Speed Up Your APIs with Nodejs and Redis Magic!  

More to come! 😊

Glad I could help😊 , it made my day

noted✅, content coming soon

Noted✅, will bring this content very soon

Working on it bro✅, Next video will be on this and coming in just couple of days

With the help of cdc

Good question, Here is the detailed answer of your question on How to ensure Redis cache has most updated data 1. Cache Invalidation on Update Whenever data is updated in the database, immediately invalidate or update the corresponding cache entry. Implementation Steps: Update the Database: Perform the update operation on the database. Invalidate Cache: Remove or update the relevant key in Redis. Example in Node.js: const redisClient = require('./redisClient'); // assuming you've set up your Redis client const db = require('./databaseClient'); // your database client async function updateData(id, newData) { // Update the database await db.update(id, newData); // Invalidate the cache await redisClient.del(`data:${id}`); } async function getData(id) { // Check the cache first let cachedData = await redisClient.get(`data:${id}`); if (cachedData) { return JSON.parse(cachedData); } // If not in cache, fetch from the database let data = await db.get(id); // Store in cache for future requests await redisClient.set(`data:${id}`, JSON.stringify(data)); return data; } 2. Write-Through Cache When updating data, write the new data to both the database and the cache simultaneously. Example in Node.js: async function updateData(id, newData) { // Update the database await db.update(id, newData); // Update the cache await redisClient.set(`data:${id}`, JSON.stringify(newData)); } 3. Cache with Expiry (TTL) Set a time-to-live (TTL) for cache entries. This ensures that even if cache invalidation is missed, the stale data will eventually expire and be refreshed from the database. Example in Node.js: async function getData(id) { let cachedData = await redisClient.get(`data:${id}`); if (cachedData) { return JSON.parse(cachedData); } let data = await db.get(id); // Store data with an expiry time (e.g., 60 seconds) await redisClient.setex(`data:${id}`, 60, JSON.stringify(data)); return data; } 4. Event-Driven Cache Invalidation Use events to trigger cache invalidation. Whenever the database updates, it emits an event that listeners (like the cache layer) can act upon. Example with Event Emitters: const EventEmitter = require('events'); const cacheInvalidationEmitter = new EventEmitter(); cacheInvalidationEmitter.on('dataUpdated', async (id) => { await redisClient.del(`data:${id}`); }); async function updateData(id, newData) { await db.update(id, newData); cacheInvalidationEmitter.emit('dataUpdated', id); } By using one or a combination of these strategies, you can ensure that your Redis cache reflects the latest data from your database. Hope this answer will resolve your query, if not? feel free to ask here again and I will make a proper practical video on your boubt and will ensure you will get a proper answer😊

@@sandeepdev0 Bhai mera be same he question tha. Ap es pher ek video he bana do Part2. Shukriya

Need video

@@sandeepdev0 nice 🙂 very soon I will be asking some more but currently little busy at ts learning

Actually good Idea, Will do work on this soon

welcome in the community bro💓

Noted✅, video will come soon on this topic

codium ai

Actually I use github copilot, but if you want same or better for absolutely free you can go for codium ai

Hey bro, first of all, thanks for commenting, but sorry I didn't get your query, Can you explain to me a bit more and with clarity what you are exactly asking?

Yes Ayush, you are absolutely right ✅

Good question, Here is the detailed answer on how you can ensure Redis always have updated data from database: 👇👇 There are various techniques like: 1. Cache Invalidation on Update Whenever data is updated in the database, immediately invalidate or update the corresponding cache entry. Implementation Steps: Update the Database: Perform the update operation on the database. Invalidate Cache: Remove or update the relevant key in Redis. Example in Node.js: javascript const redisClient = require('./redisClient'); // assuming you've set up your Redis client const db = require('./databaseClient'); // your database client async function updateData(id, newData) { // Update the database await db.update(id, newData); // Invalidate the cache await redisClient.del(`data:${id}`); } async function getData(id) { // Check the cache first let cachedData = await redisClient.get(`data:${id}`); if (cachedData) { return JSON.parse(cachedData); } // If not in cache, fetch from the database let data = await db.get(id); // Store in cache for future requests await redisClient.set(`data:${id}`, JSON.stringify(data)); return data; } 2. Write-Through Cache When updating data, write the new data to both the database and the cache simultaneously. Example in Node.js: async function updateData(id, newData) { // Update the database await db.update(id, newData); // Update the cache await redisClient.set(`data:${id}`, JSON.stringify(newData)); } 3. Cache with Expiry (TTL) Set a time-to-live (TTL) for cache entries. This ensures that even if cache invalidation is missed, the stale data will eventually expire and be refreshed from the database. Example in Node.js: async function getData(id) { let cachedData = await redisClient.get(`data:${id}`); if (cachedData) { return JSON.parse(cachedData); } let data = await db.get(id); // Store data with an expiry time (e.g., 60 seconds) await redisClient.setex(`data:${id}`, 60, JSON.stringify(data)); return data; } 4. Event-Driven Cache Invalidation Use events to trigger cache invalidation. Whenever the database updates, it emits an event that listeners (like the cache layer) can act upon. Example with Event Emitters: const EventEmitter = require('events'); const cacheInvalidationEmitter = new EventEmitter(); cacheInvalidationEmitter.on('dataUpdated', async (id) => { await redisClient.del(`data:${id}`); }); async function updateData(id, newData) { await db.update(id, newData); cacheInvalidationEmitter.emit('dataUpdated', id); } By using one or a combination of these strategies, you can ensure that your Redis cache reflects the latest data from your database. I hope this answer will resolve your query, if not? feel free to ask in comments section and I will make dedicated video on this topic and practically explain you how you can ensure Redis cache have most updated data

Good question, Here is the detailed answer of your question on How to ensure Redis cache has most updated data 1. Cache Invalidation on Update Whenever data is updated in the database, immediately invalidate or update the corresponding cache entry. Implementation Steps: Update the Database: Perform the update operation on the database. Invalidate Cache: Remove or update the relevant key in Redis. Example in Node.js: const redisClient = require('./redisClient'); // assuming you've set up your Redis client const db = require('./databaseClient'); // your database client async function updateData(id, newData) { // Update the database await db.update(id, newData); // Invalidate the cache await redisClient.del(`data:${id}`); } async function getData(id) { // Check the cache first let cachedData = await redisClient.get(`data:${id}`); if (cachedData) { return JSON.parse(cachedData); } // If not in cache, fetch from the database let data = await db.get(id); // Store in cache for future requests await redisClient.set(`data:${id}`, JSON.stringify(data)); return data; } 2. Write-Through Cache When updating data, write the new data to both the database and the cache simultaneously. Example in Node.js: async function updateData(id, newData) { // Update the database await db.update(id, newData); // Update the cache await redisClient.set(`data:${id}`, JSON.stringify(newData)); } 3. Cache with Expiry (TTL) Set a time-to-live (TTL) for cache entries. This ensures that even if cache invalidation is missed, the stale data will eventually expire and be refreshed from the database. Example in Node.js: async function getData(id) { let cachedData = await redisClient.get(`data:${id}`); if (cachedData) { return JSON.parse(cachedData); } let data = await db.get(id); // Store data with an expiry time (e.g., 60 seconds) await redisClient.setex(`data:${id}`, 60, JSON.stringify(data)); return data; } 4. Event-Driven Cache Invalidation Use events to trigger cache invalidation. Whenever the database updates, it emits an event that listeners (like the cache layer) can act upon. Example with Event Emitters: const EventEmitter = require('events'); const cacheInvalidationEmitter = new EventEmitter(); cacheInvalidationEmitter.on('dataUpdated', async (id) => { await redisClient.del(`data:${id}`); }); async function updateData(id, newData) { await db.update(id, newData); cacheInvalidationEmitter.emit('dataUpdated', id); } By using one or a combination of these strategies, you can ensure that your Redis cache reflects the latest data from your database. Hope this answer will resolve your query, if not? feel free to ask here again and I will make a proper practical video on your boubt and will ensure you will get a proper answer😊

@@sandeepdev0 Thank you 😁

Hey vammotv, first of all thanks for subscribing and joining our valuable community. Here is the short answer of your questions: How Much Data Can You Store in Redis? The amount of data you can store in Redis is limited by the available RAM on your server. For larger datasets, Redis Cluster allows sharding across multiple nodes to increase capacity. Also Redis provides various data structure which can be used for efficient data storing Instagram's Caching Strategy Although Instagram's real architecture isn't public fully, But through my research I got top know Instagram uses in-memory caches like Redis and Memcached for frequently accessed data, and CDNs for static assets. They also optimize databases and use a microservices architecture to handle large-scale data efficiently. Hope these answers were able to resolve your queries, if not? feel free to ask here again and I will try my best to make video on your questions and answer them propely and in-depth😊

@@sandeepdev0 Please tell me how can I write like this and you know every linkedin post they are posting like this and I am unable to write like this and I search a lot and last I am unable to find how can I write content on post plz make a detailed video and I will share lot of my friends and please explain in detail, please make video ASAP

Advanced Full stack project tutorials - 1 shot videos coming soon

When using Redis, it is crucial to follow best practices to ensure there are no memory leaks: 1. Use the LRU eviction policy to automatically remove the least recently used entries when memory limits are reached. 2. Set TTL (Time-to-Live) for each cache entry to automatically remove stale data. 3. Set memory limits to prevent Redis from consuming excessive memory. 4. Implement cache segmentation to manage memory usage more effectively by using distinct keys for different APIs. 5. And make sure to use effective data structure according to your system/data need, because Redis supports a variety of data structures to manage data effectively.

In production, There are various ways, like you can use Redis cloud, where Redis is hosted on the cloud or you can host Redis on your server as well, for example on AWS EC2 using the same docker image, and also AWS, Azure, etc. (famous cloud providers) provides Redis cloud in their services which are usually used in production. Hope this answer has resolved your query if not? feel free to ask in the comments section

How about upstash . For production

@@sandeepdev0 ​ How about upstash for production

Yes, upstash also can be used in production. Its also great👌

@@sandeepdev0 thanks 👍

Redis Pub/Sub can be used in these types of softwares

Atleast reply don't just like the comments

If your data changes continuously, you need a robust caching strategy to ensure data consistency while minimizing the performance impact. Here are some approaches: 1. Event-Driven Updates Emit events on data changes and have the cache update itself based on those events. Example with Event Emitters: const EventEmitter = require('events'); const cacheUpdateEmitter = new EventEmitter(); cacheUpdateEmitter.on('dataUpdated', async (id, newData) => { await redisClient.set(`data:${id}`, JSON.stringify(newData)); }); async function updateData(id, newData) { await db.update(id, newData); cacheUpdateEmitter.emit('dataUpdated', id, newData); } 2. Polling Periodically poll the database for changes and update the cache accordingly. Example: async function pollAndUpdateCache(id) { setInterval(async () => { let data = await db.get(id); await redisClient.set(`data:${id}`, JSON.stringify(data)); }, 5000); // Poll every 5 seconds } 5. Hybrid Approach Combine several strategies for optimal performance and consistency. By implementing one or a combination of these strategies, you can handle continuously changing data effectively while maintaining cache consistency.

In some cases like Chat Apps where messages are sent in realtime , you can utilize Redis Pub Sub for optimize the performance

Sorry bro for late replying, thanks for waiting, here I have given the answer, but if you still have more questions, want more clarity upon answers, feel free to comment here, I will make a dedicated practical video for resolving your queries😊

@@sandeepdev0 thanks, will try to implement once, then I will get back to you

I'm really glad that you have liked this video. One of the best comment on my channel ever😅, Also comment down on what topics you want the next videos on☺️

yes you are right, but I also said that this is just an example and its not the actual and real architecture of Instagram. That was just a simple example to make everyone understand the problem solved by Redis.

But thanks for commenting on the video☺️

@sandeepdev0 thank you sir...

Got it bro, I will be careful next time☺️

REDIS PUB SUB can be used in this case

@@sandeepdev0 can you create video on that

@@sandeepdev0 thanks

And if there is any change on data in that case ?

​@@sandeepdev0What about sockets