Build a Geospatial App with Redis 3.2- Andrew Bass, Sean Yesmunt, Sergio Prada, Deanna Shih, Coding House

•

1 j'aime•1,053 vues

We created an app to find nearby running partners, and to demonstrate Redis Data structures and functions. In this talk, we will review the data structures and walk through our NodeJS app that depends solely on Redis Geospatial Indexes. Functions demoed are GEOADD, ZREM, GEOHASH, GEOPOS, GEODIST, GEORADIUS, GEORADIUSBYMEMBER

Technologie

Please visit:
 
http://bit.do/redis
 
We will be using this data for our presentation!

Redis 3.2 and Geospatial
Andrew Bass, Deanna Shih, and Sean Yesmunt

• Students at Coding House in Fremont, CA
• Andrew Bass
• Deanna Shih
• Sean Yesmunt
• Sergio Prada 
 
paceme.us
3
Who are we?

• Stable as of May 6, 2016 
• Includes a new set of GEO
commands for geo indexing 
• Simply put, saves locations and
compares them
• Geo indexed data is a special
sorted set
4
Redis 3.2 and GEO

GEOADD key longitude latitude member 
 
GEOHASH key member 
• Longitude then Latitude
• Assumes Earth is a sphere
• Data is saved in sorted set as a Geohash
• ZREM can can be used to remove a member from the key
5
GEOADD & GEOHASH

Divide the world in half
Each half is 0 or 1
Save that bit, then keep
dividing
Images from: mapzen.com/blog/geohashes-and-you/

@RedisConf
122.393 W, 37.768 N
or
0100110110010001111011110
Or, in GEOHASH
9q8yyg72zpm0
Images from: mapzen.com/blog/geohashes-and-you/

Why?
8
Redis can compare the locations as a sorted set!
City Geohash
San Francisco 9q8yyg7zpm0
San Jose 9q9k6mjng
Los Angeles 9q5ctr186
London gcpvj0duq
Beijing wx4g0bm6c

GEOPOS key member [member ...]
• Returns Longitude and Latitude for member of a key
• Since the data is converted to a string and back, it may be
slightly different than the original GEOADD inputs
9
GEOPOS

10
Longitude and Latitude Accuracy
https://en.wikipedia.org/wiki/Decimal_degrees

GEODIST key member1 member2 [unit]
❖ Returns the distance between two members of a key
❖ Unit is defaulted to meters, but accepts:
• m for meters
• km for kilometers
• mi for miles
• ft for feet
11
GEODIST

GEORADIUS key longitude latitude radius m|km|ft|mi
[WITHCOORD] [WITHDIST] [WITHHASH] [COUNT count]
[ASC|DESC] 
• Returns members of a key within the radius from the coordinates
• Options for including coordinates, distance, limit number of results,
and sort ascending or descending
• Note, WITHHASH returns a 52bit integer, not the geohash.
12
GEORADIUS

GEORADIUSBYMEMBER key member radius m|km|ft|mi
[WITHCOORD] [WITHDIST] [WITHHASH] [COUNT count]
[ASC|DESC] 
• Returns members of a key within the radius of the given member
• Otherwise, same as GEORADIUS
13
GEORADIUSBYMEMBER

Demo App
Find people close to you
who are also at
14

Contenu connexe

En vedette

RedisConf 2016 talk - The Redis API: Simple, Composable, Powerful

DynomiteDB

Troubleshooting Redis- DaeMyung Kang, Kakao

Redis Labs

I will build from scratch in this session a Microsoft ASP.NET website that caches WebAPI REST calls with both MSOpenTech’s Redis implementation for running while developing in Visual Studio as well as running on a Windows server running IIS. I will show you how to build a safe reusable caching library in c# that can be used in any .net project. I will also demonstrate how to use the Redis cache services that are available on Microsoft’s Azure cloud platform. Further, I’ll demonstrate a real world web site that uses Azure Redis cache and show statistics on how Redis improves performance consistently and reliably.

Using Redis as Distributed Cache for ASP.NET apps - Peter Kellner, 73rd Stre...

Redis Labs

Slides for talk presented at LA Redis meetup, April 16, 2016 at Scopely. This is a draft of a session to be presented at Redis Conference 2016. Description: Scopely's portfolio of social and mid-core games generates billions of events each day, covering everything from in-game actions to advertising to game engine performance. As this portfolio grew in the past two years, Scopely moved all event analysis from third-party hosted solutions to a new event analytics pipeline on top of Redis and Kinesis, dramatically reducing operating costs and enabling new real-time analysis and more efficient warehousing. Our solution receives events over HTTP and SQS and provides real-time aggregation using a custom Redis-backed application, as well as prompt loads into HDFS for batch analyses. Recently, we migrated our realtime layer from a pure Redis datastore to a hybrid datastore with recent data in Redis and older data in DynamoDB, retaining performance while further reducing costs. In this session we will describe our experience building, tuning and monitoring this pipeline, and the role of Redis in supporting handling of Kinesis worker failover, deployment, and idempotence, in addition to its more visible role in data aggregation. This session is intended be helpful for those building streaming data systems and looking for solutions for aggregation and idempotence.

Scalable Streaming Data Pipelines with Redis

Avram Lyon

As a data scientist I frequently need to create web apps to provide interactive functionality, deliver data APIs or simply publish results. It is now easier than ever to deploy your data driven web app by using cloud based application platforms to do the heavy lifting. Cloud Foundry (http://cloudfoundry.org) is an open source public and private cloud platform that enables simple app deployment, scaling and connectivity to data services like PostgreSQL, MongoDB, Redis and Cassandra. Resources: http://www.ianhuston.net/2015/01/cloud-foundry-for-data-science-talk/

Cloud Foundry for Data Science

Ian Huston

In this session, we will build a minimum viable Spring Data web service with REST API, add a MySQL backing service as the primary data store, and a Redis Labs backing service for caching. We will demonstrate performance metrics without Redis caching enabled and then with Redis caching enabled. I will also provide an intro-level explanation of the platform capabilities within Pivotal Web Services.

Back your App with MySQL & Redis, the Cloud Foundry Way- Kenny Bastani, Pivotal

Redis Labs

Efficiently digesting data in large volumes can prove to be challenging for any database. The challenges are compounded when this influx must be analyzed on the fly, or "tasted", to satisfy the sophisticated palates of modern apps. Luckily, there are several proven remedies you can concoct with Redis to help with potential indigestion. The URLs from the presentation are also available at: https://gist.github.com/itamarhaber/325e515c1715a12ef132

Redis & MongoDB: Stop Big Data Indigestion Before It Starts

Itamar Haber

March 29, 2016 Dr. Josiah Carlson talks about using Redis as a Time Series DB

Josiah Carlson

Redis High availability and fault tolerance in a multitenant environment

Iccha Sethi

Benchmarking Redis by itself and versus other NoSQL databases

Itamar Haber

Postgres & Redis Sitting in a Tree- Rimas Silkaitis, Heroku

Redis Labs

Leveraging Probabilistic Data Structures for Real Time Analytics with Redis M...

Itamar Haber

Redis acc 2015_eng

DaeMyung Kang

En vedette (13)

RedisConf 2016 talk - The Redis API: Simple, Composable, Powerful

Troubleshooting Redis- DaeMyung Kang, Kakao

Using Redis as Distributed Cache for ASP.NET apps - Peter Kellner, 73rd Stre...

Scalable Streaming Data Pipelines with Redis

Cloud Foundry for Data Science

Back your App with MySQL & Redis, the Cloud Foundry Way- Kenny Bastani, Pivotal

Redis & MongoDB: Stop Big Data Indigestion Before It Starts

March 29, 2016 Dr. Josiah Carlson talks about using Redis as a Time Series DB

Redis High availability and fault tolerance in a multitenant environment

Benchmarking Redis by itself and versus other NoSQL databases

Postgres & Redis Sitting in a Tree- Rimas Silkaitis, Heroku

Leveraging Probabilistic Data Structures for Real Time Analytics with Redis M...

Redis acc 2015_eng

Similaire à Build a Geospatial App with Redis 3.2- Andrew Bass, Sean Yesmunt, Sergio Prada, Deanna Shih, Coding House

NAPSG 2010 Fire/EMS Conference - Data Sharing Basics

pdituri

Book Review Global Posiotining System

Anonymous Singh

GPS

Selvakumar Selva

Spatial search with geohashes

Lucidworks (Archived)

Differential Global Positioning System (DGPS) is an enhancement to Global Positioning System that provides improved location accuracy, from the 15-meter nominal GPS accuracy to about 10 cm in case of the best implementations. Differential Global Positioning System (DGPS) is a method of providing differential corrections to a Global Positioning System (GPS) receiver in order to improve the accuracy of the navigation solution. DGPS corrections originate from a reference station at a known location. The receivers in these reference stations can estimate errors in the GPS because, unlike the general population of GPS receivers, they have an accurate knowledge of their position. DGPS uses a network of fixed, ground-based reference stations to broadcast the difference between the positions indicated by the GPS (satellite) systems and the known fixed positions. These stations broadcast the difference between the measured satellite pseudoranges and actual (internally computed) pseudoranges, and receiver stations may correct their pseudoranges by the same amount. The digital correction signal is typically broadcast locally over ground-based transmitters of shorter range.

Dgps

egovindia

Dgps concept

egovindia

Dgps concept

egovindia

Geocaching citb mb presenatation day 2

Joseph Murphy

Geohash

Sandeep Bhaskar

Introduction To Geocoding: Linda Achieng

Redis Labs

Similaire à Build a Geospatial App with Redis 3.2- Andrew Bass, Sean Yesmunt, Sergio Prada, Deanna Shih, Coding House (10)

NAPSG 2010 Fire/EMS Conference - Data Sharing Basics

Book Review Global Posiotining System

GPS

Spatial search with geohashes

Dgps

Dgps concept

Geocaching citb mb presenatation day 2

Geohash

Introduction To Geocoding: Linda Achieng

Plus de Redis Labs

Redis Day Bangalore 2020 - Session state caching with redis

Redis Labs

Protecting Your API with Redis by Jane Paek - Redis Day Seattle 2020

Redis Labs

The Happy Marriage of Redis and Protobuf by Scott Haines of Twilio - Redis Da...

Redis Labs

SQL, Redis and Kubernetes by Paul Stanton of Windocks - Redis Day Seattle 2020

Redis Labs

Rust and Redis - Solving Problems for Kubernetes by Ravi Jagannathan of VMwar...

Redis Labs

Redis for Data Science and Engineering by Dmitry Polyakovsky of Oracle

Redis Labs

Practical Use Cases for ACLs in Redis 6 by Jamie Scott - Redis Day Seattle 2020

Redis Labs

Moving Beyond Cache by Yiftach Shoolman Redis Labs - Redis Day Seattle 2020

Redis Labs

Leveraging Redis for System Monitoring by Adam McCormick of SBG - Redis Day S...

Redis Labs

JSON in Redis - When to use RedisJSON by Jay Won of Coupang - Redis Day Seatt...

Redis Labs

Highly Available Persistent Session Management Service by Mohamed Elmergawi o...

Redis Labs

Anatomy of a Redis Command by Madelyn Olson of Amazon Web Services - Redis Da...

Redis Labs

Building a Multi-dimensional Analytics Engine with RedisGraph by Matthew Goos...

Redis Labs

RediSearch 1.6 by Pieter Cailliau - Redis Day Bangalore 2020

Redis Labs

RedisGraph 2.0 by Pieter Cailliau - Redis Day Bangalore 2020

Redis Labs

RedisTimeSeries 1.2 by Pieter Cailliau - Redis Day Bangalore 2020

Redis Labs

RedisAI 0.9 by Sherin Thomas of Tensorwerk - Redis Day Bangalore 2020

Redis Labs

Rate-Limiting 30 Million requests by Vijay Lakshminarayanan and Girish Koundi...

Redis Labs

Three Pillars of Observability by Rajalakshmi Raji Srinivasan of Site24x7 Zoh...

Redis Labs

Solving Complex Scaling Problems by Prashant Kumar and Abhishek Jain of Myntr...

Redis Labs

Plus de Redis Labs (20)