A Little Bit About Terracotta BigMemory
In talking to our users it is clear that applications are getting more and more data hungry. According to IDC, data requirements are growing at an annual rate of 60 percent. There is good news though. Server class machines purchased this year have a minimum of 8 Gig of RAM and likely have 32 Gig. Cisco is now selling mainstream UCS boxes with over 380 Gig of RAM. Memory has gotten big and extremely cheap compared to things like developer time and user satisfaction.
Unfortunately a problem exists as well. For Java/JVM applications it is becoming an ever increasing challenge to use all that data and memory due to GC Pauses.
In this talk I'm going to cover the problems we identified and the technology we built to solve those problems.
- A bit about it's history and the history of the problem
- The where, when why of BigMemory
- Throughput, latency, Garbage Collection, SLA and scaling characteristics
- Configuration of Ehcache with BigMemory in an existing application with just a few lines of config code
- Ehcache's tiered storage architecture: MemoryStore, the OffHeapMemoryStore and the DiskStore
- Ehcache BigMemory with scale-out
- Best practices
- Implications for your caching architecture