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4 Myths
about
in-memory databases
busted
Yiftach Shoolman – Co-Founder & CTO @ Redis Labs
@yiftachsh, @redislabsinc
Background - Redis
2
Created by Salvatore Sanfilippo (@antirez)
OSS, in-memory NoSQL k/v database/data-structure engine
# 1 # 2 # 3 # 12
Fastest growing
DB
2013-01 – to date:
DB-Engines
Most popular
database on
containers:
@DevOps.com
& ClusterHQ
Top NoSQL
databases:
DB-Engines
Top tools
developers love:
StackShare
Backgroud – Redis Labs
3
Founded in 2011. HQ in Mountain View CA, R&D in Tel-Aviv IL
The largest commercial company behind OSS Redis
•  5000+ paying customers
•  30,000+ free users
•  100,000+ databases under management
•  ±200 new databases/day
Provide enterpsie class Redis deployement
•  As a service – Redis Cloud
•  On-premises – Redis Labs Enterprise Cluster (RLEC)
$28MM VC funding
Why an in-memory operational DBMS ?
4
DatabaseApp
Servers
100msec = The new standard for E2E application response time, under any
load
50msec = Average internet latency
(round-trip)
50msec = Required app
response time
(includes processing
& multi-DB access)
1msec = Required DB
response time
Why an in-memory analytics DBMS ?
5
Query time:
Days/Hours
Disk-based
Query time:
Mins/Secs
RAM
RAM-based
Myth #1
all in-memory databases
are
equally fast
6
How many ops/sec can a single EC2 instance do?
7
1,000,000
0 200,000 400,000 600,000 800,000 1,000,000 1,200,000
1xc3.8xlarge EC2 instance
(1)  Deployed in a VPC
(2)  Enhanced Network Interface
(ENI) enabled
(3)  Placement group enabled
(4)  IRQ Distribution enabled
(5)  Interrupt coalescing enabled
(6)  4xNICs configured
(7)  Receive Packet Steering (RPS)
enabled
How many ops/sec can a single EC2 instance do?
8
1,228,432
1,000,000
0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000
1xc3.8xlarge EC2 instance
Fully optimized
(1)  Deployed in a VPC
(2)  ENI enabled
(3)  Placement group enabled
(4)  IRQ Distribution enabled
(5)  Interrupt coalescing enabled
(6)  4xNICs configured
(7)  RPS enabled
Not optimized
(1)  Deployed on Classic EC2
(2)  No tuning
(3)  Reached packets/sec limit
How many ops/sec can a single EC2 instance do?
9
1,750,000
1,228,432
1,000,000
0 200,000 400,000 600,000 800,0001,000,0001,200,0001,400,0001,600,0001,800,0002,000,000
1xc3.8xlarge EC2 instance
Not optimized
(1)  Deployed on Classic EC2
(2)  No tuning
(3)  Reached packets/sec limit
Half optimized
(1)  Deployed in a VPC
(2)  ENI enabled
(3)  RPS enabled
(4)  Only 1xNIC
Fully optimized
(1)  Deployed in a VPC
(2)  ENI enabled
(3)  Placement group enabled
(4)  IRQ Distribution enabled
(5)  Interrupt coalescing enabled
(6)  4xNICs configured
(7)  RPS enabled
How many ops/sec can a single EC2 instance do?
10
2,000,000
1,750,000
1,228,432
1,000,000
0 500,000 1,000,000 1,500,000 2,000,000 2,500,000
1xc3.8xlarge EC2 instance
Not optimized
(1)  Deployed on Classic EC2
(2)  No tuning
(3)  Reached packets/sec limit
Half optimized
(1)  Deployed in a VPC
(2)  ENI enabled
(3)  RPS enabled
(4)  Only 1xNIC
Fully optimized
(1)  Deployed in a VPC
(2)  ENI enabled
(3)  Placement group enabled
(4)  IRQ Distribution enabled
(5)  Interrupt coalescing enabled
(6)  4xNICs configured
(7)  RPS enabled
Fully optimized
How many servers to get 1M writes/sec on GCE?
300
0
50
100
150
200
250
300
350
11
How many servers to get 1M writes/sec on GCE?
300
50 50
0
50
100
150
200
250
300
350
12
How many servers to get 1M writes/sec on GCE?
300
50 50
2
0
50
100
150
200
250
300
350
In-memory databases13
Real-world write intensive app
14
Hbase+Internal Cache vs. Hbase+Redis
15
16
Same Redis core, same HW, different performance
16
So why aren’t in-memory DBs equally fast?
17
Most are written in C/C++…. but programming
language isn’t the only thing to consider
What affects in-memory DB performance?
(1) Complexity of processing commands
à How many lines of code per command ? What is the computation
complexity (e.g. in Redis most commands are O(1))?
(2) Query efficiency
à Is it limited to blob queries? Can you query a discrete value?
(3) Pipelining
à Can you send multiple requests at once to get lower latency and less
context switches?18
What affects in-memory DB performance?
(4) Protocol efficiency
à How long it takes to parse a request or to serialize a response
(5) TCP overhead
à Long-lived (connection pool) vs. short-lived connections
19
What affects in-memory DB performance?
(6) Single-threaded or multi-threaded architecture
à Lock-free vs. parallel computing
(7) Shared-nothing (the best) vs. shared-something vs. shared-everything
(8) Built-in acceleration components
20
Myth #2
A single node is not a cluster
21
The truth:
A single node can be a cluster
but not a HA cluster
22
In the new containers/VMs world a cluster is:
A bunch of processes that
together look like one big
process
23
A real-world example
A Binary Option platform; 400MB dataset; 1,000,000 ops/sec
A single node, 9 shards cluster
=
3 nodes, 9 shards cluster24
Myth #3
In-memory databases are
inconsistent and unreliable
25
A few facts/questions about consistency
Almost all NoSQL databases (not just in-memory) ack the client before
commiting to disk
Almost all in-memory databases can commit to disk before they ack the
client
However, even if you ack after everything is committed:
•  Is your driver memory buffer persistent and consistent?
•  Is your storage system cache persistent and consistent?
Most databases are NOT bulletproof26
Most in-memory databases are async most of the time
Master
Storage
Slave
Storage
2 1
4
3
6 5
Application Async replication
Async disk I/O
27
Some of them can partially sync
Master
Storage
Slave
Storage
4 1
3
2
6 5
Application Sync replication
Async disk I/O
28
A few of them fully sync
Master
Storage
Slave
Storage
6 1
5
2
4 3
Application Sync replication
Sync disk I/O
29
CP
AP
CAP and in-memory databases
CA
Memcached
in-memory
30
AP
Behavior during network splits
Y X X
Write
Inconsistency
Read CP
X X X
Full-consistency
Read
31
31
Are in-memory databases reliable?
Redis Labs facts:
•  Provisioned 100s of TBs of RAM
•  500+ node failure events à 1 failure every 2 days
•  ~30 complete data-center outages à 1 outage every month
•  Users with high availability (HA) features enabled haven’t lost a single
byte of data
32
Myth #4
In-memory databases
are expensive
33
Real-world use case:
•  500+GB
•  400K writes/sec
•  1500 reads/sec
•  37.5KB average object size
Which one costs more?
No extra
work at
app level
1.5Gbps 120Gbps
Tons of work
at app level
Others
6-node
cluster
30+ node
cluster
34
Redis Labs
Aerospike/
Couchbase Cassandra
Read
Write
Cost
Which one costs more (2)?
35
Sometimes in-memory can be very expensive
#1
200GB
#2
200GB
#50
200GB
50 x r3.8xlarge instances
#51
200GB
#51
200GB
#100
200GB
1st replica for HA
#101
200GB
#102
200GB
#150
200GB
2nd replica for quorum
HA deployment of 10TB in-memory dataset on EC2
Total cost (reserved instances) = $2,132,250/yr
36
Do we really need 2 replicas?
#1
200GB
#2
200GB
#50
200GB
50 x r3.8xlarge instances
#51
200GB
#51
200GB
#100
200GB
1 replica for HA
#101
15GB
Efficient HA deployment of 10TB in-memory dataset on EC2
Total cost (reserved instances) = $1,421,500/yr Savings = $710,750/yr
Redis Labs Patent Pending Technology
1 quorum server
37
Can we save more?
38
Price/performance of memory technologies
$10
$3
$1
0.10
1.00
10.00
100.00
1000.00
$0
$2
$4
$6
$8
$10
$12
DRAM SCM NAND (flash)
micosecond
1GB memory - cost vs read/write performance
1 GB cost Read latency Write latency
39
Redis on Flash
Flash
(slow
RAM)
RAM All keys +
‘hot’ values
‘cold’ values
●  Asynchronous
●  Multi-threaded
Flash used as a RAM extender and NOT as persistent storage
40
How to achieve optimal price/performance
By dynamically setting RAM/Flash ratio
RAM
4410 GB590 GB
88.2%11.8%
41
Single server performance - 10% in RAM / 90% in Flash
*IBM P8 with IBM FlashSystem42
RAM Hits Ratio Ops/Sec Latency
Low latency scenarios
100% 1.35M 1.00 msec
80% 340K 1.07 msec
50% 200K 0.96 msec
20% 160K 1.00 msec
High throughput scenarios
100% 2.00M 2.40 msec
80% 671K 6.20 msec
50% 483K 10.00 msec
20% 366K 14.50 msec
10TB Redis deployment on EC2
43
Redis (on RAM)
2 replicas
Redis (on RAM)
1 replicas
Redis on Flash
1 replica
Instance type r3.8xlarge r3.8xlarge i2.8xlarge
# of instances 150 100 10
RAM 30TB 20TB 2TB
Flash - - 64TB
Persistent storage (EBS) 150TB 100TB 80TB
1yr costs
(reserved instances)
$2,132,250 $1,421,500 $318,090
Yearly savings - $710,750 $1,814,160
Savings % - 33.33% 85.08%
Summary
44
4 myths about in-memory databases busted
All in-memory databases are NOT equally fast
You can create a single node in-memory cluster
In-memory databases can be consistent and reliable
With the right technology, in-memory databases are not expensive
45
Thank you
Click to get more info about
Redis, Redis Labs, Redis Cloud and RLEC

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IMCSummit 2015 - Day 2 IT Business Track - 4 Myths about In-Memory Databases Busted

  • 1. 4 Myths about in-memory databases busted Yiftach Shoolman – Co-Founder & CTO @ Redis Labs @yiftachsh, @redislabsinc
  • 2. Background - Redis 2 Created by Salvatore Sanfilippo (@antirez) OSS, in-memory NoSQL k/v database/data-structure engine # 1 # 2 # 3 # 12 Fastest growing DB 2013-01 – to date: DB-Engines Most popular database on containers: @DevOps.com & ClusterHQ Top NoSQL databases: DB-Engines Top tools developers love: StackShare
  • 3. Backgroud – Redis Labs 3 Founded in 2011. HQ in Mountain View CA, R&D in Tel-Aviv IL The largest commercial company behind OSS Redis •  5000+ paying customers •  30,000+ free users •  100,000+ databases under management •  ±200 new databases/day Provide enterpsie class Redis deployement •  As a service – Redis Cloud •  On-premises – Redis Labs Enterprise Cluster (RLEC) $28MM VC funding
  • 4. Why an in-memory operational DBMS ? 4 DatabaseApp Servers 100msec = The new standard for E2E application response time, under any load 50msec = Average internet latency (round-trip) 50msec = Required app response time (includes processing & multi-DB access) 1msec = Required DB response time
  • 5. Why an in-memory analytics DBMS ? 5 Query time: Days/Hours Disk-based Query time: Mins/Secs RAM RAM-based
  • 6. Myth #1 all in-memory databases are equally fast 6
  • 7. How many ops/sec can a single EC2 instance do? 7 1,000,000 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1xc3.8xlarge EC2 instance (1)  Deployed in a VPC (2)  Enhanced Network Interface (ENI) enabled (3)  Placement group enabled (4)  IRQ Distribution enabled (5)  Interrupt coalescing enabled (6)  4xNICs configured (7)  Receive Packet Steering (RPS) enabled
  • 8. How many ops/sec can a single EC2 instance do? 8 1,228,432 1,000,000 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 1xc3.8xlarge EC2 instance Fully optimized (1)  Deployed in a VPC (2)  ENI enabled (3)  Placement group enabled (4)  IRQ Distribution enabled (5)  Interrupt coalescing enabled (6)  4xNICs configured (7)  RPS enabled Not optimized (1)  Deployed on Classic EC2 (2)  No tuning (3)  Reached packets/sec limit
  • 9. How many ops/sec can a single EC2 instance do? 9 1,750,000 1,228,432 1,000,000 0 200,000 400,000 600,000 800,0001,000,0001,200,0001,400,0001,600,0001,800,0002,000,000 1xc3.8xlarge EC2 instance Not optimized (1)  Deployed on Classic EC2 (2)  No tuning (3)  Reached packets/sec limit Half optimized (1)  Deployed in a VPC (2)  ENI enabled (3)  RPS enabled (4)  Only 1xNIC Fully optimized (1)  Deployed in a VPC (2)  ENI enabled (3)  Placement group enabled (4)  IRQ Distribution enabled (5)  Interrupt coalescing enabled (6)  4xNICs configured (7)  RPS enabled
  • 10. How many ops/sec can a single EC2 instance do? 10 2,000,000 1,750,000 1,228,432 1,000,000 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 1xc3.8xlarge EC2 instance Not optimized (1)  Deployed on Classic EC2 (2)  No tuning (3)  Reached packets/sec limit Half optimized (1)  Deployed in a VPC (2)  ENI enabled (3)  RPS enabled (4)  Only 1xNIC Fully optimized (1)  Deployed in a VPC (2)  ENI enabled (3)  Placement group enabled (4)  IRQ Distribution enabled (5)  Interrupt coalescing enabled (6)  4xNICs configured (7)  RPS enabled Fully optimized
  • 11. How many servers to get 1M writes/sec on GCE? 300 0 50 100 150 200 250 300 350 11
  • 12. How many servers to get 1M writes/sec on GCE? 300 50 50 0 50 100 150 200 250 300 350 12
  • 13. How many servers to get 1M writes/sec on GCE? 300 50 50 2 0 50 100 150 200 250 300 350 In-memory databases13
  • 15. Hbase+Internal Cache vs. Hbase+Redis 15
  • 16. 16 Same Redis core, same HW, different performance 16
  • 17. So why aren’t in-memory DBs equally fast? 17 Most are written in C/C++…. but programming language isn’t the only thing to consider
  • 18. What affects in-memory DB performance? (1) Complexity of processing commands à How many lines of code per command ? What is the computation complexity (e.g. in Redis most commands are O(1))? (2) Query efficiency à Is it limited to blob queries? Can you query a discrete value? (3) Pipelining à Can you send multiple requests at once to get lower latency and less context switches?18
  • 19. What affects in-memory DB performance? (4) Protocol efficiency à How long it takes to parse a request or to serialize a response (5) TCP overhead à Long-lived (connection pool) vs. short-lived connections 19
  • 20. What affects in-memory DB performance? (6) Single-threaded or multi-threaded architecture à Lock-free vs. parallel computing (7) Shared-nothing (the best) vs. shared-something vs. shared-everything (8) Built-in acceleration components 20
  • 21. Myth #2 A single node is not a cluster 21
  • 22. The truth: A single node can be a cluster but not a HA cluster 22
  • 23. In the new containers/VMs world a cluster is: A bunch of processes that together look like one big process 23
  • 24. A real-world example A Binary Option platform; 400MB dataset; 1,000,000 ops/sec A single node, 9 shards cluster = 3 nodes, 9 shards cluster24
  • 25. Myth #3 In-memory databases are inconsistent and unreliable 25
  • 26. A few facts/questions about consistency Almost all NoSQL databases (not just in-memory) ack the client before commiting to disk Almost all in-memory databases can commit to disk before they ack the client However, even if you ack after everything is committed: •  Is your driver memory buffer persistent and consistent? •  Is your storage system cache persistent and consistent? Most databases are NOT bulletproof26
  • 27. Most in-memory databases are async most of the time Master Storage Slave Storage 2 1 4 3 6 5 Application Async replication Async disk I/O 27
  • 28. Some of them can partially sync Master Storage Slave Storage 4 1 3 2 6 5 Application Sync replication Async disk I/O 28
  • 29. A few of them fully sync Master Storage Slave Storage 6 1 5 2 4 3 Application Sync replication Sync disk I/O 29
  • 30. CP AP CAP and in-memory databases CA Memcached in-memory 30
  • 31. AP Behavior during network splits Y X X Write Inconsistency Read CP X X X Full-consistency Read 31 31
  • 32. Are in-memory databases reliable? Redis Labs facts: •  Provisioned 100s of TBs of RAM •  500+ node failure events à 1 failure every 2 days •  ~30 complete data-center outages à 1 outage every month •  Users with high availability (HA) features enabled haven’t lost a single byte of data 32
  • 34. Real-world use case: •  500+GB •  400K writes/sec •  1500 reads/sec •  37.5KB average object size Which one costs more? No extra work at app level 1.5Gbps 120Gbps Tons of work at app level Others 6-node cluster 30+ node cluster 34
  • 36. Sometimes in-memory can be very expensive #1 200GB #2 200GB #50 200GB 50 x r3.8xlarge instances #51 200GB #51 200GB #100 200GB 1st replica for HA #101 200GB #102 200GB #150 200GB 2nd replica for quorum HA deployment of 10TB in-memory dataset on EC2 Total cost (reserved instances) = $2,132,250/yr 36
  • 37. Do we really need 2 replicas? #1 200GB #2 200GB #50 200GB 50 x r3.8xlarge instances #51 200GB #51 200GB #100 200GB 1 replica for HA #101 15GB Efficient HA deployment of 10TB in-memory dataset on EC2 Total cost (reserved instances) = $1,421,500/yr Savings = $710,750/yr Redis Labs Patent Pending Technology 1 quorum server 37
  • 38. Can we save more? 38
  • 39. Price/performance of memory technologies $10 $3 $1 0.10 1.00 10.00 100.00 1000.00 $0 $2 $4 $6 $8 $10 $12 DRAM SCM NAND (flash) micosecond 1GB memory - cost vs read/write performance 1 GB cost Read latency Write latency 39
  • 40. Redis on Flash Flash (slow RAM) RAM All keys + ‘hot’ values ‘cold’ values ●  Asynchronous ●  Multi-threaded Flash used as a RAM extender and NOT as persistent storage 40
  • 41. How to achieve optimal price/performance By dynamically setting RAM/Flash ratio RAM 4410 GB590 GB 88.2%11.8% 41
  • 42. Single server performance - 10% in RAM / 90% in Flash *IBM P8 with IBM FlashSystem42 RAM Hits Ratio Ops/Sec Latency Low latency scenarios 100% 1.35M 1.00 msec 80% 340K 1.07 msec 50% 200K 0.96 msec 20% 160K 1.00 msec High throughput scenarios 100% 2.00M 2.40 msec 80% 671K 6.20 msec 50% 483K 10.00 msec 20% 366K 14.50 msec
  • 43. 10TB Redis deployment on EC2 43 Redis (on RAM) 2 replicas Redis (on RAM) 1 replicas Redis on Flash 1 replica Instance type r3.8xlarge r3.8xlarge i2.8xlarge # of instances 150 100 10 RAM 30TB 20TB 2TB Flash - - 64TB Persistent storage (EBS) 150TB 100TB 80TB 1yr costs (reserved instances) $2,132,250 $1,421,500 $318,090 Yearly savings - $710,750 $1,814,160 Savings % - 33.33% 85.08%
  • 45. 4 myths about in-memory databases busted All in-memory databases are NOT equally fast You can create a single node in-memory cluster In-memory databases can be consistent and reliable With the right technology, in-memory databases are not expensive 45
  • 46. Thank you Click to get more info about Redis, Redis Labs, Redis Cloud and RLEC