We would be discussing how node.js has grown as an ecosystem for building real time web apps and how new open source technologies in collaboration with node.js can be used to build high performance applications. We will discuss using elastic search as the data store in node applications for storing large amount of data and using kibana from the ELK stack to visually analyse our data from the web app.

1. What are real time
applications?
• real-time data feeds, video and audio streams,
Machine-to-Machine-to-Browser communications, and
more

2. Why Nodejs?
• non-blocking, event-driven I/O to remain lightweight
and efficient in the face of data-intensive real-time
applications that run across distributed devices.
• fast, scalable network applications, as it is capable of
handling a huge number of simultaneous connections
with high throughput

3. Mongo vs
Elasticsearch
• heavy computation could choke up Node’s single
thread and cause problems for all clients
• let elastic search do the computation and analytics.

4. Why elasticsearch
• distributed full-text search function with schema-free
JSON documents - fuzzy logic
• If you need more than 5 indexes on MongoDB,
consider using ElasticSearch because this search
engine will give you faster results. For MongoDB, it is
difficult and time-consuming to deal with large
indexes.

5. Analyse documents -
Kibana
• Flexible analytics and visualization platform
• Real-time summary and charting of streaming data
• Instant sharing and embedding of dashboards

6. Growing acceptance
of elastic search
• Scalability - shard reallocation on adding new nodes
leading to better performance
• Easy to horizontally scale with almost 0 downtime
• Elastic now provides open source monitoring tools for
measuring cluster health.