Aerospike is a high performance, highly resilient, distributed database. Since “seeing is believing,” this session will demonstrate multiple elements of Aerospike exhibiting multiple aspects of our database including: Time required to insert 10 million records How strong consistency works with only two copies Linear performance and throughput for removal then re-addition of a node and zone. Mixed workload uninterrupted performance for concurrent massive reads and writes.

1. Art of the Possible
Tim Faulkes
Chief Developer Advocate

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Agenda
• Why is Aerospike so fast?
• Linear latency even with the loss of a node / zone
• How strong consistency works with only two copies
2

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3
Aerospike’s Rapid
Reads And Writes

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Primary Index Read
A
Sprig 1 Sprig 2 Sprig 3 Sprig N
…
Indexes
Storage
Sprig Table (2N)
…
SSD 1
SSD 2
SSD 3
1. Client sends digest to server
2. Bits in digest determine sprig
3. Sprig is traversed (memory) to find Primary
Index
4. Primary Index hold metadata + storage
location
5. Data read from storage and returned
Aerospike is:
● Written in C for speed
● Optimized to reduce contention / locks
● Is typically limited by Disk IOPS / Network
● Data volume does not slow down
performance
● Is strong consistent
● Doesn’t need a cache
Client
1
2
3
5

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Primary Index Write
1. Client sends digest and data to server
2. Write is put into a Write Buffer (memory)
3. Primary Index entry created / updated
4. Replica(s) sent the write over the Fabric
5. Writes stored in Write Buffers on replica(s)
6. Replica(s) acknowledge writes
7. Master acknowledges write to client
8. Write Buffers flushed to storage when full or
on timeout (1s by default)
A
Sprig 1 Sprig 2 Sprig 3 Sprig N
…
Indexes
Storage
Sprig Table (2N)
…
SSD 1
SSD 2
SSD 3
Buffers Used Record Unused
Network Fabric (Inter-node communications)
1
2
3
4
5
6

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6
Demo: Loading
Data

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Cluster Configuration
Servers: m6gd.2xlarge
• 8 vCPUs
• 32 GiB DRAM
• 1 x 474 GB SSD (4 partitions)
• 6 nodes across 3 racks
Client: c7g.12xlarge
• Normally a range of clients!
Monitoring: t3.large
• Prometheus
• Grafana
Aerospike
• 2 copies of the data (RF = 2)
• Strong Consistency
• Session Consistency on reads
A
F
E
D
C
B
Rack 3 Rack 2
Rack 1
Monitoring
Note: 2 nodes per rack not recommended for production!

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8
Demo: Node/Rack
Loss Under Load

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9
Strong
Consistency
With 2 data copies!

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CAP Theorem
10
RDBMS
Aerospike
HBase
MongoDB
BigTable
YugabyteDB
Aerospike
Cassandra
DynamoDB

11. Proprietary & Confidential | All rights reserved. © 2023 Aerospike, Inc.
Terminology: Immediate vs Eventual
Consistency
Immediate consistency guarantees that as soon as a piece of data is written
successfully, it will be available to clients who are reading that data. There is
no delay between writing data and being able to read it.
Eventual consistency means that once the data is successfully written, it will,
at some point in the future, become available for reads. There is no guarantee
of how soon it will become available; the only guarantee is that the time will
eventually come when reads will be consistent.
Aerospike’s writes are always Immediately Consistent

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Raft Consensus (Quorum Based)
A
B C
Application (Leader)
(Followers)
RAFT is a well-known and quorum based.
● Requires an ODD number of copies of the data
● To support N node failures, need 2N+1 copies
○ 1 node failure => 3 copies
○ 2 node failures => 5 copies
● Data is consistent once a majority of nodes
(including leader) have responded
● Used by: YugabyteDB, CockroachDB, Neo4j,
MongoDB, RedisRaft, etc…
● Heartbeats are exchanged per raft group.
○ More raft groups => more CPU usage
● As data per node grows => more raft groups =>
more heartbeats => more CPU
● CPU is often a bottleneck in raft-based systems For more information, see http://thesecretlivesofdata.com/raft/

13. Proprietary & Confidential | All rights reserved. © 2023 Aerospike, Inc.
Aerospike Consensus (Non-Quorum Based)
A
B
Application (Leader)
(Follower)
Aerospike has its own consistency algorithm
● To support N failures, need N+1 copies
○ 1 failure => 2 copies
○ 2 failures => 3 copies
○ Big hardware savings!
● Data is consistent once all nodes have responded
● Used by:
○ Aerospike
● Consistency is maintained with a roster
○ Determines healthy state of the cluster
● Heartbeats are exchanged by nodes so CPU stays
constant as data density per node increases
SC Mode passes Jepsen tests: https://jepsen.io/analyses/aerospike-3-99-0-3

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Cluster Splits Data Availability
A B C D E
A B C D E
RM
R
RR
M
RM RR
M R
Rule 1: A sub-cluster is Active if it has the Roster Master and all
Roster Replicas and at least 1 is full.
Rule 2: A sub-cluster is Active if it has the majority of nodes and at
least one full Roster Master or Roster Replica OR exactly ½ the
roster nodes and the Roster Master and the partition is full.
A B C D E
RM RR
M R
Rule 3: A sub-cluster is Active if it is a Super Majority Cluster and the
partitions are full or subsets
Aerospike implements rules which guarantee that for any partition in any network split, at most one partition
can write the data.
• Conflict avoidance, not conflict resolution

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Helpful tools &
resources
15

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Join our community!
16
https://discord.gg/NfC93wJEJU
on

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Helpful tools & resources
17
https://docs.aerospike.com
https://github.com/aerospike/aerolab
https://github.com/aerospike-examples
https://developer.aerospike.com