We all know that MongoDB is one of the most flexible and feature-rich databases available. In this session we'll discuss how you can leverage this feature set and maintain high performance with your project's massive data sets and high loads. We'll cover how indexes can be designed to optimize the performance of MongoDB. We'll also discuss tips for diagnosing and fixing performance issues should they arise.
Building a Scalable Inbox System with MongoDB and Javaantoinegirbal
Many user-facing applications present some kind of news feed/inbox system. You can think of Facebook, Twitter, or Gmail as different types of inboxes where the user can see data of interest, sorted by time, popularity, or other parameter. A scalable inbox is a difficult problem to solve: for millions of users, varied data from many sources must be sorted and presented within milliseconds. Different strategies can be used: scatter-gather, fan-out writes, and so on. This session presents an actual application developed by 10gen in Java, using MongoDB. This application is open source and is intended to show the reference implementation of several strategies to tackle this common challenge. The presentation also introduces many MongoDB concepts.
Video available here: http://vivu.tv/portal/archive.jsp?flow=783-586-4282&id=1270584002677
We all know that MongoDB is one of the most flexible and feature-rich databases available. In this webinar we'll discuss how you can leverage this feature set and maintain high performance with your project's massive data sets and high loads. We'll cover how indexes can be designed to optimize the performance of MongoDB. We'll also discuss tips for diagnosing and fixing performance issues should they arise.
As your data grows, the need to establish proper indexes becomes critical to performance. MongoDB supports a wide range of indexing options to enable fast querying of your data, but what are the right strategies for your application?
In this talk we’ll cover how indexing works, the various indexing options, and use cases where each can be useful. We'll dive into common pitfalls using real-world examples to ensure that you're ready for scale.
Building a Scalable Inbox System with MongoDB and Javaantoinegirbal
Many user-facing applications present some kind of news feed/inbox system. You can think of Facebook, Twitter, or Gmail as different types of inboxes where the user can see data of interest, sorted by time, popularity, or other parameter. A scalable inbox is a difficult problem to solve: for millions of users, varied data from many sources must be sorted and presented within milliseconds. Different strategies can be used: scatter-gather, fan-out writes, and so on. This session presents an actual application developed by 10gen in Java, using MongoDB. This application is open source and is intended to show the reference implementation of several strategies to tackle this common challenge. The presentation also introduces many MongoDB concepts.
Video available here: http://vivu.tv/portal/archive.jsp?flow=783-586-4282&id=1270584002677
We all know that MongoDB is one of the most flexible and feature-rich databases available. In this webinar we'll discuss how you can leverage this feature set and maintain high performance with your project's massive data sets and high loads. We'll cover how indexes can be designed to optimize the performance of MongoDB. We'll also discuss tips for diagnosing and fixing performance issues should they arise.
As your data grows, the need to establish proper indexes becomes critical to performance. MongoDB supports a wide range of indexing options to enable fast querying of your data, but what are the right strategies for your application?
In this talk we’ll cover how indexing works, the various indexing options, and use cases where each can be useful. We'll dive into common pitfalls using real-world examples to ensure that you're ready for scale.
I inherited a MongoDB database server with 60 collections and 100 or so indexes.
The business users are complaining about slow report completion times. What can I do to improve performance?
Back to Basics Webinar 3: Schema Design Thinking in DocumentsMongoDB
This is the third webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will explain the architecture of document databases.
This presentation will demonstrate how you can use the aggregation pipeline with MongoDB similar to how you would use GROUP BY in SQL and the new stage operators coming 3.4. MongoDB’s Aggregation Framework has many operators that give you the ability to get more value out of your data, discover usage patterns within your data, or use the Aggregation Framework to power your application. Considerations regarding version, indexing, operators, and saving the output will be reviewed.
Back to Basics Webinar 4: Advanced Indexing, Text and Geospatial IndexesMongoDB
This is the fourth webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will introduce you to the aggregation framework.
Webinar: Exploring the Aggregation FrameworkMongoDB
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Watch this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo an analysis of U.S. census data.
Back to Basics Webinar 5: Introduction to the Aggregation FrameworkMongoDB
This is the fifth webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will introduce you to the aggregation framework.
Map/Confused? A practical approach to Map/Reduce with MongoDBUwe Printz
Talk given at MongoDb Munich on 16.10.2012 about the different approaches in MongoDB for using the Map/Reduce algorithm. The talk compares the performance of built-in MongoDB Map/Reduce, group(), aggregate(), find() and the MongoDB-Hadoop Adapter using a practical use case.
Webinar: Back to Basics: Thinking in DocumentsMongoDB
New applications, users and inputs demand new types of data, like unstructured, semi-structured and polymorphic data. Adopting MongoDB means adopting to a new, document-based data model.
While most developers have internalized the rules of thumb for designing schemas for relational databases, these rules don't apply to MongoDB. Documents can represent rich data structures, providing lots of viable alternatives to the standard, normalized, relational model. In addition, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense.
In this session, Buzz Moschetti explores how you can take advantage of MongoDB's document model to build modern applications.
These are slides from our Big Data Warehouse Meetup in April. We talked about NoSQL databases: What they are, how they’re used and where they fit in existing enterprise data ecosystems.
Mike O’Brian from 10gen, introduced the syntax and usage patterns for a new aggregation system in MongoDB and give some demonstrations of aggregation using the new system. The new MongoDB aggregation framework makes it simple to do tasks such as counting, averaging, and finding minima or maxima while grouping by keys in a collection, complementing MongoDB’s built-in map/reduce capabilities.
For more information, visit our website at http://casertaconcepts.com/ or email us at info@casertaconcepts.com.
MongoDB Europe 2016 - Advanced MongoDB Aggregation PipelinesMongoDB
We will do a deep dive into the powerful query capabilities of MongoDB's Aggregation Framework, and show you how you can use MongoDB's built-in features to inspect the execution and tune the performance of your queries. And, last but not least, we will also give you a brief outlook into MongoDB 3.4's awesome new Aggregation Framework additions.
In a real life almost any project deals with the
tree structures. Different kinds of taxonomies,
site structures etc require modeling of
hierarchy relations.
Typical approaches used
● Model Tree Structures with Child References
● Model Tree Structures with Parent References
● Model Tree Structures with an Array of Ancestors
● Model Tree Structures with Materialized Paths
● Model Tree Structures with Nested Sets
I inherited a MongoDB database server with 60 collections and 100 or so indexes.
The business users are complaining about slow report completion times. What can I do to improve performance?
Back to Basics Webinar 3: Schema Design Thinking in DocumentsMongoDB
This is the third webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will explain the architecture of document databases.
This presentation will demonstrate how you can use the aggregation pipeline with MongoDB similar to how you would use GROUP BY in SQL and the new stage operators coming 3.4. MongoDB’s Aggregation Framework has many operators that give you the ability to get more value out of your data, discover usage patterns within your data, or use the Aggregation Framework to power your application. Considerations regarding version, indexing, operators, and saving the output will be reviewed.
Back to Basics Webinar 4: Advanced Indexing, Text and Geospatial IndexesMongoDB
This is the fourth webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will introduce you to the aggregation framework.
Webinar: Exploring the Aggregation FrameworkMongoDB
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Watch this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo an analysis of U.S. census data.
Back to Basics Webinar 5: Introduction to the Aggregation FrameworkMongoDB
This is the fifth webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will introduce you to the aggregation framework.
Map/Confused? A practical approach to Map/Reduce with MongoDBUwe Printz
Talk given at MongoDb Munich on 16.10.2012 about the different approaches in MongoDB for using the Map/Reduce algorithm. The talk compares the performance of built-in MongoDB Map/Reduce, group(), aggregate(), find() and the MongoDB-Hadoop Adapter using a practical use case.
Webinar: Back to Basics: Thinking in DocumentsMongoDB
New applications, users and inputs demand new types of data, like unstructured, semi-structured and polymorphic data. Adopting MongoDB means adopting to a new, document-based data model.
While most developers have internalized the rules of thumb for designing schemas for relational databases, these rules don't apply to MongoDB. Documents can represent rich data structures, providing lots of viable alternatives to the standard, normalized, relational model. In addition, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense.
In this session, Buzz Moschetti explores how you can take advantage of MongoDB's document model to build modern applications.
These are slides from our Big Data Warehouse Meetup in April. We talked about NoSQL databases: What they are, how they’re used and where they fit in existing enterprise data ecosystems.
Mike O’Brian from 10gen, introduced the syntax and usage patterns for a new aggregation system in MongoDB and give some demonstrations of aggregation using the new system. The new MongoDB aggregation framework makes it simple to do tasks such as counting, averaging, and finding minima or maxima while grouping by keys in a collection, complementing MongoDB’s built-in map/reduce capabilities.
For more information, visit our website at http://casertaconcepts.com/ or email us at info@casertaconcepts.com.
MongoDB Europe 2016 - Advanced MongoDB Aggregation PipelinesMongoDB
We will do a deep dive into the powerful query capabilities of MongoDB's Aggregation Framework, and show you how you can use MongoDB's built-in features to inspect the execution and tune the performance of your queries. And, last but not least, we will also give you a brief outlook into MongoDB 3.4's awesome new Aggregation Framework additions.
In a real life almost any project deals with the
tree structures. Different kinds of taxonomies,
site structures etc require modeling of
hierarchy relations.
Typical approaches used
● Model Tree Structures with Child References
● Model Tree Structures with Parent References
● Model Tree Structures with an Array of Ancestors
● Model Tree Structures with Materialized Paths
● Model Tree Structures with Nested Sets
Quando uma aplicação começa a ficar grande e complexa, fazer buscas nos seus models torna-se uma tarefa complicada. Efetuar as buscas diretamente no banco de dados é um processo lento, ineficiente e que permite pouca ou nenhuma maleabilidade sobre a forma com que a busca é feita. Surge então o ElasticSearch, uma engine de busca utilizada por empresas como Github, Twitter e 4square para indexar e buscar literalmente milhões de documentos em tempo real. Nessa palestra, explicarei quando, como e porque utilizar o ElasticSearch para facilmente indexar e efetuar buscas complexas nos seus models.
Program 4You are to write an efficient program that will read a di.pdfezzi552
Lab Exercise — 2-D Arrays in C++
In this lab exercise, you are required to write a C++ program to add two 4 * 5 matrices and then
output the results to the screen.
Prompt the user to enter the name of two files with matrices
Compute the sum of the two matrices and output the results
A sample run is as follows:
Further requirements:
Create three functions:
readArray with void return and one 2D array argument and one string argument
printArray with void return and one 2D array argument
sumArray with void return and three 2D array arguments
Separate your code into three files as described in the lab:
main.cpp
myFunction.cpp
myFunction.h
If you can correct this and then saparete it to three files
#include
#include
#include
using namespace std;
const unsigned int R_SIZE=4;
const unsigned int C_SIZE=5;
void readArray(int p_Array[][C_SIZE], string fName); // function prototype
int main()
{
int i, j;
int Nums1[R_SIZE][C_SIZE];
string filename;
cout << \"Please enter the name of the file containing data for Array 1: \";
cin >>filename;
readArray(Nums1, filename);
for ( i=0; i < R_SIZE; i++)
{
for ( j=0; j< C_SIZE; j++)
{
cout<< Nums1[i][j] << \" \";
}
cout << endl;
}
}
// Function: readArray
// Purpose: To read values into an array.
// Parameters: Base address of an array.
// Returns: void
// --------------------------------------------------------
void readArray(int p_Array[R_SIZE][C_SIZE], string fName)
{
ifstream inF;
int i, j;
inF.open(fName.c_str());
if(inF.fail())
{
cout << fName << \"does not enist\" << endl;
}
else
{
cout << fName << \"exists\" << endl;
}
while(!inF.eof())
{
for ( i=0; i < R_SIZE; i++)
{
for ( j=0; j< C_SIZE; i++)
{
inF >> p_Array[i][j];
}
}
}
} // end InitArray function
Someone help me with this and I modified it a little bit but still can\'t read from the file!!!
This is the code
/*..............myFunction.h ...........*/
#include
#include
using namespace std;
const unsigned int R_SIZE=4;
const unsigned int C_SIZE=5;
void readArray(int p_Array[][C_SIZE], string fName); // function read prototype
void printArray(int p_Array[][C_SIZE]); // function print prototype
void sumArray (int p_Array1[][C_SIZE],int p_Array2[][C_SIZE],int p_Array3[][C_SIZE]);
/*..............myFunction.cpp ...........*/
#include \"myfunction.h\"
// Function: printArray
// Purpose: To print values of an array.
// Parameters: Base address of an array.
// Returns: void
// --------------------------------------------------------
void printArray(int p_Array[R_SIZE][C_SIZE]){
for (int i=0; i < R_SIZE; i++)
{
for (int j=0; j< C_SIZE; j++)
{
cout<< p_Array[i][j] << \" \";
}
cout << endl;
}
}
// Function: readArray
// Purpose: To read values into an array.
// Parameters: Base address of an array.
// Returns: void
// --------------------------------------------------------
void readArray(int p_Array[R_SIZE][C_SIZE], string fName)
{
ifstream inF;
int i, j;
inF.open(fName.c_str());
if(inF.fail())
{
cout << fName << \"does not enist\" << endl;
}
else
{
cout << fName .
45. Range Types db.c.find( {x:/^a/} ) "indexBounds" : { "x" : [ [ "a", "b" ], [ /^a/, /^a/ ] ] } 2 ranges scanned of 2 different types: string and regex
46. Range Types db.c.find( {x:/a/} ) "indexBounds" : { "x" : [ [ "", { } ], [ /a/, /a/ ] ] } Here the index only helps to restrict type, not efficient in practice
50. Set Match > db.c.find( {x:{$in:[3,6]}} ).explain() { "cursor" : "BtreeCursor x_1 multi", "nscanned" : 3, "nscannedObjects" : 2, "n" : 2, "millis" : 8, "nYields" : 0, "nChunkSkips" : 0, "isMultiKey" : false, "indexOnly" : false, "indexBounds" : { "x" : [ [ 3, 3 ], [ 6, 6 ] ] }} Why is nscanned 3? This is an algorithmic detail, when there are disjoint ranges for a key nscanned may be higher than the number of matching keys.
54. All Match 8 1 2 3 4 5 6 7 9 3 ? {_id:4,x:[3,6]}
55. All Match > db.c.find( {x:{$all:[3,6]}} ).explain() { "cursor" : "BtreeCursor x_1", "nscanned" : 1, "nscannedObjects" : 1, "n" : 1, "millis" : 0, "nYields" : 0, "nChunkSkips" : 0, "isMultiKey" : true, "indexOnly" : false, "indexBounds" : { "x" : [ [ 3, 3 ] ] } } The first entry in the $all match array is always used for index bounds. Note this may not be the least numerous indexed value in the $all array.
83. Index {x:1} Id would be returned by default, but isn’t in the index so we need to exclude to return only indexed fields.
84. Covered Indexes > db.c.find( {x:6}, {x:1,_id:0} ).explain() { "cursor" : "BtreeCursor x_1", "nscanned" : 1, "nscannedObjects" : 1, "n" : 1, "millis" : 0, "nYields" : 0, "nChunkSkips" : 0, "isMultiKey" : false, "indexOnly" : true, "indexBounds" : { "x" : [ [ 6, 6 ] ] } } IndexOnly is true, and isMultiKey must be false. Currently we set isMultiKey to true the first time we save a doc where the field is a multikey array.
102. Unindexed $or Clause > db.c.find( {$or:[{x:5},{y:'d'}]} ).explain() { "cursor" : "BasicCursor", "nscanned" : 9, "nscannedObjects" : 9, "n" : 3, "millis" : 0, "nYields" : 0, "nChunkSkips" : 0, "isMultiKey" : false, "indexOnly" : false, "indexBounds" : { } } Since y is not indexed, we must do a full collection scan to match y:’d’. Since a full scan is required, we don’t use the index on x to match x:5.