Struct Vector3F
A structure encapsulating three single precision floating point values.
Inherited Members
Namespace: MathStructs
Assembly: MathStructs.dll
Syntax
public struct Vector3F : IEquatable<Vector3F>, IFormattable
Remarks
Less precise than Vector3D but faster.
Constructors
| Improve this Doc View SourceVector3F(Vector2F, Single)
Declaration
public Vector3F(Vector2F value, float z)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector2F | value | The vector to use as the X and Y component. |
| System.Single | z | The Z component. |
Vector3F(Single)
Constructs a vector whose elements are all the single specified value.
Declaration
public Vector3F(float value)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Single | value | The element to fill the vector with. |
Vector3F(Single, Single, Single)
Constructs a vector with the given individual elements.
Declaration
public Vector3F(float x, float y, float z)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Single | x | X component. |
| System.Single | y | Y component. |
| System.Single | z | Z component. |
Fields
| Improve this Doc View SourceX
The X component of the vector.
Declaration
public float X
Field Value
| Type | Description |
|---|---|
| System.Single |
Y
The Y component of the vector.
Declaration
public float Y
Field Value
| Type | Description |
|---|---|
| System.Single |
Z
The Z component of the vector.
Declaration
public float Z
Field Value
| Type | Description |
|---|---|
| System.Single |
Properties
| Improve this Doc View SourceOne
Returns the vector < 1, 1, 1 >.
Declaration
public static Vector3F One { get; }
Property Value
| Type | Description |
|---|---|
| Vector3F |
UnitX
Returns the vector < 1, 0, 0 >.
Declaration
public static Vector3F UnitX { get; }
Property Value
| Type | Description |
|---|---|
| Vector3F |
UnitY
Returns the vector < 0, 1, 0 >.
Declaration
public static Vector3F UnitY { get; }
Property Value
| Type | Description |
|---|---|
| Vector3F |
UnitZ
Returns the vector < 0, 0, 1 >.
Declaration
public static Vector3F UnitZ { get; }
Property Value
| Type | Description |
|---|---|
| Vector3F |
Zero
Returns the vector < 0, 0, 0 >.
Declaration
public static Vector3F Zero { get; }
Property Value
| Type | Description |
|---|---|
| Vector3F |
Methods
| Improve this Doc View SourceAbs()
Returns a vector whose elements are the absolute values of each of this vector's elements.
Declaration
public Vector3F Abs()
Returns
| Type | Description |
|---|---|
| Vector3F |
Abs(Vector3F)
Returns a vector whose elements are the absolute values of each of the source vector's elements.
Declaration
public static Vector3F Abs(Vector3F value)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | value | The source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Add(Vector3F, Vector3F)
Adds two vectors together.
Declaration
public static Vector3F Add(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first source vector. |
| Vector3F | right | The second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Clamp(Vector3F, Vector3F)
Restricts a copy of this vector between a min and max value.
Declaration
public Vector3F Clamp(Vector3F min, Vector3F max)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | min | The minimum value. |
| Vector3F | max | The maximum value. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Clamp(Vector3F, Vector3F, Vector3F)
Restricts a vector between a min and max value.
Declaration
public static Vector3F Clamp(Vector3F value, Vector3F min, Vector3F max)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | value | The source vector. |
| Vector3F | min | The minimum value. |
| Vector3F | max | The maximum value. |
Returns
| Type | Description |
|---|---|
| Vector3F |
CopyTo(Single[])
Copies the contents of the vector into the given array.
Declaration
public void CopyTo(float[] array)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Single[] | array |
CopyTo(Single[], Int32)
Copies the contents of the vector into the given array, starting from index.
Declaration
public void CopyTo(float[] array, int index)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Single[] | array | |
| System.Int32 | index |
Exceptions
| Type | Condition |
|---|---|
| System.RankException | If array is multidimensional. |
| System.ArgumentOutOfRangeException | If index is greater than end of the array or index is less than zero. |
| System.ArgumentException | If number of elements in source vector is greater than those available in destination array. |
Cross(Vector3F)
Computes the cross product of two vectors which is a vector perpendicular to the two vectors.
Declaration
public Vector3F Cross(Vector3F vector)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | vector | The other vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Remarks
The cross product performs the following:
< v₁.Y * v₂.Z - v₁.Z * v₂.Y , v₁.Z * v₂.X - v₁.X *v₂.Z , v₁.X * v₂.Y - v₁.Y * v₂.X >Cross(Vector3F, Vector3F)
Computes the cross product of two vectors which is a vector perpendicular to the two vectors.
Declaration
public static Vector3F Cross(Vector3F vector1, Vector3F vector2)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | vector1 | The first vector. |
| Vector3F | vector2 | The second vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Remarks
The cross product performs the following:
< v₁.Y * v₂.Z - v₁.Z * v₂.Y , v₁.Z * v₂.X - v₁.X *v₂.Z , v₁.X * v₂.Y - v₁.Y * v₂.X >Deconstruct(out Vector2F, out Single)
Deconstructs this vector into its System.Single components and builds the X and Y into a new Vector2F.
Declaration
public void Deconstruct(out Vector2F vector, out float z)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector2F | vector | The X and Y of this vector as a Vector2F. |
| System.Single | z | The Z of this vector. |
Deconstruct(out Single, out Single, out Single)
Deconstructs this vector into its System.Single components.
Declaration
public void Deconstruct(out float x, out float y, out float z)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Single | x | The X of this vector. |
| System.Single | y | The Y of this vector. |
| System.Single | z | The Z of this vector. |
Distance(Vector3F)
Returns the Euclidean distance between this point and the given point.
Declaration
public float Distance(Vector3F value)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | value | The other point. |
Returns
| Type | Description |
|---|---|
| System.Single |
Remarks
More expensive than DistanceSquared(Vector3F) if you need the squared distance.
Distance(Vector3F, Vector3F)
Returns the Euclidean distance between the two given points.
Declaration
public static float Distance(Vector3F vector1, Vector3F vector2)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | vector1 | The first point. |
| Vector3F | vector2 | The second point. |
Returns
| Type | Description |
|---|---|
| System.Single |
Remarks
More expensive than DistanceSquared(Vector3F, Vector3F) if you need the squared distance.
DistanceSquared(Vector3F)
Returns the Euclidean distance squared between this point and the given point.
Declaration
public float DistanceSquared(Vector3F value)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | value | The other point. |
Returns
| Type | Description |
|---|---|
| System.Single |
Remarks
Less expensive than Distance(Vector3F) if you need the squared distance.
DistanceSquared(Vector3F, Vector3F)
Returns the Euclidean distance squared between the two given points.
Declaration
public static float DistanceSquared(Vector3F vector1, Vector3F vector2)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | vector1 | The first point. |
| Vector3F | vector2 | The second point. |
Returns
| Type | Description |
|---|---|
| System.Single |
Remarks
Less expensive than Distance(Vector3F, Vector3F) if you need the squared distance.
Divide(Vector3F, Vector3F)
Divides the first vector by the second.
Declaration
public static Vector3F Divide(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first source vector. |
| Vector3F | right | The second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Divide(Vector3F, Single)
Divides the vector by the given scalar.
Declaration
public static Vector3F Divide(Vector3F left, float right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The source vector. |
| System.Single | right | The scalar value. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Dot(Vector3F)
Returns the dot product of this instance and the given vector.
Declaration
public float Dot(Vector3F vector)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | vector | The other vector. |
Returns
| Type | Description |
|---|---|
| System.Single |
Remarks
The dot product of two vectors is the sum of the products of each of the pairs of elements from two vectors
Dot(Vector3F, Vector3F)
Returns the dot product of two vectors.
Declaration
public static float Dot(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first vector. |
| Vector3F | right | The second vector. |
Returns
| Type | Description |
|---|---|
| System.Single |
Remarks
The dot product of two vectors is the sum of the products of each of the pairs of elements from two vectors
Equals(Vector3F)
Declaration
public bool Equals(Vector3F other)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | other | The Vector3F to compare this instance to. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the other Vector3F is equal to this instance; False otherwise. |
Equals(Vector3F, Double)
Returns a boolean indicating whether the given Vector4D is equal to this Vector3F instance ± delta.
Declaration
public bool Equals(Vector3F other, double delta)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | other | The Vector3F to compare this instance to. |
| System.Double | delta | The allowable margin of error to determine equality for each element pair between the vectors. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the other Vector3F is equal to this instance ± delta; False otherwise. |
Equals(Object)
Returns a boolean indicating whether the given Object is equal to this Vector3F instance.
Declaration
public override bool Equals(object obj)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Object | obj | The Object to compare against. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the Object is equal to this Vector3F; False otherwise. |
Overrides
GetHashCode()
Returns the hash code for this instance.
Declaration
public override int GetHashCode()
Returns
| Type | Description |
|---|---|
| System.Int32 |
Overrides
Length()
Returns the length of the vector.
Declaration
public float Length()
Returns
| Type | Description |
|---|---|
| System.Single |
Remarks
LengthSquared() is cheaper to use if you need the squared length.
LengthSquared()
Returns the length of the vector squared.
Declaration
public float LengthSquared()
Returns
| Type | Description |
|---|---|
| System.Single |
Remarks
This operation is cheaper than Length() if you need the squared length.
Lerp(Vector3F, Vector3F, Single)
Linearly interpolates between two vectors based on the given weighting.
Declaration
public static Vector3F Lerp(Vector3F bounds1, Vector3F bounds2, float amount)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | bounds1 | The first source vector. |
| Vector3F | bounds2 | The second source vector. |
| System.Single | amount | Value between 0 and 1 indicating the weight of the second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Max(Vector3F, Vector3F)
Returns a vector whose elements are the maximum of each of the pairs of elements in the two source vectors.
Declaration
public static Vector3F Max(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first source vector. |
| Vector3F | right | The second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Min(Vector3F, Vector3F)
Returns a vector whose elements are the minimum of each of the pairs of elements in the two source vectors.
Declaration
public static Vector3F Min(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first source vector. |
| Vector3F | right | The second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Multiply(Vector3F, Vector3F)
Multiplies two vectors together.
Declaration
public static Vector3F Multiply(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first source vector. |
| Vector3F | right | The second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Multiply(Vector3F, Single)
Multiplies a vector by the given scalar.
Declaration
public static Vector3F Multiply(Vector3F left, float right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The source vector. |
| System.Single | right | The scalar value. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Multiply(Single, Vector3F)
Multiplies a vector by the given scalar.
Declaration
public static Vector3F Multiply(float left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Single | left | The scalar value. |
| Vector3F | right | The source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Negate(Vector3F)
Negates a given vector.
Declaration
public static Vector3F Negate(Vector3F value)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | value | The source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Normalize()
Returns a vector with the same direction as this vector, but with a length of 1.
Declaration
public Vector3F Normalize()
Returns
| Type | Description |
|---|---|
| Vector3F |
Normalize(Vector3F)
Returns a vector with the same direction as the given vector, but with a length of 1.
Declaration
public static Vector3F Normalize(Vector3F vector)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | vector | The vector to normalize. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Reflect(Vector3F)
Returns the reflection of this vector off a surface that has the specified normal.
Declaration
public Vector3F Reflect(Vector3F normal)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | normal | The normal of the surface being reflected off. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Reflect(Vector3F, Vector3F)
Returns the reflection of a vector off a surface that has the specified normal.
Declaration
public static Vector3F Reflect(Vector3F vector, Vector3F normal)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | vector | The source vector. |
| Vector3F | normal | The normal of the surface being reflected off. |
Returns
| Type | Description |
|---|---|
| Vector3F |
SquareRoot()
Returns a vector whose elements are the square root of each of this vector's elements.
Declaration
public Vector3F SquareRoot()
Returns
| Type | Description |
|---|---|
| Vector3F |
SquareRoot(Vector3F)
Returns a vector whose elements are the square root of each of the source vector's elements.
Declaration
public static Vector3F SquareRoot(Vector3F value)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | value | The source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Subtract(Vector3F, Vector3F)
Subtracts the second vector from the first.
Declaration
public static Vector3F Subtract(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first source vector. |
| Vector3F | right | The second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
ToString()
Returns a String representing this Vector3F instance.
Declaration
public override string ToString()
Returns
| Type | Description |
|---|---|
| System.String |
Overrides
ToString(String)
Returns a String representing this Vector3F instance, using the specified format to format individual elements.
Declaration
public string ToString(string format)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | format | The format of individual elements. |
Returns
| Type | Description |
|---|---|
| System.String |
ToString(String, IFormatProvider)
Returns a String representing this Vector3F instance, using the specified format to format individual elements and the given System.IFormatProvider.
Declaration
public string ToString(string format, IFormatProvider formatProvider)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | format | The format of individual elements. |
| System.IFormatProvider | formatProvider | The format provider to use when formatting elements. |
Returns
| Type | Description |
|---|---|
| System.String |
Transform(Matrix4x4F)
Transforms a copy of this vector by the given matrix.
Declaration
public Vector3F Transform(Matrix4x4F matrix)
Parameters
| Type | Name | Description |
|---|---|---|
| Matrix4x4F | matrix | The transformation matrix. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Transform(Vector3F, Matrix4x4F)
Transforms a vector by the given matrix.
Declaration
public static Vector3F Transform(Vector3F vector, Matrix4x4F matrix)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | vector | The source vector. |
| Matrix4x4F | matrix | The transformation matrix. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Transform(Vector3F, QuaternionF)
Transforms a vector by the given QuaternionF rotation value.
Declaration
public static Vector3F Transform(Vector3F value, QuaternionF rotation)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | value | The source vector to be rotated. |
| QuaternionF | rotation | The rotation to apply. |
Returns
| Type | Description |
|---|---|
| Vector3F |
TransformNormal(Matrix4x4F)
Transforms this vector normal by the given 4x4 matrix.
Declaration
public Vector3F TransformNormal(Matrix4x4F matrix)
Parameters
| Type | Name | Description |
|---|---|---|
| Matrix4x4F | matrix | The matrix. |
Returns
| Type | Description |
|---|---|
| Vector3F |
TransformNormal(Vector3F, Matrix4x4F)
Transforms a vector normal by the given 4x4 matrix.
Declaration
public static Vector3F TransformNormal(Vector3F normal, Matrix4x4F matrix)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | normal | The source vector. |
| Matrix4x4F | matrix | The matrix. |
Returns
| Type | Description |
|---|---|
| Vector3F |
TransformV4(Matrix4x4F)
Transforms this vector by the given matrix and returns a Vector4F.
Declaration
public Vector4F TransformV4(Matrix4x4F matrix)
Parameters
| Type | Name | Description |
|---|---|---|
| Matrix4x4F | matrix | The transformation matrix. |
Returns
| Type | Description |
|---|---|
| Vector4F |
TransformV4(QuaternionF)
Transforms this vector by the given QuaternionF rotation value and returns a Vector4F.
Declaration
public Vector4F TransformV4(QuaternionF rotation)
Parameters
| Type | Name | Description |
|---|---|---|
| QuaternionF | rotation | The rotation to apply. |
Returns
| Type | Description |
|---|---|
| Vector4F |
With(Nullable<Single>, Nullable<Single>, Nullable<Single>)
Record-like langword_csharp_with-style constructor
Declaration
public Vector3F With(float? x = default(float? ), float? y = default(float? ), float? z = default(float? ))
Parameters
| Type | Name | Description |
|---|---|---|
| System.Nullable<System.Single> | x | |
| System.Nullable<System.Single> | y | |
| System.Nullable<System.Single> | z |
Returns
| Type | Description |
|---|---|
| Vector3F |
Operators
| Improve this Doc View SourceAddition(Vector3F, Vector3F)
Adds two vectors together.
Declaration
public static Vector3F operator +(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first source vector. |
| Vector3F | right | The second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Division(Vector3F, Vector3F)
Divides the first vector by the second.
Declaration
public static Vector3F operator /(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first source vector. |
| Vector3F | right | The second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Division(Vector3F, Single)
Divides the vector by the given scalar.
Declaration
public static Vector3F operator /(Vector3F left, float right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The source vector. |
| System.Single | right | The scalar value. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Equality(Vector3F, Vector3F)
Returns a boolean indicating whether the two given vectors are equal.
Declaration
public static bool operator ==(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first vector to compare. |
| Vector3F | right | The second vector to compare. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the vectors are equal; False otherwise. |
Inequality(Vector3F, Vector3F)
Returns a boolean indicating whether the two given vectors are not equal.
Declaration
public static bool operator !=(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first vector to compare. |
| Vector3F | right | The second vector to compare. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the vectors are not equal; False if they are equal. |
Multiply(Vector3F, Vector3F)
Multiplies two vectors together.
Declaration
public static Vector3F operator *(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first source vector. |
| Vector3F | right | The second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Multiply(Vector3F, Single)
Multiplies a vector by the given scalar.
Declaration
public static Vector3F operator *(Vector3F left, float right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The source vector. |
| System.Single | right | The scalar value. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Multiply(Single, Vector3F)
Multiplies a vector by the given scalar.
Declaration
public static Vector3F operator *(float left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Single | left | The scalar value. |
| Vector3F | right | The source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
Subtraction(Vector3F, Vector3F)
Subtracts the second vector from the first.
Declaration
public static Vector3F operator -(Vector3F left, Vector3F right)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | left | The first source vector. |
| Vector3F | right | The second source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
UnaryNegation(Vector3F)
Negates a given vector.
Declaration
public static Vector3F operator -(Vector3F value)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | value | The source vector. |
Returns
| Type | Description |
|---|---|
| Vector3F |
UnaryPlus(Vector3F)
Returns the unary plus of the provided vector (nop).
Declaration
public static Vector3F operator +(Vector3F value)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3F | value |
Returns
| Type | Description |
|---|---|
| Vector3F |


SHINE A film review by Scott Renshaw Copyright 1996 Scott Renshaw
(Fine Line) Starring: Geoffrey Rush, Armin Mueller-Stahl, Noah Taylor, Lynn Redgrave, John Gielgud. Screenplay: Jan Sardi. Producer: Jane Scott. Director: Scott Hicks.  Reviewed by Scott Renshaw.
It has been a long time since a film inspired as profound a case of "What am I missing?" in me as SHINE. This Australian drama has been a fixture on year-end top ten lists, won a Best Picture nod from the National Board of Review, and picked up a New York Film Critics Association Best Actor for star Geoffrey Rush. So I sat myself down to watch SHINE, and I waited to be transported. And I waited. And I waited. And it never happened. SHINE is an extremely well-crafted film, but it doesn't pay off at a gut level. Director Scott Hicks' restrained approach to an uplifting story ends up choking much of the life out of it.
SHINE is based on the true story of David Helfgott, who is an emotionally troubled 40-year-old (Geoffrey Rush) when first we see him. The film then flashes back to David's youth in Perth, Australia, where he is a piano prodigy under the strict supervision of his domineering father Peter (Armin Mueller-Stahl). David's raw talent is apparent to all who see him, but Peter refuses to allow David to leave home for an opportunity to study music in the United States. The teenage David (Noah Taylor) accedes to his father's wishes once, but a scholarship to the Royal College of Music in London is too much to pass by. Disowned by his father for his disobedience, David leaves for London and studies with Professor Cecil Parkes (John Gielgud), but his latent eccentricities become a full-fledged collapse which institutionalizes him for nearly twenty years.
There is a lot to like about the first half of SHINE, which focuses on the relationship between David and his father. Peter Helfgott is a great character, a Holocaust survivor whose obsessive control over his family masks a deep fear of losing them. Armin Mueller-Stahl brings out both sides of the complex Peter -- the loving father who embraces David in the middle of the night and the tyrant who beats him and forces him to repeat "I am a very lucky boy" -- making him a sympathetic figure in spite of his cruelty. Noah Taylor carries his half of the relationship as well, making his desire to master the incredibly challenging 3rd Piano Concerto by Rachmaninoff an effort both to make his father proud and to prove his own talent without Peter's guidance. Perhaps the film's best sequence involves David's performance of the "Rach 3" at the Albert Hall, wild hair and sweat flying in slow motion as his ultimate artistic triumph becomes a mute pounding of keys.
The screenplay by Jan Sardi explores several other relationships of David's, but it soon becomes clear that none of them are as compelling as the relationship between David and Peter. There is a briefly-explored friendship between David and Socialist author Katharine Prichard (Googie Withers), the teacher-student interaction with Professor Parkes, and eventually a romance involving an astrologer named Gillian (Lynn Redgrave), yet there is little passion to any of them. Even the death of one character cannot evoke much emotional response, and David's match with Gillian is apparently the result of complementary astrological charts instead of a genuine connection between them. SHINE may be about David Helfgott, but Peter Helfgott is the real soul of the film, and when he disappears from the narrative there is nothing to drive it forward. Geoffrey Rush's performance as the adult David is superb, a portrait of mental illness which is vivid and lacking the obtrusive kookiness of many screen depictions of such conditions; his mile-a-minute monologues full of puns and streams of consciousness display a brilliant mind imploding upon itself. There simply isn't much to the second half of SHINE which leads us to a conclusion. David begins to re-enter society and to play music again, but the process of his healing is taken almost for granted.
The real fault for SHINE's failings lies with director Scott Hicks, who discovered David Helfgott's story and decided to turn it into a film. Hicks' background is in documentary film-making, and it seems that his instinct is to let David's story tell itself. Most films about characters struggling to triumph over adversity play to the broadest possible emotional canvas, and it is admirable that Hicks has chosen not to bludgeon his audience insensible with feel-good signifiers. However, he takes SHINE several miles too far in the opposite direction, and finds himself with a film which drifts when it should be building to an emotional resolution. SHINE is a good-looking, good-sounding and thoroughly professional production with a pair of excellent performances by Armin Mueller-Stahl and Geoffrey Rush. It is also too sedate by half, a story of shattered genius which opts for a dim glow when it needs a dazzling shine.
On the Renshaw scale of 0 to 10 Rach concerts: 6.
Visit Scott Renshaw's MoviePage http://www-leland.stanford.edu/~srenshaw Subscribe to receive reviews directly via email See details on the MoviePage
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


Importance of the Navigation element
Packt Publishing: Grouping Links in a specific section
Packt Publishing
Blogs
Tech Hub
Submission of Errata
Videos


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Script Spinner is composed of five STEM students who met at the University of Texas at Tyler in their Capstone course. Read bellow to find out more about the people behind Script Spinner!
Joesph Duncan
Founder
Joesph is our creator and founder!
He is graduating with his bachelor’s in Information Technology.
He is 22 years old and is from Eufaula, Oklahoma.
A fun fact is that he lived on a Native American Reservation for 4 years.
Laila Sherif
Editing & Social Media Director
Laila handles things like editing, social media, and our documentation!
She is graduating with her bachelor’s in Information Technology.
She is 20 years old and is from Los Angeles, California.
A fun fact is that she actually runs a small baking business from her house!
Hiroki Yaginuma
Programmer
Hiroki is a part of the programming duo behind Script Spinner.
He is graduating with his bachelor’s in Computer Science.
He is 23 years old and is from Japan, which is a fun fact!
Mohammad Alkrad
Programmer
Mohammad is the other part of the aforementioned programming duo!
He is graduating with his bachelor’s in Computer Science.
He is 32 years old and is from Syria.
A fun fact about him is that he is a diehard fan of soccer.
Sean Maples
Business Director
Sean is our business planner and behind all things business, marketing, and financials!
He is graduating with his bachelor’s in Computer Information Systems.
He is 26 years old and is from Inglewood, California.

Eniola Sogbesan
IT Director
Eniola is our resident IT professional!
He is graduating with his bachelor’s in Information Technology.
He is 23 years old and is from Nigeria, which is his fun fact!


Copyright (c) 2010 The Echo Nest. All rights reserved. Created by Tyler Williams on 2010-04-25.
Global configuration variables for accessing the Echo Nest web API.
The API endpoint you’re talking to
API selector... just ‘api’ for now
Version of api to use... only 4 for now
The API call timeout (seconds)
Location of your codegen binary. If not given, we will guess codegen.platform-architecture on your system path, e.g. codegen.Darwin, codegen.Linux-i386
You may change this to be a user agent string of your own choosing
If true, API calls will be traced to the console


Assestment for Bussines Process - Introduction
1. Getting Started with A4BP
What do you need
bla bla ...


Indicates if the records should be loaded from the server automatically as user scrolls.
Boolean, default = true
This property only applies if your data source for the grid is remote. As of 1.3, the default behavior of the grid is infinite
scroll, which means as user scrolls more records will be loaded. However, in some cases record generation might be processor
heavy and server will not be able to output records fast. If you set .autoLoad to false, instead
of loading records automatically, the grid will display "Load More..." button as user reaches the end of available records.
This property can be set during object creation:
Or any time during run-time:
You do not need to refresh the grid because .autoLoad will only be used when more records need to
be loaded.


I design and build things for the virtual and real world.
Coding
During my education, I self-taught microcontroller and computer programming as a side skill. Since then, I've worked on different programming projects that involved robotics, automation, and design.
Research
During my university years, I participated in different developments at the Mechatronics Department. As a result, I authored and co-authored several research papers in robotics, system control, monitoring, and related topics.
Robotics
I got my BSc and MSc degrees in Mechatronics eng. because of my interest in robotics. To enrich my team management skills, I completed a Lean eng. and a Quality control eng. postgraduate training programs.
Projects
An overview of my projects (non-exhaustive list)
Online 3D Pathfinding Algorithm Visualization Tool
VS Code ThreeJS jQuery Bootstrap bootstrap-slider.js HTML CSS JavaScript
Send me an e-mail, check my projects on GitHub, or connect with me on LinkedIn
Zsolt Molnár
Online 3D Pathfinding Algorithm Visualization Tool
My goal with the project was to teach myself WebGL based graphics and related technologies. As a result, the application has an editable 3D environment, which permits the user to build his own obstacle course for pathfinding algorithms in a 3-dimensional space. The 3D editor enables moving the start and endpoints, adding/removing walls (obstacles), and resizing the 3D space available for the algorithm.
Because of the lockdown caused by COVID-19 outbreak, the university labs were closed, where the industrial sensor measurements were carried out. To overcome this challenge, I began to work on a set of .NET framework-based applications, which are intended to model the behavior of the real measurement setup. The output values are generated using actual measurement data with random noise generation. Each interface has an image of the measurement setup, which illustrates the physical arrangement of the devices.
A Pro Micro board based, 3D printed, triple-layout macro keyboard with mechanical switches (Arduino IDE programmable). The Pro Micro board emulates an HID device which grants cross-platform and cross-device compatibility without requiring additional software.
Technologies: Arduino IDE FreeCAD Autodesk EAGLE C++
The project's goal was to control a robot based on a top view of the area and a hand-drawn path. The functionality was accomplished by processing the top view image using EmguCV, a .Net wrapper for the OpenCV library. The algorithm detects the red-blue tracker circles and determines the robot position in real-time. The steering is determined using geometry calculations. For demonstration, a LEGO NXT robot was used, connected via Bluetooth to the computer.
Technologies: Visual Studio IDE OpenCV EmguCV C#
Competition in Applied Engineering
2010 - 2014
~ 2010 ~
This was the most complex mission from the five consecutive years. The mission had multiple challenges. First, the robot (in this year called a Mars rover) needed to navigate a rugged terrain made up of river pebbles. The contest area was entirely hidden from the competitors, only a low-resolution satellite (top-view webcam) image was available, and the video stream from the rover mounted camera. The manual commands were delayed by 15 seconds. Only one team was present on the field at a given time, having 30 minutes to complete the mission.
The rover had to read a DNA sequence from a strange, flower-disguised screen with its onboard camera in the first part. In 2010 the today's smartphones were less capable, and even the basic models were expensive. For this reason, an analog black and white camera was used, connected to a TV transmitter on the robot. An analog TV set received the signal.
In the second part of the mission, the rover had to take a soil sample (represented by airsoft bullets for simplicity) from an alien plant's ground. The rover was equipped with a shovel mechanism, built from repurposed CD drive units and printer parts.
In the third part of the mission, the soil sample had to be transported to a space elevator, represented in the competition by a metal pipe climber.
From the five consecutive competitions, this was the most complex one from a technical perspective. For autonomous control, a top-view of the field was available via a live video stream. At the same time, the area was divided into different "worlds." To move between them, stargates were placed on the field. These gates contained infrared transmitters with different but known frequencies. Also, a line following option was available for the robot when it was nearby the gate. There were pyramids with varying values of score in each world, which could be collected by visiting them with the robot. The pyramids also were equipped with IR transmitters of a specific frequency.
The robot on the figure was equipped with all the necessary sensors for autonomy and the power of processing required for frequency analysis and line following by using a Microchip PIC32, nearly the fastest microcontroller available on the market at that time. The manual commands were delayed by 15 seconds.
This year's competition aimed to design a spider-like robot that positions ping-pong balls (representing spider eggs) on stepped platforms. The robot on the figure used Klann linkage to achieve the walking motion of its two times four legs, each group of four driven by a motor. The robot competed against 3 other teams present on the field at the same time.
For manual commands, a 15-second delay was necessary. From the control perspective, and a basic version of autonomous control based on image processing was implemented. This was perfected for the 2013 competition.
The goal of the competition was to place magnetic rods on marked spots. The story said that the robot was situated on a distant planet, so all the manual commands were delayed by 10 seconds. Any automatic control-related decision could be executed by the robot instantly. There was a webcam over the field, which provided a live video stream for control purposes. Another challenge was that the robot must have only two wheels, which introduced an imbalance in the system.
The robot on the figure had full autonomy, except the path planning at the beginning of the rounds, which had to be done manually. With all of these, the robot had to compete against three other teams present on the field at the same time.
The competition's goal was to place dices on different height boxes, competing against three other teams present on the field at the same time. The vehicle needed to be a hovercraft-locomotion based one. The hovercraft was hand-controlled, using a glove mounted accelerometer. In this year, the manual commands were not delayed.
Real-time accuracy monitoring system for industrial robot arms
The core of the system is a software developed in the lab, which compares the robot's preprogrammed paths with the momentary one. As a result, we get an absolute difference, which then is compared against a reference value. The reference value is the threshold for different warning levels.
The paper was presented at the Annual Session of Scientific Papers - IMT Oradea 2019 international conference.
Flexible control and monitoring system design initiative based on Industry 4.0 principles
The UI running on a touchscreen-enabled HMI
The four different control module
System demo
Programming demo
The project is a design initiative, which outlines the main requirements for a flexible and modular control system design from software and hardware perspectives. At the same time, the basics of Industry 4.0 are taken into account. As a result, the system prototype was built and tested by the creators and Mechatronics Engineer students as part of their classes. The system includes a UI and control logic, which runs on a touchscreen-enabled HMI. The hardware part consists of four different purpose Wi-Fi enabled control module.
The paper was presented at the 3rd International Symposium on Small-scale Intelligent Manufacturing Systems (SIMS 2020) international conference.
The conference presentation topic was a network-connected control system developed at the Department of Mechatronics. With wireless technology, the devices (production cell components) equipped with the in-house developed control modules can connect to a central computer. With a touch-enabled HMI, the users can redefine the connections and behavior of the controlled devices. The wireless connectivity is an essential feature for the Laboratory because it permits the production cell's reconfiguration fast and easy between educational and research activities.
The development was presented at the Process Control System 2019 (PCS 2019) international conference.
Remote controlled and monitored CNC developed for prototype component manufacturing
The development's main challenge was that we needed to design the CNC using components mostly available in a bigger hardware store. Also, the CNC frame must be sturdy enough to execute smaller machining jobs. The second challenging part was the design of the control system. This was based on a Raspberry Pi single-board computer and an Arduino board as a G-code interpreter and physical I/O interface.
Heat-sensitive electric skin based on predictive temperature measurement
The goal was to create a skin-like surface with similar heat-sensing capabilities as a human skin. Thereby it can distinguish the tangible temperature objects from hot ones. We achieved this functionality by applying the predictive temperature measurement principle to off the shelf, low heat capacity, and cheap temperature sensors mounted on a flexible PCB. The prototype was a 2 x 2 sensor matrix. The resulted concept can be applied to humanoid robot arms, prosthetic arms, and other fields where robot-human interactions happen.
The development was presented at the InterTalent Unideb 2016 international conference.


Using the PlatformingPhysics classes to implement a moving platform
Keyboard controls: Left/right buttons to move character.


All views derive from this class. it’s constructor will register your exposed urls on flask as a Blueprint.
Generally you will not implement your views deriving from this class, unless your implementing a new base appbuilder view.
This class does not expose any url’s, but provides a common base for all views.
Most importante Base Properties:
| route_base: | The root base of your view |
|---|---|
| template_folder: | |
| The base template folder | |
| base_permissions: | |
| The forced base permissions for your views, if not provided it will infer the permissions from the exposed methods and actions |
Derive from this view to provide some base processing for your costumized form views.
Notice that this class derives from BaseView so all properties from the parent class can be overrided also.
Implement form_get and form_post to implement your form pre-processing and post-processing
Most importante Base Properties:
| form_title: | The title to be presented (this is mandatory) |
|---|---|
| form_columns: | The form column names to include |
| form: | Your form class (WTFORM) (this is mandatory) |
This is the most important view. If you want to automatically implement create, edit, delete, show, and search form your database tables, derive your views from this class.
Notice that this class derives from BaseView so all properties from the parent class can be overrided also.
Most importante Base Properties:
datamodel: SQLAModel (flask.ext.appbuilder.models.datamodel), take a look at quick start. (this is mandatory)
- Titles
list_title: Title for list view show_title: Title for show view add_title: Title for add view edit_title: Title for edit view
- Include Columns: lists of column names for the views
list_columns: The columns to show on list (this is mandatory) show_columns: The columns to show on show view add_columns: The columns to show on add form, and also what will be added edit_columns: The columns to show on edit form, and also what will be edited order_columns: The columns allowed to order on lists search_columns: The search form to filter the list
- Properties for Labels and descriptions
label_columns: The labels to be shown for columns {‘<COL NAME>’: ‘<COL LABEL>’, ....} description_columns: The description to be shown for columns {‘<COL NAME>’: ‘<COL DESCRIPTION>’, ....}
- Optional Field set’s, inspired on DJANGO field sets:
fieldsets [(<’TITLE’|None>, {‘fields’:[<F1>,<F2>,...]}),....]
show_fieldsets: A list add_fieldsets: A list edit_fieldsets: A list
- Properties for overriding auto form creation with your own defined forms (WTFORM)
add_form: Override this to override the add form auto creation edit_form: Override this to override the edit form auto creation search_form: : Override this to override the search form auto creation validators_columns: Override this to implement your special validations for form columns {‘<COL NAME>’: <WTForm Validator> }
- Templates: override these to implement your own templates, AppBuilder has some variations
list_template: (default) ‘appbuilder/general/model/list.html’ edit_template: (default) ‘appbuilder/general/model/edit.html’ add_template: (default) ‘appbuilder/general/model/add.html’ show_template: (default) ‘appbuilder/general/model/show.html’
- Widgets: override these to change the default display for the views implemented on this class. AppBuilder has some variations on these.
list_widget: (default) ListWidget edit_widget: (default) FormWidget add_widget: (default) FormWidget show_widget: (default) ShowWidget search_widget: (default) SearchWidget
Provides a simple (and hopefully nice) way to draw charts on your application.
This will show Google Charts based on group by of your tables.
Most importante Base Properties:
| datamodel: | SQLAModel (flask.ext.appbuilder.models.datamodel), take a look at quick start. (this is mandatory) |
|---|---|
| chart_title: | Your Chart Title |
| chart_type: | ‘PieChart’ or ‘ColumnChart’ |
| chart_3d: | ‘true’ or ‘false’ |
| height: | The height for you chart default is: ‘400px’ |
| label_columns: | : The labels to be shown for columns {‘<COL NAME>’: ‘<COL LABEL>’, ....} (this is mandatory) |
| group_by_columns: | |
| A list for your possible group by’s for your table (select * from <TABLE> group by [...]) |
Provides a simple way to draw some time charts on your application.
This will show Google Charts based on count and group by month and year for your tables.
Most importante Base Properties:
| datamodel: | SQLAModel (flask.ext.appbuilder.models.datamodel), take a look at quick start. (this is mandatory) |
|---|---|
| chart_title: | Your Chart Title |
| chart_type: | ‘PieChart’ or ‘ColumnChart’ |
| chart_3d: | ‘true’ or ‘false’ |
| height: | The height for you chart default is: ‘400px’ |
| label_columns: | : The labels to be shown for columns {‘<COL NAME>’: ‘<COL LABEL>’, ....} (this is mandatory). |
| group_by_columns: | |
| A list for your possible group by’s for your table, only select date columns. |


Publish Queue Video Title A short Video Description Goes here Video Title A short Video Description Goes here Video Title A short Video Description Goes here Video Title A short Video Description Goes here
Recently Added Videos Video Title A short Video Description Goes here Video Title A short Video Description Goes here Video Title A short Video Description Goes here Video Title A short Video Description Goes here
Most Popular Videos Video Title A short Video Description Goes here Video Title A short Video Description Goes here Video Title A short Video Description Goes here Video Title A short Video Description Goes here
Recently Joined John Doe VP Marketing Rajbir Kaushik Front-end Developer Semad Javed Senior Designer Vasundhara Mishra Desginer, Intern
Most Active Users Roshan Ravi 52 Videos | 3 Channels John Doe 30 Videos | 4 Channels John Smith 30 Videos | 3 Channels Ajay Singh 18 Videos | 4 Channels
Most Popular Channels IT Internal Trainings A short Video Description Goes here IV Induction Videos A short Video Description Goes here JS Javascript A short Video Description Goes here A Agile A short Video Description Goes here


Data Analyst (Part-Time) - Austin, TX
ABOUT SOCIALWITHIN
Based in Austin, SocialWithin is helping build some of the best consumer brands in the world. Our mission is to create more value for others. We've built our company on three core values: empathy, teamwork, integrity. Our core values inspire us to over-deliver on expectations and to create more success for the partners we serve.
Join our team of smart, kind, passionate, collaborative, results-driven do-ers. We work hard every day to deliver remarkable experiences to our clients, and ultimately, their customers.
WHO YOU ARE
In short, you have a passion for data and unparalleled analytical skills, and a drive for solving complex problems.
If you have hands-on experience in data extraction, dashboard creation, data analysis with some marketing mix modeling, multi-touch attribution and full-funnel reporting, then we want to hear from you!
WHAT YOU'LL DO
As a Data Analyst, you will help streamline and maintain our reporting operations, data analysis deliverables, pipeline, processes, and tools; act as connective tissue for client strategy and performance our Account Management team drives. The desired outcome is to minimize reporting downtime for real-time insights and maximize customer happiness, team happiness, and operational efficiency.
You will be apart of cross-functional initiatives across Account Management, Engineering, Performance Marketing and the client-side to ensure our reporting and analysis are not only data-driven but data-informed.
The successful candidate will combine a passion for innovative analysis with attention to detail and a tenacity to overcome difficult and complex analytical challenges.
RESPONSIBILITIES
Performing ad-hoc statistical analysis (linear regression, forecasting, hypothesis testing, etc.)
Querying data with SQL and visualizing through either Excel or Periscope Data
Using Python to create statistical models and advance analysis within the current reporting infrastructure
Work cross-functionally with the Account Management team and client-side to bridge reporting gaps and drive meaningful insights
Assemble large, complex data sets that meet functional / non-functional business requirements.
Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques, and business strategies.
QUALIFICATIONS
Bachelor's degree, or on track to earning bachelor's degree in an analytical or technical field (e.g. Engineering, Computer Information Systems, Computer Science, Data Science, Business Analytics, etc.)
Working SQL knowledge and experience working with relational databases
Experience using statistical computer languages (R, Python, etc.) to manipulate data and draw insights from large data sets.
Previous classwork in upper-division Mathematics and/or Statistics.
Creative problem-solving skills and a can-do attitude to overcome any obstacle and get things done
Willingness to take on big challenges and own the results from brainstorming solutions to execution
Strong communication skills and ability to work collaboratively across functions including product, client services, and engineering
PREFERRED QUALIFICATIONS
Experience analyzing data from 3rd party providers: Google Analytics, HubSpot, AdWords, Shopify, Facebook Insights, etc.
Previous employment or internship experience in manipulating, processing and extracting value from large disconnected datasets.
Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, etc.
PERKS AND BENEFITS
Incredible company culture - we are passionate about the impact we make every day, we balance freedom with responsibility, and we aim for a consultative, transparent, and communicative approach in both our internal and client relationships
Diverse and forward-thinking environment
Direct access to agency leadership
Dogs welcome in office
Bright, collaborative and snacks-stocked office space near downtown Austin
WeWork membership, including shared workspaces, access to the global WeWork community, and complimentary coffee, fruit water, cold brew coffee and kombucha
Company events and industry conferences
24/7 access to the University Park gym and locker room
At SocialWithin, we're looking for people with passion, grit, and high integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and passion will stand out; especially if your career has taken some extraordinary twists and turns. At SocialWithin, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions. Join us.
PLEASE NOTE: as a company, we take hiring very seriously. Interviewing with SocialWithin may include phone/video interviews, written projects, and/or on-site interviews. Although we are unable to follow-up with each and every applicant, we do our best to run a thorough process for candidates with whom we identify a potential fit.


This is a link to GitHub!
resources
This is a link to GitHub!
Mozilla developer
last boss tutorials
This is a link to GitHub!
Hello there,awesome person!


Note: even though these tags look like HTML they are a little different as they're allowed to "break the rules". Something like:
Template

<tmpl_var name='body_text'>

In Dreamweaver or other editors it might be more convenient to see the vLIB markup tags. You can use "{}" instead of "<>". Most examples use this alternative syntax.
Template

{tmpl_var name='body_text'}

These markup tags are not really valid HTML (or XHTML), but it will work as planned. TMPL_VAR specifies a template variable. The attribute "name" is necessary to create the interface between the PHP script and the template.
The "TMPL_" bit of the tag is optional, and can be omitted if you prefer to use the shorter versions, i.e.: <LOOP name="LOOP_NAME">.
If you're a fanatic about valid HTML and would like your templates to conform to valid HTML syntax, you may optionally type template tags in the form of HTML comments. This may be of use to HTML authors who would like to validate their templates' HTML syntax prior to vlibTemplate parsing, or who use DTD-savvy editing tools. You can also use " instead of ' or you can disable it completly, but I would not recommend it.
Template

You can also use a curly bracket syntax '{tmpl_var}' for use within comments (as to not break them with the comment style tag), or within html tags (as to not break the tag in your editor).
Template

{tmpl_var name=param1}

All shown brackets should work with all vLIB markup tags.


Deployment synchronizer can be used to synchronize the main repository of a Carbon server with a collection in the registry. This feature is sometimes also referred to as the registry based repository synchronizer. Deployment synchronizer has a number of use cases.
Perhaps the most interesting use case of the deployment synchronizer is the second item listed above. Assume a cluster of Carbon servers that share the configuration registry. With the deployment synchronizer it is possible to maintain all the nodes in the cluster in sync through the shared registry. One of the nodes can be designated as the master node and it can upload its local repository to the registry using the deployment synchronizer. Other nodes (slave nodes) can then download the same repository from the registry and deploy locally. To support this use case, synchronizer has to be run in auto commit mode in the master node. When in auto commit mode, it will periodically upload the changed artifacts in the local repository to the registry. Similarly slave nodes should run the synchronizer in the auto checkout mode. If needed registry eventing can be employed to run the checkout operations so that a checkout will be made only when some artifact has changed in the shared registry.
Note that Carbon repository is located in the repository/deployment/server directory by default. Once enabled, deployment synchronizer uploads the contents of this directory to a collection named repository/deployment/server in the configuration registry (this is configurable).
While the deployment synchronizer can be engages and configured from the carbon.xml file the, control panel allows the user to engage it from the Carbon management console.
To access the Deployment Synchronizer Control Panel select Configure > Deployment Synchronizer from the left navigation panel of the Carbon management console. The following page will be displayed.
Figure 1: Deployment Synchronizer Control Panel (Synchronizer Home Page)
To enable the synchronizer simply check the required features (eg: auto commit) and click the 'Enable' button. Synchronizer will get initialized and start running periodically as specified in the 'Synchronization Period' parameter. When the auto checkout option is checked, an additional option named 'Use Eventing' will be enabled. This can be checked if you want to trigger checkout actions on registry events.
When the synchronizer is enabled additional information will be shown on the UI as shown below.
Figure 2: Deployment Synchronizer Enabled
You can use the additional panels to monitor the progress of auto commit and checkout operations (by observing timestamps). It is also possible to execute a commit operation or a checkout operation manually by clicking on the corresponding buttons. To disable the synchronizer simply hit the 'Disable' button at any time.


search for version 3.0 icons adds community tags to better locate version 2.0 icons
Sponsored by Infinite Red , Created by Sia Karamalegos
Star or Contribute


{% extends 'blog/base.html' %} {% block content %}
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Yellow
Green
Filled in
This is a link
Here is some more information about this product that is only revealed once clicked on.
Lorem ipsum dolor sit amet.


Haotian Shen
About
2019 Master Graduate from University of Victoria Visual Computing Group, under the supervision of Dr. Kwang Moo Yi.
Projects
Grade Distribution - This platform enables teachers to distribute student grades using a single xlxs spreadsheet when logged in as an admin account. Using student's identification number, parents can log in as guests, they are allowed to review the exam results of their own child only. This project uses django rest framework, redux and webpack.
Annotation Platform - Annotation/Training data management site, providing three main features including 1) conditional data retrieval, 2) data auto archieve and 3) Real time review/fix of data annotation. Used Docker, Nginx, Gunicorn, Django and PostgresDB.
Skylark - Predicting Proto-planetary disks using deep learning. Msc project for frame extrapolation on astronomical simulation data. Used PyTorch and was trained on Compute Canada. Currently focusing on 1) make the network to inherites sequential information such as momantum. 2) dataset expansion.
Cloud Deep Learning - A system similar to Google Vizier which performs automated parallel hyperparameter searching (Grid, Random and Bayesian Optimization) across clusters.
Full-Resolution-ResNet - Tensorflow implementation of the Full-Resolution-ResNet on cityspace data.
Sponza - A real-time renderer that stimulates a sponza structure with advanced lighting and skybox implemented in c++ and openGL.


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Serverless Architecture:
Read this comprehensive guide to understand Serverless Technology. Get to know what is function-as-a-service, how do serverless architecture works, its benefits & drawbacks and an in-depth case study on PhotoVogue.
Since the launch of serverless architecture in 2014, it is continuously redining the ways in which we develop and deploy our software products. One such revolutionary story surfaced while I was interacting with one of our client, a startup CTO.
“I recall how hard was it to build a prototype for production system which resulted in launching things even before they were ready, poorly managed continuous integration/development processes and bug fix systems that were created by an intern who left almost a year ago.
But now, due to serverless architecture, I can spontaneously take up an idea, transform into a scalable solution and outlast the business decision being taken to develop it!”
Being a startup CTO, this is a huge win when faster time-to-market is a key factor for thriving in the competing economy. However, serverless architecture brings more than just faster time-to-market and you’ll definitely need some help in getting started with this new concept.
Let’s get started and examine how serverless architecture is exactly defined!
Table of Contents
- What is Serverless Architecture?
- FaaS: Function as a Service
- What about microservices?
- How does the serverless app works?
- Case Study: Unpacking Serverless Architecture
- Benefits of serverless
- Drawbacks of serverless
- Conclusion
What is Serverless Architecture?
“Serverless computation is going to fundamentally not only change the economics of what is back-end computing, but it’s going to be the core of the future of distributed computing.” — Satya Nadella, CEO Microsoft
Methodology, mindset, culture–the heart of serverless is the approach. But taking the approach to its logical extreme yields technologies. This tech can be called serverless & they should become > the yardstick by which other technologies are measured (i.e., define the spectrum) — Ben Kehoe (@ben11kehoe) December 21, 2018
The world is still revolving around servers even with the rise of cloud computing. But it isn’t going to last for a longer time. Cloud apps are shifting into the serverless world and it will indeed bring about dramatic changes in how we create and distribute software and applications.
Once Upon a Server Time
Before the cloud-y days (pun intended), servers were one of the major parts, for developers, when it came to building an application or software. You needed a budget, good plan, connect the dots, supply the power and provide them with a house.
You’d have to buy or lease the servers, arrange the power, supplies, cooling, cabling- and then construct them all up in a data centre or a colocation facility.
Over time, the colocation facilities started providing extra facilities like racks, power, internet access, etc. But it wasn’t sufficient.
And it still required a lot of detailed planning, a huge amount of time and, most importantly, money. Hence, a shift was inevitable!
Entering the Cloud Infrastructure-as-a-service (IaaS) came to the rescue!
Since the last 2 years, the way you do computing has been changed drastically. It’s not about ‘Why cloud?’ or ‘How cloud?’ anymore.
IaaS served a perfect recipe for cost improvement, agility and scalability with added spices of reliability and perfect architecture. (I’m now hungry, maybe!)

Moreover, the total cost of ownership has reduced dramatically.
I remember buying servers at the cost of hundreds of dollars for my first company with the investment of a lot of efforts in their maintenance. For the second company, I leased the servers for years but still had to put a considerable amount of efforts.
For the third company, I leased them by month and for my current company, I’ve leased them by the hour, as per demand, on pennies.
But this was from the perspective of architecture and pricing. Anyways, the overall concept of ‘application/software’ in the cloud is quickly evolving. It’s no longer about building on a web. It’s more about building a distributed system of loosely coupled components in the cloud.
The user apps and back-end data storage are still prevailing, but the processing is increasingly taking place asynchronously outside of an app framework. And hence, the worldview is increasingly around tasks and process flows, not applications and servers.
Moreover, their units of measures for compute cycles is in seconds and minutes, not hours. In short, apps are becoming serverless
Thinking Serverless For the past 14 months or so, I’ve been working on serverless technology and let me tell you, it’s a very large break from the way we build our software/applications and deploy them.
But the phrase ‘serverless’ doesn’t mean servers are no longer involved. It just means that developers no longer have to worry about managing them. Without thinking much about their maintenance, computing resources get used as services.
Though, serverless can be understood as an amalgamation of two distinct points as follows:
#1. BaaS: First meaning of serverless could be taken as the use of 3rd party services/applications (in the cloud) to handle the server-side logic and state. For example, consider a mobile app with quite a broad ecosystem of cloud-based databases, authentication services, etc.
#2. FaaS: Second meaning of serverless could be taken as the use of 3rd party stateless compute containers to handle the server-side logic. These containers are event-triggered and may last for only one invocation i.e. ephemeral.
In this blog, you would be coming across FaaS quite frequently as it is a newer technology that is bringing about a significant change in the way we think about technical architecture and has been at the core of serverless.
Though, both BaaS and FaaS are interrelated and overlapping. For example, Auth0 started off as a BaaS ‘Authentication-as-a-Service’ but with Auth Webtask they have entered the FaaS space.
FaaS: Function-as-a-Service
FaaS is a new and improved way to build and deploy your server-side software which is more focused around deploying individual functions or operations.
FaaS is where you hear a lot of buzz about serverless comes from. I remember one of our developer saying that serverless is,in fact, FaaS. If you are thinking the same, well let me correct you, you’re missing out the complete picture.
Anyways, let’s get back to what is function-as-a-service about?
FaaS is inherently an event-driven approach. But let’s understand it better by taking an example.
When we traditionally deploy a server-side application, we start with microservices. Digging deep, we start with host instance, usually a virtual machine instance or a container.
We then deploy our application within the host. If, in case, our host is a VM or a container, then out the application is an operating system process. Usually, our application contains the code for multiple interrelated operations as shown in the below figure.
FaaS changes this model of deployment. We would shift our focus from both host instance and application process of our model to the individual operations or functions that express our application’s logic. We upload those functions, one-by-one, to a vendor-supplied FaaS platform.
These functions are not continuously active in a server process though, sitting idle until they need to be run as they would be in a traditional system.
Instead, the FaaS platform is configured in such a manner that it responds to a specific event for each operation. When that event occurs, the vendor platform initiates the function and then calls it to the triggering event.
Once the function has finished executing, the FaaS platform is free to tear it down. Alternatively, as an optimization, it might keep it for a while until there is another event to be processed.
As I said earlier, this is an event-driven approach. Moreover, FaaS vendor also integrates with various synchronous (HTTP API Gateway) and asynchronous (hosted message) event sources apart from providing a platform to host and execute the code.
What about Microservices?
I am sure you must be wondering where microservices would go?
They are here to stay! Do not worry!
This approach to digital transformation representing monolithic —> microservices —> serverless is driven by the need for greater agility and scalability. To keep up with the competition, the organizations need to update its technology stack quickly, eventually making the software a differentiating factor.
Thus microservices architecture emerged as a key method of providing development teams with flexibility and other benefits, such as the ability to deliver applications at warp speed using infrastructure as a service (IaaS) and platform as a service (PaaS) environments.
The concept of this idea was to break the monolithic applications into smaller services each with its own business logic. With a monolithic architecture, a single faulty service can bring down the entire app server and all the services running on it.
While in a microservice each service runs in its own container and thus application architects can develop, manage and scale these services independently.
The microservices can be scaled and deployed separately and written in different programming languages. But a key decision many organizations face when deploying their microservices architecture is choosing between IaaS and PaaS environments.
Microservices involve source code management, a build server, code repository, image repository, cluster manager, container scheduler, dynamic service discovery, software load balancer and a cloud load balancer. It also needs a mature agile and DevOps team to support continuous delivery.
As explained earlier FaaS takes the step further by making an application more granular to the level of functions and events. Thus, it is pretty clear that the unit of work is getting smaller and smaller.
We’ve gone from monoliths to microservices to functions. FaaS also improves the shortcoming of PaaS model ie. scaling and friction between development and operations.
Scaling a microservice hosted on PaaS is challenging and complex. The architecture might have elements written in different programming languages, deployed across multiple clouds and on-premise locations, running on different containers.
When demand increases for the app, all the underlying components have to be coordinated to scale, or you have to be able to identify which individual elements need to scale to address the surge in demand.
Even if you set up your PaaS application to auto-scale you won’t be doing this to the level of individual requests unless you know the traffic trend. So a FaaS application is much more efficient when it comes to costs.
But still, there will be always space for both microservices and FaaS to co-exist because there are certain things which you can’t do with functions at all. For example, an API/Microservice will always be to respond faster since it can keep connections to databases and other things open and ready.
Moreover, one more thing to note here is that by grouping a bundle of functions together behind an API gateway, you’ve created a microservice. This shows that both of these can co-exist in a nice way.
This high-level flow remains the same as the traditional approach. The key difference is that, in case of a function, the container is created and destroyed by algorithms used in FaaS platforms and the operational team have no control over that.
And hence, it is here to stay!
But with ease of function and reduced cost comes increasing complexity about which we will discuss later in the blog.
How does Serverless App Works?
Serverless might look like a bright sunny day but it isn’t that sunny after all. It has brought about a big difference when it comes to application architecture.
Moreover, the change with serverless isn’t gradual, but with a jolt. FaaS drives a totally different type of application architecture through a fundamentally event-driven model, a much more granular form of deployment.
So,
- How does serverless app work in actual?
- What does serverless architecture look like?
- How is it different from non-serverless architecture?
These are the questions that we’re going to tackle in this chapter. Let’s dive deeper and understand the serverless architecture and its working.
Reference Application
Let’s make some assumptions about our reference application.
- It is a multi-user app.
- It has a mobile-friendly user interface.
- User management and authentication are required.
We’ve certainly overlooked some other features that you might expect in a basic app, but the point of this exercise is not to actually build an app but to compare a serverless application architecture with a legacy, non-serverless architecture.
Monolithic Architecture
Given those requirements, a monolithic architecture for our app might look something like the below figure.
serverless architecture
- A native mobile app for iOS or Android
- A backend is written in Java & HTML
- A relational database
In this architecture, a mobile app is responsible for handling app interface and input from the user, but it delegates most actual logic to the backend. From the perspective of code, the mobile app is lightweight and quite simple. It uses HTTP to make a request to multiple API endpoints served by the backend Java application.
Authentication and user management are encapsulated with the Java application code. Moreover, it also interacts with the relational database in order to store user data.
Why Change?
You might say, “I am perfectly fine with this architecture as it meets all my requirements, so why just not stop there and call it a good?”
To which I will reply that there are multiple challenges and operational pitfalls which I will discuss in this section to which you might relate. Go on!
In building this app, you’ll need to have hands-on expertise in iOS and Java, don’t you? Apart from that, you need to have good experience in configuring, deploying and operating Java application servers as well as a relational database. Moreover, think about their host systems, whether container-based or running directly on virtual or physical hardware?
We’ve not yet touched scalability, security or high availability, all of which are critical aspects of the modern production system. The bottom line- all these complexities, at one point in time, will become friction when you’ll be fixing bugs, adding features or trying to rapidly prototype new ideas.
And hence, you need a change!
Serverless Architecture
A serverless architecture of our basic application would look something like the below figure.
According to this architecture, while the user interface will still remain a part of the native mobile app, user authentication and management will be handled by a BaaS service like AWS Cognito. These services can be called directly from the mobile app to handle user-facing tasks like registration and authentication.
Moreover, the same BaaS can be used by other backend components to retrieve user information.
With user management and authentication now handle by BaaS, the logic which was previously handled by backend Java application is simplified. You can use another amazing serverless component, AWS API Gateway, to handle routing HTTP requests between a mobile app and your backend in a secure and scalable manner.
Each distinct operation can be encapsulated in a function. A FaaS platform such as AWS Lambda takes each of these functions and runs them in parallel ‘containers’ that can be monitored and scaled separately.
Those backend FaaS functions can interact seamlessly with a NoSQL, BaaS database like AWS DynamoDB. In fact, one drastic change is that we no longer store any session state within our server-side application code, and instead will persist in NoSQL store.
While it may seem to you like a minute change but believe me, it will help you significantly with scaling.
What got better?
You might say, “There is no significant difference in terms of complexity and it has, in fact, more specific application components that our traditional monolithic architecture?”
To which I will reply that the code we will write now will be solely focused on the logic of the app. What’s more? Our components are now decoupled and separate, due to that, we can switch them or add new logic very fast without the inherent friction in the non-serverless architecture.
Scaling, high availability and security are the areas all backed up with the serverless architecture. This means that as the popularity of your app grows, you needn’t worry about renting more powerful servers, or failure in a database or troubleshoot a firewall configuration.
In short, labour cost has reduced, as well as, the risk and resources cost of running it. All of its constituent components will scale flexibly. What’s more? If you have an idea for a new feature, our lead time is highly decreased, hence, we can start getting feedback and work on it efficiently.
Pretty exciting, right?
We have more discussion on benefits and drawbacks of serverless. Stay tuned!
Before that, let’s discover more about serverless architecture through serverless architecture case study.
Case Study: Unpacking Serverless Architecture
When PhotoVogue, part of Vogue Italia, launched in 2011, it was woefully unprepared to become the next rags-to-riches online photography platform.
Within a year, the popularity increased and skyrocketing traffic overwhelmed the Italy based company. Moreover, the user experience went lethargic and its pre-existing backend limited the scalability.
Liking it to open-heart surgery, Marco Vigano, Head of Digital Development at Conde Nast Italia, was forced to perform an emergency migration from local server to AWS in just 3 months. PhotoVogue’s quick switch to the serverless likely saved it from death-by-desertion after its homebrew server failed to keep the pace.
This trend has further decreased time-to-market and delivers the scaling necessary for Uber, Pokemon Go, Airbnb, ‘Clash of Clans’ and numerous other apps characterised by large user bases and real-time data. With more than 4 million apps struggling for attention on Apple’s App Store and Google’s Play Store, developers are turning to serverless for a competitive advantage.
Sounds like a fascinating story? Let’s have a closer look!
About the Platform
PhotoVogue is an online photography platform. It is a part of Vogue Italia which is owned by Conde Nast Italia. It allows upcoming photographers to showcase their work.
It also gives them the chance to have their photos published in Vogue or to be picked up by photography agencies. Each picture is curated by Vogue’s editorial staff and it ensures that highest quality photographs appear online
The Challenge Let’s have a look at some of the key challenges faced by the PhotoVogue team.
- It currently showcases images from around 130,000 photographers from around the world. This jumbo collection houses more than 400,000 images, each of which can be up to 50 MB in size.
- With the growing demand, it failed to meet user demands. Moreover, the pre-existing IT infrastructure restricted the growth of the site.
- The need for providing better and faster experience to both photographers and editorial staff wasn’t feasible at all.
The Solution Vigano identified AWS as the perfect cloud provider that suited the firm requirements which provided them with an amazing solution.
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It offered flexible scalability, ease of maintenance and cost-effectiveness than the pre-existing physical infrastructure that supported PhotoVogue.
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Presently, it uses Amazon Simple Storage Service (Amazon S3) to store all its photos where its ability to generate a URL enabled users to upload their photos really fast.
But, do they even use FaaS? Or any serverless components? Yes!
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From Amazon S3, an AWS Lambda function is triggered, which automatically converts the uploaded image into multiple digital formats– such as GIF, JPEG, PNG and TIFF- and allows the images to be edited by PhotoVogue staff.
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The Amazon API Gateway is used as the caching layer for the REST API, and Amazon CloudFront facilitates the content delivery network (CDN).
The Benefits “With Amazon API Gateway and AWS Lambda, the user experience is up to 90% faster. That’s for both photographers uploading images and the editorial team processing them.”- Marco Viagano, Head of Digital Development.
Not just that, he has more things to say!
- Quicker provisioning of resources supports growth. “With the traditional infrastructure, it would take days to set up which now can be done in hours. IT is no longer holding back the business, in fact, we can grow faster.”
- Making savings, enabling innovation, boosting revenue. “We’ve cut IT costs by around 30%. We used to spend more time in maintaining the infrastructure which now has reduced drastically and hence we can explore new services.”
- Successful events further promoted the online platform. In November 2016, PhotoVogue held an event in Milan. “During the event, we experienced about 20% more uploads than normal daily traffic and we dealt with it seamlessly.”
Wow!
Benefits of Serverless Technology
Now, it’s time to move on to the benefits that serverless brings on the table. Since, serverless in still in evolution, some of the benefits that we will be considering might not be available after 2 years.
Moreover, this is an unproven concept and hence, before moving on to serverless, you should definitely consider its significant facets. I hope that discussing benefits and drawbacks will you in clearing the confused air regarding serverless.
Reduced Infrastructure Costs Before you set up your servers, there are various things that you need to consider. Some of them might look like below:
- How many and on what type of hosts our servers are going to run on?
- How much CPU & RAM your database needs?
- How many instances will you be provided to support scaling?
- What about high availability?
Once you figure out all these things, comes the time of planning, allocation and provisioning. Tiresome, isn’t it?
Indeed! And most of the time we overestimate things and end up paying for all those resources that we never use in the future. Sounds familiar?
On the other side, the magnanimous benefit that serverless brings to the table is that we don’t plan, allocate or provision resources. We let the vendors facilitate us precisely the amount of capacity we need at any point in time.
If we don’t have any load, we don’t use any and hence, we don’t pay for any! As simple as that! If we only have 1 TB of data, we don’t need the capacity to store 100 TB. We trust our vendors that service will be provided, on demand, and this is equally true for FaaS & BaaS services both.
For example, AWS Lambda is charged by 100 milliseconds of use, 36,000 times more precise than the hourly billing of EC2.
Flexible Scaling All of this reduction in infrastructure cost comes with the added benefit of automatic scaling.
Let me remind you, with serverless, horizontal scaling is completely automatic, elastic and managed by the provider. But how does this all happen? Let’s take an example!
When your platform receives the first event to trigger a function, it will initiate the container to run your code. If another event is triggered while the previous one is still in process, it will initiate another container to run your code. This automation will enable horizontal scaling until there are enough containers to run your code.
Pretty flexible, right?
Reduced Scaling Costs Due to this pay-as-you-go model, you save a lot with high scalability. Isn’t this a huge economic win?
#1. Infrequent requests:
If you’re using a server application, that processes 2 requests every single minute and takes 100 ms to process each request. This means, your CPU usage over an hour is 0.1%. This is highly inefficient and only if you’d wish that much other application could use your server as well.
But serverless encaptures this efficiency and gives you the benefit in the form of reduced cost. In this case, if you’d be paying for just 200ms of computing every minute, which is 0.15% of your overall time.
Such benefits in cost prove to be an amazing democratizer. As production teams have the extremely tiny amount of operational cost to bear to try out something new. In fact, if your total workload is relatively small, you have to provide nothing considering the ‘free tier’ services from some vendors.
#2. Fluctuating traffic:
Let’s assume that your website traffic spikes once in a while. The basic traffic that you receive during the day is around 10 requests/sec. but that increases to 10 times by 100 requests/sec. every 5 min. for 10 sec.
Let’s assume that during the peak hours your servers get exhausted and at the same time you don’t want to reduce your response time. Do you have any solution? I do.
If you’re functioning in a traditional environment, you may need to upgrade your hardware by the factor of 10 to handle the sudden increase in traffic.
Auto-scaling isn’t a favourable option here as it is highly unlikely to predict the time taken by the new instances of servers to come up, and moreover, by the time those servers are ready, the traffic spike phase will be over.
However, with serverless, this doesn’t seem to be an issue. You have to do nothing whether your traffic is uniform or it is at peak. All you do is pay for the extra compute capacity that you use during the spike phase. And hence, this is a huge cost saving for your company.
But wait! You’ll receive significant economic benefits only if your traffic is inconsistent. If you have stable traffic and you’re using FaaS, you may end up paying more than you should. Hence, do your math first!
Ease of Operation Management
As I said earlier, scaling is automatic and hence, FaaS has more benefits than just scaling costs. You ask me how? Let me explain.
#1. Benefit of auto-scaling
Consider a case where you’re manually managing your app server where you will need a specific person to add and remove instances to an array of servers. While with FaaS, you don’t have to worry about that as your vendor do all the things related to scaling.
Moreover, this lets you get out of the bubble of worrying about how many concurrent requests you can handle until you run out of memory with a performance hit?
#2. Reduction in packaging and deployment complexity
Packaging and deploying an API gateways aren’t simple, admit that! At the same time, FaaS functions are pretty simple in comparison to deploying an entire server. If you are just getting started with FaaS, you don’t even need to package anything, write your code in the vendor console and you’re ready to go!
If your team is quite efficient- this benefit could be as huge as- a fully serverless solution aka zero system administration.
Lower Risk of Failure
The chances of problems to occur increases when your team manages multiple systems and components. However, this is not the case with serverless. With serverless, you outsource the management and operation part, plus, you outsource having to solve any problems that occur in those systems.
This brings the added benefit as you rely on the expertise of others in solving unpredictable problems rather than solving it yourself. This sounds like a good idea because many elements of the technical ‘stack’ are the ones that we rarely alter. When a problem occurs, we may not know how to solve them which leads to prolonged and indeterminate downtime.
With serverless, there is a considerable decrease in the number of techniques that you’ll have to manage directly. And moreover, you’ll be dealing with the ones which are frequently used by your team which makes it much easier to handle.
And hence, there are rare chances of failure when you go serverless.
Shorter Lead Time
To provide enough space for innovation in our production environment, short lead time is as important as short cycle time. Lead Time means, the time from the foundation of a new product or feature to having it deployed in a minimum viable way to a production environment.
The fact that serverless architecture totally wipes out the inherent complexity of building, launching, scaling and operations is very amazing in itself.
And this is why I am so excited about serverless because of the ability it unleashes in a production environment. Plus, we can focus more now delivering awesome products to our customers!
I said enough! Let’s say what people have to say about it!AWS
“Now is a pretty great time to have your data stack on AWS. I’ve rarely felt so empowered to build a ton of things with almost literally ZERO infrastructure concerns. Serverless + data engineering is quite the combo.” Chris, Director of Data Engineering at Textio
“We are starting to see applications built in a ridiculously short time period” Adrian Cockcroft, VP at AWS Cloud Architecture Strategy
Drawbacks of Serverless Technology
**Built-in Drawbacks
#1. Vendor lock-in:
Serverless, in other words, means outsourcing your services to a 3rd party vendor. Moreover, that means that you won’t be having much control over many key aspects of system downtime, forced API upgrades, loss of functionality and much more.
This seems to be an inherent limitation that serverless applications face. Through multiple choice of integration patterns, APIs and documents, different vendors enforce different levels of lock-in models.
What lock-in means is that if you want to move from one vendor to another, you’ll need to update your operational tools, change your code and may even require changing the architecture. The fear of vendor lock-in is one of the major drawbacks of serverless.
#2. Statelessness:
This is a huge benefit which enables scaling easily as a matter of increasing concurrency but managing it is quite tricky. And moreover, it comes with the drawback of inconsistent performance which we will discuss later.
Apart from the components that are designed to store data,most of the other serverless parts are stateless. By definition, the information in stateless functions doesn’t persist beyond their immediate lifespan. And hence, this gives the birth to complexity while we consider the interaction of these components with another.
With the implementation of FaaS functions, you can have a code in your app but the cache is rarely of any use. As soon as your cache is ‘heated’ for the initial usage, it is likely to be thrown away as the FaaS instance is torn down.
#3. Local testing:
This is one of the most mind-numbing difficulties of serverless application architectures. In case of non-serverless applications, developers have local options on-hand where they can test the application and observer whether it will work or not during and after the deployment. However, with serverless architecture,end-to-end testing of application is quite more difficult.
Because, at present, most of the vendors aren’t providing with the local implementation which, in turn, forces developers to use the regular production implementation. This means remote testing for all your integrations. Sounds like a big deal? It is.
Because the units of integration with FaaS functions are a lot smaller than with other architecture. These typically consist of separate pieces and hence managing them is quite challenging. And therefore, we rely on remote testing a lot more than we do for other types of architectural style
#4. ‘Cold/Warm’ start: Serverless performance is quite inconsistent
One of the most common issues is how long does it take for a FaaS function to start which is referred to as a cold start. On the AWS Lambda, this means initialisation of our code as well as initialization of our container which houses the code. The cold starts happens when lambda function hasn’t been invoked for a while or if its configurations have been altered.
Once it has been invoked, the container can handle events without going through that same process and these ‘warm’ invocations of the Lambda functions are quite faster. It is recommended that 99.99% of events should be processed by a warm container.
However, the uncertainty between ‘cold’ and ‘warm’ is difficult to predict which leads to inconsistent serverless performance issues.
#5. Security:
As soon as you consider the serverless approach, you are exposed to a whole new level of security issues which can be summed up in the following points:
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With serverless, data is now in transit between various functions. Each time we transit data from one place to another, the chances of the data to be leaked and tampered with increases since the surface area increases.
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With greater flexibility, comes the greater opportunities for attackers to get to your system. The only way to tackle this is to treat each function with its own security protocols.
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No wonder, serverless has made the motoring extremely hard. With more number of functions, comes the hardship of monitoring them. Remember, you may create tons of functions due to its low operational cost, but keep in mind the total cost of ownership, including the increased risk of having unused, vulnerable and stale code in production.
Production Drawbacks
#1. Deployment:
The interaction between the underlying platform and the application is facilitated via API calls. Since serverless applications are, in fact, a bundle of many small individual components, deploying an entire application is typically not a good idea.
Due to this basic difference in the architecture, it is often a challenging task to align the deployments of large-scale serverless applications.
#2. Execution:
One of the publicly criticised limitations of serverless is limited execution environment of FaaS function. Unlike legacy servers, they don’t have an indefinite amount of resources and they are executed on limited CPU, I/O resources, memory and discs.
For instance, AWS Lambda functions come with an inherited memory of 1.5 GB and will be terminated after a maximum of 5 minutes. As the FaaS platform is evolving, you can expect some expansion in the execution environment. Moreover, designing the architecture within the limit of this environment will add you the benefit of more scalable architecture.
#3. Monitoring:
While you may not like the limited execution environment but at the same time monitoring of these metrics is out of your concern. However, metrics responsible for business functionality still needs to be monitored.
But the limit to which the monitoring is well supported of these functions is a mix-and-match. For example, AWS Lambda has multiple ways of monitoring can be performed but some of these are poorly documented. Also, there is a considerable lack of support at present in distributed monitoring. And hence, once all of these functions are in the commonplace, serverless systems will come off as an easy operation.
#4. Remote testing:
The ability to perform remote testing comes along with its inherent inability of local testing. This is due to the fact that certain vendors make the remote testing possible but up to a very lesser extent. For example, you can do remote testing at component level but not at the serverless application level.
It is quite exhaustive to test a series of serverless components without setting up an account with the vendor to make sure that testing isn’t affecting the production resources and also, that the limitations provided by the platform are equally met.
#5. Debugging:
At present, we are stuck on the debugging part as we have to accept whatever the vendor provides us. This might be okay in certain cases but AWS Lambda provides you with extremely basic functionalities and hence it is a big turnoff.
What we are looking for really is integration with 3rd party services for debugging and open APIs.
Conclusion
The aim of this blog is to clear the confusing clouds over the mind of people who are not sure about serverless adoption! But I am sure all those techies who are ready to roll their sleeve and eager to embrace & experiment as the way to an amazing product, will say ‘Yes!’
Though serverless architecture might not come off as a perfect solution to all your worries, it surely will decrease them. But be careful before you plunge yourself into the world of serverless and FaaS. While there are gold mines to be plundered (deployment, scaling), there are dragons as well (serverless security, debugging).
From the business perspective, the best benefit I can see is the space to experiment new features with reduced time-to-market, which is the most critical point in today’s world where new technology is released every day.
Serverless systems are still in its infancy stage and it will be wonderful to witness how they solve and fit into our architectural requirements.
Until then, subscribe to our blog and keep yourself one step ahead with updates on serverless technology and much more!


atom
information
-
class
atom.
Atom
(atom_name, x_coordinate, y_coordinate, z_coordinate, occupancy, temperature_factor, element_symbol)
- atom_name: line[12:16] from pdb file. You obtain all the name of the atom. C-α -> alfa carbon. String.
- x_coordinate: line[30:38] from pdb file. X coordinate. Float.
- y_coordinate: line[38:46] from pdb file. Y coordinate. Float.
- z_coordinate: line[46:54] from pdb file. Z coordinate. Float.
- occupancy: line[54:60] from pdb file. 1 corresponds to 100% occupancy. Float.
- temperature_factor: line[60:66] from pdb file. The temperature for each atom in the structure. Float.
- element_symbol: line[76:78] from pdb file. The element of the atom. String.