Cassandra At Wize Commerce

CASSANDRA AT WIZE COMMERCE

Eran Chinthaka Withana
Eran.Withana@wizecommerce.com

CASSANDRA AT Wize Commerce – Eran Chinthaka Withana. Cassandra Meetup (07/25/2012)

About me

• Engineer in Platform and Infrastructure team at Wize
Commerce (formerly Nextag)
• Member, PMC Member and a committer of Apache
Software Foundation
– Contributed to Web services project since 2004
• (in a different life) PhD in Computer Science from
Indiana University, Bloomington, Indiana

• Today

2

In the next hour …

• Wize Commerce
• Impact of Cassandra on Wize Commerce
– Object Cache
– Personalized Search
• Performance evaluation of Cassandra in a multi-data
center and a read/write heavy environment

3

WIZE COMMERCE


About Wize Commerce

• Helping companies maximize their eCommerce
investments
– across every channel, device and digital ecosystem
– an expertise we’ve honed for years with our eCommerce
customers
– providing them with unmatched traffic and monetization
services at incredible scale

5

About Wize Commerce

• Scale of Wize Commerce
– We drive over $1.1 Billion in annual worldwide sales
– Shopping Network includes Nextag, guenstiger.de,
FanSnap, and Calibex
– Each week, we manage
• 21 Million Keyword Searches
• 105 Million Retargeted Ads
• 140 Million Bot Crawls
• 300 Million Facebook Ads
• 700 Million Keywords
• 560 Million Product SKUs
• 1000s of Simultaneous A/B Test

6

CASSANDRA AT WIZE COMMERCE - CACHE


Cache Architecture


Cache Architecture

• Multi-tiered read-through cache, optimized for performance
• TTLs at upper levels to keep the data fresh
• JMS based infrastructure to refresh objects on-demand

9

Cache - Expectations

• For each object
– Less than 30ms 95th percentile read latency
– Less than 1-hour of update latency with 30M updates
(phase 1, with existing components)
– 10 minutes with eventing system integrated
• Fault tolerance
• Low maintenance overheads
• Ability to scale

10

Cache – Cassandra Integration

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DC1 DC2 DC3 DC4

• Replication factors to facilitate required number of copies per
region
• Consistency level to suit business requirements
• 6 multi-data center clusters with total nodes per cluster
ranging from 24 to 32
• In house monitoring system for continuous monitoring and
escalations
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• Clients
– Hector with DynamicLoadBalancing policy
– Started experimenting with Astyanax
• Maintenance
– Weekly repair and compaction tasks
• Monitoring
– System health monitoring
– End-to-end latency
– Update latency
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• Ring output of a cluster
Address DC Rack Status State Load Owns Token
148873535527910577765226390751398592512
xx.xx.xx.79 DC1 RAC1 Up Normal 90.19 GB 12.50% 0



• Column family stats of a cluster
Keyspace: XXXX
Read Count: 37060467
Read Latency: 3.0589244618800944 ms.
Write Count: 37013052
Write Latency: 0.05114632081677566 ms.
Pending Tasks: 0
Column Family: YYY
SSTable count: 11
Space used (live): 71463479840
Space used (total): 71463479840
Number of Keys (estimate): 66231424
Memtable Columns Count: 314964
Memtable Data Size: 68140546
Memtable Switch Count: 628
Read Count: 37060467
Read Latency: 3.138 ms.
Write Count: 37013052
Write Latency: 0.058 ms.
Pending Tasks: 0
Bloom Filter False Postives: 10653
Bloom Filter False Ratio: 0.01611
Bloom Filter Space Used: 173770024
Key cache capacity: 60000000
Key cache size: 13309399
Key cache hit rate: 0.9210111414757199
Row cache: disabled
Compacted row minimum size: 925
Compacted row maximum size: 8239
Compacted row mean size: 2488


Cache – Cassandra Datastore Performance

• 3-6ms average read latency across all objects in all
data centers
• 15-20ms 95th percentile read latency
• 30mins average update latency at 25M updates
• Zero downtime even with multiple node failures

16

Cache – Snapshot of Live System

Median Read Latency

Objects Scrubbed in Last
24hrs

Scrubber Latency

17

Cassandra Integration – Lessons Learned

• Try to understand the internals, read code and find solutions
on your own before getting into support requests
– Assumption: you have adventurous engineers :D
– Use IRC channels, user lists
• Never use RoundRobinLoadBalancingPolicy if you care about
performance
– DynamicLoadBalancingPolicy: based on the probability of failure of
node
• Divide keyspace within the datacenter and use token + 1
method in other data centers
• Experiment different configurations but make sure to have a
quick fallback plan

18

Cassandra Integration – Lessons Learned

• Compaction are crucial for read/write heavy
environment
• 24 x 7 automated monitoring and alerts
– Read/write latencies , read misses and node status at least
• Consistency levels are important, if you expect node
failures in a multi-data center environment
• Concentrate on key cache and forget about row
cache if you have limited resources.
– Rely on OS file cache

19

Cache: Future

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Cache: Future

• Exposing Cache system using SOA based
infrastructure
– Thrift services enabling all cache accesses
• Event based updates
– Event based pipeline for changes for system-of-record
– Based on Storm (Twitter)
• Getting rid of Memcached

21

CASSANDRA AT WIZE COMMERCE – PERSONALIZED
SEARCH


Personalized Search

• Aggregates user data from multiple data sources, e.g.
site search, banner clicks.
• Uses statistical model to re-rank search results
tailored to the user.
• Decomposes user information into model variables:
brand preference, merchant preference, product
category preference, etc.

23

Personalized Search – Cassandra Integration

• Serves 30-40MM banner ad impressions daily
• Before: rely on user cookie (stores up to 4 weeks
data)
• After: use user cookie for today's data, combined
with Cassandra Data Store to keep up to 3 months
data

24

PERFORMANCE EVALUATION OF APACHE
CASSANDRA IN A MULTI-DATA CENTER, READ/WRITE
HEAVY ENVIRONMENT


Objectives

• Understand the limitations of Cassandra when
deployed in a multi-data center environment
• Find out the best set of parameters that can
be used and tuned to improve the
performance
• Find out the limits of Cassandra cluster and for
each version.

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Objectives

• Understand its scalability characteristics with
varying amount of operations per second
– This will help us to understand how much of load
we can serve without causing any significant
performance degradations.
• Understand the implications of node failures
on its capability to efficiently serve data to
client requests

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Environment Setup

• Test Metrics
– Operation Latency for a given throughput (set in the client)
• Average, Minimum, Maximum, 95th percentile
• Test Setup
– Versions: Apache Cassandra 0.8.6 and 1.0.1
– Node Distribution: 12-nodes distributed over three
geographically distributed data centers in US
– Key Distribution: Keyspace is divided into four in each data
center and each node in the cluster is responsible for 1/4th
of the keyspace
– Replication Factor: 3. Each datacenter has a copy of the
data.
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Environment Setup

• Hardware Setup
– Dell R410
• 2 Quad-core with hyper-threading
• 8 x 4GB RAM
• PERC 6/i RAID Controller with 4 x 450GB and 15k RPM drives
• GigE Network
• CentOS 5.7
• Clients
– Uses Yahoo Cloud Serving Benchmark (YCSB)
– Two clients in each data-center, with a total of 6 clients
– Records metrics at 10s intervals
• Every test case is independent of each other 29

Workload

• Read:Write ratio is 1:1.
• Thread Counts: 256 from each client (total of 6 clients, 2 from
each data-center)
– Contacts Cassandra nodes only in its own data-center (no cross data-center
traffic)
• Key Distribution: Zipfian
• Record Count: 100 million
• Total Operations Per Test Case: 1 million

30

Workload

• Target Operations per Second: Varies
• Test Data
– Columns per row: 10
– Compacted row minimum size: 150
– Compacted row maximum size: 1331
– Compacted row mean size: 736

31

Test Cases

• Parameters varied in each test case
– Apache Cassandra version: 0.8.6 vs 1.0.1
– Concurrent read and write threads in a Cassandra node
– Number of keys cached

32

Test Cases

Test Description
Number
1 Cassandra 0.8.6 binary as it is with no changes (Concurrent reads/writes = 32
and keys cached = 200k). base case for 0.8.6
2 Cassandra 0.8.6 with 64 concurrent reads and writes. Also keys cached is
increased to 1 million.
3 Cassandra 0.8.6 with 64 concurrent reads and 32 concurrent writes. Also keys
cached is increased to 1 million
4 Cassandra 1.0.1 with 64 concurrent reads and writes. Also keys cached is
increased to 1 million.
5 Cassandra 1.0.1 with 64 concurrent reads and 32 concurrent writes. Also keys
cached is increased to 1 million
6 Failure Test: Cassandra 1.0.1 with 64 concurrent reads and 64 concurrent
writes. Also keys cached is increased to 1 million.

For each test case, we plot operations per second (varied from 3000 to 24000) vs
read/write latency
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Test Cases

• Failure Test
• Brought down a node in east coast data center (DC2) and ran the test
varying the
• Node going down has three implications on the latency.
• Our test clients timeout after 300 retries to connect to failed node.
• Our nodes in DC2 will go to DC3 to serve data that are not
available in DC2 due to the node failure 3)
• Our nodes in DC3 will have requests coming from the nodes of
DC2 putting more load on them

34

Results

Test Case 1: Varying OPS with Cassandra 0.8.6 Default
Configuration

• read performance of default configuration is increasing beyond 25ms after 3000 OPS.
35

Results

Test Case 1: Varying OPS with Cassandra 0.8.6 Default
Configuration

• Even though write performance is staying almost constant the poor read
performance will be a concern with this configuration.
36

Results
Test Case 2: Varying OPS with Cassandra 0.8.6 and Custom Configuration (64
Concurrent Reads/Writes, 1 million keys cached)

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Results

• even with good write performance, read performance after 12000 QPS is
going beyond our threshold of 25ms
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Results
Concurrent Writes, 32 Concurrent Reads and 1 million keys cached)

• latency goes beyond 25ms after reaching 18000 OPS
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Results
Concurrent Writes, 32 Concurrent Reads and 1 million keys cached)

• better and consistent write performance

40

Results

• read performance has improved significantly and even at 24000 OPS it has stayed
well below 10ms range.
41

Results

• better and consistent write performance

42

Results
Concurrent Writes, 32 Concurrent Reads, 1 million keys cached)

• a degradation of read performance compared to test case 4
• latency goes beyond 25ms after reaching 21000 QPS.
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Results
Concurrent Writes, 32 Concurrent Reads, 1 million keys cached)

• a degradation of read performance compared to test case 4
• latency goes beyond 25ms after reaching 21000 QPS.
44

Results

Test Case 6: Failure Test - Varying OPS with Cassandra 1.0.1 and
Custom Configuration (64 Concurrent Reads/Writes, 1 million
keys cached)

• Node going down has three implications on the
latency.
• Our test clients timeout after 300 retries to connect to
failed node.
• Our nodes in DC2 will go to DC3 to serve data that are not
available in DC2 due to the node failure 3)
• Our nodes in DC3 will have requests coming from the
nodes of DC2 putting more load on them

45

Results

Test Case 6: Failure Test - Varying OPS with Cassandra 1.0.1 and
keys cached)

DC2

46

Results

Test Case 6: Failure Test - Varying QPS with Cassandra 1.0.1 and
keys cached)

DC2

47

Results
Test Case 6: Failure Test - Varying QPS with Cassandra 1.0.1 and Custom
Configuration (64 Concurrent Reads/Writes, 1 million keys cached)

DC3

• increase in average latency in both DC2 and DC3 data centers but even
with the node failure the latency has stayed below 25ms.
48

Results
Test Case 6: Failure Test - Varying QPS with Cassandra 1.0.1 and Custom
Configuration (64 Concurrent Reads/Writes, 1 million keys cached)

DC3

• increase in average latency in both DC2 and DC3 data centers but even
with the node failure the latency has stayed below 25ms.
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Comparisons

Cassandra 0.8.6

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Comparisons

Cassandra 0.8.6

51

Comparisons

Cassandra 1.0.1

• 64 concurrent reads and writes with 1 millions keys cached has performed
significantly better than the other configurations in terms of read
performance
52

Comparisons

Cassandra 1.0.1

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Comparisons

Cassandra 1.0.1 vs 0.8.6 Average Read Performance Comparison

significantly better than the other configurations in terms of read performance
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Comparisons
Cassandra 1.0.1 vs 0.8.6 95th Percentile Read Performance Comparison

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significantly better than the other configurations in terms of read performance

Performance Evaluation: Conclusions

• Cassandra 1.0.1 with 64 concurrent reads and writes
and with 1 millions keys cached we could serve
24000 operations per second under 15ms
• Node failure tests prove that in this configuration we
can serve higher load in the cluster with less than
25ms
• Even the 95th percentile latency and 99th percentile
numbers for this configuration is well within our
expected limits

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Excited about the work?

We’re hiring !!

57

Thank you !!

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Questions !!
(Presentation is available at http://goo.gl/Ba9o4)

59

Cassandra At Wize Commerce

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