What is retrieval-augmented generation? RAG is an AI framework for...
Redis
Table of Contents
For Redis Consulting & Support Services
Get in touch with us
Let's break ice
Email Us
Service Offering
- Application Code Review
- Application Code Solution Proposal
- Redis Transactions and Pipelining
- Redis Shards and Lua Scripts
- Redis Sharding and Key Affinity
- Session Management
- Redis Stack Capabilities
- Search & JSON
- Time Series
- Database Scalability
- High Availability and Persistence
- Cluster Monitoring and Event Logging
- Access control lists
- Upgrade Requirements & Planning
- New Feature Workshop
- Install & Configure Cluster(s)
- Create & Populate Database(s)
- Perform Upgrade
- Document Steps
- Demonstration of Deployment Features
- Software installation & upgrades
- Cluster creation and node joining
- Database creation and replication
- Identify Requirements for Deployment
- Discuss Configuration Options
- Configuration & Delivery
Redis support
- Update Playbooks for Delivery
- Configure Inventory File Options
- Deploy Ansible Automation Release Kit
- Distribute Installation & User Guides
- Provide Assistance with Integration
- Review Automation Best Practices
- CI/CD Integration Assistance Pipeline Validation
- CI/CD Integration Assistance Pipeline Validation
- CI/CD Pipeline Review & Analysis- Identify DB Provisioning Stage
- Provide User Guide Document
- Discovery & Demonstration
- Review of Prometheus/Grafana
- Demonstration of Monitoring Features
- Grafana dashboards
- Alerting and notification
- Identify Key Metrics for Deployment
- Update Dashboards with Key Metrics
- Configure Alert and Notification Options
- Distribute Installation & User Guides
- Provide Assistance with Integration
- Review Monitoring Best Practice
- Use Case & Requirements Discovery
- Data Model Design & Development
- Document approach & recommendations
- Benchmark & analyze performance
- Document approach & recommendations
- Use Case & Requirements Discovery
- Identify Redis Stack data structures
- Expanded Design & Development
- Develop reference solution for use case –
- Document approach & recommendations
- Demonstrate Solution(s) (Q&A session)
Support Model
DURATION: 1year, 3year, renewable
RESONSE TIME:
Acknowledgement to be provided upon ticket receipt Standard Next Business Day (03:00-20:00 EST/EDT)
- Critical – Response time 1 hour, Updates provided until issue Resolved
- High – Response time 6 hrs, Updates provided until issue Resolved
- Medium – Response time 12 hrs, Updates provided until issue Resolved
- Low – Response time 24 hrs, Updates provided until issue Resolved
- Enhancements – Updates provided until issue Resolved
INCIDENTS AS MANY AS REQUIRED
Support Ticketing
All the ticketing requests will be tracked through dedicated Jira for each incident.
S1
Production outage and loss of service. Part or whole of product functionality down with no workaround available. Does not include development issues or problems in staging environments.
S2
Intermittent issues or reduced quality of service. A workaround is available. Does not include development issues or problems in staging environments.
S3
Impact in lower environments than production. Product questions, feature requests, development blockers and integration issues.
S4
No impact to any quality, performance or functionality in any environment. General knowledge gathering and troubleshooting while development.
Redis caching solutions
Cache-aside: Speed up reads when consistency is not crucial
Query caching: When there is a need to speed up simple (SQL) queries with minimal overhead
Cache-aside (lazy loading) with Redis
When to use
- Speed up reads
- Cache misses are acceptabl
- Caching a subset of the dataset
- All data operations are handled by application, which directly communicates with both the cache and DB
Challenges
- Application latency due to high read response time from the system
of record (DB) - Difficulty implementing or maintaining a cache-aside architecture
Solution
- Using Redis alongside a system of record in a cache-aside pattern
Query caching with Redis
When to use
- Implementation of the cache-aside pattern focused on speeding up often-repeatable queries against a slower system of record
- Most common for repeated SQL queries
- Can be used for a variety of purposes, but common in architectures that migrate into microservices without replatforming their current systems of record
Challenges
- Latency with often repeatable queries
Solution
- Using Redis for SQL query caching by deploying a Redis cache alongside each system of record to speed up SQL queries
- Redis SmartCache is a library that enables developers to quickly deploy a standardized query cache to simplify management and operations without needing to re-architect code.
A real-time data platform built for all your caching needs—and beyond
Session Management
Cache and Session Management
Session Management – HTTP Sessions
Redis for Microservices
Simplifying microservice architecture with Redis
How to use Redis for Microservices
- API Gateway Caching/ Rate Limiting: Caching and rate limiting reduce risk of outages
- Query Caching (Single Domain): Cache aside pattern to overcome performance issues with legacy databases
- Inter-service Communication: Lightweight message broker using Redis Streams data structure
API Gateway Caching
Problem
- Authenticating millions of users and providing session data can overwhelm API gateway causing outages (single point of failure)
- Inability to scaling API requests during peak traffic
Solution
- Caching of session and authentication data and rate limiting at the API Gateway
Redis Benefits
- Secure, scalable, and efficient authentication to streamlined user login
- High availability and resilience to avoid application outage
- Simply operations and deployment with Redis Operator for Kubernetes
- Integration with Kong API Gateway management solution for easier deployments
Interservice Communication
Problem
- Need to communicate state, events, and data among microservices without breaking isolation (stay decoupled)
- Scaling interservice communication between hundreds of microservices
- Using Kafka as message broker is too complex, time consuming, and costly
Solution
- Leverage Redis Streams as the lightweight, asynchronous event driven message broker that provides a publish-subscribe capability with message persistence
Redis Benefits
- Reduced complexity, save time and costs by using lightweight, easy to deploy
- Redis Streams message broker on single multi-tenant data platform
- Reduced message latency, improving overall application performance
- Can reduce the number of DevOps resources needed by 2X due to automation and development efficiency
~ Testimonials ~
Here’s what our customers have said.
Empowering Businesses with Exceptional Technology Consulting
"Nextbrick was able to quickly understand our Solr search requirements and provided a comprehensive solution for us. Ordinarily, having a third party provide development services with our e-commerce platform can be a challenge, but they easily managed our environment and seamlessly collaborated with our website partner. Overall, I was very pleased with their value."
"As I stated, we have a group of contractors from Nextbrick who we would like to reward for going above and beyond the call of duty and putting in extremely hard work in launching a successful summer release here at CSAA. We would like to reward the team."
"Just want to take this opportunity to thank you guys for the great job done! The core idea behind this project was to show that ES can truly be used for real time updates and how quickly we can model the data across complex tables in our source system. Your work is definitely commendable. Also, the demo was seamless and very clearly articulated."
~ Our Clients ~
~ Knowledge Hub ~
Our Latest Blogs
retrieval augmented generation
Retrieval-augmented generation Retrieval Augmented Generation (RAG) is a technique that...
Vector Search: Google’s Powerful Solution for AI-Driven Applications
Google has emerged as a leader in vector search technology,...