has been using others to buy info on Victor, and tried to get a flight out of Nice but canceled when he thought he would become accused of the murder. Evelyn is Gordon's girlfriend but is really Savarin's and is using him to get info on Victor, while receiving financial favors from both. It comes down to looking at the blackmail angle and the romantic triangle, and how the wily Charlie puts all the clues together. For comedy, Lee's misuse of French leads to some embarrassing situations and Joubert's choppy English and inflated pride in his bumbling police department leads to some amusing moments.
REVIEWED ON 7/21/2001 GRADE: C
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
========== X-RAMR-ID: 28910 X-Language: en X-RT-SourceID: 873 X-RT-AuthorID: 1315 X-RT-RatingText: C
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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Problem: you need to purge old rows from a table. This may be your weekly/monthly cleanup task. The table is large, the amount of rows to be deleted is large, and doing so in one big DELETE is too heavy.
You can use oak-chunk-update or pt-archiver to accomplish the task. You can also use server side scripting with QueryScript, offering a very simple syntax with no external scripting, dependencies and command line options.
I wish to present three cases of row deletion, with three different solutions. In all cases we assume some TIMESTAMP column exists in table, by which we choose to purge the row. In all cases we assume we wish to purge rows older than 1 month.
We assume the naive query is this:
DELETE FROM my_schema.my_table WHERE row_timestamp < CURDATE() - INTERVAL 1 MONTH
Case 1: TIMESTAMP column is indexed
I almost always index a timestamp column, if only for being able to quickly purge data (but usually also to slice data by date). In this case where the column is indexed, it’s very easy to figure out which rows are older than 1 month.
We break the naive query into smaller parts, and execute these in sequence:
This post exposes some of the internals, and the SQL behind QueryScript’s split. common_schema/QueryScript 1.1 introduces the split statement, which auto-breaks a “large” query (one which operates on large tables as a whole or without keys) into smaller queries, and executes them in sequence.
This makes for easier transactions, less locks held, potentially (depending on the user) more idle time released back to the database. split has similar concepts to oak-chunk-update and pt-archiver, but works differently, and implemented entirely in SQL on server side.
Take the following statement as example:
split (UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR)
pass;
It yields with (roughly) the following statements:
UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR WHERE ((((inventory
.inventory_id
> ‘1’)) OR ((inventory
.inventory_id
= ‘1’))) AND (((inventory
.inventory_id
< ‘1000’)) OR ((inventory
.inventory_id
= ‘1000’))));
UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR WHERE ((((inventory
.inventory_id
> ‘1000’))) AND (((inventory
.inventory_id
< ‘2000’)) OR ((inventory
.inventory_id
= ‘2000’))));
UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR WHERE ((((inventory
.inventory_id
> ‘2000’))) AND (((inventory
.inventory_id
< ‘3000’)) OR ((inventory
.inventory_id
= ‘3000’))));
UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR WHERE ((((inventory
.inventory_id
> ‘3000’))) AND (((inventory
.inventory_id
< ‘4000’)) OR ((inventory
.inventory_id
= ‘4000’))));
UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR WHERE ((((inventory
.inventory_id
> ‘4000’))) AND (((inventory
.inventory_id
< ‘4581’)) OR ((inventory
.inventory_id
= ‘4581’))));
(I say “roughly” because internally there are user defined variables at play, but for convenience, I verbose the actual values as constants.)
How does that work?
common_schema works on server side. There is no Perl script or anything. It must therefore use server-side operations to:
- Identify table to be split
- Analyze the table in the first place, deciding how to split it
- Analyze the query, deciding on how to rewrite it
- Split the table (logically) into unique and distinct chunks
- Work out the query on each such chunk
Following is an internal look at how common_schema does all the above.
(pun intended)
common_schema’s new split statement (see release announcement) auto-splits complex queries over large tables into smaller ones: instead of issuing one huge query, split breaks one’s query into smaller queries, each working on a different set of rows (a chunk).
Thus, it is possible to avoid holding locks for long times, allowing for smaller transactions. It also makes for breathing space for the RDBMS, at times boosting operation speed, and at times prolonging operation speed at will.
In this post I show how split exposes itself to the user, should the user wish so.
split can manage queries of the following forms:
- DELETE FROM table_name [WHERE]…
- DELETE FROM table_name USING <multi table syntax> [WHERE]…
- UPDATE table_name SET … [WHERE]…
- UPDATE <multiple tables> SET … [WHERE]…
- INSERT INTO some_table SELECT … FROM <single or multiple tables> [WHERE]…
- REPLACE INTO some_table SELECT … FROM <single or multiple tables> [WHERE]…
- SELECT … FROM <multiple tables> [WHERE]…
The latter being a non-obvious one at first sight.
Basically, it’ automatic
You just say:
split (UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR)
throttle 2;
And split identifies sakila.inventory as the table which needs to be split, and injects appropriate conditions so as to work on a subset of the rows, in multiple steps.
By the way, here’s how to execute a QueryScript code like the above.
Revision 68 of common_schema is out, and includes some interesting features:
- eval(): Evaluates the queries generated by a given query
- match_grantee(): Match an existing account based on user+host
- processlist_grantees: Assigning of GRANTEEs for connected processes
- candidate_keys: Listing of prioritized candidate keys: keys which are UNIQUE, by order of best-use.
- easter_day(): Returns DATE of easter day in given DATETIME’s year.
Let’s take a slightly closer look at these:
eval()
I’ve dedicated this blog post on MySQL eval() to describe it. In simple summary: eval() takes a query which generates queries (most common use queries on INFORMATION_SCHEMA) and auto-evaluates (executes) those queries. Read more
match_grantee()
As presented in Finding CURRENT_USER for any user, I’ve developed the algorithm to match a connected user+host details (as presented with PROCESSLIST) with the grantee tables (i.e. the mysql.user table), in a manner which simulates the MySQL server account matching algorithm.
This is now available as a stored function: given a user+host, the function returns with the best matched grantee. Read more
processlist_grantees
This view relies on the above, and maps the entire PROCESSLIST onto GRANTEEs. The view maps each process onto the GRANTEE (MySQL account) which is the owner of that process. Surprisingly, MySQL does not provide one with such information.
Today I have released common_schema, a utility schema for MySQL which includes many views and functions, and is aimed to be installed on any MySQL server.
What does it do?
There are views answering for all sorts of useful information: stuff related to schema analysis, data dimensions, monitoring, processes & transactions, security, internals… There are basic functions answering for common needs.
Some of the views/routines simply formalize those queries we tend to write over and over again. Others take the place of external tools, answering complex questions via SQL and metadata. Still others help out with SQL generation.
Here are a few highlights:
There’s more. Take a look at the common_schema documentation for full listing. And it’s evolving: I’ve got quite a few ideas already for future components.
Some of these views rely on heavyweight INFORMATION_SCHEMA tables. You should be aware of the impact and risks.
What do I need to install?
There’s no script or executable file. It’s just a schema. The distribution in an SQL file which generates common_schema. Much like a dump file.
An InnoDB table must have a primary key (one is created if you don't do it yourself). You may have a natural key at hand. Stop! Allow me to suggest an AUTO_INCREMENT may be better. Why should one add an AUTO_INCREMENT PRIMARY KEY on a table on which there's a natural key? Isn't an AUTO_INCREMENT a pseudo key, meaning, it doesn't have any explicit relation to the row data, other than it is a number and unique?
A simple question I’ve been asked:
Is it possible to merge two UPDATE queries, each on different WHERE conditions, into a single query?
For example, is it possible to merge the following two UPDATE statements into one?
mysql> UPDATE film SET rental_duration=rental_duration+1 WHERE rating = ‘G’;
Query OK, 178 rows affected (0.01 sec)
mysql> UPDATE film SET rental_rate=rental_rate-0.5 WHERE length < 90;
Query OK, 320 rows affected (0.01 sec)
To verify our tests, we take a checksum:
mysql> pager md5sum
PAGER set to ‘md5sum’
mysql> SELECT film_id, title, rental_duration, rental_rate FROM film ORDER BY film_id;
c2d253c3919efaa6d11487b1fd5061f3 -
Obviously, the following query is incorrect:
Risking some flames, I’d like to suggest only two options for choosing PRIMARY KEYs for InnoDB tables. I suggest they should cover 99% (throwing numbers around) of cases.
PRIMARY KEY cases
- An integer (SMALLINT / INT / BIGINT), possibly AUTO_INCREMENT column.
- The combination of two columns on a many-to-many connecting table (e.g. film_actor, which connects films to actors), the two columns being the PRIMARY KEYs of respective data tables. This rule may be extended to 3-way relation tables.
A short recap: an InnoDB must have a PRIMARY KEY. It will pick one if you don’t offer it. It can pick a really bad UNIQUE KEY (e.g. website_url(255)) or make one up using InnoDB internal row ids. If you don’t have a good candidate, an AUTO_INCREMENT PRIMARY KEY is probably the easiest way out.
A 2-column combination for a many-to-many connection table is common and viable. The PRIMARY KEY will not only provide with good join access method, but will also provide with the required UNIQUE constraint.
An integer-based PRIMARY KEY will make for more compact & shallow index tree structures, which leads to less I/O and page reads.
An AUTO_INCREMENT will allow for ascending PRIMARY KEY order of INSERT, which is InnoDB-friendly: index pages will be more utilized, less fragmented.
Here’s a few thoughts on current status and further possibilities for Facebook’s Online Schema Change (OSC) tool. I’ve had these thoughts for months now, pondering over improving oak-online-alter-table but haven’t got around to implement them nor even write them down. Better late than never.
The tool has some limitations. Some cannot be lifted, some could. Quoting from the announcement and looking at the code, I add a few comments. I conclude with a general opinion on the tool’s abilities.
“The original table must have PK. Otherwise an error is returned.”
This restriction could be lifted: it’s enough that the table has a UNIQUE KEY. My original oak-online-alter-table handled that particular case. As far as I see from their code, the Facebook code would work just as well with any unique key.
However, this restriction is of no real interest. As we’re mostly interested in InnoDB tables, and since any InnoDB table should have a PRIMARY KEY, we shouldn’t care too much.
“No foreign keys should exist. Otherwise an error is returned.”
Tricky stuff. With oak-online-alter-table, changes to the original table were immediately reflected in the ghost table. With InnoDB tables, that meant same transaction. And although I never got to update the text and code, there shouldn’t be a reason for not using child-side foreign keys (the child-side is the table on which the FK constraint is defined).
The Facebook patch works differently: it captures changes and writes them to a delta table, to be later (asynchronously) analyzed and make for a replay of actions on the ghost table.
This post follows up on Baron’s How often should you use OPTIMIZE TABLE?. I had the opportunity of doing some massive purging of data from large tables, and was interested to see the impact of the OPTIMIZE operation on table’s indexes. I worked on some production data I was authorized to provide as example.
The use case
I’ll present a single use case here. The table at hand is a compressed InnoDB table used for logs. I’ve rewritten some column names for privacy:
mysql> show create table logs \G
Create Table: CREATE TABLE logs
(
id
int(11) NOT NULL AUTO_INCREMENT,
name
varchar(20) CHARACTER SET ascii COLLATE ascii_bin NOT NULL,
ts
timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
origin
varchar(64) CHARACTER SET ascii COLLATE ascii_bin NOT NULL,
message
text NOT NULL,
level
tinyint(11) NOT NULL DEFAULT ‘0’,
s
char(16) CHARACTER SET ascii COLLATE ascii_bin NOT NULL DEFAULT “,
PRIMARY KEY (id
),
KEY s
(s
),
KEY name
(name
,ts
),
KEY origin
(origin
,ts
)
) ENGINE=InnoDB AUTO_INCREMENT=186878729 DEFAULT CHARSET=utf8 ROW_FORMAT=COMPRESSED KEY_BLOCK_SIZE=8
The table had log records starting 2010-08-23 and up till 2010-09-02 noon. Table status:
Continuing Table refactoring & application version upgrades, Part I, we now discuss code & database upgrades which require DROP operations. As before, we break apart the upgrade process into sequential steps, each involving either the application or the database, but not both.
As I’ll show, DROP operations are significantly simpler than creation operations. Interestingly, it’s the same as in life.
DROP COLUMN
A column turns to be redundant, unused. Before it is dropped from the database, we must ensure no one is using it anymore. The steps are:
- App: V1 -> V2. Remove all references to column; make sure no queries use said column.
- DB: V1 -> V2 (possibly failover from M1 to M2), change is DROP COLUMN.
DROP INDEX
A possibly simpler case here. Why would you drop an index? Is it because you found out you never use it anymore? Then all you have to do is just drop it.
Or perhaps you don’t need the functionality the index supports anymore? Then first drop the functionality:
- (optional) App: V1 -> V2. Discard using functionality which relies on index.
- DB: V1 -> V2 (possibly failover from M1 to M2), change is DROP INDEX. Check out InnoDB Plugin here.
A developer’s major concern is: How do I do application & database upgrades with minimal downtime? How do I synchronize between a DB’s version upgrade and an application’s version upgrade?
I will break down the discussion into types of database refactoring operations, and I will limit to single table refactoring. The discussion will try to understand the need for refactoring and will dictate the steps towards a successful upgrade.
Reader prerequisites
I will assume MySQL to be the underlying database. To take a major component out of the equation: we may need to deal with very large tables, for which an ALTER command may take long hours. I will assume familiarity with Master-Master (Active-Passive) replication, with possible use of MMM for MySQL. When I describe “Failover from M1 to M2”, I mean “Make the ALTER changes on M2 (passive), then switch your application from M1 to M2 (change of IPs, VIP, etc.), promoting M2 to active position, then apply same changes on M1 (now passive) or completely rebuild it”.
Phew, a one sentence description of M-M usage…
I also assume the reader’s understanding that a table’s schema can be different on master & slave, which is the basis for the “use replication for refactoring” trick. But it cannot be too different, or, to be precise, the two schemata must both support the ongoing queries for the table.
A full discussion of the above is beyond the scope of this post.
Types of refactoring needs
As I limit this discussion to single table refactoring,we can look at major refactoring operations and their impact on application & upgrades. We will discuss ADD/DROP COLUMN, ADD/DROP INDEX, ADD/DROP UNIQUE INDEX, ADD/DROP FOREIGN KEY, ADD/DROP TABLE.
We will assume the database and application are both in Version #1 (V1), and need to be upgraded to V2 or greater.
Single row tables are used in various cases. Such tables can be used for “preferences” or “settings”; for managing counters (e.g. summary tables), for general-purpose administration tasks (e.g. heartbeat table) etc.
The problem with single row tables is that, well, they must have s single row. And the question is: how can you force them to have just one row?
The half-baked solution
The common solution is to create a PRIMARY KEY and always use the same value for that key. In addition, using REPLACE or INSERT INTO ON DUPLICATE KEY UPDATE helps out in updating the row. For example:
CREATE TABLE heartbeat (
id int NOT NULL PRIMARY KEY,
ts datetime NOT NULL
);
The above table definition is taken from mk-heartbeat. It should be noted that mk-heartbeat in itself does not require that the table has a single row, so it is not the target of this post. I’m taking the above table definition as a very simple example.
So, we assume we want this table to have a single row, for whatever reasons we have. We would usually do:
REPLACE INTO heartbeat (id, ts) VALUES (1, NOW());
or
INSERT INTO heartbeat (id, ts) VALUES (1, NOW()) ON DUPLICATE KEY UPDATE ts = NOW();
Why is the above a “half baked solution”? Because it is up to the application to make sure it reuses the same PRIMARY KEY value. There is nothing in the database to prevent the following:
In a period of two weeks, I had two cases with the exact same symptoms.
Database users were experiencing low responsiveness. DBAs were seeing locks occurring on seemingly normal tables. In particular, looking at Innotop, it seemed that INSERTs were causing the locks.
In both cases, tables were InnoDB. In both cases, there was a PRIMARY KEY on the combination of all 5 columns. And in both cases, there was no clear explanation as for why the PRIMARY KEY was chosen as such.
Choosing a proper PRIMARY KEY
Especially with InnoDB, which uses clustered index structure, the PRIMARY KEY is of particular importance. Besides the fact that a bloated PRIMARY KEY bloats the entire clustered index and secondary keys (see: The depth of an index: primer), it is also a source for locks. It’s true that any UNIQUE KEY can serve as a PRIMARY KEY. But not all such keys are good candidates.
In mathematics, a monotonic function (or monotone function) is a function which preserves the given order. [Wikipedia]
To be more precise, a function f is monotonic increasing, if for every x ≤ y it holds that f(x) ≤ f(y). f is said to be strictly monotonic increasing is for every x < y it holds that f(x) < f(y).
So, if we follow values in some order, we say that f is monotonic increasing if f’s value never decreases (it either increases or stays the same), and we say that f is strictly increasing if f’s value is always changes “upwards”.
Monotonic functions play an important role in SQL. To discuss monotonic functions in SQL we must first determine what the order is, and then, what the function is.
Well, they both change according to our point of view. Let’s look at some examples. Take a look at the following table:
Ever so often a query provides a “bad” execution plan. Adding a missing index can many times solve the problem. However, not everything can be solved with an index. I wish to highlight the point of having an implicit cast, which negates the use of an index on MySQL.
I see this happening a lot on customers’ databases, and this begs for a short introduction.
MySQL doesn’t support index functions
Let’s assume the following table:
Here’s a complication of some common and useful time & date calculations and equations. Some, though very simple, are often misunderstood, leading to inefficient or incorrect implementations.
There are many ways to solve such problems. I’ll present my favorites.
Querying for time difference
Given two timestamps: ts1 (older) and ts2 (newer), how much time has passed between them?
One can use TIMEDIFF() & DATEDIFF(), or compare two UNIX_TIMESTAMP() values. My personal favorite is to use TIMESTAMPDIFF(). Reason being that I’m usually interested in a specific metric, like the number of hours which have passed, or the number of days, disregarding the smaller minute/second resolution. Which allows one to:
SELECT TIMESTAMPDIFF(HOUR, ts1, ts2)
Take, for example:
SELECT TIMESTAMPDIFF(MONTH, ‘2008-10-07 00:00:00’, ‘2009-12-06 00:00:00’)
The function correctly identifies the number of days per month, and provides with 13, being the truncated number of full months.
Doing arithmetics
One can use TIMESTAMPADD(), or DATE_SUB(), but, again, when dealing with specific resolutions, I find “+ INTERVAL” to be the most convenient:
SELECT ts1 + INTERVAL 10 HOUR
I’ve encountered the same problem twice for different customers, so I guess it’s worth a discussion.
A common task for web applications is to find out the country/region of a user, based on her IP address, as can be detected in the HTTP request. Depending on the country of origin, the website can translate dates for different time zones, can change locale settings, and, perhaps most commonly, show advertisements in her native language.
To start with, there’s a table which lists the IP ranges per country/region. Let’s assume we’re only dealing with IPv4:
CREATE TABLE regions_ip_range (
regions_ip_range_id INT UNSIGNED AUTO_INCREMENT,
country VARCHAR(64) CHARSET utf8,
region VARCHAR(64) CHARSET utf8,
start_ip INT UNSIGNED,
end_ip INT UNSIGNED,
…
PRIMARY KEY(regions_ip_range_id),
…
);
The table is fixed, and is populated. Now the question arises: how do we query this table, and which indexes should be created?
The wrong way
The form I’ve encountered is as follows: an index is declared on regions_ip_range:
KEY ip_range_idx (start_ip, end_ip)
And the query goes like this:
SELECT * FROM regions_ip_range
WHERE my_ip BETWEEN start_ip AND end_ip
I came today upon a very strange issue. It seems like MySQL is unable to utilize a compound index when evaluating a plan for a query with a range condition. I’m looking for an explanation. I’ll appreciate any insight on this.
InnoDB and MyISAM use B+ and B trees for indexes (InnoDB also has internal hash index).
In both these structures, the depth of the index is an important factor. When looking for an indexed row, a search is made on the index, from root to leaves.
Assuming the index is not in memory, the depth of the index represents the minimal cost (in I/O operation) for an index based lookup. Of course, most of the time we expect large portions of the indexes to be cached in memory. Even so, the depth of the index is an important factor. The deeper the index is, the worse it performs: there are simply more lookups on index nodes.
What affects the depth of an index?
There are quite a few structural issues, but it boils down to two important factors:
- The number of rows in the table: obviously, more rows leads to larger index, larger indexes grow in depth.
- The size of the indexed column(s). An index on an INT column can be expected to be shallower than an index on a CHAR(32) column (on a very small number of rows they may have the same depth, so we’ll assume a large number of rows).
Sometimes MySQL gets it wrong. It doesn’t use the right index.
It happens that MySQL generates a query plan which is really bad (EXPLAIN says it’s going to explore some 10,000,000 rows), when another plan (soon to show how was generated) says: “Sure, I can do that with 100 rows using a key”.
A true story
A customer had issues with his database. Queries were taking 15 minutes to complete, and the db in general was not responsive. Looking at the slow query log, I found the criminal query. Allow me to bring you up to speed:
A table is defined like this:
CREATE TABLE t (
id INT UNSIGNED AUTO_INCREMENT,
type INT UNSIGNED,
level TINYINT unsigned,
…
PRIMARY KEY(id),
KEY type
(type)
) ENGINE=InnoDB;
The offending query was this:
SELECT id FROM data
WHERE type=12345 AND level > 3
ORDER BY id
The facts were:
t
has about 10,000,000 rows.
- The index on
type
is selective: about 100 rows per value on average.
- The query took a long time to complete.
- EXPLAIN has shown that MySQL uses the PRIMARY KEY, hence searches 10,000,000 rows, filtered “using where”.
- The other EXPLAIN has shown that by using the
type
key, only 110 rows are expected, to be filtered “using where”, then sorted “using filesort”
So MySQL acknowledged it was generating the wrong plan. The other plan was better by its own standards.
Solving the problem
Let’s walk through 7 ways to solve the problem, starting with the more aggressive solutions, refining to achieve desired behavior through subtle changes.
A set of useful queries on INFORMATION_SCHEMA follows. These queries can be used when approaching a new database, to learn about some of its properties, or they can be regularly used on an existing schema, so as to verify its integrity.
I will present queries for:
- Checking on database engines and size
- Locating duplicate and redundant indexes
- Checking on character sets for columns and tables, looking for variances
- Checking on processes and long queries (only with MySQL 5.1)
In Part I of this article, we have seen how the internal structure of the storage engine’s index can affect an execution plan. We’ve seen that some plans are inherent to the way engines are implemented.
We wish to present a second scenario in which execution plans vary for different storage engines. Again, we will consider MyISAM and InnoDB. Again, we will use the world database for testing. This time, we will see how confident the storage engines are in their index search capabilities.
Many newcomers to databases often believe that an index search is always preferable to full table scan. This is not the case. If I were to look for 10 rows in a 1,000,000 rows table, using an indexed column - I could benefit from an index search. However, if I’m looking for 200,000 rows on that table (that’s 20% of the rows) - an index search can actually be much more expensive than a full table scan.
A popping question is: “Can an execution plan change for different storage engines?”
The answer is “Yes”. I will present two such cases, where the MySQL optimizer will choose different execution plans, based on our choice of storage engine.
We will consider MyISAM and InnoDB, the two most popular engines. The two differ in many respects, and in particular, the way they implement indexes and statistics: two major players in the optimizer’s point of view.


build without bootstrap
Publish Date: 16/03/30
Publisher: IBM
Status: RECRUITING
build with nodejs and react
Publish Date: 16/03/30
Publisher: IBM
Status: RECRUITING
Need one ios engineer
Publish Date: 16/03/30
Publisher: IBM
Status: ENDED
Need a team with at least 4 people
Publish Date: 16/03/30
Publisher: IBM
Status: IN PROGRESS


| 1 |
% |
viruses |
bronchiolitis |
HRV |
HMPV |
hbov |
assay |
influenza |
hmpv |
95 |
WU |
CAP |
asthma |
load |
hcov |
| 2 |
respiratory |
virus |
severity |
strains |
ARI |
© |
sensitivity |
ILI |
hmpv infection |
CI |
wupyv |
pneumoniae |
exacerbations |
viral load |
hcovs |
| 3 |
children |
influenza |
infants |
hadv |
aris |
inc. |
assays |
illness |
antibody |
ALRI |
recipients |
bacteria |
exacerbation |
RTI |
hcov-nl63 |
| 4 |
patients |
respiratory |
RSV |
species |
HPIV |
med |
culture |
influenza-like |
human metapneumovirus |
controls |
immunocompromised |
bacterial |
AECOPD |
AOM |
hcov-hku1 |
| 5 |
detected |
parainfluenza |
admission |
phylogenetic |
china |
virol |
detection |
pandemic |
infection |
odds |
transplant |
pathogens |
chronic |
hbov1 |
human coronaviruses |
| 6 |
study |
human |
unit |
HRV-C |
seasonal |
J. |
PCR |
H1N1 |
metapneumovirus |
ratio |
kipyv |
mycoplasma |
RV |
rtis |
HKU1 |
| 7 |
infections |
respiratory syncytial virus |
disease severity |
EV-D68 |
countries |
bocavirus |
rapid |
influenza-like illness |
vaccine |
adjusted |
KI |
pneumonia |
COPD |
media |
hcov-oc43 |
| 8 |
infection |
syncytial |
intensive |
genotypes |
peaks |
human bocavirus |
sensitive |
2009 |
tract |
odds ratio |
polyomaviruses |
mycoplasma pneumoniae |
de |
otitis |
NL63 |
| 9 |
= |
adenovirus |
levels |
hrvs |
seasonality |
wiley |
RT-PCR |
illnesses |
review |
interval |
polyomavirus |
atypical |
atopic |
URI |
hcov-229e |
| 10 |
samples |
respiratory viruses |
clinical |
sequences |
health |
periodicals |
specificity |
influenza‐like |
elderly |
confidence |
patients |
influenzae |
asthmatic |
RSV-A |
coronaviruses |
| 11 |
clinical |
metapneumovirus |
disease |
phylogenetic analysis |
acute respiratory infections |
wiley‐liss |
conventional |
surveillance |
vaccines |
confidence interval |
HSCT |
streptococcus |
obstructive |
- |
229E |
| 12 |
viruses |
viral |
hospitalization |
HRV-A |
years |
RT‐PCR |
multiplex |
RSV |
les |
regression |
WUV |
community-acquired |
asthma exacerbations |
material |
staff |
| 13 |
years |
coronavirus |
longer |
sequencing |
developing countries |
hbov infection |
test |
influenza‐like illness |
family |
logistic |
bronchoalveolar |
community-acquired pneumonia |
stable |
SUPPLEMENTARY |
coronavirus |
| 14 |
symptoms |
rhinovirus |
PICU |
gene |
temperature |
hmpv |
method |
SARI |
pathogenesis |
LRTD |
BAL |
streptococcus pneumoniae |
wheezing |
online |
neonates |
| 15 |
common |
human metapneumovirus |
hospital |
analysis |
artis |
tract |
® |
outpatients |
igg |
maternal |
hematopoietic |
antibiotic |
chronic obstructive pulmonary disease |
quantitative |
saudi |
| 16 |
chain |
parainfluenza virus |
oxygen |
sequenced |
year |
human |
xtag |
participants |
fusion |
hiv-infected |
HCT |
haemophilus |
el |
users |
residents |
| 17 |
hospitalized |
influenza a |
stay |
sequence |
HPIV-3 |
hcov‐nl63 |
diagnostic |
epidemic |
suggests |
associated with |
cancer |
M. |
airway |
authorized |
OC43 |
| 18 |
reaction |
influenza virus |
acute bronchiolitis |
genome |
hpivs |
2005 |
designed |
a(h1n1)pdm09 |
CNS |
risk |
transplantation |
haemophilus influenzae |
RV-C |
ELECTRONIC |
hcov infection |
| 19 |
age |
type |
rsv infection |
genotype |
meteorological |
hbov. |
conventional methods |
elderly |
shown |
HIV |
KIV |
S. |
rhinovirus |
online version |
AGE |
| 20 |
2 |
molecular |
factors |
hadv-7 |
patterns |
DNA |
clinical specimens |
sentinel |
été |
aor |
stem |
antibiotics |
los |
supplementary |
fecal |
| 21 |
respiratory viruses |
RSV |
intensive care unit |
strain |
incidence |
parvovirus |
developed |
sore |
protection |
AFE |
lavage |
bacterial infection |
cystic fibrosis |
version |
fecal samples |
| 22 |
positive |
coronaviruses |
length |
hadv-55 |
highest |
npas |
detect |
sore throat |
immunity |
95%CI |
failure |
community‐acquired |
fibrosis |
acute otitis media |
hcov infections |
| 23 |
tested |
parainfluenza viruses |
outcome |
isolates |
age |
hrsv |
DFA |
transmission |
host |
% |
cell |
filmarray |
allergic |
surgery |
pilgrims |
| 24 |
specimens |
including |
coinfection |
genetic |
distribution |
co‐infections |
fast |
outpatient |
hmpv infections |
RV |
LRTI |
respiratory pathogens |
cystic |
KD |
hcov-nl63 infection |
| 25 |
polymerase |
rhinoviruses |
duration |
region |
ALRTI |
hbov-positive |
antigen |
week |
biology |
multivariable |
bronchoalveolar lavage |
community‐acquired pneumonia |
sputum |
MATERIAL |
rotavirus |
| 26 |
viral |
agents |
severe |
human rhinoviruses |
public |
real‐time |
real-time |
definition |
immune |
RV‐A |
wu virus |
bacterial pathogens |
en |
viral |
gastroenteritis |
| 27 |
cases |
infections |
care |
diversity |
prevention |
hbov dna |
clinical samples |
symptom |
humans |
TB |
malignancy |
s. pneumoniae |
CRS |
ear |
households |
| 28 |
PCR |
influenza viruses |
viral |
EV‐D68 |
epidemiological |
acute wheezing |
viral culture |
adults |
recent |
FLU |
wu polyomavirus |
catarrhalis |
RV-A |
middle |
arabia |
| 29 |
results |
bocavirus |
higher |
HEV |
rural |
causative |
performance |
non-influenza |
development |
1000 |
CMV |
H. |
rhinitis |
VRI |
mers-cov |
| 30 |
group |
adenoviruses |
single |
VP1 |
5 |
agent |
seeplex |
alris |
studies |
population-based |
lung |
febrile |
asthmatics |
nasopharynx |
saudi arabia |
| 31 |
1 |
enterovirus |
admitted |
ON1 |
summer |
children |
RT-LAMP |
individuals |
includes |
.001 |
organ |
chlamydia |
subjects |
article |
NH |
| 32 |
identified |
detection |
= |
HRV-B |
humidity |
× |
RVP |
influenza virus |
paramyxoviridae |
HRV/ENT |
malignancies |
m. pneumoniae |
CP‐COPD |
OME |
human coronavirus |
| 33 |
frequently |
influenza b |
infection |
identified |
HPIV-4 |
hcov‐hku1 |
specimens |
ilis |
dans |
AF |
mortality |
cultures |
acute exacerbations |
loads |
middle east respiratory syndrome coronavirus |
| 34 |
acute |
influenza a virus |
compared |
nucleotide |
understood |
recently |
diagnosis |
training |
myocarditis |
fvdes |
hematologic |
chlamydophila |
ifn-γ |
complicating |
hajj |
| 35 |
frequent |
common |
score |
partial |
data |
causative agent |
accurate |
influenza c virus |
lower |
hiv-uninfected |
neutropenia |
mp |
asthma exacerbation |
probiotics |
hcov species |
| 36 |
rate |
detected |
children |
enterovirus |
worldwide |
hbov infections |
IFA |
adult |
virus |
nepal |
LRT |
chlamydophila pneumoniae |
se |
NPA |
hpev |
| 37 |
detection |
viral etiology |
ventilation |
hexon |
annual |
acute |
laboratories |
active surveillance |
discovered |
pneumonia |
allogeneic |
pathogen |
PLT |
hpyv6 |
kenya |
| 38 |
12 |
A/B |
syncytial |
showed |
RSV |
samples |
vial |
influenza c |
immune response |
hospitalization |
lung transplant |
infectious |
con |
psps |
staff members |
| 39 |
tract |
% |
parameters |
serotype |
surveillance |
wheezing |
mrt-pcr |
season |
simplex |
control |
ribavirin |
aetiology |
acute asthma |
RNA |
hcov. |
| 40 |
conclusions |
rhinovirus/enterovirus |
ICU |
clade |
public health |
hcov‐oc43 |
agreement |
women |
respiratory-tract |
case-control study |
oncology |
moraxella catarrhalis |
CFLI |
presence |
norovirus |
| 41 |
frequency |
diagnostics |
respiratory syncytial virus |
hadv-3 |
years old |
pulmonol |
positive |
pregnant |
des |
hazard ratio |
CHD |
h. influenzae |
niños |
low |
hcov nl63 |
| 42 |
virus |
1–4 |
months |
types |
rainy |
found |
maripoc |
virus |
hmpv-associated |
life-threatening disease |
leukemia |
staphylococcus |
por |
high |
NRVI |
| 43 |
11 |
respiratory virus infections |
viral coinfection |
hadv infection |
human parainfluenza viruses |
pediatr pulmonol |
samples |
headache |
measles |
virus-virus |
polyomaviruses ki |
chlamydia pneumoniae |
allergic rhinitis |
018 |
astrovirus |
| 44 |
18 |
coronavirus oc43 |
mechanical |
rhinoviruses |
seasonal patterns |
multiplex rt‐pcr |
duplex |
PIV |
à |
rural thailand |
hematopoietic stem cell transplant |
assessment |
acute exacerbation |
PCR/ESI-MS |
NVRI |
| 45 |
aim |
MPV |
associated with |
VP4/VP2 |
hpiv |
nasopharyngeal |
one‐step |
data |
human |
A/H3 |
hematologic malignancy |
bacterium |
pacientes |
rhinovirus rna |
hcov oc43 |
| 46 |
9 |
influenza virus a |
mechanical ventilation |
hrv-positive |
guangzhou |
2003–2004 |
probes |
mpcr |
respiratory tract |
RV‐C |
tract |
pneumophila |
OSA |
MEE |
rotaviruses |
| 47 |
25 |
acute |
lower |
molecular |
MP |
genotype b |
rt-pcr assays |
pregnant women |
infections |
CAAP |
EBV |
HCAP |
AE‐COPD |
quantitative pcr |
ltcfs |
| 48 |
and/or |
b viruses |
mxa |
hadvs |
% |
hbovs |
specific |
control |
enzyme immunoassay |
adjusting |
HM |
ACS |
attacks |
otitis media |
SETTING |
| 49 |
difference |
flua |
infants hospitalized |
typing |
children |
lower |
RT-PCR/ESI-MS |
patients |
respiratoires |
compared |
days |
PSI |
adenotonsillar |
017 |
RT-PCR |
| 50 |
presentation |
multiplex rt-pcr |
< |
NA1 |
seasonal peaks |
enteric viruses |
PRV |
KCDC |
EIA |
factors |
lower |
bacterial pathogen |
pulmonary |
asymptomatic |
npss |