In this talk, we describe the latest trends and advancements in storage-class memory (a.k.a. Non-Volatile Memory) and its impact on data infrastructure as it exists today. We also describe how Ampool is building a product that allows Big Data analysis solutions to work together with a smart storage class memory layer to allow fast & complex end to end analytical pipelines, thus lowering the time-to-insights significantly.

1. Non Volatile Memory &
Implications on Data Infrastructure
Robert Geiger and Shankar Hundekar, Ampool Inc.

2. Memory and Storage Directions
Robert Geiger, Chief Architect and VP Engineering, Ampool Inc.

3. Previous Storage Class Memory (SCM)
• SSD and “keep the power on” solutions
• SATA SSD; NAND Flash
§ NOTE: NOR flash is byte addressable but more expensive (due to lower density)
• NVMe SSD; NAND Flash (very fast; 2GBytes/sec on new Mac laptop)
• Battery backed DRAM (replacement an issue)
• NVDIMM-F (block device; high capacity, high latency) – PCIe
• NVDIMM-N (byte addressable at or near DRAM speeds) – PCIe

4. Emerging Storage Class Memory (SCM)
• Actually Storage Class Byte Addressable Memory
• Near DRAM latency and throughput
• Based on one of several types of memory technology
• MRAM (magnetic)
• ReRAM (resistive)
• FRAM (ferroelectric), PCM (phase change)
• XPoint (Intel/Micron won’t tell you exactly)
• 3-D XPoint (Intel-Micron) - DDR or PCIe

5. SCM in the Memory/Storage Hierarchy

6. SCM Interfaces
• File system based (standard & memory mapped)
• File systems getting really fast
§ Test a new Mac laptop w/ SSD- you can do 2 GBytes/sec sustained read/write
• Linux (fedora core/RedHat) and MS adding SCM support in ‘16
• Capacity will still push big data to disk for data lake, cold data
• What does this mean for in-memory plays?
§ Being in-memory is no longer enough
§ Sophistication must increase
• PMEM.IO => Direct access to SCM
• Most likely interface for in-memory focused solutions

7. In-Memory Future
• Line between memory and storage is blurring
• Storage is just memory with a specific Quality of Service (QoS)
• Managing multiple layers/types of memory will differentiate
• No longer in-memory vs. disk
• Now file system vs. managed byte addressable object store

8. What Matters for App, DB, and Compute
• The flexibility and ease of use of the interfaces provided
• How memory is managed transparently to provide QoS
• The service guarantee abstractions (QoSchoices) provided
• How conflicts are managed and prevented

9. What it Means to Big Data and Analytics
• Throughput, latency, and capacity: what QoS is needed?
• Avoiding pipeline conflicts with multiple solutions using memory
• In-memory solutions can control allocations
• This will allow multiple frameworks to co-exist better
• “Yarn for memory”
• More types of memory: eviction algorithms need adjustment
• Hot, cold => hot, warm, lukewarm, cold

10. Implications on Data Infrastructure
Shankar Hundekar. Senior Software Engineer, Ampool Inc.

11. Apache Geode Project
*Source apache geode
A real time in-memory data management platform

12. Apache Geode Architecture
Locator
Server
Server
Server
Server
+

…

13. Persistence in Apache Geode…
*Source apache geode
Ÿ High availability
Ÿ Low latency, high throughput
Ÿ Durability
Ÿ Minimal impact to in memory speed
– Minimize IO operations
– Minimize locking
– Minimize deserialization

14. …is a Shared-nothing architecture
*Source apache geode
Server 3Server 2
Bucket 3
*primary*
Bucket 1
secondary
Bucket 3
secondary
Bucket 2
*primary*
Server 1
Bucket 2
secondary
Bucket 1
*primary*

15. ampool
• Powered by Apache Geode
• Broad support for Big Data
solutions
• Pluggable, distributed memory
layer

16. In-Memory Object Store optimized for Analyticsampool
PLUGGABLE distributed memory layer


17. In-Memory Object Store optimized for Analyticsampool

MANAGED FLEXIBILITY
✅‘Best of breed’
engines
✅Extensible Core
✅Pluggable stores

18. ampool
In-Memory Distributed Sys
Low-latency Comms.
Key-Value Store
Function Execution
+
High Throughput
Table Store
Native InterfacePluggable Store Manager
Java API
MASH (CLI Ext)
Java API
Building on PROVEN In-memory Technology

19. In memory Storage Manager for Analytics
Storage Manager
SCM
DRAM
• Leverages Storage characteristics
• Based on the usage of data
• Tiered Storage architecture …

20. About the pmem.io library
• Open source library that simplifies development
of apps using SCM
• Works with existing CPU hardware
• Focuses specifically on how persistent memory is
exposed to server-class applications
• Applications will explicitly manage the placement
of data among the three tiers (volatile memory,
persistent memory, and storage)
*Source pmem.io

21. Libraries provided by pmem
• libpmemprovides low level persistent memory support
• libpmemobjlibrary provides a transactional object store, providing memory
allocation, transactions, and general facilities for persistent memory programming.
Developers new to persistent memory probably want to start with this library
• libpmemblksupports arrays of pmem-resident blocks, all the same size, that
are atomically updated
• libpmemlog provides a pmem-resident log file. This is useful for programs like
databases that append frequently to a log file
*Source pmem.io

22. Conclusion
• New Storage Class Memories are changing the in-memory
landscape
• Both opportunities and challenges for in-memory solutions
• Apache Geode is a proven in-memory technology platform
• Ampool is working to meet the challenges of these changes!