Today, if events change the decision model, we wait until the next batch model build for new insights. By extending fast “time-to-decisions” into the world of Big Data Analytics to get fast “time-to-insights”, apps will get what used to be batch insights in near real time. The technology enabling this includes smart in-memory data storage, new storage class memory, and products designed to do one or more parts of an analysis pipeline very well. In this talk we describe how Ampool is building on Apache Geode to allow Big Data analysis solutions to work together with a scalable smart storage class memory layer to allow fast and complex end-to-end pipelines to be built -- closing the loop and providing dramatically lower time to critical insights.

1. Democratizing Fast Analytics with Ampool
(Powered by Apache Geode-incubating)
Avinash Dongre and Robert Geiger, Ampool Inc.

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Analytics
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Analytics needs to work in CLOSED LOOP with AppsAnalytics Needs to be Faster!

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What are the CHALLENGES?
⚠ Many data users/
stakeholders
⚠ Disparate tools &
processing needs
⚠ Long time to
insights

4. Meet the ChallengesDISTRIBUTED MEMORY LAYER …
Smart Distributed In-Memory Object Store

5. Smart Distributed In-Memory Object Store
CHOICE in Best of Breed Engines, …

6. Smart Distributed In-Memory Object Store
… PLUGGABLE distributed memory layer…
+  …

7. AnalyticsIngest App UseETL
… FAST OBJECT ACCESS across the pipeline
# # #
, ,
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. Data Architect
Data Developers
. . Business Analysts
Data Scientists
.

8. What ENABLERS can help here?
In-Memory Fabric Technology
• Apache Geode!
• Flexible, stable, and proven distributed in-memory technology
New memory technologies and fast network fabrics
• Storage Class Memory
• low latency, high throughput, persistent
• Initially exposed via file system interface
• Regular or memory mapped

9. Emerging Storage Class Memory (SCM) is DISRUPTIVE
Challenges the value proposition of in-memory solutions
Near DRAM latency and throughput at lower cost
Based on one of several types of memory technology
• MRAM (magnetic)
• ReRAM (resistive)
• FRAM (ferroelectric), PCM (phase change)
• 3D-XPointTM (Intel/Micron)
Accessible via Java and C/C++ libraries
• Mnemonic (Java)
• Pmem.io (C++)

10. SCM is ATTRACTIVE in the Memory/ Storage Hierarchy

11. In-Memory Technology CHALLENGES
Line between memory and storage is blurring
File systems getting really fast, so the speed gap is closing
• SCM File Systems will also be low latency
• File system overhead still limits latency improvements
• Before: disk based vs. in-memory
• After: file system vs. byte addressable object store
Managing multiple layers and types of memory

12. Fast Closed Loop Analytics,
Powered by a Smart, Distributed In-Memory Fabric…
High throughput and large data handling matters
• Throughput, latency, and capacity:
• each pipeline stage values these differently
Common interfaces, multiple region types
• Meet the needs of many types of best of breed engines
Managing multiple layers of memory and storage
• Speed (latency, throughput) differentiator will diminish
More classifications for data now
• Hot, cold => hot, warm, lukewarm, cold

13. …must handle MULTIPLE needs in one fabric
Need for High Throughput
Need for Low latency
Early stages (ingest, ETL)
Later stages
(data driven
insights & actions)

14. What Matters for App, DB, and Compute?
The flexibility, suitability, and ease of use of the interfaces
Memory & storage are managed transparently to provide QoS
The service guarantee abstractions are provided
Conflicts are managed and prevented
Freeing developersfrom re-inventing the wheel

15. A Distributed, Memory-Centric, Object Store
for Closed Loop Analytics
Introducing….

16. Smart Distributed In-Memory Object Store
PLUGGABLE distributed memory layer …
+  3D XPointTM
......

17. Smart Distributed In-Memory Object Store
… for MANAGED FLEXIBILITY...
+  3D XPointTM
......
✅ Flexible regions
and interfaces for
‘Best of breed’
engines
✅ Extensible Core
✅ Pluggable stores

18. AnalyticsIngest App UseETL
…and FAST OBJECT ACCESS across the pipeline
# # #
, ,
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-
,
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…
. Data Architect
Data Developers
. . Business Analysts
Data Scientists
.

19. In-Memory Distributed Sys
Low-latency Comms.
Key-Value Store
Function Pushdown
+
High Throughput
Table Store
Native InterfacePluggable Store Manager
Java API
MASH (CLI Ext)
Java API
Building on PROVEN In-memory Technology
Smart Data Tiering
Mature Event Model
Tunable Consistency
Metadata/ Catalog
Security AuthZ

20. ampool
+
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ORC
…
First release covers MULTIPLE analytical needs…

21. No change in data
application code
Config. changes only
No change in user
experience
Performance benefits
No added hassles
Current mgmt. tools
…and deliver VALUE to all Analytics stakeholders
. Data Architect
Data Developers
.
. Business Analysts
Data Scientists
.
. Data Admins
Infra/ Sys Admins
.

22. Contributing Back
Plan for contributions back to Apache Geode:
• Storage plug-ability layer
• Off-heap memory plug-ability
• SCM plugin (Mnemonic)
• Impersonation support for security
• Region type plug-ability

23. Thank You!
Avinash Dongre
Architect, Ampool India Pvt. Limited
avinash@ampool.io
Robert Geiger
Chief Architect & VP Engineering, Ampool Inc.
robert@ampool.io