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W H AT NON-VOLATILE MEMORY
M E A N S F O R T H E F U T U R E O F
DATABASE SYSTEMS
1973
1974
1978
198 6
1994
2 010
2 016
The Future
Non-Volatile Memory
1 1
• Persistent storage with byte-addressable operations. • Fast read/write latencies. • No difference between random vs. sequential access.
What does NVM mean for DBMSs?
1 2
• Thinking of NVM as just a faster SSD is not interesting. • We want to use NVM as permanent storage for the
database, but this has major implications. –Operating System Support –Cloud Provider Provisioning –Database Management System Architectures
Existing Systems
NVM-Only Storage
Hybrid DBMS
1 3
Existing Systems
NVM-Only Storage
Hybrid DBMS
Chapter I – Existing Systems
1 4
• Investigate how existing systems perform with NVM for write-heavy transaction processing (OLTP) workloads.
• Evaluate two types of DBMS architectures. –Disk-oriented (MySQL) – In-Memory (H-Store)
A PROLEGOMENON ON OLTP DATABASE SYSTEMS FOR NON-VOLATILE MEMORY ADMS@VLDB 2015
1 5
DISK-ORIENTED Buffer Pool
Table Heap
Log Snapshots
IN-MEMORY Table Heap
Log Snapshots
Intel Labs NVM Emulator
1 6
• Instrumented motherboard that slows down access to the memory controller with tunable latencies.
• Special assembly to emulate upcoming Xeon instructions for flushing cache lines.
STORE STORE L1 Cache
L2 Cache
PCOMMIT
Experimental Evaluation
1 7
• Compare architectures on Intel Labs NVM emulator. • Yahoo! Cloud Serving Benchmark:
– 10 million records (~10GB) –8x database / memory –Variable skew
YCSB //
1 8
0
50,000
100,000
150,000
200,000MySQL H-Store
Read-Only Workload 2x Latency Relative to DRAM
SKEW AMOUNT (HIGH→LOW) TXN/SEC
YCSB //
1 9
0
10,000
20,000
30,000
40,000
TXN/SEC
MySQL H-Store
50% Reads / 50% Writes Workload 2x Latency Relative to DRAM
SKEW AMOUNT (HIGH→LOW)
8x Latency
LESS
ONS
2 0
Logging is a major performance bottleneck.
2
1 NVM Latency does not have a large impact.
Legacy DBMSs are not prepared to run on NVM.
3
What would Larry Ellison do?
Chapter II – NVM-only Storage • Evaluate storage and recovery methods for a system
that only has NVM. • Testbed DBMS with a pluggable storage engines. • We had to build our own NVM-aware memory allocator.
LET'S TALK ABOUT STORAGE & RECOVERY METHODS FOR NON-VOLATILE MEMORY DATABASE SYSTEMS SIGMOD 2015
2 3
Copy-on-Write Table Heap No Logging
Log-Structured No Table Heap
Log-only Storage
DBMS Architectures
2 4
In-Place Table Heap
Log + Snapshots
Copy-on-Write Table Heap No Logging
Log-Structured No Table Heap
Log-only Storage
In-Place Engine
2 5
Table Heap
Log Snapshots 1
2
3
UPDATE table SET val=ABC WHERE id=123
Delta Record
New Tuple
New Tuple
NVM
NVM-Optimized Architectures
2 6
• Use non-volatile pointers to only record what changed rather than how it changed.
• Be careful about how & when things get flushed from CPU caches to NVM.
NVM-Aware In-Place Engine
2 7
Table Heap
Log 1
2
Tuple Pointers
New Tuple Log Record
TxnId Pointer
UPDATE table SET val=ABC WHERE id=123
Evaluation
2 8
• Testbed system using the Intel NVM hardware emulator. • Yahoo! Cloud Serving Benchmark
–2 million records + 1 million transactions –High-skew setting
YCSB //
2 9
0
400,000
800,000
1,200,000
In-Place Copy-on-Write Log-Structured
NVM-Optimized Traditional
10% Reads / 90% Writes Workload 2x Latency Relative to DRAM
↑63%
↑122% ↑50%
TXN/SEC
YCSB //
3 0
0
100
200
300
400
In-Place Copy-on-Write Log-Structured
NVM-Optimized Traditional
10% Reads / 90% Writes Workload 2x Latency Relative to DRAM
NVM STORES (M)
↓40%
↓25%
↓20%
YCSB //
3 1
NVM-Optimized Traditional
Elapsed time to replay log with varying log sizes 2x Latency Relative to DRAM
RECOVERY TIME (MS)
0.01
0.1
1
10
100
1000
10^3 10^4 10^5 10^3 10^4 10^5 10^3 10^4 10^5 In-Place Copy-on-Write Log-Structured
No Recovery Needed
LESS
ONS
3 2
Avoid block-oriented components.
2
1 Using NVM correctly improves throughput & reduces weadown.
NVM-only systems are 15-20 years away
3
What would Nikita Kahn do?
Chapter III – Hybrid DBMS • Design and build a new in-memory DBMS that will be
ready for NVM when it becomes available. • Hybrid Storage + Hybrid Workloads
–DRAM + NVM oriented architecture –Fast Transactions + Real-time Analytics
3 5
Adaptive Storage
3 6
Original Data Adapted Data
SELECT AVG(B) FROM myTable WHERE C < “yyy”
UPDATE myTable SET A = 123, B = 456, C = 789 WHERE D = “xxx”
A B C D
BRIDGING THE ARCHIPELAGO BETWEEN ROW-STORES AND COLUMN-STORES FOR HYBRID WORKLOADS SIGMOD 2016
A B C D
Cold
Hot
A B C D
LESS
ONS
3 9
NVM Ready Query Compilation Vectorized Execution Autonomous Apache Licensed
Anthony Tomasic
Todd Mowry
Prashanth Menon
Michael Zhang
Lin Ma
Matthew Perron
Dana Van Aken
Yingjun Wu
Ran Xian
Runshen Zhu
Jiexi Lin
Jianhong Li
Ziqi Wang
http://pelotondb.org
Joy Arulraj
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