Markus Kett
In-Memory Computing - The Big Picture
#1about 3 minutes
The critical need for performance in modern applications
Latency is a significant cost for businesses, making high-performance, in-memory computing essential for modern applications.
#2about 2 minutes
Understanding the fundamental speed of in-memory operations
In-memory operations are orders of magnitude faster, measured in microseconds, compared to database access which is measured in milliseconds.
#3about 3 minutes
The core problem of object-relational impedance mismatch
Object-oriented programming languages are inherently incompatible with relational database models, leading to complex and slow data mapping.
#4about 3 minutes
Why NoSQL and mapping layers don't solve the bottleneck
Even with NoSQL databases, the need for data conversion and mapping layers like ORMs persists, creating a significant performance bottleneck.
#5about 3 minutes
Using distributed caches to reduce database load
A distributed cache cluster sits between the application and the database to store frequently accessed data in memory, reducing database load.
#6about 2 minutes
Differentiating in-memory data grids from distributed caches
In-memory data grids extend distributed caches by adding computational capabilities, allowing for distributed processing across the cluster.
#7about 3 minutes
The architecture and limitations of in-memory databases
In-memory databases run the DBMS in memory but often on a separate cluster, which still introduces network latency and requires data mapping.
#8about 4 minutes
A new paradigm: Database-less processing and system prevalence
The system prevalence architecture keeps the entire application state as an object graph in memory, leveraging native language APIs for ultra-fast queries.
#9about 3 minutes
Simplifying architecture and costs with Eclipse Store
Eclipse Store provides a persistence engine that stores the in-memory object graph directly to cloud blob storage, eliminating database clusters and reducing costs.
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