Speedment SQL Reflector is a software solution that allows applications to get automatically updated data in real time. The SQL Reflector loads data from your existing SQL database and feeds it into an in-memory data grid e.g. GridGain. When started, the SQL reflector will load your selected existing relational data into your map cluster. Also, any subsequent changes that are made to the relational database (regardless how, via your application, script, SQL commands or even stored procedures) are then continuously fed to your GridGain nodes. Even SQL-transactions are preserved so that your maps will always reflect a valid state of the underlying SQL database.

1. WORK WITH MULTIPLE HOT TERABYTES IN
JVMS
PER MINBORG
@PMINBORG
CTO, SPEEDMENT, INC.
See all the presentations from the In-Memory Computing
Summit at http://imcsummit.org

2. SPEEDMENT, INC.

3. 3
ABOUT PER

4. SCENARIO
>1TB
Application
Source ofTruth
In-JVM-Cache
In-Memory
Solution
Web Shop
StockTrade
Bank
Machine learning
Etc.

5. PROS OF IN-MEMORY
 Improved performance
 Consistent performance
 Cost reduction (server, AWS and licenses)

6. CHALLENGES OF IN-MEMORY
 Optimized Speed
 Cost and size of Memory
 Consistency, Restart, DB impact, etc.
 Organization and size of JVMs

7. CHALLENGES OF IN-MEMORY
 Optimized Speed
 Cost and size of Memory
 Consistency, Restart, DB impact, etc.
 Organization and size of JVMs

8. OPTIMIZED SPEED
 No matter how advanced database you may ever use, it is really the data locality that counts
 Eventually, memory will cost less than x $/GB (Pick any x)

9. LATENCIES USING THE SPEED OF LIGHT
 Database query (1 s)

10. LATENCIES USING THE SPEED OF LIGHT
 Disk Seek – LA
 TCP (DC) – SJ
 SSD - Oakland

11. LATENCIES USING THE SPEED OF LIGHT
 Main Memory
 CPU L3 Cache

12. LATENCIES USING THE SPEED OF LIGHT
 CPU L2 Cache
 CPU L1 Cache

13. CHALLENGES OF IN-MEMORY
 Optimized Speed
 Cost and size of Memory
 Consistency, Restart, DB impact, etc.
 Organization and size of JVMs

14. TITLE OF SLIDE GOES HEREHow much
does 1 GB
cost?

15. BACK TO THE FUTURE
$ 5
$ 0.04
$ 720,000
$ 67,000,000,000
Source: http://www.jcmit.com/memoryprice.htm

16. BACK TO THE FUTURE

17. CHALLENGES OF IN-MEMORY
 Optimized Speed
 Cost and size of Memory
 Consistency, Restart, DB impact, etc.
 Organization and size of JVMs

18. CACHE SYNCHRONIZATION STRATEGIES
• Dumps are reloaded periodically
• All data elements are reloaded
• Data remains unchanged between
reloads
• System restart is just a reload
DUMP AND LOAD
• Data evicted, refreshed or marked as old
• Evicted element are reloaded
• Data changes all the time
• System restart either warm-up the cache
or use a cold cache
POLL

19. CACHE SYNCHRONIZATION STRATEGIES
• Changed data is captured in the Database
• Changed data events are pushed into the cache
• Events are grouped in transactions
• Cache updates are persisted
• Data changes all the time
• System restart, replay the missed events
REACTIVE PERSISTANT
CACHING

20. COMPARISON
Dump and Load
Caching
Poll Caching Reactive
Persistance
Caching
Max Data Age Dump period Eviction time Replication Latency -
Lookup
Performance
Consistently Instant ~20% slow Consistently Instant
Consistency Eventually Consistent Inconsistent - stale Eventually Consistent
Database Cache
Update Load
Total Size Depends on Eviction
Time and Access
Rate of Change
Restart Complete Reload Eviction Time Down time update
-> 10% of down time
*

21. CHALLENGES OF IN-MEMORY
 Optimized Speed
 Cost and size of Memory
 Consistency, Restart, DB impact, etc.
 Organization and size of JVMs

22. BIG JVMS WITH TERABYTES OF DATA
 Scale Up
 One large JVM handles all data
 Map memory to (SSD backed) files
 Several JVMs can share data via the file system
 Instant restart
 Scale Out
 Have several JVMs in a network
 Use sharding between nodes
 Redundant nodes

23. CONVENTIONAL JAVA APPLICATIONS
 Java Objects live on the Heap and are Garbage Collected periodically
 Garbage Collection times increases with the Java Heap size
 Garbage Collection times increases with the Java Heap mutation rate
 “The app has hit the GC wall”
 Hard to meet reasonable SLAs with more than 16:ish GB JVMs
 10 TB data and 10 GB JVMs -> ~1000 JVMs

24. OFF HEAP STORAGE
 Stores data outside of the Java heap
 The Garbage Collector does not see the content
 Scales up to terra bytes of main memory in a single JVM
 Use any number of nodes for scale out solutions

25. PERSISTENT SCALE OUT CACHE
 Persists data in files or memory mapped files
 SSD backing device recommended
 1.3 GB/s reload per node
 10 GB in 6s
 100 GB in 1 min
 1 TB in 10 min
 6.5 GB/s reload in a system with 10 nodes (1 active and 1 backup)
 10 GB in 1 s
 100 GB in 12 s
 1 TB in 2 min
 65 GB/s reload in a system with 100 nodes, 1 TB in 12 s

26. COMPRESSED OOPS IN JAVA 8
 Using the default of
–XX:+UseCompressedOops
–XX:ObjectAlignmentInBytes=16
 In a 64-bit JVM, it can use “compressed” memory references.
 This allows the heap to be up to 64 GB without the overhead of 64-bit object references.
 As all object must be 8 or 16-byte aligned, the lower 3 or 4 bits of the address are always zeros and
don’t need to be stored. This allows the heap to reference 4 billion * 16-bytes or 64 GB.
 Uses 32-bit references.

27. JVM SIZE SWEET SPOT
 50 GB off heap per node
 20 nodes per terabyte
 40 nodes per terabyte with minimum redundancy

28. CONCLUSIONS
 Get speed by keeping your data close to the application
 RAM is cheap and getting bigger and ever cheaper
 Consistent solution with Reactive Persistent Caching
Reactive Persistent Caching imposes minimum load on restart and on the DB
 Scale up solutions can be in the terabytes with virtual memory or file mapped memory
Scale out solutions can use 50 GBish nodes

29. SOLUTION
>1TB
Application
In-JVM-Cache
Web Shop
StockTrade
Bank
Machine learning
Etc.
Source ofTruth

30. SPEEDMENT
 Java Application Development Tool
 In-JVM-memory cache
 Database SQL Reflector (CDC, Change Data Capture)
 Pluggable storage engines (Speedment, Chronicle Map, Hazelcast, Grid Gain, etc.)
 Code generation tool -> Automatic domain model extraction from databases
 Transaction-aware

31. SPEEDMENT SCALE UP ULTRA-LOW LATENCY CACHE
 Ultra-low latency (Runs in the same JVM as the application)
 Millions of TPS
 Latencies measured in microseconds
 Supports file mapping
 Terabytes of data
 O(1) for equality operations
 O(log(N)) for other operations

32. SPEEDMENT SQL REFLECTOR
 Detects changes in a database
 Buffers the changes
 Can replay the changes later on
 Will preserve order
 Will preserve transactions
 Sees data as it was persisted
 Detects changes from any
source
Database
INSERT
UPDATE
DELETE

33. DOWNLOAD TRIAL @ WWW.SPEEDMENT.COM

34. CONNECT TO YOUR EXISTING SQL DB

35. AUTOMATIC SCHEMA ANALYSIS

36. PUSH AND PLAY

37. OFFERINGS
 Complete solutions for in-memory hot big data
 Software licenses
 Service and support
 Consulting

38. sales@speedment.com
@Speedment
www.speedment.com