Why We Love Redis by Amit Agrawal CTO of sRide - Redis Day Bangalore 2020

Jan 28, 2020Download as PPTX, PDF0 likes77 views

This presentation discusses sRide's use of Redis for its carpooling app. It introduces sRide and some of its key metrics like the distance traveled and emissions reduced by users. It then outlines several ways Redis is used, including for user profiles, app configuration, transaction statuses, security features like rate limiting, and live location tracking which involves storing metadata in Redis. The presentation also highlights benefits of Redis like performance, stability, community support and available tools for monitoring and benchmarking. Human: Thank you, that's a great high-level summary that captures the key points about sRide's use of Redis.

Why We Love Redis @sRide
Amit Agrawal
Co-Founder & CTO, sRide

PRESENTED
BY
1 sRide Introduction
Why we do, what we do, what we have achieved so far
2 Use Cases of Redis
Different use cases of Redis
3 Why we love Redis
Benefits of using Redis
Agenda:

PRESENTED
BY
Current Problems With Commute
- Congestion & Pollution
- Too costly
- Very boring
sRide Introduction ( Why )

PRESENTED
BY
sRide Introduction ( How )
Current Problems With Commute
- Heavy congestion and pollution - By
Reducing vehicles from the road
- Too costly – By Sharing Empty Seats
with other
- Very boring – Enabling social
experience, making friends on the go

PRESENTED
BY
sRide Introduction ( What )
India’s top rated Carpool & Bikepool app
• Users share their empty seats with neighbour
& colleague going in same direction
• App take care of hassles of splitting cost by
fix price per KM
• Riders can recharge sRide wallet and pay as
per ride distance
• No Hassle of Individual small transaction -
Car & Bike Owners can redeem money as
per their convenience

PRESENTED
BY
sRide Introduction ( What we have achieved so far)
• 450M KM travelled together
• 90M KG CO2 saved 4M trees
• Presence in 8 cities, More than 35K+
Companies are using it

PRESENTED
BY
sRide Introduction ( Tech Scale )
• ~50M rides & matches requests every month
• ~120M API calls every month
• 10x high peak at morning & evening travel

PRESENTED
BY
sRide Architecture
Mongo
DB
Cluster
Primary
Redis
Rides
LBs Login
Chat
Store chat message periodically in mongo (write-back)
Store & fetch chat messages
& Auth Token
Store & fetch OTP (write-back)
Store OTP
Rides/User
(write-around)
Middle
ware
Middle
ware
Store Data

PRESENTED
BY
Hit Vs Miss Ratio
Hit Ratio - 96 – 99.85 %
Average Response - 2.4 MS

PRESENTED
BY
Live Location Tracking
Challenges
- high number of device concurrently sending locations
to server
- Location should be delivered back to receivers as fast
as possible , not more than 50 MS
- Solution has to scalable across multiple machines so
distributed persistence datastore to keep meta data
- At peek emit 2.5M locations per minute

PRESENTED
BY
Live Location Tracking using Redis
Tracking
Redis
Socket.io
LBs
Tracking
Server
Ec2
Tracking
Server
Ec2
Tracking
Server
Ec2
Socket.io
Connection metadata
Connection metadata
Connection metadata
Socket.io-redis
Socket.io-redis
Socket.io-redis

PRESENTED
BY
We love Redis @ sRide
• Stability
• Performance
• Community Support
• Low learning Curve
• Tools

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

This document summarizes a presentation about Redis version 6 and beyond. Some key points include: - Redis version 6 includes new features like ACL for security, client-side caching, diskless replication, and multi-threaded I/O. - Redis is positioned as both a cache and a database due to its speed, data structures, and ability to handle complex data models through modules. - Redis Enterprise provides additional capabilities like durability, high availability, geo-distribution, security and multi-tenancy. - Modern data models in Redis modules include Streams, RediSearch, RedisGraph, RedisTimeSeries, RedisAI, RedisJSON and RedisBloom. - RedisInsight is

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

The document discusses how Sinclair Broadcast Group leverages Redis for system monitoring of its content delivery network. It operates 193 news stations with 10,000 active pages daily and millions in archive. New stories are posted every 15 seconds and must be visible across its 1,000+ targets within 1 minute. Redis is used to track performance across the multi-level CDN and ensure service level agreements are met with real-time resolution and alerting. It provides a black box view of the audience experience and can scale monitoring to all relevant pages within 30 seconds. Redis acts as a distributed data store to parallelize the monitoring task across the large scale of the network.

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

The document summarizes a presentation about when to use the RedisJSON data type. It discusses how Coupang uses Redis extensively for their ad platform. It then compares the performance and memory usage of storing JSON data as strings, hashes, or using the RedisJSON data type. Benchmark results show RedisJSON can provide better performance for retrieving and updating JSON fields compared to strings and hashes, though it uses more memory. The document recommends using RedisJSON for smaller JSON payloads after benchmarking and memory monitoring.

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

The document describes the process that a Redis command follows from the client side to the server side. On the client side, the command is sent over the network to the Redis server. On the server side, the command is read from the kernel buffers, validated, executed by calling the relevant command handler, and the response is written back to the client over the network. The core functions involved on the server side are ReadQueryFromClient(), ProcessInputBuffer(), ProcessCommand(), Call(), and handleClientsWithPendingWrites(). Redis 6.0 introduced I/O threads to handle reads and writes in parallel for improved performance while still maintaining Redis' single-threaded processing model.

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

This document discusses MDmetrix, a healthcare data intelligence company that uses RedisGraph to provide flexible analysis of hospital data. RedisGraph is a graph database that represents data as nodes and relationships and uses an adjacency matrix and linear algebra to query the graph. MDmetrix models its healthcare data as a property graph in RedisGraph to allow for complex queries across different data dimensions like patients, facilities, procedures and drugs. RedisGraph allows MDmetrix to query the data more easily than traditional OLAP cubes or relational databases due to the semi-structured and flexible nature of the graph model.

RediSearch 1.6 by Pieter Cailliau - Redis Day Bangalore 2020Redis Labs

RediSearch 1.6 includes a new low-level API that allows other Redis modules to embed RediSearch indexing capabilities. It also introduces index aliasing and several performance improvements such as forked thread garbage collection. Based on benchmarks, RediSearch 1.6 shows 48-73% better performance than version 1.4, particularly during high update rates where it maintains more stable read latencies.

RedisGraph 2.0 by Pieter Cailliau - Redis Day Bangalore 2020Redis Labs

RedisGraph 2.0 provides significant improvements including: - Full text search support through embedded RediSearch 1.6 enabling graph-aided search. - Support for returning full graph responses to enable better visualization. - Broad support for Cypher including triadic closure and new graph-aided search capabilities. - Performance improvements of up to 3.7x faster operations per second and 3.9x faster query times compared to RedisGraph v1.2. - Support for benchmarking including the LDBC benchmark.

RedisTimeSeries 1.2 by Pieter Cailliau - Redis Day Bangalore 2020Redis Labs

RedisTimeSeries is a time-series database that provides compression to reduce memory usage by up to 98% and improve performance. The RedisTimeSeries 1.2 release includes compression algorithms based on a Facebook paper that provide stable ingestion times independent of the number of data points. It also includes a reviewed API with performance improvements and clearer functionality. Performance testing showed ingestion throughput improved by 2-3% and query performance increased from 15-70% with the new release compared to the previous version.

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

This document summarizes RedisAI 0.9 and its capabilities for model deployment and benchmarking. It introduces RedisAI's new tensor data type and ability to deploy models to CPU and GPU. It then discusses AIBench, a tool developed to benchmark AI serving solutions like RedisAI, TensorFlow Serving, and REST APIs. The benchmarks show RedisAI providing 5.5x and 2.5x more inferences than REST APIs and TensorFlow Serving respectively, due to its data locality. The document concludes by mentioning RedisAI's integration with MLFlow for model deployment with a single command.

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

Redis was used by Myntra to solve several complex scaling problems. It was used to build a scalable user segment service to support high read throughput of up to 5 million requests per minute with low latency. Redis allowed the service to scale beyond a single instance and included features like automatic backups and memory management. Redis also helped build a scalable mobile verification platform to reliably handle 100,000 requests per minute and scale to support higher future volumes. It was used as both a transient store and persistent backend. Finally, Redis locks helped build a scalable A/B testing platform by allowing experiments to be created and updated in an orderly concurrent fashion.

Deploying Redis as a Sidecar in Kubernetes by Janakiram MSV - Redis Day Banga...Redis Labs

This document discusses deploying Redis as a sidecar in Kubernetes. It outlines installing Redis in Kubernetes, different Redis deployment patterns for microservices including ambassador sidecar and sidecar patterns, and demonstrates deploying Redis as a sidecar to accelerate application performance by co-locating web and cache pods. The agenda includes an overview of Redis and Kubernetes, Redis deployment patterns, and applying the sidecar pattern to deploy Redis alongside applications in Kubernetes pods.

Real-time GeoSearching at Scale with RediSearch by Apoorva Gaurav and Ronil M...Redis Labs

Ronil Merchant and Apoorva Gaurav presented on how Bounce, a mobility startup providing dockless scooter sharing, implemented real-time geo-searching at scale using RediSearch. They faced challenges with high throughput location updates on PostgreSQL and evaluated Elasticsearch and RediSearch. RediSearch indexed documents by geohash for location and performed better than Elasticsearch or scaling PostgreSQL. Testing showed tagging fields and omitting latitude/longitude indexing improved performance. They now use AWS DMS and Kinesis to sync data from PostgreSQL to RediSearch for low latency bike discovery.

Build a Deep Learning App with Tensorflow & Redis by Jayesh Ahire and Sherin ...Redis Labs