Aerospike AdTech Gets Hacked in Lower Manhattan

© 2013 Aerospike, Inc. All rights reserved. Confidential. | <Title of Presentation> | 1
Aerospike aer . o . spike [air-oh- spahyk]
noun, 1. tip of a rocket that enhances speed and stability
YOU SNOOZE YOU LOSE
OR
HOW TO WIN IN AD TECH?
THE ONLY FLASH-OPTIMIZED DATABASE
BRIAN BULKOWSKI
FOUNDER, CTO, PRODUCT
STACK EXCHANGE MEETUP
APRIL 10, 2014

Aerospike: the gold standard for high throughput,
low latency, high reliability transactions
Performance
• Over ten trillion transactions per
month
• 99% of transactions faster than 2
ms
• 150K TPS per server
Scalability
• Billions of Internet users
• Clustered Software
• Automatic Data Rebalancing
Reliability
• 50 customers; zero service down-
time
• Immediate Consistency
• Rapid Failover; Data Center
Replication
Price/Performance
• Makes impossible projects
affordable
• Flash-optimized
• 1/10 the servers required

Aerospike Proven in Production
■  AppNexus - #2 RTB after Google
■  45 Billion auctions per day
■  2M QPS
■  3 12 server clusters
■  4.8T Flash per server
■  120K read TPS, 60K write TPS
■  Chango – #2 Search after Google
■  Sees more Searches than
Yahoo! + bing
■  Data on 300 Million users
■  TradeDesk – first Ad Exchange
■  Facebook Exchange partner
■  FBX serves 25% of Ads on the Internet
■  Snapdeal
■  2 servers replace 10 mongo servers
■  10GB data
■  “changed our company”
“Aerospike has operated
without interruptions
and easily scaled to meet
our performance demands.”
– Mike Nolet, CTO, AppNexus
© 2013 Aerospike. All rights reserved. Pg. 3

MILLIONS OF CONSUMERS
BILLIONS OF DEVICES
AEROSPIKE CLUSTER
APP SERVERS RDBMS
DATA
WAREHOUSE
SEGMENTS
WRITE REAL-TIME CONTEXT
READ RECENT CONTENT
PROFILE STORE
Cookies, email, deviceID, IP address,
location, segments, clicks, likes,
tweets, search terms...
REAL-TIME ANALYTICS
Best sellers, top scores, trending
tweets
BATCH
ANALYTICS
Discover
patterns,
segment data:
location patterns,
audience affinity
TYPICAL REAL-TIME DATABASE
DEPLOYMENT
TRANSACTIONS
WRITE CONTEXT

KEY CHALLENGES
1.  Handle extremely high rates of read/write transactions
over persistent data
2.  Avoid hot spots to maintain tight latency SLAs
3.  Provide immediate consistency with replication
4.  Ensure long running tasks do not slow down
transactions
5.  Scale linearly as data sizes and workloads increase
6.  Add capacity with no service interruption

SYSTEM ARCHITECTURE FOR 100%
UPTIME

SHARED-NOTHING SYSTEM:100% DATA
AVAILABILITY
■  Every node in a cluster is identical,
handles both transactions and long
running tasks
■  Data is replicated synchronously with
immediate consistency within the cluster
■  Data is replicated asynchronously
across data centers
OHIO Data Center
© 2013 Aerospike. All rights reserved Pg. 7

ROBUST DHT TO ELIMINATE HOT SPOTS
How Data Is Distributed (Replication Factor 2)
■  Every key is hashed into a
20 byte (fixed length) string
using the RIPEMD160 hash function
■  This hash + additional data
(fixed 64 bytes)
are stored in RAM in the index
■  Some bits from this hash value are
used to compute the partition id
■  There are 4096 partitions
■  Partition id maps to node id
based on cluster membership
cookie-abcdefg-12345678
182023kh15hh3kahdjsh
Partition
ID
Master
node
Replica
node
… 1 4
1820 2 3
1821 3 2
4096 4 1

REAL-TIME PRIORITIZATION TO MEET SLA
1.  Write sent to row master
2.  Latch against simultaneous writes
3.  Apply write to master memory and replica
memory synchronously
4.  Queue operations to disk
5.  Signal completed transaction (optional
storage commit wait)
6.  Master applies conflict resolution policy
(rollback/ rollforward)
master replica
1.  Cluster discovers new node via gossip
protocol
2.  Paxos vote determines new data
organization
3.  Partition migrations scheduled
4.  When a partition migration starts, write
journal starts on destination
5.  Partition moves atomically
6.  Journal is applied and source data deleted
transactions
continue
Writing with Immediate Consistency Adding a Node

INTELLIGENT CLIENT TO MAKE APPS
SIMPLER
■  Implements Aerospike API
■  Optimistic row locking
■  Optimized binary protocol
■  Cluster tracking
■  Learns about cluster changes,
partition map
■  Gossip protocol
■  Transaction semantics
■  Global transaction ID
■  Retransmit and timeout
■  Linear scale
■  No extra hop
■  No load balancers

OTHER DATABASE
OS FILE SYSTEM
PAGE CACHE
BLOCK INTERFACE
SSD HDD
BLOCK INTERFACE
SSD SSD
OPEN NVM
SSD
OTHER
DATABASE
AEROSPIKE FLASH OPTIMIZED
IN-MEMORY DATABASE
Ask me and I’ll tell you the answer.Ask me. I’ll look up the answer and then tell it to
you.
AEROSPIKE
HYBRID MEMORY SYSTEM™
•  Direct device access
•  Large Block Writes
•  Indexes in DRAM
•  Highly Parallelized
•  Log-structured FS “copy-on-write”
•  Fast restart with shared memory
FLASH OPTIMIZED HIGH
PERFORMANCE

Storage type DRAM & NoSQL SSD & DRAM
Storage per server 180 GB (196 GB Server) 2.4 GB (4 x 700 GB)
TPS per server 500,000 500,000
Cost per server $8,000 $11,000
Server costs $1,488,000 $154,000
Power/server 0.9 kW 1.1 kW
Power (2 years) $0.12 per kWh ave.
US
$352,000 $32,400
Maintenance (2 years) $3,600 per
server
$670,000 $50,400
Total $2,510,000 $236,800
THE BOTTOM LINE
Actual customer analysis.
Customer requires 500K TPS,
10 TB of storage, with
2x replication factor.
186 SERVERS REQUIRED 14 SERVERS REQUIRED
OTHER DATABASES
ONLY

Only up in 2013, 2014
Everyone wants that “Facebook architecture”
Facebook and Apple bought at least
$200+M in FusionIO cards in 2012
+ =
$200+M

© 2012 Aerospike. All rights reserved. Pg. 14
Measure your drives!
Aerospike Certification Tool (ACT)
http://github.com/aerospike/act
Transactional database workload
Reads: 1.5KB
(can’t batch / cache reads, random)
Writes: 128K blocks
(log based layout)
(plus defragmentation)
Turn up the load until
latency is over required SLA

➤  Super Storm Sandy 2012
§  NYC down for 17 hours
§  Back up and synched in 1 hour via
Aerospike Cross-Data Center Replication (XDR)
Replication that Works
“Aerospike allows us to
handle business continuity
and reliability across 4 data
centers seamlessly. And we
can now expand our
deployment to new data
centers in less than a week.”
- Elad Efraim, CTO

HOT ANALYTICS BY ROW

➤  Namespaces (policy containers)
§  Determine storage - DRAM or Flash
§  Determine replication factor
§  Contain records and sets
➤  Sets (tables) of records
§  Arbitrary grouping
➤  Records (rows)
§  Max 128k, contain key and bins
§  Bin with same name can contain
values of different types
u  String, integer, bytes (raw, blob, etc)
u  list ( an ordered collection of
values )
u  map ( a collection of keys and
values )
§  Bins can be added anytime
NOSQL EXTENSIBILITY

DISTRIBUTED QUERIES
1.  “Scatter” requests to all nodes
2.  Indexes in DRAM for fast map of secondary à primary keys
3.  Indexes co-located with data to guarantee ACID,
manage migrations
4.  Records read in parallel from all SSDs
using lock free concurrency control
5.  Aggregate results on each node
6.  “Gather” results from all nodes on client
STREAM AGGREGATIONS
1.  Push Code/ Security Policies/ Rules to Data with UDFs
2.  Pipe Query results through UDFs to
Filter, Transform, Aggregate.. Map, Reduce
REAL-TIME ANALYTICS on OPERATIONAL DATA (No ETL)
➤  In Database, within the same Cluster
➤  On the same Data, on XDR Replicated Clusters
Real-time Analytics on Operational Data

LESSONS LEARNED

NATIVE FLASH à PERFORMANCE
■  Low Latency at High Throughput
0
2.5
5
7.5
10
0 50,000 100,000 150,000 200,000
AverageLatency,ms
Throughput, ops/sec
Balanced Workload Read Latency (Full view)
Aerospike
Cassandra
MongoDB
0
4
8
12
16
0 50,000 100,000 150,000 200,000
AverageLatency,ms
Throughput, ops/sec
Balanced Workload Update Latency (Full view)
Aerospike
Cassandra
MongoDB
0
1
2
3
4
0 75,000 150,000 225,000 300,000
AverageLatency,ms
Throughput, ops/sec
Read-Heavy Workload Read Latency (Full view)
Aerospike
Cassandra
MongoDB
0
6
12
18
24
0 75,000 150,000 225,000 300,000
AverageLatency,ms
Throughput, ops/sec
Read-Heavy Workload Update Latency (Full view)
Aerospike
Cassandra
MongoDB

LESSONS
1.  Keep architecture simple
■  No hot spots (e.g., robust DHT)
■  Scales up easily (e.g., easy to size)
■  Avoids points of failure (e.g., single node type)
2.  Avoid manual operation – automate, automate!
■  Self-managed cluster responds to node failures
■  Data rebalancing requires no intervention
■  Real-time prioritization allows unattended system operation
3.  Keep system asynchronous
■  Shared nothing – nodes are autonomous
■  Async writes across data centers
■  Independent tuning parameters for different classes of tasks

LESSONS (cont’d)
4.  Monitor the Health of the System Extensively
■  Growth in load sneaks up on you over weeks
■  Early detection means better service
■  Most failures can be predicted (e.g., capacity, load, …)
5.  Size clusters properly
■  Have enough capacity ALWAYS!
■  Upgrade SSDs every couple years
■  Reduce cluster sizes to make operations simple
6.  Have geographically distributed data centers
■  Size the distributed data centers properly
■  Use active-active configurations if possible
■  Size bandwidth requirements accurately

LESSONS (CONT’D)
7.  Have plan for unforeseen situations
■  Devise scenarios and practice during normal work time
■  Ensure you can do rolling upgrades during high load time
■  Make sure that your nodes can restart fast (< 1 minute)
8.  Constantly test and monitor app end-to-end
■  Application level metrics are more important than DB metrics
■  Most issues in a service are due to a combination of application, network,
database, storage, etc.
9.  Separate online and offline workloads
■  Reserve real-time edge database for transactions and hot analytics queries
(where newest data is important)
■  Avoid ad-hoc queries on on-line system
■  Perform deep analysis in offline system (Hadoop)
10.  Use the Right Data Management System for the job
■  Fast NoSQL DB for real-time transactions and hot analytics on rapidly
changing data
■  Hadoop or other comparable systems for exhaustive analytics on mostly
read-only data

AEROSPIKE REAL-TIME BIG DATA
PLATFORM
Rapid Development Complete Customizability
➤  Support for popular languages and
tools
§  ASQL and Aerospike Client in
Java, C#, Ruby, Python..
➤  Complex data types
§  Nested documents
(map, list, string, integer)
§  Large (Stack, Set, List) Objects
➤  Queries
§  Single record
§  Batch multi-record lookups
§  Equality and range
§  Aggregations and MapReduce
➤  User Defined Functions
(UDFs)
§  In-DB processing
➤  Aggregation Framework
§  UDF Pipeline
§  MapReduce ++
➤  Time Series Queries
§  Just 2 IOPs for most r/w
independent of object size

HOW TO GET AEROSPIKE?
Free Community
Edition Enterprise Edition
➤  For developers looking
for speed and stability
and transparently scale
as they grow
➤  No transaction limits
➤  No time limit
➤  No production limit
➤  Data per cluster limit
➤  Community support
➤  For mission critical apps
needing to scale right from
the start
§  Unlimited number of
nodes, clusters, data
centers
§  Cross data center
replication
§  Premium 24x7 support
§  Priced by TBs of unique
data (not replicas)

QUESTIONS?
brian@aerospike.com
www.aerospike.com

Aerospike AdTech Gets Hacked in Lower Manhattan

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