Amazon Redshift & Amazon DynamoDB Michael Hanisch, Amazon Web Services Erez Hadas-Sonnenschein, clipkit GmbH Witali Stohler, clipkit GmbH

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1 Amazon Redshift & Amazon DynamoDB Michael Hanisch, Amazon Web Services Erez Hadas-Sonnenschein, clipkit GmbH Witali Stohler, clipkit GmbH Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

2 Amazon Redshift & Amazon DynamoDB

3 Amazon Redshift

4 Amazon Redshift Fast, simple, petabyte-scale data warehousing for less than $1,000/TB/Year

5 A fully managed data warehouse service Massively parallel relational data warehouse Takes care of cluster management and distribution of your data Columnar data store with variable compression Optimized for complex queries across many large tables Use standard SQL & standard BI tools Amazon Redshift

6 Amazon DynamoDB

7 A fully managed fast key-value store Fast, predictable performance Simple and fast to deploy Easy to scale as you go, up to millions of IOPS Pay only for what you use: Read / write IOPS + storage Data is automatically replicated across data centers Amazon DynamoDB

8 Amazon DynamoDB Amazon Redshift Fast insert & update Limited query capability (single table only) NoSQL database Fast queries Flexible queries (JOINs, aggregation functions, ) SQL

9 Queries in Amazon DynamoDB

10 Queries in Amazon DynamoDB Query or BatchQuery APIs retrieve items Scan & filter to comb through a whole table You have to join tables in your own code! Amazon DynamoDB

11 Queries in Amazon DynamoDB (2) Apache Hive on Amazon EMR can access data in DynamoDB Run HiveQL queries for bulk processing Can integrate data in HDFS, Amazon S3, HiveQL queries on Amazon EMR Amazon DynamoDB

12 Queries in Amazon DynamoDB (3) Import data into Amazon Redshift Use SQL queries, use BI tools etc. Powerful analytics and aggregation functions Amazon Redshift Amazon DynamoDB

13 Importing Data into Amazon Redshift

14 TMTOWTDI

15 Query & Insert #1 Query / BatchQuery #3 INSERT INTO ( ) Amazon DynamoDB #2 Retrieve Items Amazon Redshift

16 Query & Insert The Good Full control over queries Decide which items you want to move to Redshift Process data on the way The Bad Slow Inefficient on the Redshift side of things Does not scale well

17 The COPY Command #1 COPY FROM #2 Politely ask for a table Amazon DynamoDB #3 Return whole table Amazon Redshift

18 The COPY Command #1 COPY FROM Amazon DynamoDB #2 Parallel Scans Amazon Redshift

19 The COPY Command #1 COPY FROM Amazon DynamoDB #3 Return Items Amazon Redshift

20 The COPY Command COPY a single table at a time From one Amazon DynamoDB table into one Amazon Redshift table Fast executed in parallel on all data nodes in the Amazon Redshift cluster Can be limited to use a certain percentage of provisioned throughput on the DynamoDB table

21 The COPY Command COPY <table_name> (col1, col2, ) FROM 'dynamodb://<table_name2>' CREDENTIALS 'aws_access_key_id= ;aws_secret_access_key= ' READRATIO use 10% of available read capacity COMPROWS 0 [ other options ] -- how many rows to read to determine -- compression

22 The COPY Command Attributes are mapped to columns by name Case of column names is ignored Attributes that do not map are ignored Missing attributes are stored as NULL or empty values Only works for STRING and NUMBER attributes

23 The COPY Command The Good Easy to use Fast Efficient use of resources Scales linearly with cluster size Only uses certain percentage of read throughput The Bad Whole tables only No processing in between Can only copy from DynamoDB in same region Only works with STRING and NUMBER types

24 Query & Insert at Scale #1 Query / BatchQuery in parallel #3 INSERT INTO ( ) in parallel Amazon DynamoDB #2 Retrieve Items Amazon Redshift

25 Query & Insert at Scale #1 Query / BatchQuery in parallel #3 INSERT INTO ( ) in parallel Amazon EMR Amazon DynamoDB #2 Retrieve Items Amazon Redshift

26 Query & Insert at Scale #1 Query / BatchQuery in parallel #3 INSERT INTO ( ) in parallel Amazon EMR Amazon DynamoDB #2 Retrieve Items Amazon Redshift

27 Query & Import using Amazon EMR #1 Query / BatchQuery in parallel Amazon EMR #3 Export to file(s) on S3 #4 COPY FROM s3:// Amazon DynamoDB #2 Retrieve Items Amazon S3 #5 Retrieve files Amazon Redshift

28 Query & Import using Amazon EMR #1 Query / BatchQuery in parallel #3 COPY FROM emr:// Amazon EMR #4 Retrieve files from HDFS Amazon DynamoDB #2 Retrieve Items Amazon Redshift

29 Query & Import using Amazon EMR The Good Decide which items you want to move to Redshift Full control over queries Process data on the way Scales well Integrates with other data sources easily The Bad Additional complexity Additional cost (for EMR) Slower than direct COPY from Amazon DynamoDB

30 Please welcome Erez Hadas-Sonnenschein, Sr. Product Manager Witali Stohler, Datawarehouse & BI Specialist clipkit GmbH

31 Video Syndication The Possibilities

32 Content Partner Overview News Sports Cars/motor Business/finances Music Gaming Cinema Cooking/food Lifestyle/fashion Traveling Computer/mobile Fitness/wellness Knowledge/hobby entertaintment

33 clipkit Player Analytics (Metrics) Full Screen Category Playlist Pos. Play / Pause Progress Pos. Mute / Unmute Volume

34 clipkit Player Analytics (Metrics) Location (Country, City) Language Browser Operating System Video Id Publisher URL Etc

35

36

37

38

39 First Implementation (Expensive and Slow) designed in starting days not calculated to such amount of data slow copy process from S3 to DB (PHP application old architecture) fix EC2 price (expensive to support peak hours) PostgreSQL scalability limitations sometimes the copy process was so slow that the delay was ~3 days.

40 Analytics / Metrics (Requests Graph)

41 Analytics / Metrics (Numbers) ~ 6,000,000 New Entries per day ~ 1,000 Requests per second (Peak Hours) ~ 25 Requests per second (Off-peak Hours) 4000% Requests Growth during the day.

42 Second Implementation (Expensive and Slow) Inserting only for one (big) Table The copy command only works for whole tables The minimum delay was one day Our solution have increase the provisioned throughput and that was expensive NO REAL-TIME DATA

43 Third Implementation (Cheap and Fast)

44 Third Implementation Dynamo DB Java SDK AmazonDynamoDBAsyncClient (Fire and Go) Easy to Create and Delete Tables Write Latency ~5ms Throughput auto scale with Dynamic DynamoDB One Table per day Continuous Iteration and copy to Redshift We just pay for what we use

45 Third Implementation Redshift Standard PostgreSQL JDBC Fully managed by Amazon Automated Backups and Fast Restores ~7000 Insert Items per Second Less than 2 seconds Queries to > 1 billion entries Real-time available data (maximum 1 minute delay)

46 Third Implementation Conclusions Java Web Application Auto Scale (Off-Peak - 1 Small Instance) Dynamo DB One Table per day (After copied it will be deleted) Auto Scale ~5 ms Put Item Latency Redshift Insert ~7000 Items per second Fully managed

47 Thank You!

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