Frankfurt 26 & 27 September 2018

Transcription

1 Frankfurt 26 & 27 September 2018

2 Production-Ready Serverless Java Applications in 3 Weeks with S3, Lambda, API Gateway, SNS, DynamoDB and Aurora Serverless by Elmar Warken and Vadym Kazulkin, ip.labs GmbH

3 Contact Vadym Kazulkin, ip.labs (Meetup) Elmar Warken, ip.labs GmbH

4 ip.labs GmbH

5 Revenue Share Project

6 Revenue Share Calculator Service Requirements 1. Automatic calculation for the complete previous month on the 1 st of each month for each customer 2. Manual calculation via a simple GUI For the current month so far Recalculation for the previous months 3. Calculation results as downloads, download link per 4. Archive with calculated results for each customer 5. For internal use only!

7 Serverless decision for RevShare project spike workloads only at the beginning of a month service is idle more than 99% of the time We use Function as a Service, to never pay for idle. 7

8 RS calculator app extract customer, year and month from the file path customer, year, month Turnover info Central DB Management Server RS core calculator input data: order data & calculation rules with path to download RS calculation result output data: RS calculation & input data Key example: revsharetool-storage-demo/input-data/ /import/201806/revshare-final.json

9 Amazon API Gateway AWS Lambda AWS X-Ray

10 Implementing Revenue Share Calculation Lambda with Java SDK

11 RevenueShare Service Lambda memory setting

12 Selecting memory size for a Lambda function Price 0, USD per GB-second CPU power allocation proportional to amount of memory more GB will often just improve performance and not increase costs! cheaper GB setting may save costs, when lambda function often has to wait 12

13 13 Lambda profiling with X-Ray

14 14 Request references from X-Ray and CloudWatch

15 15 Billed Duration and Max Memory Used

16 16 XRay activation

17 17 XRay activation in Java

18 Lambda free tier sec. per month with the most powerful CPU setting = more than 1h per day with max 1 call every 2 sec. 18

19 Amazon API Gateway Amazon API Gateway Expose Lambda functions as RESTful web service API Management functionality built in: authorization and access control for API calls with IAM request monitoring and analytics overload protection version management APIs as products (SaaS, AWS Marketplace) SDK generation

20 API Gateway for different AWS services Access different AWS services in a consistent manner

21 API Gateway for Lambda function in RevShare App

22 Cost influence of API gateway cost per million API calls in $ for a 128 MB function 3,908 0,408 1,032 4,532 1,656 5,156 2,28 5,78 2,904 6,404 3,528 7,028 4,152 7,652 4,776 8, MS 400MS 700MS 1000MS 1300MS 1600MS 1900MS 2200MS duration per call without API Gateway with API Gateway 22

23 Defining APIs for Lambdas manual approach Resources Operations

24 24 Automated API definitions with Serverless framework

25 25 Automated API definitions with Serverless framework

26 26

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28 Aggregations Source: Advanced Design Patterns for Amazon DynamoDB

29 Aggregations with DynamoDB Streams and Lambda

30 Aggregations with DynamoDB Streams and Lambda

31 Aggregations with DynamoDB Lambda Execution Role

32 Aggregations with DynamoDB Streams and Lambda

33 Auto Scaling Source: Yan Cui: "The problems with DynamoDB Auto Scaling and how it might be improved"

34 Scheduled Auto Scaling

35 Scheduled Auto Scaling Example

36 DynamoDB Pricing DynamoDB works by provisioning throughput at the table level. The throughput is set up as follows: Each write capacity unit (WCU) gives 1KB/s of write throughput Each read capacity unit (RCU) gives 4KB/s of read throughput Read and write throughput are independent

37 Partitioning Math By Capacity = (Total RCU)/ (Total WCU)/1000 By Size = Total size / 10 GB Number of partitions = CEILING (MAX (Capacity, Size)) Max RCUs per partition=3000; Max WCUs per partition=1000 Throughput (RCU & WCU) is uniformly spread across partitions In case of exceeding throughput per partition - ProvisionedThroughputExceededException

38 Partitioning Math Table Size=40 GB, RCUs=1500, WCUs=400 By Capacity = 1500/ /1000=0.9 By Size = 40 GB / 10 GB =4 Number of partitions = CEILING (MAX (0.9,4)) =4 RCUs per partition=1500/4 =375 WCUs per partition=400/4 =100 Data per partition=40 GB/4 = 10GB

39 Example of ProvisionedThroughputExceededException DynamoDB Table with 40 GB of data -> gives 4 partitions Source:

40 DynamoDB Exceeding Throughput Causes: Uneven distribution of data due to the wrong choice of partition key Frequent accessing of the same key in a partition (the most popular item, also known as a hot key) A request rate greater than the provisioned throughput

41 Solutions to the hot key problems Distribute your key accross partitions Avoid mixing cold and hot keys Split the hot key (key+ random (0,X)) Source: Deep Dive on DynamoDB Source: Advanced Design Patterns for Amazon DynamoDB

42 New Feature : DynamoDB Adaptive Capacity Source:

43 DynamoDB canonical use cases Key-value lookups on well-distributed records Avoiding complex queries (and joins) Limiting hot keys

44 Challenges with DynamoDB DynamoDB is not the right choice for each solution Think of the access pattern in advance Difficult to change access patterns (partition&sort key) & table structure afterwards Difficult to replace DynamoDB with another even (NoSQL) database in the future

45 Why NoSQL database DynamoDB? Project use case and challenges: Fixed Scheme (argument against NoSQL DB) Small size of the table and the entities Learning curve for the new technology No aggregate functions was sum, avg, count and as expected no joins

46 Amazon Aurora Serverless (GA for MySql in us-east-1 N. Virginia since )

47 What Is Amazon Aurora Serverless?

48 Amazon Aurora (MySql/Postgres) Source: AWS Summit - London twitch.tv/aws Aurora Serverless Preview Update

49 What Is Amazon Aurora Serverless?

50 Amazon Aurora Serverless Architeсture Source:

51 Amazon Aurora Serverless Architeсture Source: AWS Summit - London twitch.tv/aws Aurora Serverless Preview Update

52 Amazon Aurora Serverless Pricing & Settings Source:

53 Amazon Aurora Serverless CloudWatch Monitoring Source:

54 Amazon Aurora Serverless Pricing & Settings Pause the cluster Downscaling 4 -> 2 ACUs 26 sec Cold-start Source:

55 Use cases for Amazon Aurora Serverless Seldom usage (weekly jobs) + additional spikes Noticable initial latencies acceptable Aurora and therefore Lambda are behind VPC Drastically increases also Lambda cold start #AWSWishList: Aurora Serverless works like DynamoDB: HTTP+IAM Role Dev/Test Database Source: Ajay Nair Become a Serverless Black Belt

56 Questions?

57 Thank You!