In version 2.4, there have been significant enhancements to the geospatial indexing capabilities in MongoDB, such as polygon intersections, a more accurate spherical model, and better integration with MongoDB's aggregation framework. In this presentation, you'll learn about the new enhancements and how they are enabling developers to more quickly and easily develop spatially-aware applications.
GORM is one of the keys for the success of Grails, but for a Grails beginner some concepts may be a bit confusing. Even for a long time developer there can be some missconceptions due to the abstractions layers of the framework.
In this talk I’ll try to cover some of the basics of GORM, Hibernate and how to interact with transactions and sessions. I’ll show some of the problems that I had starting with the Grails framework and how I think they are best solved.
Some other topics that I’ll go over are the interaction with GPars, and the differences between “session” and “transaction”.
Getting Started with Geospatial Data in MongoDBMongoDB
MongoDB supports geospatial data and specialized indexes that make building location-aware applications easy and scalable.
In this session, you will learn the fundamentals of working with geospatial data in MongoDB. We will explore how to store and index geospatial data and best practices for using geospatial query operators and methods. By the end of this session, you should be able to implement basic geolocation functionality in an application.
In this webinar, you will learn:
- Getting geospatial data into MongoDB and how to build geospatial indexes.
- The fundamentals of MongoDB's geospatial query operators and how to design queries that meet the needs of your application.
- Advanced geospatial capabilities with Java geospatial libraries and MongoDB.
Spatial Data is very important for the new applications, related with Data Visualization and BI. Microsoft Azure offers possibility to use advantages of spatial data suing cloud computing. In this lecture will talk about the use of spatial data in the Microsoft Azure - loading data from Windows Azure SQL Database Spatial, optimizing Windows Azure applications and their use of different types of customers: WEB based, WPF, WP. We will learn how to import spatial data in different formats in Microsoft Azure SQL Database Spatial and will create a several demo applications, that use this data. We will also discuss the specifics, when you need to create and deploy claus applications like Azure Web Sites, Azure Cloud Services using spatial data.
Covers the new Apache Lucene 4 spatial module. Includes Solr usage info. Applicable to ElasticSearch too.
Presented the 2012 Open Source Search in Government conference by Basis Technologies.
GORM is one of the keys for the success of Grails, but for a Grails beginner some concepts may be a bit confusing. Even for a long time developer there can be some missconceptions due to the abstractions layers of the framework.
In this talk I’ll try to cover some of the basics of GORM, Hibernate and how to interact with transactions and sessions. I’ll show some of the problems that I had starting with the Grails framework and how I think they are best solved.
Some other topics that I’ll go over are the interaction with GPars, and the differences between “session” and “transaction”.
Getting Started with Geospatial Data in MongoDBMongoDB
MongoDB supports geospatial data and specialized indexes that make building location-aware applications easy and scalable.
In this session, you will learn the fundamentals of working with geospatial data in MongoDB. We will explore how to store and index geospatial data and best practices for using geospatial query operators and methods. By the end of this session, you should be able to implement basic geolocation functionality in an application.
In this webinar, you will learn:
- Getting geospatial data into MongoDB and how to build geospatial indexes.
- The fundamentals of MongoDB's geospatial query operators and how to design queries that meet the needs of your application.
- Advanced geospatial capabilities with Java geospatial libraries and MongoDB.
Spatial Data is very important for the new applications, related with Data Visualization and BI. Microsoft Azure offers possibility to use advantages of spatial data suing cloud computing. In this lecture will talk about the use of spatial data in the Microsoft Azure - loading data from Windows Azure SQL Database Spatial, optimizing Windows Azure applications and their use of different types of customers: WEB based, WPF, WP. We will learn how to import spatial data in different formats in Microsoft Azure SQL Database Spatial and will create a several demo applications, that use this data. We will also discuss the specifics, when you need to create and deploy claus applications like Azure Web Sites, Azure Cloud Services using spatial data.
Covers the new Apache Lucene 4 spatial module. Includes Solr usage info. Applicable to ElasticSearch too.
Presented the 2012 Open Source Search in Government conference by Basis Technologies.
OSDC 2012 | Building a first application on MongoDB by Ross LawleyNETWAYS
MongoDB – from "humongous" – is an open source, non-relational, document-oriented database. Trading off a few traditional features of databases (notably joins and transactions) in order to achieve much better performance, MongoDB is fast, scalable, and designed for web development. The goal of the MongoDB project is to bridge the gap between key-value stores (which are fast and highly scalable) and traditional RDBMS systems (which provide rich queries and deep functionality).
This talk will introduce the features of MongoDB by walking through how one can building a simple location-based application using MongoDB. The talk will cover the basics of MongoDB's document model, query language, map-reduce framework and deployment architecture.
Efficient Query Processing in Geographic Web Search EnginesYen-Yu Chen
Geographic web search engines allow users to constrain and order search results in an intuitive manner by focusing a query on a particular geographic region. Geographic search technology, also called local search, has recently received significant interest from major search engine companies. Academic research in this area has focused primarily on techniques for extracting geographic knowledge from the web. In this paper, we study the problem of efficient query processing in scalable geographic search engines. Query processing is a major bottleneck in standard web search engines, and the main reason for the thousands of machines used by the major engines. Geographic search engine query processing is different in that it requires a combination of text and spatial data processing techniques. We propose several algorithms for efficient query processing in geographic search engines, integrate them into an existing web search query processor, and evaluate them on large sets of real data and query traces.
Presented by David Smiley, Software Systems Engineer, Lead, MITRE
Lucene’s former spatial contrib is gone and in its place is an entirely new spatial module developed by several well-known names in the Lucene/Solr spatial community. The heart of this module is an approach in which spatial geometries are indexed using edge-ngram tokenized geohashes searched with a prefix-tree/trie recursive algorithm. It sounds cool and it is! In this presentation, you’ll see how it works, why it’s fast, and what new things you can do with it. Key features are support for multi-valued fields, and indexing shapes with area -- even polygons, and support for various spatial predicates like “Within”. You’ll see a live demonstration and a visual representation of geohash indexed shapes. Finally, the session will conclude with a look at the future direction of the module.
Webinar: Building Your First Application with MongoDBMongoDB
This webinar will introduce the features of MongoDB by walking through how one can building a simple location-based checkin application using MongoDB. The talk will cover the basics of MongoDB's document model, query language, map-reduce framework and deployment architecture.
2012 URISA Track, Geologic Mapping 101: Common Pitfalls and Suggestions for a...GIS in the Rockies
Creating maps composed of polygons and polylines within a Geographic Information System (GIS) software, such as ArcGIS, is a common task for many GIS professionals across multiple disciplines. The
[22]
development of these types of maps can be a complex and labor intensive process. This is especially true when creating geologic maps, which represent a complex network of geologic units, faults, joints, and other features, often with cross-cutting relationships. Those who have tried to create a map like this probably realized, early in the process, that it is not as straight-forward as they imagined due to the many different work-flow patterns that exist for creating maps in a GIS. In this presentation, we will discuss common problems and how to manage them, as well as give suggestions that will make your next geologic mapping project more streamlined and organized.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
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MongoDB – from "humongous" – is an open source, non-relational, document-oriented database. Trading off a few traditional features of databases (notably joins and transactions) in order to achieve much better performance, MongoDB is fast, scalable, and designed for web development. The goal of the MongoDB project is to bridge the gap between key-value stores (which are fast and highly scalable) and traditional RDBMS systems (which provide rich queries and deep functionality).
This talk will introduce the features of MongoDB by walking through how one can building a simple location-based application using MongoDB. The talk will cover the basics of MongoDB's document model, query language, map-reduce framework and deployment architecture.
Efficient Query Processing in Geographic Web Search EnginesYen-Yu Chen
Geographic web search engines allow users to constrain and order search results in an intuitive manner by focusing a query on a particular geographic region. Geographic search technology, also called local search, has recently received significant interest from major search engine companies. Academic research in this area has focused primarily on techniques for extracting geographic knowledge from the web. In this paper, we study the problem of efficient query processing in scalable geographic search engines. Query processing is a major bottleneck in standard web search engines, and the main reason for the thousands of machines used by the major engines. Geographic search engine query processing is different in that it requires a combination of text and spatial data processing techniques. We propose several algorithms for efficient query processing in geographic search engines, integrate them into an existing web search query processor, and evaluate them on large sets of real data and query traces.
Presented by David Smiley, Software Systems Engineer, Lead, MITRE
Lucene’s former spatial contrib is gone and in its place is an entirely new spatial module developed by several well-known names in the Lucene/Solr spatial community. The heart of this module is an approach in which spatial geometries are indexed using edge-ngram tokenized geohashes searched with a prefix-tree/trie recursive algorithm. It sounds cool and it is! In this presentation, you’ll see how it works, why it’s fast, and what new things you can do with it. Key features are support for multi-valued fields, and indexing shapes with area -- even polygons, and support for various spatial predicates like “Within”. You’ll see a live demonstration and a visual representation of geohash indexed shapes. Finally, the session will conclude with a look at the future direction of the module.
Webinar: Building Your First Application with MongoDBMongoDB
This webinar will introduce the features of MongoDB by walking through how one can building a simple location-based checkin application using MongoDB. The talk will cover the basics of MongoDB's document model, query language, map-reduce framework and deployment architecture.
2012 URISA Track, Geologic Mapping 101: Common Pitfalls and Suggestions for a...GIS in the Rockies
Creating maps composed of polygons and polylines within a Geographic Information System (GIS) software, such as ArcGIS, is a common task for many GIS professionals across multiple disciplines. The
[22]
development of these types of maps can be a complex and labor intensive process. This is especially true when creating geologic maps, which represent a complex network of geologic units, faults, joints, and other features, often with cross-cutting relationships. Those who have tried to create a map like this probably realized, early in the process, that it is not as straight-forward as they imagined due to the many different work-flow patterns that exist for creating maps in a GIS. In this presentation, we will discuss common problems and how to manage them, as well as give suggestions that will make your next geologic mapping project more streamlined and organized.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
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These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
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Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
4. MongoDB has had geo for a
while
• `2d` index
– Store points on 2d plane
– Search for points within a:
• Rectangle ($box)
• Polygon ($polygon)
• Circle ($center)
• Circle on a sphere ($centerSphere)
– Search for nearest points ($near, $nearSphere)
2.4 Geospatial features – Ian Bentley
5. Some desirable things!
• Storing non-point geometries
• Within searches on a sphere
• Searching for intersecting geometries on a
sphere
• Better support for compound indexes
2.4 Geospatial features – Ian Bentley
6. Storing non-point geometries
• GeoJSON – A collaborative community project
that produced a specification for encoding
geometric entities in JSON
• Gaining wide support
– OpenLayers
– PostGIS
– Libraries in several languages
2.4 Geospatial features – Ian Bentley
7. GeoJSON allows us to
encode
Points:
{
geo: {
type: "Point",
coordinates: [100.0, 0.0]
}
}
2.4 Geospatial features – Ian Bentley
8. GeoJSON allows us to
encode
LineStrings:
{
geo: {
type: "LineString",
coordinates: [ [100.0, 0.0], [101.0, 1.0] ]
}
}
2.4 Geospatial features – Ian Bentley
9. GeoJSON allows us to
encode
Polygons:
{ geo: {
type: "Polygon",
coordinates: [
[ [100.0, 0.0], [101.0, 0.0],
[101.0, 1.0], [100.0, 1.0],
[100.0, 0.0] ]
]
} }
2.4 Geospatial features – Ian Bentley
10. Within searches on a sphere
• $geoWithin operator
• Takes a GeoJSON polygon geometry as a
specifier
• Returns any geometries of any type that are fully
contained within the polygon
• Works without any index.
2.4 Geospatial features – Ian Bentley
11. Intersecting geometries on a
sphere
• $geoIntersects operator
• Takes any GeoJSON geometry as a specifier
• Returns any geometries that have a non-empty
intersection
• Lots of edge cases – intersecting edges, points
on lines.
• Works without any index.
2.4 Geospatial features – Ian Bentley
12. Better support for compound
indexes
• Unlike 2d indexes, 2dsphere indexes aren’t
required to be the first field of a compound index
– Filtering potential documents before doing geo query can
drastically improve the performance of some queries
• “Find me Hot Dog Stands within New York state”
• “Find me geometries in New York state that are
Hot Dog Stands”
• Multiple geo fields can be in the same index
– “Find routes with start location 50miles from JFK and end
location 100miles from YYC”
2.4 Geospatial features – Ian Bentley
14. • You can find all the code, and data powering the
demo on github, and read about it on my blog
• Let’s take a close look at the python that does
the actual work.
2.4 Geospatial features – Ian Bentley
15. It’s this simple - within
def find_within(points):
# When defining a polygon, the first point should
# also appear as the last point.
points.append(points[0])
poly = {
"type": "Polygon",
"coordinates": [points]
}
places = collection.find(
{"geo": { "$within": { "$geometry": poly } } } )
places.limit(500)
return places
2.4 Geospatial features – Ian Bentley
16. It’s this simple - intersects
def find_intersects(points):
line = {
"type": "LineString",
"coordinates": points
}
places = collection.find(
{"geo":{ "$geoIntersects":
{ "$geometry": line } } } )
places.limit(50)
return places
2.4 Geospatial features – Ian Bentley
17. It’s this simple - near
def find_nearest(point):
point = {
"type": "Point",
"coordinates": point
}
places = collection.find(
{"geo": { "$near": { "$geometry": point } } })
places.limit(10)
return places
2.4 Geospatial features – Ian Bentley
19. How do you index a spherical
coordinate?
• Divide the geometry that you are indexing into a
grid.
• For each cell in the grid, calculate a key, based
upon its position on the sphere.
• Insert each cell into a standard B-tree
• MongoDB uses google’s S2 C++ library for the
heavy lifting.
2.4 Geospatial features – Ian Bentley
21. Coverings
• A covering of a geometry is a minimal set of cells
that completely cover’s a geometry
• S2 can efficiently generate coverings for arbitrary
geometries.
2.4 Geospatial features – Ian Bentley
22. Covering of Grid of the UK
2.4 Geospatial features – Ian Bentley
23. Covering of A4 surrounding
Trafalgar Square
2.4 Geospatial features – Ian Bentley
24. Cells
• S2 defines cell sizes from level 1 to level 31
• The higher the level, the smaller the cell
• Different levels are optimized for different queries
– If you have densely packed geometries, and you are
doing a $near search, a higher level will be efficient
– If you are doing a $within search with a large polygon, a
lower level will be more efficient
• By default we use all levels between 500m and
100km on a side
2.4 Geospatial features – Ian Bentley
25. Near search
2.4 Geospatial features – Ian Bentley
26. Near search
2.4 Geospatial features – Ian Bentley
27. Near search
2.4 Geospatial features – Ian Bentley
28. Near search
2.4 Geospatial features – Ian Bentley
29. Near search
2.4 Geospatial features – Ian Bentley
30. Near search
2.4 Geospatial features – Ian Bentley
Hit Record and make sure it recordsOpen your demo.Move your mouse.Make announcement about QA five minutes before and as you start
This is 6th grade geometry on the cartesian plane. Often called (inexactly) Euclidean geometryAn plane is infinite in all directions. This is a convenient way of reasoning about geometry because math on the plane is easy. As a simplification of a sphere, however, it has pretty big problems as soon as you start to worry about large polygons, long lines, or any degree of accuracy.
As is excellently highlighted by Randall Munroe of xkcd, projecting a sphere onto a plane is non-obvious. It’s similarly not easy the other direction.Managing the math for sphere’s is much more difficult than on a plane, and definitely not something most of us want to implement.
The 2d index was introduced in Mongodb 2.2End this slide by saying: “All this is great, but there are some additional features that we might like.”
Points are great, but we want to store arbitrary polygons, lines, etc.
Notice that the first point is the same as the last point.This is the simplest polygon form. The coordinate specification is a list of list of point specs. The first list of point specifications describes the exterior shell of the polygon, and each subsequent list of points describes a hole in the polygon.MongoDB will reject any polygons that self intersect with a parse error.
Within searches on the plane with large polygons can be significantly different than on the sphere because they follow the curvature of the sphere.
Re: edge cases: Some are documented on mongodb.org, but there are far too many to detail, so make sure to play around with your particular edge cases.
If you have a collection of documents that are all the businesses in America, filtering for type Hot Dog Stand will reduce the set of results significantly, and searching for an exact match string compare on a normal mongo index is a very quick operation, compared to a geo index search. Because of that stating the question in the first order will be much faster than stating it in the second way.Indexing multiple geo fields was not possible between 2.4, and make possible a whole suite of queries that weren’t possible before.
1st point and 2nd point define the first line.2nd point and 3rd point define the second line.So on.
$maxDistance operator is an optional operator that allows us to specify a maximum distance away from a point, which to go looking.
Tricky bitsHow do you use that index efficiently?How do you decide the size of the cells? How do you calculate thebtree key
Works by looking at concentric donuts starting from the center point.Here we are searching for pubs near a point on Leicester SquareNothing in donut 1
The porcupine is within the second donut, but although the Brewmaster is within the covering for the second donut, it isn’t actually within the donut
This continues until we have found enough points to fill a batch