Hapyrus: Amazon RedshiftBENCHMARK Series 02

Scalability of Amazon RedshiftData Loading andQuery Speed

Comparisons between the performance of different instances

Amazon Redshift can load 1.2TB data using:

•an XL instance, taking 17 hours

•a two node XL instance, taking 10 hours

•a two node 8XL instance (equivalent to XL 16x), taking 2

hours

Load speed is almost proportional to number of nodes

Running identical queries on Amazon Redshift,

•an XL instance took 155 seconds

•a two node XL instance took 55 seconds

•a two node 8XL instance (equivalent to XL 16x) took 31

seconds

The more nodes, the faster a query runs (but not by much, seems more inversely

proportional)

The most important feature of Amazon Redshift is that it has flexible scalability compared to other columnar data warehouses such as Vertica, Netezza and Teradata.

We have run benchmarks to compare the cost and speed of Redshift instances using:

• 1.2TB of data and identical queries– same data as previous benchmark

http://www.slideshare.net/Hapyrus/amazon-redshift-is-10x-faster-and-cheaper-than-hadoop-hive

• 3 different types of instances: Single node XL, two node XL, and two node 8XL

1. Data Loading

• XL x1 instance took 17 hours

• XL x2 instance took 10 hours

• 8XL x2 instance (equivalent to XL 16x) took 2 hours

• XL x1 instance took 17 hours

• XL x2 instance took 10 hours

• 8XL x2 instance (equivalent to XL 16x) took 2 hours

* The query and data used can be referenced in our Appendix

2h 36m

17h 19m

10h 38m

Data loading by COPY command. All the data is copied at once.

2

2. Query Speed

• XL x1 instance took 155 seconds

• XL x2 instance took 55 seconds

• 8XL x2 instance (equivalent to XL 16x) took 31 seconds

• XL x1 instance took 155 seconds

• XL x2 instance took 55 seconds

• 8XL x2 instance (equivalent to XL 16x) took 31 seconds

* The query used can be referenced in our Appendix

155.02s

54.77s

30.61s

2

Redshift Data Loading Results

* The data used can be referenced in our Appendix

The biggest data (imp_logs: 1.2TB)

Other Tables

Instance Type Nodes Machine Spec Time (hours) Speed Cost per TB

dw.hs1.xlarge1 1 17.32 70MB/h $12.27

2 2 10.63 110MB/h $15.07

dw.hs1.8xlarge 2 16 2.62 460MB/h $29.64

Loading Time

Table Name XL x1 XL x2 8XL x2

ad_campaigns(100MB)

5.82s 8.97s 8.5s

Publishers(10MB)

552ms 2.83s 2.22s

Advertisers(10MB)

770ms 3.71s 4.75s

imp_logs(1.2TB)

17h 19m 22.27s 10h 38m 5.66s 2h 36m 55.49s

click_logs(6GB)

11m 31.37s 10m 6.24s 37.87s

Redshift Query Results

* The query used can be referenced in our Appendix

  Processing Time

Trial XL x1 XL x2 8XL x2

1 2m 28.65s 1m 1.44s 39.11s

2 2m 37.65s 52.89s 26.77s

3 2m 35.91s 53.76s 29.9s

4 2m 29.04s 53.52s 27.51s

5 2m 36.9s 52.22s 29.75s

Average 2m 35s 54.77s 30.69s

Discussion

• Redshift can load data in parallel– Almost proportional to number of nodes

• Redshift query speed is drastically affected by number of nodes– Big difference between Single and Multiple nodes

– Two nodes are faster than two times the speed of one node

– Additional experimentation may be helpful in future

APPENDIX - Data

TSV files, gzip compressed

Imp_log

1.2TB / 1.2B record

date datetimepublisher_id integerad_campaign_id integerbid_price realcountry varchar(30)attr1-4 varchar(255)

click_log

5.6GB / 6M record

date datetimepublisher_id integerad_campaign_id integercountry varchar(30)attr1-4 varchar(255)

ad_campaign

100MB / 100k record

publisher

10MB / 10k record

advertiser

10MB / 10k record

Using 5 tables, we run a query which join tables and creates a report.

appendix – Sample Query

select ac.ad_campaign_id as ad_campaign_id, adv.advertiser_id as advertiser_id, cs.spending as spending, ims.imp_total as imp_total, cs.click_total as click_total, click_total/imp_total as CTR, spending/click_total as CPC, spending/(imp_total/1000) as CPMfrom ad_campaigns acjoin advertisers adv on (ac.advertiser_id = adv.advertiser_id)

join(select il.ad_campaign_id, count(*) as imp_total from imp_logs il group by il.ad_campaign_id) ims on (ims.ad_campaign_id = ac.ad_campaign_id)join(select cl.ad_campaign_id, sum(cl.bid_price) as spending, count(*) as click_total from click_logs cl group by cl.ad_campaign_id) cs on (cs.ad_campaign_id = ac.ad_campaign_id);

The query generates a basic report for ad campaigns performance, imp, click numbers,advertiser spending, CTR, CPC and CPM.

APPENDIX – Additional Comments

• A Redshift 8XL instance cannot be selected as single node (you must choose at least 2 nodes)

(as of Feb. 24, 2013)

APPENDIX – Additional Information

• All resources for our benchmark are on our github repository– https://github.com/hapyrus/redshift-

benchmark– The dataset we use is open on S3, so you

can reproduce the benchmark

About Us - FlyData

• FlyData Enterprise

– Enables continuous loading to Amazon Redshift, with real-time data loading

– Automated ETL process with multiple supported data formats

– Auto scaling, data Integrity and high durability– FlyData Sync feature allows real-time

replication from RDBMS to Amazon Redshift

Contact us at: info@flydata.com

We are an official data integration partner of Amazon Redshift

Formerly known as Hapyrus